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SSRN 2024年

作者： Stamatiou, Yannis C. Liagkou, Vasia Spirakis, Paul G. Nastou, Panayiotis E. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Koatakioi Arta 47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece

The formal study of computer malware was initiated in the seminal work of Fred Cohen in the mid-80s who applied elements of Computation Theory in the investigation of the theoretical limits of using the Turing Machine formal model of computation in detecting viruses. Cohen gave a simple but realistic, formal, definition of the characteristic actions of a computer virus as a Turing Machine that replicates itself and proved that detecting this behaviour, in general. is an undecidable problem. In this paper, we complement Cohen's approach by providing a simple generalization of his definition of a computer virus so as to model any type of malware behaviour and showing that the malware/nonmalware classification problem is, again, undecidable. Most importantly, beyond Cohen's work, our work provides a generic theoretical framework for studying anti-malware applications and identifying, at an early stage, before their deployment, several of their inherent vulnerabilities which may lead to the construction of zero-day exploits and malware strains with stealth properties. To this end, we show that for any given formal system, which can be seen as an anti-malware formal model, there are infinitely many, effectively constructible, programs for which no proof can be produced by the formal system that they are either malware or non-malware programs. Moreover, infinitely many of these programs are, indeed, malware programs which evade the detection powers of the given formal system. © 2024, The Authors. All rights reserved.

关键词： Turing machines

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POLYNOMIAL-TIME RECOGNITION AND MAXIMUM INDEPENDENT SET IN BURLING GRAPHS

arXiv

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arXiv 2024年

作者： Rzążewski, Pawel Walczak, Bartosz Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Institute of Informatics University of Warsaw Warsaw Poland Department of Theoretical Computer Science Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland

A Burling graph is an induced subgraph of some graph in Burling’s construction of triangle-free high-chromatic graphs. We provide a polynomial-time algorithm which decides whether a given graph is a Burling graph and if it is, constructs its intersection model by rectangular frames. That model enables a polynomial-time algorithm for the maximum independent set problem in Burling graphs. As a consequence, we establish Burling graphs as the first known hereditary class of graphs that admits such an algorithm while not being χ-bounded. Copyright © 2024, The Authors. All rights reserved.

关键词： Polynomial approximation

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Adaptation of AI-Accelerated CFD Simulations to the IPU Platform 1

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14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Rościszewski, Pawel Krzywaniak, Adam Iserte, Sergio Rojek, Krzysztof Gepner, Pawel Graphcore Gdańsk Poland Department of Construction and Mechanical Engineering Universitat Jaume I Castellón de la Plana Spain Barcelona Supercomputing Center Barcelona Spain Institute of Computer and Information Sciences Czȩstochowa University of Technology Czȩstochowa Poland Faculty of Mechanical and Industrial Engineering Warsaw University of Technology Warszawa Poland Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of AI for simulation, where traditional numerical simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a computational fluid dynamics application. We use custom TensorFlow provided by the Poplar SDK to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We show how to utilize the popdist library to overcome a performance bottleneck in feeding training data to the IPU on the host side, achieving up to 34% speedup. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from 2 to 16 improves the throughput from 560.8 to 2805.8 samples/s. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vita...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mahdi Momeni Adrian Radomski Ulkuhan Guler Daniel Teichmann SDU Health Informatics and Technology The Maersk Mc-Kinney Moller Institute University of Southern Denmark Denmark Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness.

关键词： Heart rate Current control Accuracy Power demand Inverters Robustness Sensors Biomedical monitoring Monitoring Magnetic circuits

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Designing a Low-cost High-end Android-based Wireless Board for the EMULSION IoT Platform 3

Designing a Low-cost High-end Android-based Wireless Board f...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Ganchev, Ivan Ji, Zhanlin O'Droma, Mairtin Department of Computer Systems University of Plovdiv 'Paisii Hilendarski' Plovdiv Bulgaria Institute of Mathematics and Informatics Sofia Bulgaria Limerick Ireland North China University of Science and Technology College of Artificial Intelligence China

ISBN: (纸本)9789463968058

This paper proposes an innovative generic wireless low-cost high-end Android-based board for use by the EMULSION IoT platform, which is being elaborated as IoT-service and IoT-system prototyping ready. Based on the AllWinner A33 quad core Cortex-A7 CPU with a Mali400MP2 GPU and 512MB RAM, the board supports a 5MP CMOS sensor camera, 1080p video, MIDP display, and Hi-Fi audio. The paper reveals the basics of the design of this proprietary board. © 2022 URSI.

关键词： Costs

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Intelligent System for Recommendation of Mobile Services to Consumers 3

Intelligent System for Recommendation of Mobile Services to ...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

ISBN: (纸本)9789463968058

This paper presents an intelligent system for recommendation of services to mobile users (consumers) by considering the current context. The system builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers' choices for getting the best 'cost / quality of experience (QoE)' ratio when utilizing different mobile services. © 2022 URSI.

关键词： Intelligent systems

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DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection

DeepVD: Toward Class-Separation Features for Neural Network ...

