Human Resources College Essay Example | Topics and Well Written Essays - 1000 words

Human Resources College - Essay Example The strategy of UPS Supply Chain Solutions, as stated in the UPS plan of action are to leverage on technology to create new services and to strengthen its operations and networks, attract and develop the most talented people, continually study customers' behavior, anticipate their needs, and design its products and services to exceed their expectations, create a culture of innovation that leads to sustainable growth, and maintain an environment that enables it to treat every customer as if they are the only one (United Parcel Service n.d. a). In job/task analysis, the focus is on the needs of each group or department. Under the strategy of leveraging on technology to create new services, the information systems department needs to be trained in developing novel online tracking programs (United Parcel Service n.d. b), the customer service department has to be trained in how to use the tracking programs developed by the information systems department, and the finance department has to be trained in billing applications that generate accurate and timely billing to the customers. Under the strategy of attracting and developing the most talented people, supervisors of all groups have to be trained in developing and mentoring employees. ... In person needs analysis, the training and development needs of each employee is identified. For example, the information system developer for external applications may need to have knowledge of Java programming to develop the tracking programs, whereas the information system developer for internal applications may need to have database programming knowledge to develop applications that would enable the manipulation of customer data by the rest of the organisation. The accounts payables finance staff might need to be trained in using the accounts payables module of the billing program, while the accounts receivables finance staff might need to be trained in using the accounts receivables module of the billing program. Depending on the prior training of the supervisors, some might be lacking in performance appraisal skills, while others might be lacking in motivational skills. The same applies for customer service personnel. Some might find telephone skills more appropriate, while oth ers might find computer skills more relevant. 2. The primary stimulus for change remains those factors in the external environment, including the political, economic, socio-cultural, technological, environmental, and legal environment (Cook 2004, p. 34-35). Political influences include government or constitutional policies that may affect the organisation. For instance, the recent expansion of EU boundaries may influence certain organisations (Cook 2004, p. 35). Economic influences are concerned with the economy. Inflation has an effect, for example, on levels of spending. Socio-cultural influences include such factors as class, age and gender as well as issues such as culture and diversity. For example, the

Legal and Ethical Issues a Healthcare Provider May Face

Legal and Ethical Issues a Healthcare Provider May Face EMMYLOU GAY J. DOLOTINA 6. ETHICAL ISSUES AND CONFLICTS 6.1 MALPRACTICE This pertains to careless acts and/or not doing what needs to be done in accordance with the standards of practice of health care. For example the nurse cannot perform indwelling catheterization unless she was trained to do so, and without the doctor’s order for it. This works the same way as when doctors prescribe medications to the elderly without prior laboratory tests made. On the other hand, a caregiver who fails to put the bed down, lock the brake and put the sensor mat on after doing the turning to sides of the resident is also an act of malpractice as this will lead to injury or even death of a resident. MANAGEMENT: Provide information materials in the staff room and the nurses’ station about the risks and possible outcomes including the lawsuit that can be filled when proven guilty Provide a seminar with open forum about the professional practice – what’s new and what not to do in the healthcare setting. Reorientation of the staff on the policies and standards to be followed though meetings and written communications. 6.2 CONFIDENTIALITY The healthcare team members of Kindly Residential facility are obliged to follow strict safekeeping of the resident’s information among the team members which include the medical history, past treatments, even the address and phone numbers relevant to the care of the patient. For example, if somebody calls the nurses station and asks for some information about the resident, the nurse/ healthcare assistant has to refuse to release any information to the caller. If in turn, the caller asks for mobile phone number of the attending physician, still the nurse/caregiver should not give the information. This is also applicable to the rest of the members of the team. The mobile numbers, addresses should not be given without permission from the particular person. MANAGEMENT: Formulate a protocol with regard to information releasing over the phone. The family may sign up an agreement of having a code when acquiring information on present condition of the elderly member in the facility. Implement a colour coding of the folders in order for the staff to recognize the types of files, so as not to accidentally open confidential files of other people. Implementation of disciplinary actions of whoever breaks confidentiality rules leading to mistrust of the older people and their families. 6.3 CONSENT Consent is an agreement between the person, allowing the healthcare professional to perform care, medical intervention and obtain history of the elderly person after being properly explained on the steps and risks of the procedures. This is usually put into writing or recorded for the protection of both parties. The issue arises when the elderly person is assessed to be incapable of making decisions for his care due to dementia and other geriatric conditions. The immediate family is given the power of authority by the elderly to execute actions for the resident’s care and treatment. In some cases, the authorised person readily decides for the elderly without consulting the older person on his/her preference of care. An example is when the doctor prescribes medications for palliative care for the patient after thorough assessment. The family decided to sign the consent for the induction of the medication, but then the elderly refused to be given the medication. Still, the elderly has the right to refuse even with the consent signed. MANAGEMENT: Ensure acquisition of consent forms with signature prior to implementing care such as catheterization, intravenous tubing, etc. including admission forms. Upon admission, approval for authorised person is to be assigned for clearance from the legal counsel of the facility. Any queries regarding the matter will be addressed by the legal counsel office. 7. ETHICAL APPROACHES 7.1 PRINCIPLISM The following are parts of the ideologies of Principlism: a. AUTONOMY This is said to be the free will of man, to choose and decide things that affect his life. Elderly people suffering from dementia and other health conditions of the elderly have diminished autonomy. These people are given protection. APPLICATION: Informed Consent for the following reasons Information – provides knowledge of the whole process: procedure, steps, risks and benefits, other alternative actions, answer questions and give an opportunity to withdraw Comprehension- for understanding Voluntariness- the freedom b. BENEFICENCE and NON MALEFICENCE Beneficence is to do good, while non maleficence is the practice to do no harm. In some instances, a caregiver has to choose between two actions which both give a benefit to the elderly, but in another way has a negative side. For instance, the patient is on liquid restriction, however, the patient is crying a lot because she wanted to drink more water. The caregiver can safely wipe her mouth with wet cotton from time to time. In this way she is giving the patient comfort, at the same time, avoiding the practice to do harm by keeping the restriction of fluid even with the constant calling of the resident. APPLICATION: Assessment in the risks and benefits The avoidance of adding up to the nature of the problem The system of assessment of these risks and benefits is made clear c. JUSTICE This refers to the balance between the reason for performing the procedure or treatment of an elderly person and its impact towards the totality of his well-being. The positive impact must outweigh the painful process that the elderly patient may undergo. For instance, a 90 year old resident is complaining of knee pains and is tired of taking the pain killers. The only long term treatment that the doctor sees in order to alleviate the pain for the patient is to undergo knee cap replacement surgery. The doctor has to assess the patient’s condition if he can still endure the process, and if he can still recover properly from the operation. It is not justifiable to put the elderly for surgery for the reason that the resident can afford it and just wanted to do it if he will end up without pain but bedridden. APPLICATION: Procedures Decision making guide in performing the procedures Weighing positive outcomes of treatment versus pain along the process of doing it. 7.2 VIRTUE ETHICS In the field of healthcare, the healthcare team undeniably possess the character of patience and good virtues. This is one of the reasons why these people are able to endure the stressful environment of caring for the elderly with dementia and other medical conditions that the older people suffer. Virtue ethics refers to the positive inner aspects that a healthcare team member holds naturally, that radiates in the workplace. This helps in keeping the job well done. For instance, a person may say that he/she will not commit murder because he/she can’t bear to do it. 7.3 DUTY ETHICS In the principle of deontology, the team member does the job well by adhering to the rules of the facility and the standards of practice. Therefore there is a higher authority that is being based upon in order to fulfil a task. Obedience is the key to doing the tasks. For example, a person may say he/she will not commit murder because it is against the law of the country. 7.4 CONSEQUENTIALISM The judgement of whether the job is properly done lies in the consequences and the outcomes of the effort done. Therefore, a person must not perform the action if the outcome is perceived to be negative. For example, a person may say he/she will not commit murder because it will bring me to jail. REFERENCES: Greenwood, B. (2015).Legal and ethical issues that health care professionals face. Retrieved from http://work.chron.com/legal-ethical-issues-health-care-professionals-face-5648.html Wikipedia (2014). Principlism. Retrieved from http://en.wikipedia.org/wiki/Principlism Wikipedia (2014). Virtue Ethics. Retrieved from http://en.wikipedia.org/wiki/Virtue_ethics

Cat Stevens & His Journey to Islam :: essays research papers

Before you read this : Yusuf Islam ( formely Cat Stevens ) used to be one of the most famous POP singers in England in the 70th till he decided to be a Muslim and Alhamdulillah he found the light of Allah ! All I have to say is all what you know already, to confirm what you already know, the message of the Prophet (Sallallahu alaihi wa sallam) as given by God - the Religion of Truth. As human beings we are given a consciousness and a duty that has placed us at the top of creation. Man is created to be God's deputy on earth, and it is important to realize the obligation to rid ourselves of all illusions and to make our lives a preparation for the next life. Anybody who misses this chance is not likely to be given another, to be brought back again and again, because it says in Qur'an Majeed that when man is brought to account, he will say, "O Lord, send us back and give us another chance." The Lord will say, "If I send you back you will do the same." MY EARLY RELIGIOUS UPBRINGING I was brought up in the modern world of all the luxury and the high life of show business. I was born in a Christian home, but we know that every child is born in his original nature - it is only his parents that turn him to this or that religion. I was given this religion (Christianity) and thought this way. I was taught that God exists, but there was no direct contact with God, so we had to make contact with Him through Jesus - he was in fact the door to God. This was more or less accepted by me, but I did not swallow it all. I looked at some of the statues of Jesus; they were just stones with no life. And when they said that God is three, I was puzzled even more but could not argue. I more or less believed it, because I had to have respect for the faith of my parents. POP STAR Gradually I became alienated from this religious upbringing. I started making music. I wanted to be a big star. All those things I saw in the films and on the media took hold of me, and perhaps I thought this was my God, the goal of making money.

