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arXiv 2024年

作者： Umm-E-Habiba Haug, Markus Bogner, Justus Wagner, Stefan Institute of Software Engineering University of Stuttgart Stuttgart70569 Germany Department of Software Engineering University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands TUM School of Communication Information and Technology Technical University of Munich Heilbronn Germany

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward. ∗ © 2024, CC BY.

关键词： Requirements engineering

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Smart Gradational Vehicle Disassembling and Troubleshooting Support Assistant

Smart Gradational Vehicle Disassembling and Troubleshooting ...

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Advancements in Computing (ICAC), International Conference on

作者： Suvin Tennakoon Pamuditha Wickramasinha Hasini Ranathunga Chathuranga Ulpathakumbura Dharshana Kasthurirathna Udara Srimath S. Samaratunge Arachchillage Department of Computer Science and Software Engineering Sri Lanka Institute Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute Information Technology Malabe Sri Lanka

The world of vehicle service and troubleshooting can be daunting for individuals without specialized training. Vehicle manuals are often complex and challenging to comprehend, while relying on experienced mechanics for assistance can be inconvenient and expensive. To address these challenges, this research paper presents the development of an interactive vehicle service assistance system that empowers vehicle owners to independently service and troubleshoot their vehicles. The proposed system encompasses four key components: a knowledgebase construction, write semantic rules (SWRL), real-time object identification using YOLO model, audio feedback and stepwise guidance for customer. The knowledgebase is used for stepwise guidance for disassembling engine through employing marker-less Augmented Reality (AR). The knowledgebase component offers user-friendly access to troubleshooting techniques based on insights from experienced mechanics. By providing stepwise instructions accompanied by AR based visualization, users can effectively learn the process of deconstructing and assembling different components of their automobiles. The proposed system enhances vehicle troubleshooting experiences by enabling owners to acquire the necessary skills to self-troubleshooting without involving external assistance and empower individuals to take control of their own vehicles.

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AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design

arXiv

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arXiv 2023年

作者： Zhao, Qi Yan, Bai Hu, Taiwei Chen, Xianglong Duan, Qiqi Yang, Jian Shi, Yuhui The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science Johns Hopkins University BaltimoreMD United States The School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China

Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the rigorous mathematical assumptions of analytical solvers. The canonical manual optimizer design could be laborious, untraceable and error-prone, let alone human experts are not always available. This arises increasing interest and demand in automating the optimizer design process. In response, this paper proposes AutoOptLib, the first platform for accessible automated design of metaheuristic optimizers. AutoOptLib leverages computing resources to conceive, build up, and verify the design choices of the optimizers. It requires much less labor resources and expertise than manual design, democratizing satisfactory metaheuristic optimizers to a much broader range of researchers and practitioners. Furthermore, by fully exploring the design choices with computing resources, AutoOptLib has the potential to surpass human experience, subsequently gaining enhanced performance compared with human problem-solving. To realize the automated design, AutoOptLib provides 1) a rich library of metaheuristic components for continuous, discrete, and permutation problems;2) a flexible algorithm representation for evolving diverse algorithm structures;3) different design objectives and techniques for different optimization scenarios;and 4) a graphic user interface for accessibility and practicability. AutoOptLib is fully written in Matlab/Octave;its source code and documentation are available at https://***/qz89/AutoOpt and https://***/, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： User interfaces

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TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

arXiv

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arXiv 2023年

作者： Xiong, Weidan Zhang, Hongqian Peng, Botao Hu, Ziyu Wu, Yongli Guo, Jianwei Huang, Hui Shenzhen University China Shenzhen University China MAIS Institute of Automation Chinese Academy of Sciences China Shenzhen University College of Computer Science & Software Engineering China

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Efficient Relational Context Perception for Knowledge Graph Completion

arXiv

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arXiv 2024年

作者： Tu, Wenkai Wan, Guojia Shang, Zhengchun Du, Bo School of Computer Science Wuhan University Luojiashan Road Hubei Province Wuhan430072 China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Luojiashan Road Hubei Province Wuhan430072 China Institute of Artificial Intelligence School of Computer Science Wuhan University Luojiashan Road Hubei Province Wuhan430072 China

