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arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

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ADOD: Adaptive Density Outlier Detection 24

ADOD: Adaptive Density Outlier Detection

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Qian, Li Qian, Jing Sun, Xin Guo, Wengang Bohm, Christian Institute of Informatics Ludwig Maximilian University of Munich Munich Germany University of Vienna Faculty of Computer Science Vienna Austria Carbon Neutral R&D Center China Railway Hi-Tech Industry Corporation Limited Beijing China City University of Macau Faculty of Data Science China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798331506681

Outlier detection plays a dual role in data analysis: cleansing data to optimize the performance of downstream tasks and identifying potentially rare valuable events or patterns. Proximity-based methods, which are independent of data distribution assumptions, are plagued by parameter selection and performance challenges when handling data with varying densities. This study proposed a novel unsupervised algorithm named Adaptive Density Outlier Detection (ADOD) to address these challenges. The core innovation of ADOD involves two main aspects: adaptive neighborhood boundaries and density consistency scoring. First, instead of relying on a predefined fixed radius, ADOD employs perplexity to calculate the local scale of each data point. It then dynamically adjusts the neighborhood boundaries according to this scale to adapt to data with varying densities. Second, ADOD estimates local density using a mutual neighbor graph and combines the density differences between data points and their neighbors to compute outlier scores, effectively distinguishing outliers that significantly deviate from their surroundings. This study evaluated ADOD on one synthetic and 32 real datasets, and compared it with 14 classical and state-of-the-art algorithms from different categories. Extensive experimental results demonstrated the superior performance of ADOD, achieving the highest average accuracy across ROC, P@N, and AP metrics. This study promotes the development of outlier detection techniques and expands their potential for real-time applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of Enhanced Dynamic Video Streaming Mechanism for Improving the Throughput Comparing with Ad Hoc on-Demand Distance Vector Routing Mechanism in Video Streaming Application

Design of Enhanced Dynamic Video Streaming Mechanism for Imp...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： P. Dilli Prasad R. Balamanigandan R. Mahaveerakannan A.AntoSagaya Priscilla Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Ad hoc, on-demand distance vector routing and dynamic video streaming techniques are used with the aim of increasing the throughput of the video streaming application. Two categories have been distinguished. Dynamic video streaming is the purview of Group 1, and vector routing is the domain of Group 2. The dynamic video streaming method was run with a sample size of 155, an enrolment ratio of 1.1, alpha and beta values of 0.05 and 0.5, and a 95% confidence level. It was also run with a variable number of trials. For this research, the G-power Pretest value is 85%. When it comes to wormhole prediction, the dynamic video streaming mechanism yields 1.57 throughput, while the distance vector navigation on-demand and ad hoc mechanism yields 1.76. A statistically significant difference $(\mathbf{p}=\mathbf{0.008} < \mathbf{0.05})$ with a $\mathbf{99}\%$ confidence interval exists between the two groups. Therefore, ad hoc, on-demand, distance vector routing mechanisms are inferior to dynamic video streaming mechanisms. In summary, the findings indicate that the dynamic video streaming mechanism outperforms the distance vector navigation on-demand and ad hoc mechanisms in terms of throughput.

关键词： Navigation Data integrity Streaming media Throughput Routing Vectors AODV

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A House Divided: How U.S. Politics Could Shape Contact-Tracing Adoption in Future Pandemics 25

A House Divided: How U.S. Politics Could Shape Contact-Traci...

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

作者： Smith, Garrett Chapman, Kirsten Weng, Tzu-Yu Hao, Haijing Mondal, Mainack Smith, Staci Chen, Yunan Page, Xinru Brigham Young University ProvoUT United States Computer Science Brigham Young University ProvoUT United States Bentley University WalthamMA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India Informatics University of California Irvine IrvineCA United States Department of Computer Science Brigham Young University ProvoUT United States

ISBN: (纸本)9798400713941

Contact tracing has shown to be an effective tool in limiting the spread of transmittable diseases in countries where it is widely adopted. During the COVID-19 pandemic, contact tracing app adoption in the United States was low despite having the highest number of recorded cases worldwide. To better understand why, we conducted a survey (N=302, matched to U.S. census demographics) and found that political orientation overwhelmingly predicted attitudes towards COVID-19 and the adoption of contact tracing apps. These attitudes also overwhelmingly shaped people's willingness to participate in contact tracing for diseases in future pandemics. Our findings suggest that the politically charged environment surrounding COVID-19 in the U.S. may have a long-term impact on American's willingness to utilize contact tracing for diseases in future pandemics. We conclude with recommendations for technology designers and policymakers on how to overcome the sharp divide that has been driven by the political discourse in the U.S. © 2025 Copyright held by the owner/author(s).