Conclusion and Recommendations

A Study of Criminal Justice Major Undergraduate College Students And Their Perceptions of the Criminal Justice System in Comparison with other Individuals who have Lesser Knowledge of the SystemChapter 1: IntroductionCriminal Justice is among the most misunderstood factors of the society. There appears to be different sides of perception regarding this matter from different sectors of the human community. The divisions primarily are based from two major views. The first division is where the knowledgeable individuals belong.They are those who are more aware of the situation and the theories that underlie it. While on the other side of the division are those who primarily base their understanding upon what is actually obvious. They intend to comprehend with what Criminal Justice through the basis of the actual situations that happen in the society. This is where misconception of the whole idea of what crime, what justice and what Criminal Justice is as a whole.In this paper, the diffe rence of the two major perceptions addressed earlier with regards the reality of Criminal Justice shall be discussed. More likely, this study shall be directly focused upon the importance as well as impact that education has on individuals who have not gained deeper understanding of what Criminal Justice is. The idea is to compare their perceptions with others who are simply basing their beliefs regarding the said issue on obvious occurrences.A Study of Criminal Justice  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Page 21.1  Ã‚  Ã‚  Ã‚   Background of the StudyThe American society, although they are noted to be among the nations having the most number of laws and regulations with regards crime becomes the primary victim of crime itself during the past centuries. Why does this happen? Why the American society and how come they are becoming the target?It could not be denied that there are numerous members of the American population that belongs to the minorities. This population particularly includes th e immigrants from other countries and other ethnic groups present within the American territories.For this reason, cultural diversity is indeed obvious within the said society. Cultural diversity then particularly causes the differences of the people living in one community making personal conflicts not that impossible to occur.As a result, more and more crimes are being committed by those who are non-Americans as they are overwhelmed with the life that faces them in a foreign land which is a lot different from the way they live their lives within their own countries. One fruit of the said conflicts is that people are forced at times to commit mistakes against the law, worse, they commit crimes against their fellowmen bringing grief and alarm to the whole community.How then do people view these dreadful situations in the society? Sadly, not everyone understands the whole picture of the story in one glance alone. This means that there is a need for deeper understandingA Study of Crim inal Justice  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Page 3 to really be able to comprehend with what is happening. The application of justice in the society also becomes a lot more questionable as the years pass. This is the reason why many individuals have a different perception as to the uprightness and the effectiveness of the Criminal Justice System in the society.1.2  Ã‚  Ã‚  Ã‚   Statement of the ProblemTo be able to have a deeper understanding of the study being made, the problem statement that follows shall be addressed carefully within the paper:â€Å"Criminal Justice is a factor of the social regulation where several groups of people are involved. Understanding the said matter in a more objective way is not that easy. At times, only those who are able to gain knowledge regarding the said issue could actually comprehend to the said system that governs the human conflicts in the society today.This is the reason why the clarification of the said idea should be given closer attention by r esearchers of the said field.†1.3  Ã‚  Ã‚  Ã‚   Research QuestionsTo be able to have a guide or a determining outline for the completion of this study, the following questions shall be answered during the process of this research: ·Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Does education have an influence on beliefs and understanding regarding Criminal Justice?   