Knowledge Graphs (KGs) provide a structured representation of knowledge but often suffer from challenges of incompleteness. To address this, link prediction or knowledge graph completion (KGC) aims to infer missing new facts based on existing facts in KGs. Previous knowledge graph embedding models are limited in their ability to capture expressive features, especially when compared to deeper, multi-layer models. These approaches also assign a single static embedding to each entity and relation, disregarding the fact that entities and relations can exhibit different behaviors in varying graph contexts. Due to complex context over a fact triple of a KG, existing methods have to leverage complex non-linear context encoder, like transformer, to project entity and relation into low dimensional representations, resulting in high computation cost. To overcome these limitations, we propose Triple Receptance Perception (TRP) architecture to model sequential information, enabling the learning of dynamic context of entities and relations. Then we use tensor decomposition to calculate triple scores, providing robust relational decoding capabilities. This integration allows for more expressive representations. Experiments on benchmark datasets such as YAGO3-10, UMLS, FB15k, and FB13 in link prediction and triple classification tasks demonstrate that our method performs better than several state-of-the-art models, proving the effectiveness of the integration. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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SliceLocator: Locating Vulnerable Statements with Graph-based Detectors

arXiv

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arXiv 2024年

作者： Cheng, Baijun Wang, Kailong Gao, Cuiyun Luo, Xiapu Li, Li Guo, Yao Chen, Xiangqun Wang, Haoyu School of Computer Science Peking University Beijing China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China School of Computer Science and Technology Harbin Institute of Technolgy Guangdong Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Software Beihang University Beijing China

Vulnerability detection is a crucial component in the software development lifecycle. Existing vulnerability detectors, especially those based on deep learning (DL) models, have achieved high effectiveness. Despite their capability of detecting vulnerable code snippets from given code fragments, the detectors are typically unable to further locate the fine-grained information pertaining to the vulnerability, such as the precise vulnerability triggering locations. Although explanation methods can filter important statements based on the predictions of code fragments, their effectiveness is limited by the fact that the model primarily learns the difference between vulnerable and non-vulnerable samples. In this paper, we propose SliceLocator, which, unlike previous approaches, leverages the detector’s understanding of the differences between vulnerable and non-vulnerable samples—essentially, vulnerability-fixing statements. SliceLocator identifies the most relevant taint flow by selecting the highest-weighted flow path from all potential vulnerability-triggering statements in the program, in conjunction with the detector. We demonstrate that SliceLocator consistently performs well on four state-of-the-art GNN-based vulnerability detectors, achieving an accuracy of around 87% in flagging vulnerability-triggering statements across six common C/C++ vulnerabilities. It outperforms five widely used GNN-based explanation methods and two statement-level detectors. Copyright © 2024, The Authors. All rights reserved.

关键词： software design

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CETIS: Retrofitting Intel CET for Generic and Efficient Intra-process Memory Isolation 22

CETIS: Retrofitting Intel CET for Generic and Efficient Intr...

引用

28th ACM SIGSAC Conference on computer and Communications Security, CCS 2022

作者： Xie, Mengyao Wu, Chenggang Zhang, Yinqian Xu, Jiali Lai, Yuanming Kang, Yan Wang, Wei Wang, Zhe Sklp Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science and Engineering SUSTech Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Sklp Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9781450394505

Intel control-flow enforcement technology (CET) is a new hardware feature available in recent Intel processors. It supports the coarse-grained control-flow integrity for software to defeat memory corruption attacks. In this paper, we retrofit CET, particularly the write-protected shadow pages of CET used for implementing shadow stacks, to develop a generic and efficient intra-process memory isolation mechanism, dubbed CETIS. To provide user-friendly interfaces, a CETIS framework was developed, which provides memory file abstraction for the isolated memory regions and a set of APIs to access said regions. CETIS also comes with a compiler-assisted tool chain for users to build secure applications easily. The practicality of using CETIS to protect CPI, CFIXX, and JIT-compilers was demonstrated, and the evaluation reveals that CETIS is performed better than state-of-the-art intra-memory isolation mechanisms, such as MPK. © 2022 Owner/Author.

关键词： Retrofitting

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Objective Full-Reference Image Assessment Metrics for Estimating the Quality of Remote Sensing Images

Objective Full-Reference Image Assessment Metrics for Estima...

