642 research outputs found

    Integrating TrustZone Protection with Communication Paths for Mobile Operating System

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    Nowadays, users perform various essential activities through their smartphones, including mobile payment and financial transaction. Therefore, users’ sensitive data processed by smartphones will be at risk if underlying mobile OSes are compromised. A technology called Trusted Execution Environment (TEE) has been introduced to protect sensitive data in the event of compromised OS and hypervisor. This dissertation points out the limitations of the current design model of mobile TEE, which has a low adoption rate among application developers and has a large size of Trusted Computing Base (TCB). It proposes a new design model for mobile TEE to increase the TEE adoption rate and to decrease the size of TCB. This dissertation applies a new model to protect mobile communication paths in the Android platform. Evaluations are performed to demonstrate the effectiveness of the proposed design model

    Investigating the User Experience with a 3D Virtual Anatomy Application

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    Decreasing hours dedicated to teaching anatomy courses and declining use of human cadavers have spurred the need for innovative solutions in teaching anatomy in medical schools. Advancements in virtual reality (VR), 3D visualizations, computer graphics, and medical graphic images have enabled the development of highly interactive 3D virtual applications. Over recent years, variations of interactive systems on computer-mediated environments have been used as supplementary resource for learners. However, despite the growing sophistication of these resources for learning anatomy, studies show that students predominantly prefer traditional methods of learning and hands-on cadaver-based learning over computer-mediated platforms. There is limited research on evaluating user experience in the use of interactive 3D anatomy systems, even though Human-Computer Interaction (HCI) studies show that usability (ease of use) and user engagement are essential to technology adoption and satisfaction. The addressable problem of the research was to investigate how ease of use and flow affected aspects of the students’ engagement experience with the use of a 3D virtual anatomy application. The aim of the study was to evaluate the use of a 3D virtual application in performing dissection learning tasks and to understand aspects of user engagement as assessed by ease of use and flow experience. The flow experience was quantified using the Short Flow State Scale (S FSS-2) and the System Usability Scale (SUS) to measure perceptions about ease of use and user satisfaction. The research questions included: (1) What consequences of flow do students experience? (2) What aspects of the 3D virtual platform are distracting to performing the learning tasks? (3) How do students’ perception of ease of use affect the flow experience based on the SUS and S FSS-2 scores? (4) How do students rate their level of engagement as measured by flow based on their S FSS-2 scores? (5) How does flow help explain student satisfaction and motivation? (6) How do students perceive use of the application to learn anatomy compared with cadaver-based dissection? The study consisted of medical student participants who were asked to complete virtual dissection activities associated with learning objectives in the Structure of the Human Body course to perform using a 3D virtual anatomy application. A subset of participants who completed the learning task and the surveys had a follow-up Cognitive Walkthrough with Think-Aloud Protocol observation activity with an interview segment to gain deeper insights into their user experience with the application. The data from the convergent mixed method analysis indicated that ease of use had some impact on the flow experience and that perceived user satisfaction and motivation were attributed to the interactive 3D visualization design. Seven super-ordinate themes were identified: Ease of Use, Learnability, Interface-Technical, User Satisfaction, Visuospatial, Focus/In the Zone, and CA vs Cadaver. The results have implications for educators (particularly anatomists), educational technologists, and HCI and UX practitioners. Additional research should be conducted using the long version of the Flow State Scale to provide a better understanding of each flow dimension. Further study is recommended with students who have hands-on experience with human cadaver dissection that are also able to compare their experience with the use of a 3D virtual anatomy platform for a direct side-by-side assessment. It would also be helpful to conduct the study as part of the entire duration of the anatomy course and assess how the flow experience impacts student learning performance

    Political artifacts and personal privacy : the Yenta multi-agent distributed matchmaking system

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    Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (p. 119-128).Technology does not exist in a social vacuum. The design and patterns of use of any particular technological artifact have implications both for the direct users of the technology, and for society at large. Decisions made by technology designers and implementors thus have political implications that are often ignored. If these implications are not made a part of the design process, the resulting effects on society can be quite undesirable. The research advanced here therefore begins with a political decision: It is almost always a greater social good to protect personal information against unauthorized disclosure than it is to allow such disclosure. This decision is expressly in conflict with those of many businesses and government entities. Starting from this premise, a multi-agent architecture was designed that uses both strong cryptography and decentralization to enable a broad class of Internet-based software applications to handle personal information in a way that is highly resistant to disclosure. Further, the design is robust in ways that can enable users to trust it more easily: They can trust it to keep private information private, and they can trust that no single entity can take the system away from them. Thus, by starting with the explicit political goal of encouraging well-placed user trust, the research described here not only makes its social choices clear, it also demonstrates certain technical advantages over more traditional approaches. We discuss the political and technical background of this research, and explain what sorts of applications are enabled by the multi-agent architecture proposed. We then describe a representative example of this architecture--the Yenta matchmaking system. Yenta uses the coordinated interaction of large numbers of agents to form coalitions of users across the Internet who share common interests, and then enables both one-to-one and group conversations among them. It does so with a high degree of privacy, security, and robustness, without requiring its users to place unwarranted trust in any single point in the system.by Leonard Newton Foner.Ph.D