User Authentication Through Mouse Dynamics

16 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 User Authentication Through Mouse Dynamics Chao Shen, Student Member, IEEE, Zhongmin Cai, Member, IEEE, Xiaohong Guan, Fellow, IEEE, Youtian Du, Member, IEEE, and Roy A. Maxion, Fellow, IEEE Abstract—Behavior-based user authentication with pointing devices, such as mice or touchpads, has been gaining attention. As an emerging behavioral biometric, mouse dynamics aims to address the authentication problem by verifying computer users on the basis of their mouse operating styles.This paper presents a simple and ef? cient user authentication approach based on a ? xed mouse-operation task. For each sample of the mouse-operation task, both traditional holistic features and newly de? ned procedural features are extracted for accurate and ? ne-grained characterization of a user’s unique mouse behavior. Distance-measurement and eigenspace-transformation techniques are applied to obtain featur e components for ef? ciently representing the original mouse feature space.Then a one-class learning algorithm is employed in the distance-based feature eigenspace for the authentication task. The approach is evaluated on a dataset of 5550 mouse-operation samples from 37 subjects. Extensive experimental results are included to demonstrate the ef? cacy of the proposed approach, which achieves a false-acceptance rate of 8. 74%, and a false-rejection rate of 7. 69% with a corresponding authentication time of 11. 8 seconds. Two additional experiments are provided to compare the current approach with other approaches in the literature.Our dataset is publicly available to facilitate future research. Index Terms—Biometric, mouse dynamics, authentication, eigenspace transformation, one-class learning. I. INTRODUCTION T HE quest for a reliable and convenient security mechanism to authenticate a computer user has existed since the inadequacy of conventional password mechanism was reali zed, ? rst by the security community, and then gradually by the Manuscript received March 28, 2012; revised July 16, 2012; accepted September 06, 2012. Date of publication October 09, 2012; date of current version December 26, 2012.This work was supported in part by the NSFC (61175039, 61103240, 60921003, 60905018), in part by the National Science Fund for Distinguished Young Scholars (60825202), in part by 863 High Tech Development Plan (2007AA01Z464), in part by the Research Fund for Doctoral Program of Higher Education of China (20090201120032), and in part by Fundamental Research Funds for Central Universities (2012jdhz08). The work of R. A. Maxion was supported by the National Science Foundation under Grant CNS-0716677. Any opinions, ? dings, conclusions, or recommendations expressed in this material are those of the authors, and do not necessarily re? ect the views of the National Science Foundation. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sviatoslav Voloshynovskiy. C. Shen, Z. Cai, X. Guan, and Y. Du are with the MOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China (e-mail: [email  protected] xjtu. edu. cn; [email  protected] xjtu. edn. cn; [email  protected] xjtu. edu. cn; [email  protected] jtu. edu. cn). R. A. Maxion is with the Dependable Systems Laboratory, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: [email  protected] cmu. edu). Color versions of one or more of the ? gures in this paper are available online at http://ieeexplore. ieee. org. Digital Object Identi? er 10. 1109/TIFS. 2012. 2223677 public [31]. As data are moved from traditional localized computing environments to the new Cloud Computing paradigm (e. g. , Box. net and Dropbox), the need for better authentication has become more pressing.Recently, several large-scale password leakages exposed users to an unprecedented risk of disclosure and abuse of their information [47], [48]. These incidents seriously shook public con? dence in the security of the current information infrastructure; the inadequacy of password-based authentication mechanisms is becoming a major concern for the entire information society. Of various potential solutions to this problem, a particularly promising technique is mouse dynamics. Mouse dynamics measures and assesses a user’s mouse-behavior characteristics for use as a biometric.Compared with other biometrics such as face, ? ngerprint and voice [20], mouse dynamics is less intrusive, and requires no specialized hardware to capture biometric information. Hence it is suitable for the current Internet environment. When a user tries to log into a computer system, mouse dynamics only requires her to provide the login name and to perform a certain sequence of mouse operations. Extracted behavioral features, based on mouse movements and clicks, are compared to a legitimate user’s pro? le. A match authenticates the user; otherwise her access is denied.Furthermore, a user’s mouse-behavior characteristics can be continually analyzed during her subsequent usage of a computer system for identity monitoring or intrusion detection. Yampolskiy et al. provide a review of the ? eld [45]. Mouse dynamics has attracted more and more research interest over the last decade [2]–[4], [8], [14]–[17], [19], [21], [22], [33], [34], [39]–[41], [45], [46]. Although previous research has shown promising results, mouse dynamics is still a newly emerging technique, and has not reached an acceptable level of performance (e. . , European standard for commercial biometric technology, which requires 0. 001% false-acceptance rate and 1% false-rejection rate [10]). Most existing approaches for mouse-dynamics-based user authentication result in a low authentication accuracy or an unreasonably long authenticatio n time. Either of these may limit applicability in real-world systems, because few users are willing to use an unreliable authentication mechanism, or to wait for several minutes to log into a system.Moreover, previous studies have favored using data from real-world environments over experimentally controlled environments, but this realism may cause unintended side-effects by introducing confounding factors (e. g. , effects due to different mouse devices) that may affect experimental results. Such confounds can make it dif? cult to attribute experimental outcomes solely to user behavior, and not to other factors along the long path of mouse behavior, from hand to computing environment [21], [41]. 1556-6013/$31. 00  © 2012 IEEE SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 17It should be also noted that most mouse-dynamics research used data from both the impostors and the legitimate user to train the classi? cation or detection model. However, in the scenario of mouse-d ynamics-based user authentication, usually only the data from the legitimate user are readily available, since the user would choose her speci? c sequence of mouse operations and would not share it with others. In addition, no datasets are published in previous research, which makes it dif? cult for third-party veri? cation of previous work and precludes objective comparisons between different approaches.A. Overview of Approach Faced with the above challenges, our study aims to develop a mouse-dynamics-based user authentication approach, which can perform user authentication in a short period of time while maintaining high accuracy. By using a controlled experimental environment, we have isolated inherent behavioral characteristics as the primary factors for mouse-behavior analysis. The overview of the proposed approach is shown in Fig. 1. It consists of three major modules: (1) mouse-behavior capture, (2) feature construction, and (3) training/classi? cation. The ? st module serves to create a mouse-operation task, and to capture and interpret mouse-behavior data. The second module is used to extract holistic and procedural features to characterize mouse behavior, and to map the raw features into distance-based features by using various distance metrics. The third module, in the training phase, applies kernel PCA on the distance-based feature vectors to compute the predominant feature components, and then builds the user’s pro? le using a one-class classi? er. In the classi? cation phase, it determines the user’s identity using the trained classi? r in the distance-based feature eigenspace. B. Purpose and Contributions of This Paper This paper is a signi? cant extension of an earlier and much shorter version [40]. The main purpose and major contributions of this paper are summarized as follows: †¢ We address the problem of unintended side-effects of inconsistent experimental conditions and environmental variables by restricting usersâ€℠¢ mouse operations to a tightly-controlled environment. This isolates inherent behavioral characteristics as the principal factors in mouse behavior analysis, and substantially reduces the effects of external confounding factors. Instead of the descriptive statistics of mouse behaviors usually adopted in existing work, we propose newly-de? ned procedural features, such as movement speed curves, to characterize a user’s unique mouse-behavior characteristics in an accurate and ? ne-grained manner. These features could lead to a performance boost both in authentication accuracy and authentication time. †¢ We apply distance metrics and kernel PCA to obtain a distance-based eigenspace for ef? ciently representing the original mouse feature space.These techniques partially handle behavioral variability, and make our proposed approach stable and robust to variability in behavior data. †¢ We employ one-class learning methods to perform the user authentication task, so that the detection model is Fig. 1. Overview of approach. built solely on the data from the legitimate user. One-class methods are more suitable for mouse-dynamics-based user authentication in real-world applications. †¢ We present a repeatable and objective evaluation procedure to investigate the effectiveness of our proposed approach through a series of experiments.As far as we know, no earlier work made informed comparisons between different features and results, due to the lack of a standard test protocol. Here we provide comparative experiments to further examine the validity of the proposed approach. †¢ A public mouse-behavior dataset is established (see Section III for availability), not only for this study but also to foster future research. This dataset contains high-quality mouse-behavior data from 37 subjects. To our knowledge, this study is the ? rst to publish a shared mouse-behavior dataset in this ? eld. This study develops a mouse-dynamics-based user authenticat ion approach that performs user authentication in a short time while maintaining high accuracy. It has several desirable properties: 1. it is easy to comprehend and implement; 2. it requires no specialized hardware or equipment to capture the biometric data; 3. it requires only about 12 seconds of mouse-behavior data to provide good, steady performance. The remainder of this paper is organized as follows: Section II describes related work. Section III presents a data-collection process. Section IV describes the feature-construction process.Section V discusses the classi? cation techniques for mouse dynamics. Section VI presents the evaluation methodology. Section VII presents and analyzes experimental results. Section VIII offers a discussion and possible extensions of the current work. Finally, Section IX concludes. 18 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 II. BACKGROUND AND RELATED WORK In this section, we provide background on mouse- dynamics research, and various applications for mouse dynamics (e. g. , authentication versus intrusion detection).Then we focus on applying mouse dynamics to user authentication. A. Background of Mouse Dynamics Mouse dynamics, a behavioral biometric for analyzing behavior data from pointing devices (e. g. , mouse or touchpad), provides user authentication in an accessible and convenient manner [2]–[4], [8], [14]–[17], [19], [21], [22], [33], [34], [39]–[41], [45], [46]. Since Everitt and McOwan [14] ? rst investigated in 2003 whether users could be distinguished by the use of a signature written by mouse, several different techniques and uses for mouse dynamics have been proposed.Most researchers focus on the use of mouse dynamics for intrusion detection (sometimes called identity monitoring or reauthentication), which analyzes mouse-behavior characteristics throughout the course of interaction. Pusara and Brodley [33] proposed a reauthentication scheme using m ouse dynamics for user veri? cation. This study presented positive ? ndings, but cautioned that their results were only preliminary. Gamboa and Fred [15], [16] were some of the earliest researchers to study identity monitoring based on mouse movements.Later on, Ahmed and Traore [3] proposed an approach combining keystroke dynamics with mouse dynamics for intrusion detection. Then they considered mouse dynamics as a standalone biometric for intrusion detection [2]. Recently, Zheng et al. [46] proposed angle-based metrics of mouse movements for reauthentication systems, and explored the effects of environmental factors (e. g. , different machines). Yet only recently have researchers come to the use of mouse dynamics for user authentication (sometimes called static authentication), which analyzes mouse-behavior characteristics at particular moments.In 2007, Gamboa et al. [17] extended their approaches in identity monitoring [15], [16] into web-based authentication. Later on, Kaminsky e t al. [22] presented an authentication scheme using mouse dynamics for identifying online game players. Then, Bours and Fullu [8] proposed an authentication approach by requiring users to make use of the mouse for tracing a maze-like path. Most recently, a full survey of the existing work in mouse dynamics pointed out that mouse-dynamics research should focus on reducing authentication time and taking the effect of environmental variables into account [21]. B.User Authentication Based on Mouse Dynamics The primary focus of previous research has been on the use of mouse dynamics for intrusion detection or identity monitoring. It is dif? cult to transfer previous work directly from intrusion detection to authentication, however, because a rather long authentication period is typically required to collect suf? cient mouse-behavior data to enable reasonably accurate veri? cation. To our knowledge, few papers have targeted the use of mouse dynamics for user authentication, which will be the central concern of this paper. Hashia et al. [19] and Bours et al. 8] presented some preliminary results on mouse dynamics for user authentication. They both asked participants to perform ? xed sequences of mouse operations, and they analyzed behavioral characteristics of mouse movements to authenticate a user during the login stage. Distance-based classi? ers were established to compare the veri? cation data with the enrollment data. Hashia et al. collected data from 15 participants using the same computer, while Bours et al. collected data from 28 subjects using different computers; they achieved equal-error rates of 15% and 28% respectively.Gamboa et al. [17] presented a web-based user authentication system based on mouse dynamics. The system displayed an on-screen virtual keyboard, and required users to use the mouse to enter a paired username and pin-number. The extracted feature space was reduced to a best subspace through a greedy search process. A statistical model based on the Weibull distribution was built on training data from both legitimate and impostor users. Based on data collected from 50 subjects, the researchers reported an equal-error rate of 6. 