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Dangerous art Paintings: artistic Adversarial Attacks on Salient Object Detection 4

Dangerous Art Paintings: Artistic Adversarial Attacks on Sal...

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2023 4th international conference on Computer engineering and Intelligent Control, ICCEIC 2023

作者： Chen, Qian Zhao, Haichao Wan, Chi Yang, Tianrun Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Communication University of China School of Information and Communication Engineering Beijing100024 China Northeastern University School of Mechanical Engineering and Automation Shenyang110819 China Harbin Institute of Technology School of Software Harbin150001 China

ISBN: (纸本)9798350308877

Recently, deep Convolutional Neural Networks (CNNs) are found to be vulnerable to inputs with some imperceptible perturbations. However, existing adversarial attacks add noise that conforms to certain distribution which can be detected and defended easily. In this paper, we propose a novel attack approach for Salient Object Detection (SOD) network: artistic adversarial attacks. Inspired by the style transfer task, we work on adding learnable adversarial noise by changing texture. Specifically, we propose a hybrid loss function to add attack texture in the original image. Meanwhile, we present a color fusion method to make the adversarial texture more imperceptible and authentic. We come up with a white-box training and black-box testing strategy to stimulate real attack. Experiments demonstrate that our style-transferred images have fooled 10 state-of-the-art detection networks with enough authenticity and naturalness. the source code is available at: https://***/NorthForest233/art. © 2023 IEEE.

关键词： Textures

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Game programming Meets Parallel Computing: Teaching Parallel Computing Concepts to Game Programmers 4th

Game Programming Meets Parallel Computing: Teaching Parallel...

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4th international conference on Advances in Computational science and engineering, ICACSE 2023

作者： Del Gallego, Neil Patrick De La Salle University 2401 Taft Ave Malate Metro Manila Manila1004 Philippines Lab De La Salle University 2401 Taft Ave Malate Metro Manila Manila1004 Philippines

ISBN: (纸本)9789819729760

With the rapid advancement of the gaming industry and the demand to create high-performance games, game programmers must be well-versed in concurrent programming and parallel computing concepts. this paper presents a set of project-based learning activities for teaching parallel computing concepts to game programmers. We propose four project-based activities: (1) asynchronous image loader, (2) multi-threaded ray tracer, (3) interactive loading screen, and (4) interactive 3D model viewer. Our projects require computer graphics and game programming concepts, differentiating our projects from other parallel computing course assignments. Such projects may be of interest to undergraduate students who wish to pursue game programming in the future. Similarly, the projects may be taught to game programmers who have yet to learn about parallel computing. A pilot course delivery was conducted at De La Salle University, where 39 students have enrolled. Students have generally achieved high scores (>80%), implying the effectiveness of the learning content in developing the projects. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Research on the method of abnormal physiological indicators diagnosis for subway drivers. 4

Research on the method of abnormal physiological indicators ...

引用

4th international conference on Smart City engineering and Public Transportation, SCEPT 2024

作者： Deng, Jian Ma, Ziyan Zheng, Rongshu School of Urban Rail Transportation Shanghai University of Engineering Science Shanghai China Anhui Institute of Information Technology Wuhu China

ISBN: (数字)9781510679825

ISBN: (纸本)9781510679818

A real-time physiological index anomaly detection method for subway drivers has been proposed. We use smart wristbands to collect indicator data and determine whether the current physiological indicator values of subway drivers are in an abnormal state. We use historical data of three human physiological indicators, heart rate, body temperature, and respiration, and generate confidence intervals for various indicators of drivers based on the principle of statistical normal distribution, to establish a model for judging abnormal physiological indicators of drivers. Python programming is applied to simulate historical data, establish confidence intervals, collect indicator data, and calculate results for judgment. the simulation results can determine the current state of the driver through measurement values, and the historical data generated by the measurement is in a continuously updated state. the confidence intervals of each indicator can be continuously adjusted, which is more applicable than other methods. Moreover, monitoring indicators can be added on the basis of technology and equipment. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Normal distribution

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Electric-vehicle routing problem with time windows and energy minimization: green logistics with same-day delivery approaches 4

Electric-vehicle routing problem with time windows and energ...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Moradi, Nima Kayvanfar, Vahid Baldacci, Roberto Concordia University Information Systems Engineering Montreal Canada College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Division of Engineering Management and Decision Sciences Doha34110 Qatar

ISBN: (纸本)9798350395914

Electric Vehicle (EV)-based last-mile delivery has been studied in recent years due to its theoretical and practical importance. EVs are known for their eco-friendliness and no air pollution in Green Logistics. However, there is no work on integrating the energy consumption and same-day delivery approaches with Electric-Vehicle Routing Problem with Time Windows (EVRP- TW). the influence of payload weights on the EVs energy consumption is considerable and should be considered when planning routes. this work presents the Prize-Collecting EVRP- TW with Energy Minimization (PC-EVRP- TW-EM), finding the optimal routes of EV s to visit the customers with prime membership (same-day service) and consume less battery energy. A mixed-integer program models the PC-EVRP- TW-EM. the results show the efficiency of the proposed approach, reducing the energy consumption and usage cost of EVs by an average of 32.97% and 29.17%, respectively. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Co-creation Lab and Its Liminal Pedagogy as an Innovation Tool for Shaping a Local and Global Future 4th

Co-creation Lab and Its Liminal Pedagogy as an Innovation To...

