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arXiv 2024年

作者： Guri, Mordechai Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Air-Gap Research Lab United States

Personal data has become one of the most valuable assets and lucrative targets for attackers in the modern digital world. This includes personal identification information (PII), medical records, legal information, biometric data, and private communications. To protect it from hackers, 'air-gap' measures might be employed. This protective strategy keeps sensitive data in networks entirely isolated (physically and logically) from the Internet. Creating a physical 'air gap' between internal networks and the outside world safeguards sensitive data from theft and online threats. Air-gap networks are relevant today to governmental organizations, healthcare industries, finance sectors, intellectual property and legal firms, and others. In this paper, we dive deep into air-gap security in light of modern cyberattacks and data privacy. Despite this level of protection, publicized incidents from the last decade show that even air-gap networks are not immune to breaches. Motivated and capable adversaries can use sophisticated attack vectors to penetrate the air-gapped networks, leaking sensitive data *** focus on different aspects of air gap security. First, we overview cyber incidents that target air-gap networks, including infamous ones such as ***. Second, we introduce the adversarial attack model and different attack vectors attackers may use to compromise air-gap networks. Third, we present the techniques attackers can apply to leak data out of air-gap networks and introduce more innovative ones based on our recent research. We demonstrate that despite the disconnection from the Internet, attackers can exploit special types of covert channels to exfiltrate confidential data. We present the different types of covert channels, including electromagnetic, electrical, optical, thermal, and acoustic techniques. We show how attackers can use them stealthily. Our paper shows how advanced persistent threats (APTs) can leak private and confidential information using t

关键词： Information leakage

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia Computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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A Study of Two Types of Introductory Exercise Problems for Network Programming Learning with Python 23

A Study of Two Types of Introductory Exercise Problems for N...

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11th International Conference on Computer and Communications Management, ICCCM 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Department of Information and Communication Systems Distributed System Design Lab. Okayama University Okayama Japan Department of Computer Science Control Software Lab. Nihon University Koriyama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798400707735

Network programming is the act of writing computer codes for communications between programs or processes in different computers across networks. It is crucial for applications and services using networks, including e-commerce, gaming, and social media. However, university courses of educating it are very limited. As a result, it has become important to develop and offer IT tools to support self-studies of network programming by novices. In this paper, we present a study of two types of introductory exercise problems, namely, Grammar-concept Understanding Problem (GUP) and Comment Insertion Problem (CIP), for self-studies of five basic network protocols of TCP/IP, ARP, SMTP, POP3, and SSH with Python. A GUP instance requests to answer the keyword such as a library function in the given source code that corresponds to the grammar concept described in the question. A CIP instance requests to insert the comment at each blank of the code. String matching is adopted to mark any answer at the browser-based answer interface. Through solving them, it is expected to understand the meaning of important words and the code structure for network programming. As preliminary evaluations, we generated six GUP and CIP instances respectively, and assigned them to 10 students in Okayama University, Japan. The results confirmed that the proposal is proper for novices to learn network programming with Python. © 2023 ACM.

关键词： Python

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Optimal Strategies for the Game with Two Faster 3D Pursuers and One Slower 2D Evader 41

Optimal Strategies for the Game with Two Faster 3D Pursuers ...

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第41届中国控制会议

作者： Runqi Zhang Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LabCollege of EngineeringPeking University National Engineering Research Center of Software Engineering Peking University Center for Multi-Agent Research Institute for Artificial IntelligencePeking University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A pursuit-evasion differential game with two faster pursuers in 3 D space and one slower evader in a 2 D plane is *** pursuers,which have different speeds,attempt to cooperate to capture the evader while the evader struggles to avoid or delay the *** differential games and the concept of isochron,we present a complete solution to this game and develop the optimal strategies for both the pursuers and the *** order to guarantee the optimality of the proposed strategies,the value function of the game is given and is further proved to be the solution of the corresponding Hamilton-Jacobi-Isaacs ***,simulations are carried out with comparisons between the proposed strategies and other typical strategies,the results of which show the optimality of the proposed strategies.

关键词： Pursuit-evasion differential games multi-agent system optimal strategy

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Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Large Language Models are Few-Shot Summarizers: Multi-Intent...

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International Conference on software engineering (ICSE)

作者： Mingyang Geng Shangwen Wang Dezun Dong Haotian Wang Ge Li Zhi Jin Xiaoguang Mao Xiangke Liao College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Key Lab of High Confidence Software Technology Peking University Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.

关键词： Computer languages Codes Supervised learning Natural languages Semantics Task analysis software engineering

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy systems 2025年第6期33卷 1899-1912页

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots

Reinforcement Learning for Blind Stair Climbing with Legged ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Simon Chamorro Victor Klemm Miguel de La Iglesia Valls Christopher Pal Roland Siegwart Department of Computer and Software Engineering École Polytechnique de Montréal Québec Canada Autonomous Systems Lab ETH Zurich Switzerland Robotic Systems Lab ETH Zurich Switzerland Ascento Robotics Zurich Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across various domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in multi-story environments. This study proposes a method aimed at addressing this limitation, employing reinforcement learning to develop a versatile controller applicable to a wide range of robots. In contrast to the conventional velocity-based controllers, our approach builds upon a position-based formulation of the RL task, which we show to be vital for stair climbing. Furthermore, the methodology leverages an asymmetric actor-critic structure, enabling the utilization of privileged information from simulated environments during training while eliminating the reliance on exteroceptive sensors during real-world deployment. Another key feature of the proposed approach is the incorporation of a boolean observation within the controller, enabling the activation or deactivation of a stair-climbing mode. We present our results on different quadrupeds and bipedal robots in simulation and showcase how our method allows the balancing robot Ascento to climb 15cm stairs in the real world, a task that was previously impossible for this robot.