2%, without explicitly reporting authentication time.The test data were also used for feature selection, which may lead to an overly optimistic estimate of authentication performance [18]. Recently, Revett et al. [34] proposed a user authentication system requiring users to use the mouse to operate a graphical, combination-lock-like GUI interface. A small-scale evaluation involving 6 subjects yielded an average false-acceptance rate and false-rejection rate of around 3. 5% and 4% respectively, using a distance-based classi? er. However, experimental details such as experimental apparatus and testing procedures were not explicitly reported. Aksari et al. 4] presented an authentication framework for verifying users based on a ? xed sequence of mouse movements. Features were extracted from nine move ments among seven squares displayed consecutively on the screen. They built a classi? er based on scaled Euclidean distance using data from both legitimate users and impostors. The researchers reported an equal-error rate of 5. 9% over 10 users’ data collected from the same computer, but authentication time was not reported. It should be noted that the above two studies were performed on a small number of users—only 6 users in [34], and 10 users in [4]—which may be insuf? ient to evaluate de? nitively the performance of these approaches. The results of the above studies have been mixed, possibly due to the realism of the experiments, possibly due to a lack of real differences among users, or possibly due to experimental errors or faulty data. A careful reading of the literature suggests that (1) most approaches have resulted in low performance, or have used a small number of users, but since these studies do not tend to be replicated, it is hard to pin the discr epancies on any one thing; (2) no research group provided a shared dataset.In our study, we control the experimental environment to increase the likelihood that our results will be free from experimental confounding factors, and we attempt to develop a simple and ef? cient user authentication approach based on mouse dynamics. We also make our data available publicly. III. MOUSE DATA ACQUISITION In this study, we collect mouse-behavior data in a controlled environment, so as to isolate behavioral characteristics as the principal factors in mouse behavior analysis. We offer here SHEN et al. USER AUTHENTICATION THROUGH MOUSE DYNAMICS 19 considerable detail regarding the conduct of data collection, because these particulars can best reveal potential biases and threats to experimental validity [27]. Our data set is available 1. A. Controlled Environment In this study, we set up a desktop computer and developed a Windows application as a uniform hardware and software platform for the coll ection of mouse-behavior data. The desktop was an HP workstation with a Core 2 Duo 3. 0 GHz processor and 2 GB of RAM.It was equipped with a 17 HP LCD monitor (set at 1280 1024 resolution) and a USB optical mouse, and ran the Windows XP operating system. Most importantly, all system parameters relating to the mouse, such as speed and sensitivity con? gurations, were ? xed. The Windows application, written in C#, prompted a user to conduct a mouse-operation task. During data collection, the application displayed the task in a full-screen window on the monitor, and recorded (1) the corresponding mouse operations (e. g. , mouse-single-click), (2) the positions at which the operations occurred, and (3) the timestamps of the operations.The Windows-event clock was used to timestamp mouse operations [28]; it has a resolution of 15. 625 milliseconds, corresponding to 64 updates per second. When collecting data, each subject was invited to perform a mouse-operations task on the same desktop computer free of other subjects; data collection was performed one by one on the same data-collection platform. These conditions make hardware and software factors consistent throughout the process of data collection over all subjects, thus removing unintended side-effects of unrelated hardware and software factors. B.Mouse-Operation Task Design To reduce behavioral variations due to different mouse-operation sequences, all subjects were required to perform the same sequence of mouse operations. We designed a mouse-operation task, consisting of a ? xed sequence of mouse operations, and made these operations representative of a typical and diverse combination of mouse operations. The operations were selected according to (1) two elementary operations of mouse clicks: single click and double click; and (2) two basic properties of mouse movements: movement direction and movement distance [2], [39].As shown in Fig. 2, movement directions are numbered from 1 to 8, and each of them is sel ected to represent one of eight 45-degree ranges over 360 degrees. In addition, three distance intervals are considered to represent short-, middle- and long-distance mouse movements. Table I shows the directions and distances of the mouse movements used in this study. During data collection, every two adjacent movements were separated by either a single click or a double click. As a whole, the designed task consists of 16 mouse movements, 8 single clicks, and 8 double clicks.It should be noted that our task may not be unique. However, the task was carefully chosen to induce users to perform a wide variety of mouse movements and clicks that were both typical and diverse in an individual’s repertoire of daily mouse behaviors. 1The mouse-behavior dataset is available from: http://nskeylab. xjtu. edu. cn/ projects/mousedynamics/behavior-data-set/. Fig. 2. Mouse movement directions: sector 1 covers all operations performed degrees and degrees. with angles between TABLE I MOUSE MO VEMENTS IN THE DESIGNED MOUSE-OPERATION TASK C.Subjects We recruited 37 subjects, many from within our lab, but some from the university at large. Our sample of subjects consisted of 30 males and 7 females. All of them were right-handed users, and had been using a mouse for a minimum of two years. D. Data-Collection Process All subjects were required to participate in two rounds of data collection per day, and waited at least 24 hours between collections (ensuring that some day-to-day variation existed within the data). In each round, each subject was invited, one by one, to perform the same mouse-operation task 10 times.A mouse-operation sample was obtained when a subject performed the task one time, in which she ? rst clicked a start button on the screen, then moved the mouse to click subsequent buttons prompted by the data-collection application. Additionally, subjects were instructed to use only the external mouse device, and they were advised that no keyboard would be needed. S ubjects were told that if they needed a break or needed to stretch their hands, they were to do so after they had accomplished a full round. This was intended to prevent arti? cially anomalous mouse operations in the middle of a task.Subjects were admonished to focus on the task, as if they were logging into their own accounts, and to avoid distractions, such as talking with the experimenter, while the task was in progress. Any error in the operating process (e. g. , single-clicking a button when requiring double-clicking it) caused the current task to be reset, requiring the subject to redo it. 20 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 TABLE II MOUSE DYNAMICS FEATURES Subjects took between 15 days and 60 days to complete data collection.Each subject accomplished 150 error-free repetitions of the same mouse-operation task. The task took between 6. 2 seconds and 21. 3 seconds, with an average of 11. 8 seconds over all subjects. The ? nal dataset contained 5550 samples from 37 subjects. IV. FEATURE CONSTRUCTION In this section, we ? rst extract a set of mouse-dynamics features, and then we use distance-measurement methods to obtain feature-distance vectors for reducing behavioral variability. Next, we utilize an eigenspace transformation to extract principal feature components as classi? er input. A.Feature Extraction The data collected in Section III are sequences of mouse operations, including left-single-clicks, left-double-clicks, and mouse-movements. Mouse features were extracted from these operations, and were typically organized into a vector to represent the sequence of mouse operations in one execution of the mouse-operation task. Table II summarizes the derived features in this study. We characterized mouse behavior based on two basic types of mouse operations—mouse click and mouse movement. Each mouse operation was then analyzed individually, and translated into several mouse features.Our study divi ded these features into two categories: †¢ Holistic features: features that characterize the overall properties of mouse behaviors during interactions, such as single-click and double-click statistics; †¢ Procedural features: features that depict the detailed dynamic processes of mouse behaviors, such as the movement speed and acceleration curves. Most traditional features are holistic features, which suf? ce to obtain a statistical description of mouse behavior, such as the mean value of click times. They are easy to compute and comprehend, but they only characterize general attributes of mouse behavior.In our study, the procedural features characterize in-depth procedural details of mouse behavior. This information more accurately re? ects the ef? ciency, agility and motion habits of individual mouse users, and thus may lead to a performance boost for authentication. Experimental results in Section VII demonstrate the effectiveness of these newly-de? ned features. B. Dis tance Measurement The raw mouse features cannot be used directly by a classi? er, because of high dimensionality and behavioral variability. Therefore, distance-measurement methods were applied to obtain feature-distance vectors and to mitigate the effects of these issues.In the calculation of distance measurement, we ? rst used the Dynamic Time Warping (DTW) distance [6] to compute the distance vector of procedural features. The reasons for this choice are that (1) procedural features (e. g. , movement speed curve) of two data samples are not likely to consist of the exactly same number of points, whether these samples are generated by the same or by different subjects; (2) DTW distance can be applied directly to measure the distance between the procedural features of two samples without deforming either or both of the two sequences in order to get an equal number of points.Next, we applied Manhattan distance to calculate the distance vector of holistic features. The reasons for th is choice are that (1) this distance is independent between dimensions, and can preserve physical interpretation of the features since its computation is the absolute value of cumulative difference; (2) previous research in related ? elds (e. g. , keystroke dynamics) reported that the use of Manhattan distance for statistical features could lead to a better performance [23]. ) Reference Feature Vector Generation: We established the reference feature vector for each subject from her training feature vectors. Let , be the training set of feature vectors for one subject, where is a -dimensional mouse feature vector extracted from the th training sample, and is the number of training samples. Consider how the reference feature vector is generated for each subject: Step 1: we computed the pairwise distance vector of procedural features and holistic features between all pairs of training feature vectors and .We used DTW distance to calculate the distance vector of procedural features for measuring the similarity between the procedural components of the two feature vectors, and we applied Manhattan distance to calculate the distance vector of holistic features . (1) where , and represents the procedural components of represents the holistic components. SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 21 Step 2: we concatenated the distance vectors of holistic features and procedural features together to obtain a distance vector for the training feature vectors and by (2) Step 3: we normalized vector: to get a scale-invariant feature nd sample covariance . Then we can obtain the mean of such a training set by (5) (6) (3) is the mean of all where pairwise distance vectors from the training set, and is the corresponding standard deviation. Step 4: for each training feature vector, we calculated the arithmetic mean distance between this vector and the remaining training vectors, and found the reference feature vector with minimum mean distance. (4) 2) Feature-Dis tance Vector Calculation: Given the reference feature vector for each subject, we then computed the feature-distance vector between a new mouse feature vector and the reference vector.Let be the reference feature vector for one subject; then for any new feature vector (either from the legitimate user or an impostor), we can compute the corresponding distance vector by (1), (2) and (3). In this paper, we used all mouse features in Table II to generate the feature-distance vector. There are 10 click-related features, 16 distance-related features, 16 time-related features, 16 speed-related features, and 16 acceleration-related features, which were taken together and then transformed to a 74-dimensional feature-distance vector that represents each mouse-operation sample. C.Eigenspace Computation: Training and Projection It is usually undesirable to use all components in the feature vector as input for the classi? er, because much of data will not provide a signi? cant degree of uniquene ss or consistency. We therefore applied an eigenspace-transformation technique to extract the principal components as classi? er input. 1) Kernel PCA Training: Kernel principal component analysis (KPCA) [37] is one approach to generalizing linear PCA to nonlinear cases using kernel methods. In this study, the purpose of KPCA is to obtain the principal components of the original feature-distance vectors.The calculation process is illustrated as follows: For each subject, the training set represents a set of feature-distance vectors drawn from her own data. Let be the th feature-distance vector in the training set, and be the number of such vectors. We ? rst mapped the measured vectors into the hyperdimensional feature space by the nonlinear mapping Here we centered the mapped point with the corresponding mean as . The principal components were then computed by solving the eigenvalue problem: (7) where and . Then, by de? ning a kernel matrix (8) we computed an eigenvalue problem for t he coef? ients is now solely dependent on the kernel function , that (9) For details, readers can refer to B. Scholkopf et al. [37]. Generally speaking, the ? rst few eigenvectors correspond to large eigenvalues and most information in the training samples. Therefore, for the sake of providing the principal components to represent mouse behavior in a low-dimensional eigenspace, and for memory ef? ciency, we ignored small eigenvalues and their corresponding eigenvectors, using a threshold value (10) is the accumulated variance of the ? st largest eigenwhere values with respect to all eigenvalues. In this study, was chosen as 0. 