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Abstract: Michael Braun, Florian Weber, Florian Alt
Affective technology offers exciting opportunities to improve road safety by catering to human emotions. Modern car interiors enable the contactless detection of user states, paving the way for a systematic promotion of safe driver behavior through emotion regulation. We review the current literature regarding the impact of emotions on driver behavior and analyze the state of emotion regulation approaches in the car. We summarize challenges for affective interaction in the form of technological hurdles and methodological considerations, as well as opportunities to improve road safety by reinstating drivers into an emotionally balanced state. PubDate: Fri, 17 Sep 2021 00:00:00 GMT
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Abstract: Sophie Dramé-Maigné, Maryline Laurent, Laurent Castillo, Hervé Ganem
The Internet of Things is taking hold in our everyday life. Regrettably, the security of IoT devices is often being overlooked. Among the vast array of security issues plaguing the emerging IoT, we decide to focus on access control, as privacy, trust, and other security properties cannot be achieved without controlled access. This article classifies IoT access control solutions from the literature according to their architecture (e.g., centralized, hierarchical, federated, distributed) and examines the suitability of each one for access control purposes. Our analysis concludes that important properties such as auditability and revocation are missing from many proposals while hierarchical and federated architectures are neglected by the community. PubDate: Fri, 17 Sep 2021 00:00:00 GMT
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Abstract: Yohan Bonescki Gumiel, Lucas Emanuel Silva e Oliveira, Vincent Claveau, Natalia Grabar, Emerson Cabrera Paraiso, Claudia Moro, Deborah Ribeiro Carvalho
Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. PubDate: Fri, 17 Sep 2021 00:00:00 GMT
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Abstract: Uttam Chauhan, Apurva Shah
We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word embedded topic models, and topic models in multilingual perspectives. Besides, the research work for topic modeling in a distributed environment, topic visualization approaches also have been explored. PubDate: Fri, 17 Sep 2021 00:00:00 GMT
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Abstract: Lynda Tamine, Lorraine Goeuriot
The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. PubDate: Fri, 17 Sep 2021 00:00:00 GMT
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Abstract: Christian Berger, Philipp Eichhammer, Hans P. Reiser, Jörg Domaschka, Franz J. Hauck, Gerhard Habiger
Internet-of-Things (IoT) ecosystems tend to grow both in scale and complexity, as they consist of a variety of heterogeneous devices that span over multiple architectural IoT layers (e.g., cloud, edge, sensors). Further, IoT systems increasingly demand the resilient operability of services, as they become part of critical infrastructures. This leads to a broad variety of research works that aim to increase the resilience of these systems. In this article, we create a systematization of knowledge about existing scientific efforts of making IoT systems resilient. In particular, we first discuss the taxonomy and classification of resilience and resilience mechanisms and subsequently survey state-of-the-art resilience mechanisms that have been proposed by research work and are applicable to IoT. PubDate: Fri, 17 Sep 2021 00:00:00 GMT
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Unprecedented attention towards blockchain technology is serving as a game-changer in fostering the development of blockchain-enabled distinctive frameworks. However, fragmentation unleashed by its underlying concepts hinders different stakeholders from effectively utilizing blockchain-supported services, resulting in the obstruction of its wide-scale adoption. To explore synergies among the isolated frameworks requires comprehensively studying inter-blockchain communication approaches. These approaches broadly come under the umbrella of Blockchain Interoperability (BI) notion, as it can facilitate a novel paradigm of an integrated blockchain ecosystem that connects state-of-the-art disparate blockchains. Currently, there is a lack of studies that comprehensively review BI, which works as a stumbling block in its development. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Lefeng Zhang, Tianqing Zhu, Ping Xiong, Wanlei Zhou, Philip S. Yu
The vast majority of artificial intelligence solutions are founded on game theory, and differential privacy is emerging as perhaps the most rigorous and widely adopted privacy paradigm in the field. However, alongside all the advancements made in both these fields, there is not a single application that is not still vulnerable to privacy violations, security breaches, or manipulation by adversaries. Our understanding of the interactions between differential privacy and game theoretic solutions is limited. Hence, we undertook a comprehensive review of literature in the field, finding that differential privacy has several advantageous properties that can make more of a contribution to game theory than just privacy protection. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Yuantian Miao, Chao Chen, Lei Pan, Qing-Long Han, Jun Zhang, Yang Xiang
Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Luciano Ignaczak, Guilherme Goldschmidt, Cristiano André Da Costa, Rodrigo Da Rosa Righi
The growth of data volume has changed cybersecurity activities, demanding a higher level of automation. In this new cybersecurity landscape, text mining emerged as an alternative to improve the efficiency of the activities involving unstructured data. This article proposes a Systematic Literature Review (SLR) to present the application of text mining in the cybersecurity domain. Using a systematic protocol, we identified 2,196 studies, out of which 83 were summarized. As a contribution, we propose a taxonomy to demonstrate the different activities in the cybersecurity domain supported by text mining. We also detail the strategies evaluated in the application of text mining tasks and the use of neural networks to support activities involving unstructured data. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes
Machine learning–based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Salonik Resch, Ulya R. Karpuzcu
Benchmarking is how the performance of a computing system is determined. Surprisingly, even for classical computers this is not a straightforward process. One must choose the appropriate benchmark and metrics to extract meaningful results. Different benchmarks test the system in different ways, and each individual metric may or may not be of interest. Choosing the appropriate approach is tricky. The situation is even more open ended for quantum computers, where there is a wider range of hardware, fewer established guidelines, and additional complicating factors. Notably, quantum noise significantly impacts performance and is difficult to model accurately. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Hugo B. Lima, Carlos G. R. Dos Santos, Bianchi S. Meiguins
