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Proceedings of the Tenth USENIX Conference on Usable Privacy and Security

作者： Hui Xu Yangfan Zhou Michael R. Lyu Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong and MoE Key Laboratory of High Confidence Software Technologies (CUHK Sub-Lab) Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong and Dept. of Computer Science & Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781931971133

Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user's touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A one-month experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication.

关键词： smartphone touch biometrics continuous authentication

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A ROBUST REPUTATION-BASED GROUP RANKING SYSTEM AND ITS RESISTANCE TO BRIBERY

arXiv

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arXiv 2020年

作者： Saúde, João Ramos, Guilherme Boratto, Ludovico Caleiro, Carlos Department of Electrical and Computer Engineering Carnegie-Melon University Pittsburgh United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal Data Science and Big Data Analytics EURECAT Barcelona Spain SQIG - Instituto de Telecomunicações Dept. of Mathematics Instituto Superior Técnico University of Lisbon Lisbon Portugal

The spread of online reviews and opinions and its growing influence on people’s behavior and decisions, boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case. Copyright © 2020, The Authors. All rights reserved.

关键词： data mining

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Decision-Focused Learning: Through the Lens of Learning to Rank

arXiv

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arXiv 2021年

作者： Mandi, Jayanta Bucarey, Víctor Mulamba, Maxime Guns, Tias Data Analytics Laboratory Vrije Universiteit Brussel Belgium Institute of Engineering Sciences Universidad de O’Higgins Rancagua Chile Dept. Computer Science KU Leuven Belgium

In the last years, decision-focused learning, also known as predict-and-optimize, has received increasing attention. In this setting, the predictions of a machine learning model are used as estimated cost coefficients in the objective function of a discrete combinatorial optimization problem for decision making. Decision-focused learning proposes to train the ML models, often neural network models, by directly optimizing the quality of decisions made by the optimization solvers. Based on a recent work that proposed a noise contrastive estimation loss over a subset of the solution space, we observe that decision-focused learning can more generally be seen as a learning-to-rank problem, where the goal is to learn an objective function that ranks the feasible points correctly. This observation is independent of the optimization method used and of the form of the objective function. We develop pointwise, pairwise and listwise ranking loss functions, which can be differentiated in closed form given a subset of solutions. We empirically investigate the quality of our generic methods compared to existing decision-focused learning approaches with competitive results. Furthermore, controlling the subset of solutions allows controlling the runtime considerably, with limited effect on regret. © 2021, CC BY.

关键词： Decision making

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

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IFAC-PapersOnLine 2021年第7期54卷 463-468页

作者： Konstantin Usevich Philippe Dreesen Mariya Ishteva Université de Lorraine CNRS CRAN Nancy France KU Leuven Dept. Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium KU Leuven Department of Computer Science ADVISE-NUMA campus Geel Belgium

We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decompositions of the kernels. However, in the case of parallel Wiener-Hammerstein systems, such methods require nontrivial constraints on the factors of the decompositions. In this paper, we propose an entirely different approach: by using special sampling (operating) points for the Jacobian of the nonlinear map from past inputs to the output, we can show that the Jacobian matrix becomes a linear projection of a tensor whose rank is equal to the number of branches. This representation allows us to solve the identification problem as a tensor recovery problem.

关键词： Block structured system identification parallel Wiener-Hammerstein systems Volterra kernels low-rank tensor recovery canonical polyadic decomposition

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Probability estimation and structured output prediction for learning preferences in last mile delivery

arXiv

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arXiv 2022年

作者： Canoy, Rocsildes Bucarey, Victor Molenbruch, Yves Mulamba, Maxime Mandi, Jayanta Guns, Tias Data Analytics Laboratory Vrije Universiteit Brussel Belgium Vrije Universiteit Brussel Belgium Institute of Engineering Sciences Universidad de o'Higgins Rancagua Chile Dept. Computer Science KU Leuven Belgium

We study the problem of learning the preferences of drivers and planners in the context of last mile delivery. Given a data set containing historical decisions and delivery locations, the goal is to capture the implicit preferences of the decision-makers. We consider two ways to use the historical data: one is through a probability estimation method that learns transition probabilities between stops (or zones). This is a fast and accurate method, recently studied in a VRP setting. Furthermore, we explore the use of machine learning to infer how to best balance multiple objectives such as distance, probability and penalties. Specifically, we cast the learning problem as a structured output prediction problem, where training is done by repeatedly calling the TSP solver. Another important aspect we consider is that for last-mile delivery, every address is a potential client and hence the data is very sparse. Hence, we propose a two-stage approach that first learns preferences at the zone level in order to compute a zone routing;after which a penalty-based TSP computes the stop routing. Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further. We hence showcase a successful combination of both probability estimation and machine learning, all the while using standard TSP solvers, both during learning and to compute the final solution;this means the methodology is applicable to other, real-life, TSP variants, or proprietary solvers. © 2022, CC BY.

关键词： Decision making

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Gamification and Serious Games for Cybersecurity Awareness and First Responders Training: An overview

TechRxiv

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TechRxiv 2023年

作者： Batzos, Zisis Saoulidis, Theocharis Margounakis, Dimitrios Fountoukidis, Eleftherios Grigoriou, Elisavet Moukoulis, Achilleas Sarigiannidis, Antonios Liatifis, Athanasios Karypidis, Paris-Alexandros Bibi, Stamatia Filippidis, Adam Kazanidis, Ioannis Nifakos, Sokratis Kasig, Timo Heydari, Mohammad Mouratidis, Haralambos Sidroco Holdings Ltd. Nicosia Cyprus Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Computer Science International Hellenic University Kavala Campus Greece Massive Dynamic Sweden Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Institute for Analytics and Data Science University of Essex United Kingdom

Gamification has been characterized as the usage of video game elements in non-game environments. Gamification in cybersecurity awareness training can lead to more engaging interactions, greater pleasure, and increased security. Serious games are becoming increasingly popular since they allow learning in a natural setting. Although many of the principles covered in cybersecurity awareness training are universal, many forms of training fail because they rely on rote learning and do not require users to think about security concepts. The primary goal of this research is to conduct a review of the literature on cybersecurity awareness training methodologies and frameworks, as well as serious games that may be used to raise awareness among people, organizations, and first responders. © 2023, CC BY.