95 for all subjects, with a range from 0 to 1. Note that we used the same for different subjects, so may be different from one subject to another. Speci? cally, in our experiments, we observed that the number of principal components for different subjects varied from 12 to 20, and for an average level, 17 principal components are identi? ed under the threshold of 0. 95. 2) Kernel PCA Projection: For the selected subject, taking the largest eigenvalues and he associated eigenvectors, the transform matrix can be constructed to project an original feature-distance vector into a point in the -dimensional eigenspace: (11) As a result, each subject’s mouse behavior can be mapped into a manifold trajectory in such a parametric eigenspace. It is wellknown that is usually much smaller than the dimensionality of the original feature space. That is to say, eigenspace analysis can dramatically reduce the dimensionality of input samples. In this way, we used the extracted principal components of the feature-distance vectors as input for subsequent classi? ers. 22IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 V. CLASSIFIER IMPLEMENTATION This section explains the classi? er that we used, and introduces two other widely-used classi? ers. Each classi? er analyzes mouse-behavior data, and discriminates between a legitimate user and impostors. A. One-Class Classi? er Overview User authentication is still a challenging task from the pattern-classi? cation perspective. It is a two-class (legitimate user versus impostors) problem. In the scenario of mouse-dynamicsbased user authentication, a login user is required to provide the user name and to perform a speci? mouse-operation task which would be secret, like a password. Each user would choose her own mouse-operations task, and would not share that task with others. Thus, when building a model for a legitimate user, the only behavioral samples of her speci? c task are her own; other users’ (considered as impostors in our scenario) samples of this task are not readily available. In this scenario, therefore, an appropriate solution is to build a model based only on the legitimate user’s data samples, and use that model to detect impostors. This type of problem is known as one-class classi? ation [43] or novelty/anomaly detection [25], [26]. We thus focused our attention on this type of problem, especially because in a real-world situation we would not have impostor renditions of a legitimate user’s mouse operations anyway. B. Our Classi? er—One-Class Support Vector Machine Traditional one-class classi? cation methods are often unsatisfying, frequently missing some true positives and producing too many false positives. In this study, we used a one-class Support Vector Machine (SVM) classi? er, introduced by Scholkopf et al. [36], [38]. One-class SVMs have been successfully applied to a number of real-life classi? ation problems, e. g. , face authentication, signature veri? cation and keystroke authentication [1], [23]. In our context, given training samples belonging to one subject, , each sample has features (corresponding to the principal components of the feature-distance vector for that subject). The aim is to ? nd a hyperplane that separates the data points by the largest margin. To separ ate the data points from the origin, one needs to solve the following dual quadratic programming problem [36], [38]: the origin, and is the kernel function. We allow for nonlinear decision boundaries. Then the decision function 13) will be positive for the examples from the training set, where is the offset of the decision function. In essence, we viewed the user authentication problem as a one-class classi? cation problem. In the training phase, the learning task was to build a classi? er based on the legitimate subject’s feature samples. In the testing phase, the test feature sample was projected into the same high-dimensional space, and the output of the decision function was recorded. We used a radial basis function (RBF) in our evaluation, after comparative studies of linear, polynomial, and sigmoid kernels based on classi? ation accuracy. The SVM parameter and kernel parameter (using LibSVM [11]) were set to 0. 06 and 0. 004 respectively. The decision function would gen erate â€Å" † if the authorized user’s test set is input; otherwise it is a false rejection case. On the contrary, â€Å" † should be obtained if the impostors’ test set is the input; otherwise a false acceptance case occurs. C. Other Classi? ers—Nearest Neighbor and Neural Network In addition, we compared our classi? er with two other widely-used classi? ers, KNN and neural network [12]. For KNN, in the training phase, the nearest neighbor classi? r estimated the covariance matrix of the training feature samples, and saved each feature sample. In the testing phase, the nearest neighbor classi? er calculated Mahalanobis distance from the new feature sample to each of the samples in the training data. The average distance, from the new sample to the nearest feature samples from the training data, was used as the anomaly score. After multiple tests with ranging from 1 to 5, we obtained the best results with , detailed in Section VII. For the neural network, in the training phase a network was built with input nodes, one output node, and hidden nodes.The network weights were randomly initialized between 0 and 1. The classi? er was trained to produce a 1. 0 on the output node for every training feature sample. We trained for 1000 epochs using a learning rate of 0. 001. In the testing phase, the test sample was run through the network, and the output of the network was recorded. Denote to be the output of the network; intuitively, if is close to 1. 0, the test sample is similar to the training samples, and with close to 0. 0, it is dissimilar. VI. EVALUATION METHODOLOGY This section explains the evaluation methodology for mouse behavior analysis.First, we summarize the dataset collected in Section III. Next, we set up the training and testing procedure for our one-class classi? ers. Then, we show how classi? er performance was calculated. Finally, we introduce a statistical testing method to further analyze experimental results. (12) where is the vector of nonnegative Lagrangian multipliers to be determined, is a parameter that controls the trade-off between maximizing the number of data points contained by the hyperplane and the distance of the hyperplane from SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 23A. Dataset As discussed in Section III, samples of mouse-behavior data were collected when subjects performed the designed mouseoperation task in a tightly-controlled environment. All 37 subjects produced a total of 5550 mouse-operation samples. We then calculated feature-distance vectors, and extracted principal components from each vector as input for the classi? ers. B. Training and Testing Procedure Consider a scenario as mentioned in Section V-A. We started by designating one of our 37 subjects as the legitimate user, and the rest as impostors. We trained the classi? er and ested its ability to recognize the legitimate user and impostors as follows: Step 1: We trained the classi? er to b uild a pro? le of the legitimate user on a randomly-selected half of the samples (75 out of 150 samples) from that user. Step 2: We tested the ability of the classi? er to recognize the legitimate user by calculating anomaly scores for the remaining samples generated by the user. We designated the scores assigned to each sample as genuine scores. Step 3: We tested the ability of the classi? er to recognize impostors by calculating anomaly scores for all the samples generated by the impostors.We designated the scores assigned to each sample as impostor scores. This process was then repeated, designating each of the other subjects as the legitimate user in turn. In the training phase, 10-fold cross validation [24] was employed to choose parameters of the classi? ers. Since we used a random sampling method to divide the data into training and testing sets, and we wanted to account for the effect of this randomness, we repeated the above procedure 50 times, each time with independently selected samples drawn from the entire dataset. C. Calculating Classi? r Performance To convert these sets of classi? cation scores of the legitimate user and impostors into aggregate measures of classi? er performance, we computed the false-acceptance rate (FAR) and false-rejection rate (FRR), and used them to generate an ROC curve [42]. In our evaluation, for each user, the FAR is calculated as the ratio between the number of false acceptances and the number of test samples of impostors; the FRR is calculated as the ratio between the number of false rejections and the number of test samples of legitimate users.Then we computed the average FAR and FRR over all subjects. Whether or not a mouse-operation sample generates an alarm depends on the threshold for the anomaly scores. An anomaly score over the threshold indicates an impostor, while a score under the threshold indicates a legitimate user. In many cases, to make a user authentication scheme deployable in practice, minimizing the possibility of rejecting a true user (lower FRR) is sometimes more important than lowering the probability of accepting an impostor [46]. Thus we adjusted the threshold according to the FRR for the training data.Since calculation of the FRR requires only the legitimate user’s data, no impostor data was used for determining the threshold. Speci? cally, the threshold is set to be a variable ranging from , and will be chosen with a relatively low FRR using 10-fold cross validation on the training data. After multiple tests, we observe that setting the threshold to a value of 0. 1 yields a low FRR on average2. Thus, we show results with a threshold value of 0. 1 throughout this study. D. Statistical Analysis of the Results To evaluate the performance of our approach, we developed a statistical test using the half total error rate (HTER) and con? ence-interval (CI) evaluation [5]. The HTER test aims to statistically evaluate the performance for user authentication, which is de ? ned by combining false-acceptance rate (FAR) and falserejection rate (FRR): (14) Con? dence intervals are computed around the HTER as , and and are computed by [5]: (15) % % % (16) where NG is the total number of genuine scores, and NI is the total number of impostor scores. VII. EXPERIMENTAL RESULTS AND ANALYSIS Extensive experiments were carried out to verify the effectiveness of our approach. First, we performed the authentication task using our approach, and compared it with two widely-used classi? rs. Second, we examined our primary results concerning the effect of eigenspace transformation methods on classi? er performance. Third, we explored the effect of sample length on classi? er performance, to investigate the trade-off between security and usability. Two additional experiments are provided to compare our method with other approaches in the literature. A. Experiment 1: User Authentication In this section, we conducted a user authentication experiment, and compared our c lassi? er with two widely-used ones as mentioned in Section V-C. The data used in this experiment consisted of 5550 samples from 37 subjects.Fig. 3 and Table III show the ROC curves and average FARs and FRRs of the authentication experiment for each of three classi? ers, with standard deviations in parentheses. Table III also includes the average authentication time, which is the sum of the average time needed to collect the data and the average time needed to make the authentication decision (note that since the latter of these two times is always less than 0. 003 seconds in our classi? ers, we ignore it in this study). Our ? rst observation is that the best performance has a FAR of 8. 74% and a FRR of 7. 96%, obtained by our approach (one-class SVM).This result is promising and competitive, and the behavioral samples are captured over a much shorter period of time 2Note that for different classi? ers, there are different threshold intervals. For instance, the threshold interval fo r neural network detector is [0, 1], and for one. For uniform presentation, we mapped all of intervals class SVM, it is . to 24 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 TABLE IV HTER PERFORMANCE AND CONFIDENCE INTERVAL AT CONFIDENCE LEVELS DIFFERENT Fig. 3. ROC curves for the three different classi? rs used in this study: oneclass SVM, neural network, and nearest neighbor. TABLE III FARs AND FRRs OF USER AUTHENTICATION EXPERIMENT (WITH STANDARD DEVIATIONS IN PARENTHESES) information about mouse behavior, which could enhance performance. Finally, we conducted a statistical test, using the HTER and CI evaluation as mentioned in Section VI-D, to statistically evaluate the performance of our approach. Table IV summarizes the results of this statistical evaluation at different con? dence levels. The result shows that the proposed approach provides the lowest HTER in comparison with the other two classi? ers used in our study; the 95% con? ence interval lies at % %. B. Experiment 2: Effect of Eigenspace Transformation This experiment examined the effect of eigenspace-transformation methods on classi? er performance. The data used were the same as in Experiment 1. We applied a one-class SVM classi? er in three evaluations, with the inputs respectively set to be the original feature-distance vectors (without any transformations), the projection of feature-distance vectors by PCA, and the projection of feature-distance vectors by KPCA. Fig. 4 and Table V show the ROC curves and average FARs and FRRs for each of three feature spaces, with standard deviations in parentheses.As shown in Fig. 4 and Table V, the authentication accuracy for the feature space transformed by KPCA is the best, followed by the accuracies for feature spaces by PCA and the original one. Speci? cally, direct classi? cation in the original feature space (without transformations) produces a FAR of 15. 45% and FRR of 15. 98%. This result is not encouraging c ompared to results previously reported in the literature. However, as mentioned in Experiment 1, the samples may be subject to more behavioral variability compared with previous work, because previous work analyzed mouse behaviors over a longer period of observation.Moreover, we observe that the authentication results of % % by PCA, and % % by KPCA are much better than for direct classi? cation. This result is a demonstration of the effectiveness of the eigenspace transformation in dealing with variable behavior data. Furthermore, we ? nd that the performance of KPCA is slightly superior to that of PCA. This may be due to the nonlinear variability (or noise) existing in mouse behaviors, and KPCA can reduce this variability (or noise) by using kernel transformations [29].It is also of note that the standard deviations of FAR and FRR based on the feature space transformed by KPCA and PCA are smaller than those of the original feature space (without transformations), indicating that th e eigenspace-transformation technique enhances the stability and robustness of our approach. compared with previous work. It should be noted that our result does not yet meet the European standard for commercial biometric technology, which requires near-perfect accuracy of 0. 