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International Conference on Software Engineering (ICSE)

作者： Wenbo Wang Tien N. Nguyen Shaohua Wang Yi Li Jiyuan Zhang Aashish Yadavally Department of Informatics New Jersey Institute of Technology New Jersey USA Computer Science Department The University of Texas at Dallas Texas USA Computer Science Department University of Illinois Urbana-Champaign Illinois USA

The advances of machine learning (ML) including deep learning (DL) have enabled several approaches to implicitly learn vulnerable code patterns to automatically detect software vulnerabilities. A recent study showed that despite successes, the existing ML/DL-based vulnerability detection (VD) models are limited in the ability to distinguish between the two classes of vulnerability and benign code. We propose DeepVD, a graph-based neural network VD model that emphasizes on class-separation features between vulnerability and benign code. DeepVDleverages three types of class-separation features at different levels of abstraction: statement types (similar to Part-of-Speech tagging), Post-Dominator Tree (covering regular flows of execution), and Exception Flow Graph (covering the exception and error-handling flows). We conducted several experiments to evaluate DeepVD in a real-world vulnerability dataset of 303 projects with 13,130 vulnerable methods. Our results show that DeepVD relatively improves over the state-of-the-art ML/DL-based VD approaches 13%–29.6% in precision, 15.6%–28.9% in recall, and 16.4%–25.8% in F-score. Our ablation study confirms that our designed features and components help DeepVDachieve high class-separability for vulnerability and benign code.

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Identifying Critical Points of Departure for the Design of Self-Fashioning Technologies 25

Identifying Critical Points of Departure for the Design of S...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Blanco Cardozo, Rebeca Garrett, Rachael Samuelsson-Gamboa, Mafalda Haresamudram, Kashyap Lisy, Dominika Rogg, Maria Núñez-Pacheco, Claudia Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Interaction Design CSE Chalmers University of Technology University of Gothenburg Gothenburg Sweden Department of Technology and Society Lund University Lund Sweden Department of Thematic Studies Division of Gender Studies Linköping University Linköping Sweden Department for Informatics and Media Uppsala University Uppsala Sweden Department of Computer Science and Media Technology Malmö University Malmö Sweden

ISBN: (纸本)9798400713941

Designing technologies that clothe, adorn, or are otherwise placed on the body raises questions concerning the role they will play in dressing ourselves. We situate self-fashioning - or the process through which we stylise and present our bodies - as a complex practice where a series of social, material, and contextual factors shape how we present ourselves. Informed by reflective discussions and projective design tools, we contribute three critical points of departure for self-fashioning technologies: (i) Purposeful examining discomfort as an ongoing phenomenon, (ii) Supporting mimesis and visibility as qualities to be negotiated, and (iii) Envisioning the multiplicity of the body. We call for the design community to help devise fashionable technologies that are sensitive, caring, and responsive to the complexities of fashioning our bodies. © 2025 Copyright held by the owner/author(s).

关键词： Design

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Robust Transmission Design in Multiobjective RIS-Aided SWIPT IoT Communications

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IEEE Internet of Things Journal 2024年第10期11卷 18605-18618页

作者： Sharma, Vaibhav Allu, Raviteja Singh, Sandeep Kumar Singh, Keshav Duong, Trung Q. Tsiftsis, Theodoros A. Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj211004 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided Internet of Things (IoT) communications under imperfect channel state information (CSI). We formulate a multiobjective optimization problem (MOOP) to design a transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm-bounded CSI error model. Due to the conflicting objective functions and nonconvex nature of the above optimization problem, the MOOP is simplified using the ϵ-constraint method and subsequently adopting advanced optimization tools, such as the Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy, and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters, such as the number of RIS elements, available transmit power at BS, and the minimum HP on the performance of the considered system. © 2014 IEEE.

关键词： Energy efficiency

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A Multi-View Learning-Based Bayesian Ruleset Extraction Algorithm For Accurate Hepatotoxicity Prediction

A Multi-View Learning-Based Bayesian Ruleset Extraction Algo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Siyang Wu, Lianlian Xu, Yong Liu, Kunhong He, Song Bo, Xiaochen Xiamen University School of Informatics Xiamen China Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin China Fujian University of Technology School of Computer Science and Mathematics Fuzhou China Xiamen University School of Film Department of Digital Media Technology Xiamen China Institute of Health Service and Transfusion Medicine Department of Bioinformatics Beijing China

ISBN: (纸本)9798350337488

The interpretable machine learning method is important in drug discovery. Unlike traditional ensemble learning methods, this paper proposes an interpretable algorithm based on Bayesian rule extraction to obtain reliable and explainable results for hepatotoxicity prediction. To extract information from different types of omics data, our algorithm employs a multi-view learning strategy to enhance performance. Specifically, a random forest is trained in each view, and then the Bayesian rule extraction algorithm is designed to select an optimal rule subset, controlling the size and accuracy of the ruleset through probabilities. These rule sets are integrated through multi-view voting to get the final decisions. The performance of our algorithm is tested on the hepatotoxicity dataset, demonstrating that compared to traditional machine learning algorithms and rule-based algorithms, our approach maintains excellent performance while achieving high interpretability in most cases. Our python source code and the related Supplementary Materials are available at: ***/MLDMXM2017/MV-BRS. © 2023 IEEE.

关键词： Bayesian Ruleset Hepatotoxicity Prediction Interpretable Machine Learning Multi-view Learning

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