Music Information Research (MIR) comprises all the research topics involved in modeling and understanding music. Visualizations are frequently adopted to convey better understandings about music pieces, and the association of music with visual elements has been practiced historically and extensively. We investigated papers related to music visualization and organized the proposals into categories according to their most prominent aspects: their input features, the aspects visualized, the InfoVis technique(s) used, if interaction was provided, and users’ evaluations. The MIR and the InfoVis community can benefit by identifying trends and possible new research directions within the music visualization topic. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Palina Tolmach, Yi Li, Shang-Wei Lin, Yang Liu, Zengxiang Li
A smart contract is a computer program that allows users to automate their actions on the blockchain platform. Given the significance of smart contracts in supporting important activities across industry sectors including supply chain, finance, legal, and medical services, there is a strong demand for verification and validation techniques. Yet, the vast majority of smart contracts lack any kind of formal specification, which is essential for establishing their correctness. In this survey, we investigate formal models and specifications of smart contracts presented in the literature and present a systematic overview to understand the common trends. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Jihyeok Park, Hongki Lee, Sukyoung Ryu
Understanding program behaviors is important to verify program properties or to optimize programs. Static analysis is a widely used technique to approximate program behaviors via abstract interpretation. To evaluate the quality of static analysis, researchers have used three metrics: performance, precision, and soundness. The static analysis quality depends on the analysis techniques used, but the best combination of such techniques may be different for different programs. To find the best combination of analysis techniques for specific programs, recent work has proposed parametric static analysis. It considers static analysis as black-box parameterized by analysis parameters, which are techniques that may be configured without analysis details. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Douglas Paulo De Mattos, Débora C. Muchaluat-Saade, Gheorghita Ghinea
The mulsemedia (Multiple Sensorial Media (MulSeMedia)) concept has been explored to provide users with new sensations using other senses beyond sight and hearing. The demand for producing such applications has motivated various studies in the mulsemedia authoring phase. To encourage researchers to explore new solutions for enhancing the mulsemedia authoring, this survey article reviews several mulsemedia authoring tools and proposals for representing sensory effects and their characteristics. The article also outlines a set of desirable features for mulsemedia authoring tools. Additionally, a multimedia background is discussed to support the proposed study in the mulsemedia field. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Jie Zhang, Zhihao Qu, Chenxi Chen, Haozhao Wang, Yufeng Zhan, Baoliu Ye, Song Guo
Machine Learning (ML) has demonstrated great promise in various fields, e.g., self-driving, smart city, which are fundamentally altering the way individuals and organizations live, work, and interact. Traditional centralized learning frameworks require uploading all training data from different sources to a remote data server, which incurs significant communication overhead, service latency, and privacy issues. To further extend the frontiers of the learning paradigm, a new learning concept, namely, Edge Learning (EL) is emerging. It is complementary to the cloud-based methods for big data analytics by enabling distributed edge nodes to cooperatively training models and conduct inferences with their locally cached data. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Alexandro Baldassin, João Barreto, Daniel Castro, Paolo Romano
The recent rise of byte-addressable non-volatile memory technologies is blurring the dichotomy between memory and storage. In particular, they allow programmers to have direct access to persistent data instead of relying on traditional interfaces, such as file and database systems. However, they also bring new challenges, as a failure may render the program in an unrecoverable and inconsistent state. Consequently, a lot of effort has been put by both industry and academia into making the task of programming with such memories easier while, at the same time, efficient from the runtime perspective. This survey summarizes such a body of research, from the abstractions to the implementation level. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Julio Juárez, Cipriano (Pano) Santos, Carlos A. Brizuela
With a growing interest in high-performing work teams and how to form them, a new computational challenge, denominated Team Formation Problem (TFP), has emerged. After almost two decades of research on this problem, many works continue to raise particular conceptions of what a TFP is. Any new practitioner, unfamiliar with the problem, may be hindered in discerning what is essential and what is particular in each proposal. Until now, there was a lack of a document serving as a guide, synthesizing and framing what has been done to date. In this review, we mainly introduce two things: (1) a taxonomy proposal for the TFPs and (2) the description of the main components of a TFP. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, Defu Lian
Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Nathan Magrofuoco, Paolo Roselli, Jean Vanderdonckt
The expansion of touch-sensitive technologies, ranging from smartwatches to wall screens, triggered a wider use of gesture-based user interfaces and encouraged researchers to invent recognizers that are fast and accurate for end-users while being simple enough for practitioners. Since the pioneering work on two-dimensional (2D) stroke gesture recognition based on feature extraction and classification, numerous approaches and techniques have been introduced to classify uni- and multi-stroke gestures, satisfying various properties of articulation-, rotation-, scale-, and translation-invariance. As the domain abounds in different recognizers, it becomes difficult for the practitioner to choose the right recognizer, depending on the application and for the researcher to understand the state-of-the-art. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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Abstract: Djalel Chefrour
We expose the state of the art in the topic of one-way delay measurement in both traditional and software-defined networks. A representative range of standard mechanisms and recent research works, including OpenFlow and Programming Protocol-independent Packet Processors (P4)-based schemes, are covered. We classify them, discuss their advantages and drawbacks, and compare them according to their application environment, accuracy, cost, and robustness. The discussion extends to the reuse of traditional schemes in software-defined networks and the benefits and limitations of the latter with respect to reducing the overhead of network wide measurements. PubDate: Sun, 18 Jul 2021 00:00:00 GMT
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