001% FAR and 1% FRR [10]. But it does demonstrate that mouse dynamics could provide valuable information in user authentication tasks.Moreover, with a series of incremental improvements and investigations (e. g. , outlier handling), it seems possible that mouse dynamics could be used as, at least, an auxiliary authentication technique, such as an enhancement for conventional password mechanisms. Our second observation is that our approach has substantially better performance than all other classi? ers considered in our study. This may be due to the fact that SVMs can convert the problem of classi? cation into quadratic optimization in the case of relative insuf? ciency of prior knowledge, and still maintain hig h accuracy and stability.In addition, the standard deviations of the FAR and FRR for our approach are much smaller than those for other classi? ers, indicating that our approach may be more robust to variable behavior data and different parameter selection procedures. Our third observation is that the average authentication time in our study is 11. 8 seconds, which is impressive and achieves an acceptable level of performance for a practical application. Some previous approaches may lead to low availability due to a relatively-long authentication time. However, an authentication time of 11. seconds in our study shows that we can perform mouse-dynamics analysis quickly enough to make it applicable to authentication for most login processes. We conjecture that the signi? cant decrease of authentication time is due to procedural features providing more detailed and ? ne-grained SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 25 TABLE VI FARs AND FRRs OF DIFFERENT SAMPLE LENGTH S Fig. 4. ROC curves for three different feature spaces: the original feature space, the projected feature space by PCA, and the projected feature space by KPCA.TABLE V FARs AND FARs FOR THREE DIFFERENT FEATURE SPACES (WITH STANDARD DEVIATIONS IN PARENTHESES) the needs of the European Standard for commercial biometric technology [10]. We ? nd that after observing 800 mouse operations, our approach can obtain a FAR of 0. 87% and a FRR of 0. 69%, which is very close to the European standard, but with a corresponding authentication time of about 10 minutes. This long authentication time may limit applicability in real systems. Thus, a trade-off must be made between security and user acceptability, and more nvestigations and improvements should be performed to secure a place for mouse dynamics in more pragmatic settings. D. Comparison User authentication through mouse dynamics has attracted growing interest in the research community. However, there is no shared dataset or baseline algor ithm for measuring and determining what factors affect performance. The unavailability of an accredited common dataset (such as the FERET database in face recognition [32]) and standard evaluation methodology has been a limitation in the development of mouse dynamics.Most researchers trained their models on different feature sets and datasets, but none of them made informed comparisons among different mouse feature sets and different results. Thus two additional experiments are offered here to compare our approach with those in the literature. 1) Comparison 1: Comparison With Traditional Features: As stated above, we constructed the feature space based on mouse clicks and mouse movements, consisting of holistic features and procedural features. To further examine the effectiveness of the features constructed in this study, we provide a comparative experiment. We chose the features used by Gamboa et al. 17], Aksari and Artuner [4], Hashia et al. [19], Bours and Fullu [8], and Ahmed a nd Traore [2], because they were among the most frequently cited, and they represented a relatively diverse set of mouse-dynamics features. We then used a one-class SVM classi? er to conduct the authentication experiment again on our same dataset with both the feature set de? ned in our study, and the feature sets used in other studies. Hence, the authentication accuracies of different feature sets can be compared. Fig. 5 and Table VII show the ROC curves and average FARs and FRRs for each of six feature sets, with standard deviations in parentheses.We can see that the average error rates for the feature set from our approach are much lower than those of the feature sets from the literature. We conjecture that this may be due to the procedural features providing ? ne-grained information about mouse behavior, but they may also be due, in part, to: (1) partial adoption of features de? ned in previous approaches C. Experiment 3: Effect of Sample Length This experiment explored the effe ct of sample length on classi? er performance, to investigate the trade-off between security (authentication accuracy) and usability (authentication time).In this study, the sample length corresponds to the number of mouse operations needed to form one data sample. Each original sample consists of 32 mouse operations. To explore the effect of sample length on the performance of our approach, we derived new datasets with different sample lengths by applying bootstrap sampling techniques [13] to the original dataset, to make derived datasets containing the same numbers of samples as the original dataset. The new data samples were generated in the form of multiple consecutive mouse samples from the original dataset. In this way, we considered classi? r performance as a function of the sample length using all bootstrap samples derived from the original dataset. We conducted the authentication experiment again (using one-class SVM) on six derived datasets, with and 800 operations. Table VI shows the FARs and FRRs at varying sample lengths, using a one-class SVM classi? er. The table also includes the authentication time in seconds. The FAR and FRR obtained using a sample length of 32 mouse operations are 8. 74% and 7. 96% respectively, with an authentication time of 11. 8 seconds. As the number of operations increases, the FAR and FRR drop to 6. 7% and 6. 68% for the a data sample comprised of 80 mouse operations, corresponding to an authentication time of 29. 88 seconds. Therefore, we may conclude that classi? er performance almost certainly gets better as the sample length increases. Note that 60 seconds may be an upper bound for authentication time, but the corresponding FAR of 4. 69% and FRR of 4. 46% are still not low enough to meet 26 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 Fig. 5. ROC curves for six different feature sets: the feature set in our study, and the features sets in other studies.RESULTS OF TABLE VII CO MPARISON WITH SOME TRADITIONAL FEATURES (WITH STANDARD DEVIATIONS IN PARENTHESES) Note that this approach [2] is initially applied to intrusion detection, and we extracted parts of features closely related to mouse operations in our dataset. The reason for this decision is that we want to examine whether the features employed in intrusion detection can be used in user authentication. because of different data-collection environments; (2) using different types of thresholds on the anomaly scores; (3) using less enrollment data than was used in previous experiments.The improved performance based on using our features also indicates that our features may allow more accurate and detailed characterization of a user’s unique mouse behavior than was possible with previously used features. Another thing to note from Table VII is that the standard deviations of error rates for features in our study are smaller than those for traditional features, suggesting that our features might be more stable and robust to variability in behavior data. One may also wonder how much of the authentication accuracy of our approach is due to the use of procedural features or holistic features.We tested our method using procedural features and holistic features separately, and the set of procedural features was the choice that proved to perform better. Specifically, we observe that the authentication accuracy of % % by using the set of procedural features is much better than for the set of holistic features, which have a FAR of 19. 58% and a FRR of 17. 96%. In combination with the result when using all features, it appears that procedural features may be more stable and discriminative than holistic features, which suggests that the procedural features contribute more to the authentication accuracy.The results here only provide preliminary comparative results and should not be used to conclude that a certain set of mouse features is always better than others. Each feature set has it s own unique advantages and disadvantages under different conditions and applications, so further evaluations and comparisons on more realistic and challenging datasets are needed. 2) Comparison 2: Comparison With Previous Work: Most previous approaches have either resulted in poor performance (in terms of authentication accuracy or time), or have used data of limited size.In this section, we show a qualitative comparison of our experimental results and settings against results of previous work (listed in Table VIII). Revett et al. [34] and Aksari and Artuner [4] considered mouse dynamics as a standalone biometric, and obtained an authentication accuracy of ERR around 4% and 5. 9% respectively, with a relatively-short authentication time or small number of mouse operations. But their results were based on a small pool of users (6 users in [34] and 10 users in [4]), which may be insuf? ient to obtain a good, steady result. Our study relies on an improved user authentication methodolo gy and far more users, leading us to achieve a good and robust authentication performance. Ahmed and Traore [2] achieved a high authentication accuracy, but as we mentioned before, it might be dif? cult to use such a method for user authentication since the authentication time or the number of mouse operations needed to verify a user’s identity is too high to be practical for real systems. Additionally, Hashia et al. 19] and Bours and Fulla [8] could perform user authentication in a relatively-short time, but they reported unacceptably high error rates (EER of 15% in [19], and EER of 26. 8% in [8]). In our approach we can make an authentication decision with a reasonably short authentication time while maintaining high accuracy. We employ a one-class classi? er, which is more appropriate for mouse-dynamics-based user authentication. As mentioned in Experiment 3, we can make an authentication decision in less than 60 seconds, with corresponding error rates are FAR of 4. 9% and FRR of 4. 46%. Although this result could be improved, we believe that, at our current performance level, mouse dynamics suf? ce to be a practical auxiliary authentication mechanism. In summary, Comparison 1 shows that our proposed features outperform some traditional features used in previous studies, and may be more stable and robust to variable behavior data. Comparison 2 indicates that our approach is competitive with existing approaches in authentication time while maintaining high accuracy.More detailed statistical studies on larger and more realistic datasets are desirable for further evaluations. VIII. DISCUSSION AND EXTENSION FOR FUTURE WORK Based on the ? ndings from this study, we take away some messages, each of which may suggest a trajectory for future work. Additionally, our work highlights the need for shared data and resources. A. Success Factors of Our Approach The presented approach achieved a short authentication time and relatively-high accuracy for mouse-dynami cs-based user SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 27 TABLE VIII COMPARISON WITH PREVIOUS WORKAuthentication time was not explicitly reported in [4], [8], [17]; instead, they required the user to accomplish a number of mouse operations for each authentication (15 clicks and 15 movements for [17]; 10 clicks and 9 movements for [4]; 18 short movements without pauses for [8]). Authentication time was not explicitly stated in [2]; however, it can be assumed by data-collection progress. For example, it is stated in [2] that an average of 12 hours 55 minutes of data were captured from each subject, representing an average of 45 sessions. We therefore assume that average session length is 12. 5 60/45 17. 22 minutes 1033 seconds. authentication. However, it is quite hard to point out one or two things that may have made our results better than those of previous work, because (1) past work favored realism over experimental control, (2) evaluation methodologies were incons istent among previous work, and (3) there have been no public datasets on which to perform comparative evaluations. Experimental control, however, is likely to be responsible for much of our success. Most previous work does not reveal any particulars in controlling experiments, while our work is tightly controlled.We made every effort to control experimental confounding factors to prevent them from having unintended in? uence on the subject’s recorded mouse behavior. For example, the same desktop computer was used for data collection for all subjects, and all system parameters relating to the mouse were ? xed. In addition, every subject was provided with the same instructions. These settings suggest strongly that the differences in subjects were due to individually detectable mouse-behavior differences among subjects, and not to environmental variables or experimental conditions.We strongly advocate the control of potential confounding factors in future experiments. The reaso n is that controlled experiments are necessary to reveal causal connections among experimental factors and classi? er performance, while realistic but uncontrolled experiments may introduce confounding factors that could in? uence experimental outcomes, which would make it hard to tell whether the results of those evaluations actually re? ect detectable differences in mouse behavior among test subjects, or differences among computing environments.We had more subjects (37), more repetitions of the operation task (150), and more comprehensive mouse operations (2 types of mouse clicks, 8 movement directions, and 3 movement distance ranges) than most studies did. Larger subject pools, however, sometimes make things harder; when there are more subjects there is a higher possibility that two subjects will have similar mouse behaviors, resulting in more classi? cation errors. We proposed the use of procedural features, such as the movement speed curve and acceleration curve, to provide mor e ? egrained information about mouse behavior than some traditional features. This may allow one to accurately describe a user’s unique mouse behavior, thus leading to a performance improvement for mouse-dynamics-based user authentication. We adopted methods for distance measurement and eigenspace transformation for obtaining principal feature components to ef? ciently represent the original mouse feature space. These methods not only overcome within-class variability of mouse behavior, but also preserve between-class differences of mouse behavior. The improved authentication accuracies demonstrate the ef? acy of these methods. Finally, we used a one-class learning algorithm to perform the authentication task, which is more appropriate for mousedynamics-based user authentication in real applications. In general, until there is a comparative study that stabilizes these factors, it will be hard to be de? nitive about the precise elements that made this work successful. B. Oppor tunities for Improvement While previous studies showed promising results in mouse dynamics, none of them have been able to meet the requirement of the European standard for commercial biometric technology.In this work, we determined that mouse dynamics may achieve a pragmatically useful level of accuracy, but with an impractically long authentic