    DISPLAYING DANTE’S DIVINE COMEDY MINIATED MANUSCRIPTS IN EXHIBITIONS

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    Ancient manuscripts are part of many collections belonging to historic libraries and museums: due to their fragile nature and to the difficulties to display most of their contents during exhibitions, their study is often complicated for scholars who also need generally special permissions to examine them, mostly for a limited time window. Beginning from these premises, this paper introduces the outcomes of the digital replication and presentation of three manuscripts related to Dante’s Divine Comedy, as proposed on a real exhibition, “Dall’Alma Mater al Mondo. Dante at the University of Bologna”, held in 2021. Some of the principles related to the production of their replicas and the fruition of their contents through dedicated applications targeted to visitors and scholars are presented, with care to the reproduction of details such as the ability to explore 3D replicas of detailed elected pages or to browse many of them on dedicated touch screens

    La détection d'anomalies comme outil de renforcement d'analyse des données et de prédiction dans l'éducation

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    Les établissements d'enseignement cherchent à concevoir des mécanismes efficaces pour améliorer les résultats scolaires, renforcer le processus d'apprentissage et éviter l'abandon scolaire. L'analyse et la prédiction des performances des étudiants au cours de leurs études peuvent mettre en évidence certaines lacunes d'une formation et détecter les étudiants ayant des problèmes d'apprentissage. Il s'agit donc de développer des techniques et des modèles basés sur des données qui visent à améliorer l'enseignement et l'apprentissage. Les modèles classiques ignorent généralement les étudiants présentant des comportements et incohérences inhabituels, bien qu'ils puissent fournir des informations importantes aux experts du domaine et améliorer les modèles de prédiction. Les profils atypiques dans l'éducation sont à peine explorés et leur impact sur les modèles de prédiction n'a pas encore été étudié dans la littérature. Cette thèse vise donc à étudier les valeurs anormales dans les données éducatives et à étendre les connaissances existantes à leur sujet. La thèse présente trois études de cas de détection de données anormales pour différents contextes éducatifs et modes de représentation des données (jeu de données numériques pour une université allemande, jeu de données numériques pour une université russe, jeu de données séquentiel pour les écoles d'infirmières françaises). Pour chaque cas, l'approche de prétraitement des données est proposée en tenant compte des particularités du jeu de données. Les données préparées ont été utilisées pour détecter les valeurs anormales dans des conditions de vérité terrain inconnue. Les caractéristiques des valeurs anormales détectées ont été explorées et analysées, ce qui a permis d'étendre les connaissances sur le comportement des étudiants dans un processus d'apprentissage. L'une des principales tâches dans le domaine de l'éducation est de développer des mécanismes essentiels qui permettront d'améliorer les résultats scolaires et de réduire l'abandon scolaire. Ainsi, il est nécessaire de construire des modèles de prédiction de performance qui sont capables de détecter les étudiants ayant des problèmes d'apprentissage, qui ont besoin d'une aide spéciale. Le deuxième objectif de la thèse est d'étudier l'impact des valeurs anormales sur les modèles de prédiction. Nous avons considéré deux des tâches de prédiction les plus courantes dans le domaine de l'éducation: (i) la prédiction de l'abandon scolaire, (ii) la prédiction du score final. Les modèles de prédiction ont été comparés en fonction de différents algorithmes de prédiction et de la présence de valeurs anormales dans les données d'entraînement. Cette thèse ouvre de nouvelles voies pour étudier les performances des élèves dans les environnements éducatifs. La compréhension des valeurs anormales et des raisons de leur apparition peut aider les experts du domaine à extraire des informations précieuses des données. La détection des valeurs aberrantes pourrait faire partie du pipeline des systèmes d'alerte précoce pour détecter les élèves à haut risque d'abandon. De plus, les tendances comportementales des valeurs aberrantes peuvent servir de base pour fournir des recommandations aux étudiants dans leurs études ou prendre des décisions concernant l'amélioration du processus éducatif.Educational institutions seek to design effective mechanisms that improve academic results, enhance the learning process, and avoid dropout. The performance analysis and performance prediction of students in their studies may show drawbacks in the educational formations and detect students with learning problems. This induces the task of developing techniques and data-based models which aim to enhance teaching and learning. Classical models usually ignore the students-outliers with uncommon and inconsistent characteristics although they may show significant information to domain experts and affect the prediction models. The outliers in education are barely explored and their impact on the prediction models has not been studied yet in the literature. Thus, the thesis aims to investigate the outliers in educational data and extend the existing knowledge about them. The thesis presents three case studies of outlier detection for different educational contexts and ways of data representation (numerical dataset for the German University, numerical dataset for the Russian University, sequential dataset for French nurse schools). For each case, the data preprocessing approach is proposed regarding the dataset peculiarities. The prepared data has been used to detect outliers in conditions of unknown ground truth. The characteristics of detected outliers have been explored and analysed, which allowed extending the comprehension of students' behaviour in a learning process. One of the main tasks in the educational domain is to develop essential tools which will help to improve academic results and reduce attrition. Thus, plenty of studies aim to build models of performance prediction which can detect students with learning problems that need special help. The second goal of the thesis is to study the impact of outliers on prediction models. The two most common prediction tasks in the educational field have been considered: (i) dropout prediction, (ii) the final score prediction. The prediction models have been compared in terms of different prediction algorithms and the presence of outliers in the training data. This thesis opens new avenues to investigate the students' performance in educational environments. The understanding of outliers and the reasons for their appearance can help domain experts to extract valuable information from the data. Outlier detection might be a part of the pipeline in the early warning systems of detecting students with a high risk of dropouts. Furthermore, the behavioral tendencies of outliers can serve as a basis for providing recommendations for students in their studies or making decisions about improving the educational process