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4th EAI international conference on Technology, Innovation, Entrepreneurship and Education, TIE 2023

作者： Rodriguez Morales, Delma Pre-doctoral Fellow at the International University of La Rioja La Rioja Logroño Spain

ISBN: (纸本)9783031593826

the article presents the co-creative process carried out in a distributed laboratory on the Internet, crossing continents and different cultures to generate something new in networking. From a transdisciplinary approach of art, science, technology and society, the outcomes, lessons learned and main prospective features for further research and intervention of the project in e-learning, e-culture, multilingual platform, etc. are evidenced. the text covers the first edition of the Co-creation Lab 2019 and its process to date. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： E-learning

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Effective Summarization Techniques for Legal Document Analysis 4

Effective Summarization Techniques for Legal Document Analys...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gurrapu, Neelima Krishna, J S V Gopala Ram Pavan Kumar, V.T.M. Dhumpati, Raghu Mantena, Jeevana Sujitha Reddy Penubaka, Kiran Kumar Sr University Department of Computer Science and Artificial Intelligence Warangal India Sir C R Reddy College of Engineering Department of Cse Eluru India Kakaraparti Bhavanarayana College Department of Computer Science Vijayawada India Bahrain Polytechnic College Department of Cse Bahrain Srkr Engineering College Department of Cse Bhimavaram India Mlr Institute of Technology Department of CSE-AIML Hyderabad India

ISBN: (纸本)9798331523923

Summarizing legal texts is a critical task in natural language processing (NLP), which addresses the growing demands for efficient processing of large legal documents. Due to the complexity and structured nature of legal texts, the development of models capable of generating accurate and concise summaries presents significant challenges. this study examines the use of three state-of-the-art transformer-based models for summarizing legal texts. Each model is evaluated for its ability to capture nuanced semantics and preserve the contextual integrity of legal documents. the research uses a specialized dataset containing different types of legal documents, including summaries of case law, statutes and regulatory texts. Preprocessing steps include tokenization, normalization, and removal of non-informative content, ensuring that model inputs reflect the structure and verbosity contained in legal texts. Performance benchmarking is done using standard metrics such as ROUGE, BLEU, and BERT Score, along with domain-specific criteria to assess factual consistency and relevance. Experimental consequences exhibit that Bart-base excels in generating fluent and human-like summaries, LED-base proves effective for longer documents due to its prolonged context-handling ability, and T5 showcases its versatility in domain edition. A specified analysis famous the strengths and limitations of every version in dealing with domain-unique demanding situations consisting of felony terminology, quotation references, and logical reasoning. © 2025 IEEE.

关键词： Abstractive Summarization Bart BLEU CLS Tokenization Distil Bart LED Legal Text Summarization ROUGE T5 Transformer Models

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A Structured Architecture Development for 6G Technology for the Accurate Communication 4

A Structured Architecture Development for 6G Technology for ...

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4th IEEE international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Verma, Ankit Bagyalakshmi, P. Pavaiyarkarasi, R. Al-Fatlawy, Ramy Riad Rajalakshmi, G. Arunkumar, B. Ies College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Engineering and Technology Department of Mechanical Engineering Tamil Nadu T.M.Palayam India R.M.K. Engineering College Department of Ece Tamilnadu RSM Nagar India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology TN India Karpagam Academy of Higher Education Department of Computer Science Engineering India

ISBN: (纸本)9798350360165

Following the worldwide pandemic, the next age will bring with it never-before-seen possibilities as well as problems due to the revolutionary merging of the biological, digital, and physical domains of human experience. In order to seamlessly link these realms, a whole new infrastructure is needed to envision 6 G, a dramatic paradigm change. Breaking down architecture into its component parts of platform, works, orchestration, and specialization yields new ideas. Within a heterogeneous cloud architecture, the 6 G landscape that is being envisioned embraces an attitude of openness, scalability, flexibility, and platform agnosticism. Extremely specific architecture supports the complex decomposition of converged applications as well as services into micro-services with serverless operations. this innovative framework also has an open resource orchestration architecture. Important characteristics of these architectural possibilities are explained, with a focus on how they might be adjusted to the constantly changing technology landscapes of the future. At the airinterface leveled 6 G plans to use sub-Terahertz spectrum, use state-of-the-art technologies for sharing spectrum, optimize air-interface design using AI/ML methods, combine radio sensing and communication, and meet strict latency, reliability, while synchronization requirements. As this thorough review explains, to fully use these radio technology breakthroughs, 6 G network design advances must occur concurrently. © 2024 IEEE.