关键词： Human computer interaction

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Axle bearing condition monitoring, diagnosis and maintenance: The MAXBE project

Axle bearing condition monitoring, diagnosis and maintenance...

引用

作者： Vale, C. Bonifácio, C. Seabra, J. Calçada, R. Mazzino, N. Elis, M. Terribile, S. Anguita, D. Fumeo, E. Saborido, C. Vanhonacker, T. De Donder, E. Laeremans, M. Vermeulen, F. Grimes, D. Dias, D. Construct-LESE Faculty of Engineering of University of Porto Portugal Ansaldo STS S.p.A. Genoa Italy Polytechnic School of University of Genoa Italy COMSA EMTE Barcelona Spain Dynamics Structures and Systems International Leuven Belgium Vlaamse Vervoermaatschappij de Lijn Antwerp Belgium I-MOSS N.V. Bertem Belgium Insight Center for Data Analytics Dept. of Computer Science University College Cork Ireland NOMADTECH Portugal

The reliability of axle bearings has significant safety and economic implications on rail operations. The MAXBE project, an FP7 project funded by the European Commission appeared in this context. The project focuses on the development of novel wayside and on-board monitoring systems for an early detection of axle bearings faults and for defining a condition-based maintenance model for axle bearings. Different wayside monitoring systems were developed based on diverse approaches as well as two on-board monitoring systems. To support the decision making of the responsible stakeholders synchronized measurements from the onboard and the wayside monitoring systems were integrated into a platform. The wayside monitoring systems were tested in several locations in Europe. The onboard systems were tested in one train that circulate in the Portuguese Northern Railway Line. The research has been complemented through extensive laboratory tests and multibody dynamic modelling used to correlate the in-situ measurements with the status of the axle bearing life. A condition-based maintenance model for axle bearings was also developed as well as a smart diagnostics for an early detection of faults and a software tool for the optimal physical distribution of the diagnostic systems. This paper presents the main results and findings of the project. © Civil-Comp Press, 2016.

关键词： Condition based maintenance

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Achieving Linear Speedup with Network-Independent Learning Rates in Decentralized Stochastic Optimization

Achieving Linear Speedup with Network-Independent Learning R...

引用

IEEE Conference on Decision and Control

作者： Hao Yuan Sulaiman A. Alghunaim Kun Yuan Center for Machine Learning Research Peking University Beijing P. R. China Dept. Electrical Engr. Kuwait University Safat Kuwait AI for Science Institute Beijing P. R. China National Engineering Labratory for Big Data Analytics and Applications Beijing P. R. China

Decentralized stochastic optimization has become a crucial tool for addressing large-scale machine learning and control problems. In decentralized algorithms, all computing nodes are connected through a network topology, and each node communicates only with its direct neighbors. Decentralized algorithms can significantly reduce communication overhead by eliminating the need for global communication. However, existing research on the linear speedup analysis of decentralized stochastic algorithms is limited to the condition of network-dependent learning rates, which rarely holds in practice since the network connectivity is typically unknown to each node. As a result, it remains an open question whether a linear speedup bound can be achieved using network-independent learning rates. This paper provides an affirmative answer. By utilizing a new analysis framework, we prove that D-SGD and Exact-Diffusion, two representative decentralized stochastic algorithms, can achieve linear speedup with network-independent learning rates. Simulations are provided to validate our theories.

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Advancements in Multiple Sclerosis Disease Classification Through Machine Learning

Advancements in Multiple Sclerosis Disease Classification Th...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Darshanaben D. Pandya Sheshang Degadwala Dhairya Vyas Vishal Narendrakumar Solanki Sharma Vishalkumar Sureshbhai Hiteshkumar Gunvantbhai Patel Dept. of Comp. Sci. SCJCCS (BCA) Sankalchand Patel University Visnagar Gujarat India Dept. of Comm. Engg. Sigma University Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India D L Patel Institute of Mgt. & Technology (MCA College) Himatnagar Gujarat India Department of Computer Science (MCA) Sankalchand Patel University Visnagar Gujrat Quality Engineering - Data Tiger Analytics Chennai Tamil Nadu

In recent years, the field of medical diagnosis has witnessed significant advancements due to the application of machine learning algorithms, especially in complex neurological diseases like Multiple Sclerosis (MS). This study delves into the progress made in MS disease classification using various machine learning techniques, including SVM, KNN, DT, RF, NB, and Extra Trees. Through a diverse dataset of clinical and neuroimaging data, this study has systematically compared the performance of these algorithms in identifying different MS subtypes. SVM and Random Forest exhibited the highest accuracy, while KNN and Decision Trees showed competitive performance. Naive Bayes and Extra Trees also demonstrated promising results in specific scenarios. The study discusses the strengths and weaknesses of each approach and explores the interpretability of the models to understand the key features influencing the classification process. These findings contribute to the growing knowledge base of machine learning in MS disease classification and open avenues for more efficient and accurate diagnostic tools, ultimately leading to personalized patient care and improved treatment planning.

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