关键词： Legged locomotion Training Prototypes Reinforcement learning Switches Stairs Robot sensing systems

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Interruptions detection in video conferences

arXiv

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arXiv 2023年

作者： Horowitz, Shmuel Kagan, Dima Alpert, Galit Fuhrmann Fire, Michael The Data4Good Lab Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

In recent years, video conferencing (VC) popularity has skyrocketed for a wide range of activities. As a result, the number of VC users surged sharply. The sharp increase in VC usage has been accompanied by various newly emerging privacy and security challenges. VC meetings became a target for various security attacks, such as Zoombombing. Other VC-related challenges also emerged. For example, during COVID lockdowns, educators had to teach in online environments struggling with keeping students engaged for extended periods In parallel, the amount of available VC videos has grown exponentially. Thus, users and companies are limited in finding abnormal segments in VC meetings within the converging volumes of data. Such abnormal events that affect most meeting participants may be indicators of interesting points in time, including security attacks or other changes in meeting climate, like someone joining a meeting or sharing a dramatic content. Here, we present a novel algorithm for detecting abnormal events in VC data. We curated VC publicly available recordings, including meetings with interruptions. We analyzed the videos using our algorithm, extracting time windows where abnormal occurrences were detected. Our algorithm is a pipeline that combines multiple methods in several steps to detect users' faces in each video frame, track face locations during the meeting and generate vector representations of a facial expression for each face in each frame. Vector representations are used to monitor changes in facial expressions throughout the meeting for each participant. The overall change in meeting climate is quantified using those parameters across all participants, and translating them into event anomaly detection. This is the first open pipeline for automatically detecting anomaly events in VC meetings. Our model detects abnormal events with 92.3% precision over the collected dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Video conferencing

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GUIDE: LLM-Driven GUI Generation Decomposition for Automated Prototyping

GUIDE: LLM-Driven GUI Generation Decomposition for Automated...

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International Conference on software engineering - Companion, ICSE, Companion

作者： Kristian Kolthoff Felix Kretzer Christian Bartelt Alexander Maedche Simone Paolo Ponzetto Institute for Software and Systems Engineering Clausthal University of Technology Clausthal Germany Human-Centered Systems Lab Karlsruhe Institute of Technology Karlsruhe Germany Data and Web Science Group University of Mannheim Mannheim Germany

ISBN: (数字)9798331536831

ISBN: (纸本)9798331536848

Graphical user interface (GUI) prototyping serves as one of the most valuable techniques for enhancing the elicitation of requirements, facilitating the visualization and refinement of customer needs and closely integrating the customer into the development activities. While GUI prototyping has a positive impact on the software development process, it simultaneously demands significant effort and resources. The emergence of Large Language Models (LLMs) with their impressive code generation capabilities offers a promising approach for automating GUI prototyping. Despite their potential, there is a gap between current LLM-based prototyping solutions and traditional user-based GUI prototyping approaches which provide visual representations of the GUI prototypes and direct editing functionality. In contrast, LLMs and related generative approaches merely produce text sequences or non-editable image output, which lacks both mentioned aspects and therefore impede supporting GUI prototyping. Moreover, minor changes requested by the user typically lead to an inefficient regeneration of the entire GUI prototype when using LLMs directly. In this work, we propose GUIDE, a novel LLM-driven GUI generation decomposition approach seamlessly integrated into the popular prototyping framework Figma. Our approach initially decomposes high-level GUI descriptions into fine-granular GUI requirements, which are subsequently translated into Material Design GUI prototypes, enabling higher controllability and more efficient adaption of changes. To efficiently conduct prompting-based generation of Material Design GUI prototypes, we propose a retrieval-augmented generation (RAG) approach to integrate the component library. Our preliminary evaluation demonstrates the effectiveness of GUIDE in bridging the gap between LLM generation capabilities and traditional GUI prototyping workflows, offering a more effective and controlled user-based approach to LLM-driven GUI prototyping. Video presentation

关键词： Visualization Translation Large language models Retrieval augmented generation Prototypes Controllability Libraries Graphical user interfaces software engineering software development management

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GUIDE: LLM-Driven GUI Generation Decomposition for Automated Prototyping

arXiv

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arXiv 2025年

作者： Kolthoff, Kristian Kretzer, Felix Bartelt, Christian Maedche, Alexander Ponzetto, Simone Paolo Institute for Software and Systems Engineering Clausthal University of Technology Clausthal Germany Human-Centered Systems Lab Karlsruhe Institute of Technology Karlsruhe Germany Data and Web Science Group University of Mannheim Mannheim Germany

关键词： Graphical user interfaces

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