22 research outputs found

    A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

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    People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

    A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

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    Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

    Recent Advances in Embedded Computing, Intelligence and Applications

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    The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

    Politiche e pratiche per l’educazione linguistica, il multilinguismo e la comunicazione interculturale

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    Uno dei compiti fondamentali dell’educazione linguistica è valorizzare la diversità, non solamente perché ci si trova spesso dinanzi ad apprendenti di nazionalità diverse, ma anche perché vari sono i motivi per cui si apprendono le lingue oggi, sovente determinati da esigenze di integrazione sociale e di opportunità lavorative. Tramite il multilinguismo si valorizzano competenze linguistiche e si creano opportunità di comunicazione interculturale. Nel contempo le politiche linguistiche vanno valutate e rinnovate in continuazione. Questi temi vengono affrontati in questo volume grazie a contributi che si diversificano sia sul piano delle lingue oggetto di studio sia quello teorico-concettuale, pur avendo in comune l’interesse per la linguistica applicata e per l’educazione linguistica.peer-reviewe

    Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

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    Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed

    Preventing the release of illegitimate applications on mobile markets

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    The popularity of mobile applications has been growing worldwide over the last few decades. This popularity is attracting more and more authors of malicious applications called malwares. To detect those malwares, mobile markets have implemented analysis methods that suffer from several limitations. Those we have identified and which we propose to solve in the scope of this thesis are mainly two . The first is the inability to cope with a new method of malware publication consisting in anticipating the mobile version of a company that does not yet have one. The second limitation is the difficulty, due to app tracing, encountered by dynamic analysis solutions to be able to scale. To solve the first limitation we designed and implemented a security check system called IMAD (Illegitimate Mobile App Detector), which is based mainly on online search engines and machine learning techniques. To solve the second problem, we introduced a scalable tracing approach, that we call delegated instrumentation. It leverages Android's instrumentation module and mainly relies on ART (Android RunTime) reverse engineering and hacking. The evaluation results show that IMAD can protect companies from anticipation attacks with an acceptable error rate and at a low cost for MMPs. And we demonstrated the effectiveness of the delegated instrumentation with a prototype named ODILE that traces various app types (including benign apps and malwares) on Samsung Galaxy A7 2017. In particular, we show how much ODILE outperforms Frida, the state-of-the-art tool in the domain

    The Schulze Method of Voting

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    We propose a new single-winner election method ("Schulze method") and prove that it satisfies many academic criteria (e.g. monotonicity, reversal symmetry, resolvability, independence of clones, Condorcet criterion, k-consistency, polynomial runtime). We then generalize this method to proportional representation by the single transferable vote ("Schulze STV") and to methods to calculate a proportional ranking ("Schulze proportional ranking"). Furthermore, we propose a generalization of the Condorcet criterion to multi-winner elections. This paper contains a large number of examples to illustrate the proposed methods
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