The Game of Life

The Game of Life I steadily reach out and pick up the polished marble piece. The beginning tactic is always the same, push for center development so you can ensure a strong center, a strong defense, a tactical base that can‚t be shaken.Pawn to E4.My move is simple, the professionals call it a King‚s opening. The purpose is even more natural to me, it‚s what I was instructed to do since I was a kid, my Dad always said that E5 was the best opening move. It was a move that was the norm in the life of a chess game, it was a standard that allowed for perfect development, a standard beginning that would lead to success.Pawn to E5I notice the move is an uneventful response, it simply reeks of the conformity that is evident in the game. I close my eyes for a brief second, thinking about the outcomes of the response, the unavoidable consequences of my actions.English: Starting position of a chess game. House ...I decide that what I did was completely logical, it followed what had been trained into me. Yet, I find myself longing for a change - a variation from the harmony - I find myself looking for the solo.Pawn to D4.It‚s a change, the teachers would call it a gambit. I like the change in the setting of the game, it feels as if I‚ve lost control as the pieces move around me without motivation, a whirling dervish in a ballroom waltz. I picture the game‚s development, wondering where the next move of my match will take me. I try to keep the game under my control, but still giving it breathing room to allow the game to continue without being tied down.E5 Captures D4I smile to myself, the plans are working out perfectly. I steady my hand,

Why was there an economic crisis in Germany in 1923 Essay Example

Why was there an economic crisis in Germany in 1923 Essay Example Why was there an economic crisis in Germany in 1923 Paper Why was there an economic crisis in Germany in 1923 Paper There are several causes to the economic crisis in Germany in 1923. The Great War contributed greatly to this, but Germanys problems had begun even before the War had started. In 1914 the German Government decided to come off of the Gold Standard. This was a system in which every bank note had an equivalent sum of gold, kept in the vaults of the German Central Bank. You could go to the bank and would be able to swap your note for gold, although people would normally use the notes to buy things, like an IOU, as it was easier to do this than to use pieces of gold. When the First World War began in 1914, Germany needed more money to pay for food and weapons. The German Government found that the easiest way to obtain more money was to print more notes. This decreased the actual value of the money, as there was more money in circulation. During the War there were food shortages, this meant that there was too few goods to buy for the amount of money that there was in circulation. We can see that inflation occurred, because of the drastic fall in the value of money. In January 1921, there were 64 marks to the dollar, by November 1923; this amount had risen drastically to 4. trillion marks to the dollar! Inflation had been occurring since 1914, so was a long-term cause of the economic crisis. When Germany lost the Great War to the allies, they had to agree with the terms of the treaty of Versailles. One of the terms of this treaty was that Germany had to lose land to France, Belgium, Denmark, Poland and the League of Nations. When losing this land, the German Government lost the tax income from it. The tax income could have helped to pay the reparations of  £6,600 million, which was also a term of the Treaty of Versailles. Reasons for the economic crisis, involving the Treaty of Versailles were short-term causes because they happened quite suddenly, rather than over a long period of time. The land lost contained 14% of arable farmland. It produced food and work for the surrounding area. There was now even less food, making the problem of food shortages even worse. This land also contained 74% of Germanys iron ore, 68% of her zinc ore and 26% of her coal- most of the countrys wealth. Germany could not afford to pay her reparations. This was linked to the invasion of the Ruhr. The Treaty of Versailles also meant that Germany had to reduce its army to 100,000 men. As France no longer felt as strongly under threat by the German army and wanted their reparations paid up, they felt that they could invade the Ruhr. The Ruhr is the most industrialised part of Germany. Poincarre, the French leader, sent in five divisions of French soldiers to acquire the reparations they were owed. A division of Belgian soldiers joined them. They intended to take control of the German factories and mines in this area. All goods produced would be shipped off to France, until the reparations owed to the French were paid. This was a short-term cause to the economic crisis, as it only happened over a matter of days and weeks, unlike inflation, which was occurring for years. Germany could do nothing to stop this. This made Germany very ashamed, especially after losing the War and having to agree with the terms of the Treaty of Versailles as well. The Government ordered passive resistance. The French had no one to work for them as the Ruhr workers were ordered to go on strike. They were ordered to refuse to co-operate with the occupiers. The French then kicked 150,000 German workers out of the Ruhr. They fled as refugees. This produced yet another problem for the already suffering Government and people of Germany. How could they afford to feed, clothe and house the refugees? All their money was already being used to pay reparations, how could they afford this? These problems were solved, but by causing another problem. It seemed that the only way to overcome the problem of the refugees was to print more money. The money in circulation now was growing greatly and the cost of living was increasing. The economic crisis was a vicious circle, by solving one problem they were creating another. It seemed as though it was never going to end. Although there were many causes for the economic crisis, I believe that one made a bigger impact than the others. The Great War, the costs of which were astronomical, both during and after. The War increased the effect of inflation, as the Government printed more notes to try and cope with the problems the War was bringing, therefore decreasing the value and making problems worse. After the War, the Treaty of Versailles produced many difficulties. All of the problems caused by this linked with each other and then back to inflation. The German economic crisis was inevitable because the Government did not know that coming off of the Gold Standard would cause inflation- it was going to happen, they thought it would be better, not worse. They could not have prevented the Treaty of Versailles as they lost the War. They didnt purposely lose the War to cause themselves problems,the problems were inevitable. The economic crisis was waiting to happen, Germany could not have stopped it!