引用

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： R. Maruthi P. Anusha P. Sankar K. Thiyagaragan Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Department of Electronics and Communication Engg Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Image Quality (IQ) assessment is a very complex task and it is extremely important to evaluate the images with the metrics. The metrics applied can be a full reference, partial reference or no-reference metric and it depends on the application and availability of the ground truth. Most of the IQ metrics are developed by considering the Visual System (VS) of humans. The assessment methods studied in this paper focuses on some of the Full-Reference (FR) measures and it is used to estimate the remote sensing noisy images. The effectiveness of the measures demonstrates a considerable outcome and demonstrates how well the noisy remote sensing images are being quantified.

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Flexible Memory Rotation (FMR): Rotated Representation with Dynamic Regularization to Overcome Catastrophic Forgetting in Continual Knowledge Graph Learning

Flexible Memory Rotation (FMR): Rotated Representation with ...

引用

IEEE International Conference on Big Data

作者： Lijing Zhu Dong Hyun Jeon Wenbo Sun Li Yang Yixin Xie Shuteng Niu Computer Science Department Bowling Green State University Bowling Green USA University of Michigan Transportation Research Institute Ann Arbor USA Computer Science Department University of North Carolina at Charlotte Charlotte USA College of Computing and Software Engineering Kennesaw State University Marietta USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

As a special type of Knowledge Graph (KG), Continual Knowledge Graph Learning (CKGL) plays a pivotal role in various areas such as recommendation systems, search engines, and personalized services, where knowledge dynamically evolves. A challenging problem in CKGL is catastrophic forgetting, where models forget previously learned knowledge upon being trained on new data. To overcome the challenge, this study proposes Flexible Memory Rotation (FMR), a dual-level regularization technique that focuses on both parameter level and structural level. Our idea is inspired by the natural human learning process, which tends to memorize correctly learned knowledge, leverage the learned to acquire new knowledge, and refine incorrectly learned knowledge with newly obtained information. Commonly, existing regularization-based methods fail to mimic this human nature by having a fixed constraint strategy for all model parameters. To this end, the proposed FMR offers flexible constraints based on qualities of learned knowledge evaluated by the Fisher Information Matrix (FIM). Additionally, we identified a limitation of FIM in CKGL, which is the assumption of independence of time steps does not always hold. To overcome this, FMR rotates the parameter space to diagonalize the FIM. This work has four major contributions: 1) develop a novel regularization technique, FMR, a flexible regularization technique, 2) reveal the unexpected failure of FIM in CKGL and provides an easy remedy via parameter space rotation, 3) the comparison experiments on four benchmark datasets designed for CKGL demonstrates improvement over various state-of-the-art (SOTA) CKGL models, and 4) a comprehensive ablation study investigates each component of the proposed model. The source code is available at https://***/lijingzhu1/FMR.

关键词： Source coding Knowledge graphs Search engines Big Data Benchmark testing Data models Recommender systems

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Multi Model Approach to Evaluate and Enhance Student - Teacher Interactions

Multi Model Approach to Evaluate and Enhance Student - Teach...

引用

Advancements in Computing (ICAC), International Conference on

作者： Nisal Daksith Sathsara Wanaguru Uresha Kolonne Amaya Dolawatta Lakmini Abeywardhana Dharshana Kasthurirathna Department of Information Technology Sri Lanka Institute of Inoformation Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Inoformation Technology Malabe Sri Lanka

Advancements in artificial intelligence (AI) and machine learning (ML) have enabled the development of tools to address issues in Virtual Classrooms. This research focuses on utilizing AI and ML to monitor student behavior in virtual classrooms by analyzing facial expressions, background noise, and keystroke patterns. An AI based approach is proposed to analyze real-time webcam data, detecting patterns of inattention, and notifying teachers accordingly. The application also monitors background noise levels, identifying non-conducive learning environments, and analyzes keystroke patterns to detect unrelated activities. The research draws from academic literature to evaluate the effectiveness of the application while considering ethical implications such as potential bias and student privacy protection. Overall, this study contributes to the existing literature on AI and ML-based applications in education, demonstrating how these technologies can revolutionize student behavior monitoring, leading to enhanced outcomes for virtual classroom learning.

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