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Automated graph representation learning: A survey

Automated graph representation learning: A survey

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2021 IEEE International Conference on engineering, Technology and Innovation, ICE/ITMC 2021

作者： Madurapperuma, Sanjula Welihindha, Sudharshana Computer Science and Engineering University of Westminster London United Kingdom School of Computing Informatics Institute of Technology Colombo 6 Sri Lanka

ISBN: (纸本)9781665449632

Research on Graph Representation Learning has seen many innovations in the recent years given the close resemblance of real-world data to graphs and its already wide usage. Graph data are considerably more difficult to analyse than traditional Euclidean data structures, which is the gap that Graph Representation Learning aims to cater to. However, automating Graph Representation Learning is a research area that is only starting to gain the focus it deserves. Most of the work that has been made available is not adequately documented or clear in their approach. It would be greatly beneficial for such work to be thoroughly analysed and evaluated under a common criterion. This paper introduces a novel automated Graph Representation Learning workflow that is utilized by an ongoing research project called AutoGRL which is built by the authors. It provides a standardized and modular production-ready platform for automated Graph Representation Learning with a level of autonomy and performance that has not been discovered. This paper also delivers an analysis of the existing work in the automated Graph Representation Learning domain, the benefits and limitations of the existing work, including comparisons of features along with new and unique research directions that are currently under-explored in the field. © 2021 IEEE.

关键词： Automation

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Automatic Difficulty Balance in Two-Player Games with Deep Reinforcement Learning

Automatic Difficulty Balance in Two-Player Games with Deep R...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Simão Reis Rita Novais Luís Paulo Reis Nuno Lau Artificial Intelligence and Computer Science Laboratory (LIACC) Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Aveiro Portugal

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

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DESIGNING A SKILLED SOCCER TEAM FOR ROBOCUP: EXPLORING SKILL-SET-PRIMITIVES THROUGH REINFORCEMENT LEARNING

arXiv

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arXiv 2023年

作者： Abreu, Miguel Reis, Luis Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D Soccer Simulation League serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance is based on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the Proximal Policy Optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast sprint-kick developed in 2021 and progress to the most recent skill set, which includes a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push tackles both low-level collision-prone scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the codebase of our team to the RoboCup community, enabling other teams to transition to Python more easily and providing new teams with a robust and modern foundation upon which they can build new features. © 2023, CC BY.

关键词： High level languages

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Contactless Respiratory Rate Monitoring Using FMCW Radar

Contactless Respiratory Rate Monitoring Using FMCW Radar

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Widiyasari, Diyah Ahmadi, Nur Adiono, Trio Purwarianti, Ayu Purnomo, Ariana Tulus Lin, Ding-Bing Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Bandung40132 Indonesia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Sampoerna University Departement of Information System Jakarta Selatan12780 Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Vital sign monitoring plays a very important role in the early diagnosis and prognosis of a disease, as well as for patient health management. Among other vital signs, respiratory rate (RR) is rarely recorded and often forgotten despite the fact that it is an essential predictor of serious events such as cardiac arrest and deteriorating clinical condition. In this paper, we propose a frequency-modulated continuous-wave (FMCW) radar for contactless RR monitoring. We employ FFT to calculate RR in frequency-domain and combine it with Kalman filter to smooth out the estimated RR. We use a public dataset measuring RR from 50 children under 13 years of age recorded with 60 GHz FMCW radar. The experimental results show that the proposed method yields MAE and RMSE of 3.20 and 4.10, respectively, with respect to the reference device. Incorporating Kalman filter reduces the percentage of error by 14.83% and 13.36% compared to without it. The overall results suggest that radar-based technology can be potentially used for contactless RR monitoring which is suitable for those who cannot use contact-based monitoring devices. © 2022 IEEE.

关键词： Kalman filters

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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LiSD: An Efficient Multi-Task Learning Framework For Lidar Segmentation and Detection

LiSD: An Efficient Multi-Task Learning Framework For Lidar S...

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IEEE International Conference on Image Processing

作者： Jiahua Xu Si Zuo Chenfeng Wei Wei Zhou Wuxi Intelligent Control Research Institute Hunan University China College of Mechanical and Vehicle Engineering Hunan University China School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

With the rapid proliferation of autonomous driving, there has been a heightened focus on the research of lidar-based 3D semantic segmentation and object detection methodologies, aiming to ensure the safety of traffic participants. In recent decades, learning-based approaches have emerged, demonstrating remarkable performance gains in comparison to conventional algorithms. However, the segmentation and detection tasks have traditionally been examined in isolation to achieve the best precision. To this end, we propose an efficient multi-task learning framework named LiSD which can address both segmentation and detection tasks, aiming to optimize the overall performance. Our proposed LiSD is a voxel-based encoder-decoder framework that contains a hierarchical feature collaboration module and a holistic information aggregation module. Different integration methods are adopted to keep sparsity in segmentation while densifying features for query initialization in detection. Besides, cross-task information is utilized in an instance-aware refinement module to obtain more accurate predictions. Experimental results on the nuScenes dataset and Waymo Open Dataset demonstrate the effectiveness of our proposed model. It is worth noting that LiSD achieves the state-of-the-art performance of $83.3 \% \mathrm{mIoU}$ on the nuScenes segmentation benchmark for lidar-only methods.

关键词： Laser radar Three-dimensional displays Semantic segmentation Collaboration Object detection Performance gain Multitasking

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