8 Ways to Make Your Job Search Easier

8 Ways to Make Your Job Search Easier If you are thinking of starting a job search, it is a good idea to decide what you need to do beforehand. Laying out a plan in advance and using modern job-search techniques can help you get a job more quickly and find just the position you want. A few handy tips can get you started on the road to satisfying employment and make it easier while you are looking. 1. Connect Using Social Media and FriendsYou’ve updated your resume and are ready to look at classified job ads, but today’s resumes are usually no more than two pages. You might not have enough room to list everything you want. There are other ways to get added space to sing your own praises. Now is a good time to jazz up your LinkedIn page, use Facebook to mention that you are job hunting, and take advantage of your personal webpage if you have one. Sometimes, jobs can come from introductions or referrals, so let friends and former coworkers know you are scouting.2. Examine What Makes You Special and Use ItYou k now you have talents and experience that can help you get a job. Examine your qualifications and talents and job search in that direction. Confidence in what you have to offer to a company is more likely to land you the position you want. Let your resume reflect your expertise. Companies will notice.3. Don’t Take It PersonallyYou probably won’t hear back from many of the job applications you put in, but shouldn’t take it personally. Recruiters can receive hundreds of job applications per week. Their main focus is finding the right person for the job. If you don’t hear back, send an email or make a phone call to make sure your application wasn’t overlooked, and then move on if you don’t receive a positive response.4. Don’t Waste Your TimeYou’ve spotted what you think is the perfect job but don’t have the necessary qualifications. Should you pursue it anyway? This is a major problem faced by recruiters, and the reason many applicants do not hear back. It not only wastes their time but yours also. Apply for jobs for which you are qualified. If you want to move up on the ladder, get the necessary skills first.5. Focusing on Your Job SearchBe smart about your job search. While some job openings are still posted in newspapers, many companies use online job search sites to post openings. Other companies do the job search for you, freeing up your time so you can concentrate on other aspects of your search, such as doing follow ups. Before starting a job search, think about the most efficient way to approach it and follow through.6. Using a Stock ResumeApplicants for a job are more likely to get a callback if the resume is focused on the position for which they are applying. Sending out the same resume for every position is less likely to be noticed by a recruiter, no matter how good that resume is. If you spot a job opening that you find exciting, focus your resume on that job, and it can put you at an adv antage over other candidates.7. Don’t Get OverwhelmedThere are many jobs available, and you know you are a talented worker. However, spreading yourself too thin by applying to many different types of jobs within your category can be a mistake. Try to concentrate on one or two areas to conduct the most effective search, and you are more likely to be rewarded with good results.8. Think About Salary Before Being InterviewedIf you are applying for a job where the salary is listed, can you live on it or does it have opportunities for advancement? If not, move on to other job listings. Many positions these days require that you state your salary requirements. This can be awkward in an interview if you haven’t done your research. If you are called for an interview, research the company to come up with a salary range and present a range, not a fixed salary. The company for which you are applying for a job probably has a salary range already in mind, and you are more likely to get hired if your salary requirements fall within that range.Beginning a job search in these days of strong competition takes skill and preparation. Classified job ads can be outdated and have many applicants. TheJobNetwork takes the stress out of job hunting by doing the heavy work for you. All you have to do is list your job interests and qualifications, and you will receive prompt notices of job openings that match in the form of email alerts. Sign up with TheJobNetwork, and you can job hunt the easy way.

Success Factors in the World Toy Industry Essay - 23

Success Factors in the World Toy Industry - Essay Example This is because wherever there is a fair competition market structure, the number of competitors becomes many, with each having its own business strategy. Whiles doing this, ways in which the company can take advantage of globalization through international expansion shall also be reviewed. Quote (year) noted that every industry has its own success factors, some of which are internal and others, external. As far as the toy industry is concerned, a similar situation can be seen. From the outcome of the market research by Key Note (2014), one identifies three major success factors that influence the outcome of the toy industry as to whether a particular company will be successful or not. The first of these is the child population in the market, which in this case is the UK market. This is an important success factor given that children serve as the direct consumers of toys and toy products. From Appendix A, it can be seen that there is a very favorable trend of child population whereby the number of children within the population keeps increasing by the year. The second success factor that is also reflected in the Key Note report has to do with the household activity of families in the UK. Quote (year) noted that household activity forms an important demographic factor that directly influences the purchasing behavior of families. As a success factor, it is expected that it is expected that most households will be involved in economic and social activities that make it possible for them to buy toys for their children. With this said, it can be seen from appendix B that there are several homes without dependent children. This is a bad indication for the toy industry because the more dependent children there are the more likely it is for there to be patronage of toys. Meanwhile, the number of workless households keeps reducing by the year, which means that the likelihood of homes to be able to afford different kinds of toys is higher.

Econometrics 'A' project for MSc Economics degree course Essay

Econometrics 'A' project for MSc Economics degree course - Essay Example However, in this research we are only interested in examining the effect of total experience and attainment of a specific level of education on the wage differential between the sexes. Total experience was obtained by adding ‘months since hire’ and ‘previous Experience’ This study utilized secondary sources for data collection. The use of secondary data is useful because it is cost efficient and saves time for the independent researcher (Glaser, 1963; Hyman, 1978; Hayashi, 2000). The independent researcher has limited financial resources because he or she works alone. Such researchers are usually professionals who attempt to research some significant social problem on a part-time basis. Description of the Research Design A two-step attempt was made to discover if the income of professional females increases at the same rate as that of males with comparable education as the experience increase. During the first step the existence and strength of a relationshi p between salary and experience for male and female professionals with a given university degree in a specific job were researched using correlational methodology. During the second step a comparison was made between the incomes of males and females with the same experience. Attainment of an educational degree was held constant for each given professional position. The correlational research method was selected as the primary methodology because of its ability to discover relationships among logical variables (Wooldridge, 2008). Research Hypothesis It appears that there is a lack of congruence in the literature in establishing an association between level of income, experience and gender, and also a lack of significant research efforts towards establishing a link between income and experience by gender identification, therefore, the hypothesis was stated as a null hypothesis (Albrect et al., 2003; Atkinson et al., 2003; Blau et al., 2006). Locke, Spirduso, and Silverman (1987) recom mend that if one researches a question where no well-established theory exists one should use the format of the null-hypothesis. There will be no difference in the salary of males and females at given experience when other factors, such as academic degree (bachelor's, master's, or associate degree), and professional position are held constant. Variables The variables in this study's regression analysis are as follows: Male/female relationship (the numerical value of 0 was assigned to the male and 1 to the female variable), education and experience were considered indepen ­dent variables; annual salary depends on the aforementioned variables and therefore is considered the dependent vari ­able. A dummy variable was introduced by multiplying experience by the value of the male/female relationship for the reason mentioned below. The first independent variable, that is, male/female, was selected for its possible effect on the difference in earnings between the sexes. The second inde pendent variable, experience (in months), was chosen for its possible effect on the earning of any individual. The third indepen ­dent variable, education, allows the individual to enter a given professional position. And finally the fourth independent variable, the dummy variable, was supposed to trigger possible changes over time, that is, the closing of the male/female

Discuss the reasons for the recent weakness of the US Dollar Essay

Discuss the reasons for the recent weakness of the US Dollar - Essay Example It still rules as the de facto world currency. Foreign Exchange market is liquid where investors and players participate freely... Its daily turnover in 2004 as per the Bank of International settlements was reported to be$1.9 trillion. London was the biggest foreign exchange market followed by New York â€Å"Somewhere between 40 to 60 percent of international financial transactions are dominated in dollars. For decades the dollar has also been the world’s principal reserve currency. In 1996, the dollar accounted for approximately two thirds of the world’s foreign exchange reserves† (255) writes Robert Gilpin in his ‘Global Political Economy: Understanding Economic Order (2001) The exchange rate of dollar against many currencies in the world has fallen down leading to depreciation of dollar value exchange rate of euro, yen and Indian rupee from January 2007 to June 2007 shown below.. Dollar depreciated by 9% against Indian rupee, and by 1.2% Rate against euro The dollar-pound exchange rate that was settled at 0.6 to 0.64 in 1999 is set at 1.999 in July 2007 i.e. dollar has depreciated by 217% against sterling pound in the same period from 1999 t0 2007 July. Apart from the introduction of Euro in 1999 that squeezed the strength of dollar the Iraqi war turned to be dampener for US dollar. The heavy burden on US economy for fighting the war had an adverse impact on the domestic prices and psychology of other nations. The value of dollar is very much indirectly proportional to gold price in the international market. The following table shows gold price has raised by 135% over 1999 prices. This is one of the principal reasons for dollar price depreciation. The dollar deprecated by 25% against euro from the level of one in 1999 to 0.744 in July 2007. The acute adverse balance of trade and burgeoning debt of America added fuel to the fire. The America’s balance of payment is highly critical. There is a persistent