    Enhanced Password Security on Mobile Devices

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    <p>Sleek and powerful touchscreen devices with continuous access to high-bandwidth wireless data networks have transformed mobile into a first-class development platform. Many applications (i.e., "apps") written for these platforms rely on remote services such as Dropbox, Facebook, and Twitter, and require users to provide one or more passwords upon installation. Unfortunately, today's mobile platforms provide no protection for users' passwords, even as mobile devices have become attractive targets for password-stealing malware and other phishing attacks.</p><p>This dissertation explores the feasibility of providing strong protections for passwords input on mobile devices without requiring large changes to existing apps.</p><p>We propose two approaches to secure password entry on mobile devices: ScreenPass and VeriUI. ScreenPass is integrated with a device's operating system and continuously monitors the device's screen to prevent malicious apps from spoofing the system's trusted software keyboard. The trusted keyboard ensures that ScreenPass always knows when a password is input, which allows it to prevent apps from sending password data to the untrusted servers. VeriUI relies on trusted hardware to isolate password handling from a device's operating system and apps. This approach allows VeriUI to prove to remote services that a relatively small and well-known code base directly handled a user's password data.</p>Dissertatio

    Detecting Software Attacks on Embedded IoT Devices

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    Internet of Things (IoT) applications are being rapidly deployed in the context of smart homes, automotive vehicles, smart factories, and many more. In these applications, embedded devices are widely used as sensors, actuators, or edge nodes. The embedded devices operate distinctively on a task or interact with each other to collectively perform certain tasks. In general, increase in Internet-connected things has made embedded devices an attractive target for various cyber attacks, where an attacker gains access and control remote devices for malicious activities. These IoT devices could be exploited by an attacker to compromise the security of victim’s platform without requiring any physical hardware access. In order to detect such software attacks and ensure a reliable and trustworthy IoT application, it is crucial to verify that a device is not compromised by malicious software, and also assert correct execution of the program. In the literature, solutions based on remote attestation, anomaly detection, control-flow and data-flow integrity have been proposed to detect software attacks. However, these solutions have limited applicability in terms of target deployments and attack detection, which we inspect thoroughly. In this dissertation, we propose three solutions to detect software attacks on embedded IoT devices. In particular, we first propose SWARNA, which uses remote attestation to verify a large network of embedded devices and ensure that the application software on the device is not tampered. Verifying the integrity of a software preserves the static properties of a device. To secure the devices from various software attacks, it is imperative to also ensure that the runtime execution of a program is as expected. Therefore, we focus extensively on detecting memory corruption attacks that may occur during the program execution. Furthermore, we propose, SPADE and OPADE, secure program anomaly detection that runs on embedded IoT devices and use deep learning, and machine learning algorithms respectively to detect various runtime software attacks. We evaluate and analyse all the proposed solutions on real embedded hardware and IoT testbeds. We also perform a thorough security analysis to show how the proposed solutions can detect various software attacks

    The New Employment Verification Act: The Functionality and Constitutionality of Biometrics in the Hiring Process Note

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    In 1990, Congress created the U.S. Commission on Immigration Reform to assess and make recommendations regarding the implementation and impact of U.S. immigration policy. Unanimously, the Commission proposed employment-based immigration reforms that have lead to the creation of E-Verify, an Internet-based electronic verification system used by employers to verify a prospective worker’s eligibility. Today, the system compares a prospective worker’s identification information, such as her name, date of birth, and social security number with information contained in databases housed by the Department of Homeland Security and Social Security Administration. Several members of Congress, however, have proposed legislation that would require prospective workers to submit biometric information to curb identity fraud and existing shortfalls in the verification process. This Note examines the practical and legal implications of a nationally mandated biometric verification system and whether such a system is constitutionally viable under current Fourth Amendment jurisprudence. Ultimately this Note argues that no matter how unsettling the collection of biometric information by the government may be, at least in the employment hiring context, a nationally mandated biometric verification system will most likely pass constitutional muster

    Consulting services manual : AICPA integrated practice system

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    https://egrove.olemiss.edu/aicpa_guides/2058/thumbnail.jp
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