关键词： Terahertz spectroscopy

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Self-Organisation programming: A Functional Reactive Macro Approach

Self-Organisation Programming: A Functional Reactive Macro A...

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4th IEEE international conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Casadei, Roberto Dente, Francesco Aguzzi, Gianluca Pianini, Danilo Viroli, Mirko Alma Mater Studiorum Univ Bologna Dept Comp Sci & Engn Cesena Italy

ISBN: (纸本)9798350337440

engineering self-organising systems - e.g., robot swarms, collectives of wearables, or distributed infrastructures - has been investigated and addressed through various kinds of approaches: devising algorithms by taking inspiration from nature, relying on design patterns, using learning to synthesise behaviour from expectations of emergent behaviour, and exposing key mechanisms and abstractions at the level of a programming language. Focussing on the latter approach, most of the state-of-the-art languages for self-organisation leverage a round-based execution model, where devices repeatedly evaluate their context and control program fully: this model is simple to reason about but limited in terms of flexibility and fine-grained management of sub-activities. By inspiration from the so-called functional reactive paradigm, in this paper we propose a reactive self-organisation programming approach that enables to fully decouple the program logic from the scheduling of its sub-activities. Specifically, we implement the idea through a functional reactive implementation of aggregate programming in Scala, based on the functional reactive library Sodium. the result is a functional reactive self-organisation programming model, called FRASP, that maintains the same expressiveness and benefits of aggregate programming, while enabling significant improvements in terms of scheduling controllability, flexibility in the sensing/actuation model, and execution efficiency.

关键词： self-organising systems self-organisation programming multi-agent systems emergent behaviour emergence steering functional reactive programming aggregate computing

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Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach 4

Forecasting EV Charging Demand: A Graph Convolutional Neural...

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4th international conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Fahim, Shahriar Rahman Atat, Rachad Kececi, Cihat Takiddin, Abdulrahman Ismail, Muhammad Davis, Katherine R. Serpedin, Erchin Texas A&M University Electrical & Computer Engineering Department College StationTX77843 United States Texas A&M University at Qatar Electrical & Computer Engineering Department Doha23874 Qatar Florida State University Electrical & Computer Engineering Department TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States

ISBN: (纸本)9798350306262

Electric vehicles (EVs) are expected to revolutionize the global transportation sector by promoting sustainability and eco-friendliness. the continuous proliferation of EVs requires an expansion of the existing charging infrastructure to meet the corresponding increase in electricity demand. Such an expansion requires an accurate forecasting of charging demand in both space and time domains for a well-planned allocation of charging stations (CSs). this paper proposes a Graph Convolutional Neural Networks (GCNN) based approach combined with a Long Short-Term Memory (LSTM) to predict the future charging demands of EVs. the proposed architecture fuses the benefits of both GCNN and LSTM to extract the underlying spatio-temporal features from the collected dataset. the training dataset reflects the coupling between the power and transportation systems, and thereby it helps the proposed deep learning architecture to capture the spatio-temporal patterns of the inter-connected environment. A comparative analysis is conducted with other state-of-the-art EV charging load prediction models to assess the prediction performance of the proposed load forecasting strategy. © 2024 IEEE.

关键词： Forecasting

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Multi-scale attention graph convolutional network for heterogeneous graph representation 4

Multi-scale attention graph convolutional network for hetero...

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4th international conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Zhang, Wengjian Shi, Zhengquan Nantong University School of Information Science and Technology Nantong226019 China Nantong University School of Transportation and Civil Engineering Nantong226019 China

ISBN: (纸本)9798350394375

Existing heterogeneous graph neural network models focus on improving the efficiency of meta-path aggregation or extending GNN based on node and relationship type parameters, but they overlook the differences in attending to the hierarchical structural information of the graph. We adapt the multi-scale structure originally used for semantic segmentation in images to graph structures, paying more attention to node features and detailed information at higher scales, and focusing on the topological structure information at lower scales. For this, this paper proposes a heterogeneous graph representation learning method based on multi-scale attention graph convolution, MSA-GCN. We map attributes specific to node types into the same feature space, utilizing the hierarchical structure among different node types and relationship types in the heterogeneous graph to explore potential feature correlations. We decouple the heterogeneous graph based on relationship types and partition scales on each subgraph according to the length of meta-paths, aggregating heterogeneous information. Additionally, we apply multi-head attention to each scale of each subgraph, adaptively exploring the importance of the output at each scale in the process of heterogeneous graph representation learning based on the task and dataset. this allows for a more refined and comprehensive aggregation of heterogeneous features and structural information at different levels, enhancing the expressive power of node embeddings. We compared our model with state-of-the-art heterogeneous graph representation models on three different real-world datasets, and the results demonstrated that our model significantly outperformed the best baseline. the maximum performance improvement achieved was 14.81%. © 2024 IEEE.

关键词： Semantic Segmentation

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