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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics. © 2024 The Authors.

关键词： Data assimilation

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WEB-ANEULYSIS: A Web-based Application for the Analysis of Aneurysm Data

WEB-ANEULYSIS: A Web-based Application for the Analysis of A...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Preßler, Rebecca Meuschke, Monique Voigt, Henrik Lawonn, Kai Institute of Computer Science University of Jena Germany Department of Simulation and Graphics University of Magdeburg Germany

ISBN: (纸本)9783658416560

The analysis and treatment of cerebral aneurysms are important in neuroradiology. To support physicians in these tasks a desktop application for the visual analysis of aneurysm data called Aneulysis was developed. Because desktop applications complicate collaborative data exploration by physicians working at different locations,we present Web-Aneulysis, aweb-based version of Aneulysis written with the *** library. Especially the collaborative treatment planning is supported by our application. Therefore, Web-Aneulysis provides features for calculating morphological descriptors, positioning landmarks on the vessel surface, and deploying medical implants inside the vessel. We used Web-Aneulysis for the treatment planning of different cerebral aneurysm data sets and performed a qualitative evaluation together with two neuroradiologists. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Web services

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Transparency vs Explanation of Machine Learning Algorithms: Perspectives from Recent Legal Proceedings 23rd

Transparency vs Explanation of Machine Learning Algorithms: ...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Nnawuchi, Uchenna George, Carlisle Kammueller, Florian ALERT Research Group Department of Computer Science Faculty of Science and Technology Middlesex University London United Kingdom

ISBN: (纸本)9783031734960

The extensive use of Machine Learning (ML) algorithms has significantly altered decision-making processes, societal structures, and power dynamics. These algorithms, known for their efficiency, are increasingly making critical decisions. However, concerns about their opacity and biases persist. Some legal scholars and data scientists argue that the GDPR includes a right to explanation to address ML opacity, while others advocate for transparency as a better solution. This study explores the debate between transparency and explanation within the GDPR’s regulatory framework, focusing on Articles 15, 22 and recent EU court rulings. By examining judicial developments and scholarly perspectives, the study highlights the GDPR’s efforts to promote transparency and accountability in automated decision-making. Despite the GDPR’s provisions, the lack of an explicit right to explanation has led to ongoing legal and ethical discussions. This research aims to provide insights into how transparency, explanation, and individual rights intersect in the realm of algorithmic decision-making under the GDPR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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PSO-CHS Routing Protocol for Energy Consumption Enhancement in Wireless Sensor Networks 4

PSO-CHS Routing Protocol for Energy Consumption Enhancement ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Fakhri, Israa Sabri Marhoon, Haydar Abdulameer Hussein, Mohsin Hasan College of Computer Science and Information Technology University of Kerbala Department of Computer Science Karbala Iraq Scientific Research Center Al-Ayen University Department of Computer Science Information and Communication Technology Research Group Thi-Qar Iraq

ISBN: (纸本)9798350352931

A region's Wireless sensor networks consist of sensors distributed over a region that collect and send environmental data to a central station. It measures humidity and temperature. The formation is based on many key ideas, including the routing system that optimizes data flow to the base station. Routing introduces limits and obstacles to better results. One challenge is choosing the cluster head, who aggregates data from nearby sensors and sends it to the base station. Many methods are used to choose the cluster head. Instead of GSTEB, DCBRP, and ETSRP, which are laborious and time-consuming, modern cluster head identification methods use artificial intelligence. This study selects the cluster head quickly and accurately using PSO-CHS (particle swarm optimization-Cluster Head Selection Routing Protocol). It improves execution time by 84.1%, delay matrix by 44.3%, and energy matrix by 80.4% over previous protocols. PSO-CHS design promotes energy efficiency, network durability, and packet loss reduction. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Deep Hierarchical Variational Autoencoders for World Models in Reinforcement Learning 5

Deep Hierarchical Variational Autoencoders for World Models ...

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Ayyalasomayajula, Sriharshitha Rekabdar, Banafsheh Mousas, Christos Portland State University Department of Computer Science PortlandOR United States Purdue University Department of Computer Graphics Technology West LafayetteIN United States

ISBN: (纸本)9798350358018

With the increasing demand for sample-efficient and robust reinforcement learning agents, particularly in intricate domains like robotics, healthcare, and gaming, there is a strong need to minimize the computational overhead caused by the interactions between real and virtual agents. This necessitates highly accurate models to simulate virtual agents and limit the number of such interactions. To this effect, model-based reinforcement learning (MBRL) has been proven very effective in formulating an environment with superior decision-making and higher learning efficiency. A known approach in MBRL is World Models, which uses a generative engine called Variational Autoencoders (VAE). VAE utilizes a relatively simple architecture constrained in processing power for complex image inputs. Therefore, the image reconstruction error is high. Recent research in VAEs has shown poor reconstruction quality. This paper proposes a Nouveau VAE (NVAE) based World Models to address the abovementioned limitations. NVAE, which employs deep convolutions in its architecture, is employed as the visual sensory component of the World Models and is used to encode the environment dynamics into a latent representation. We show that NVAE-based World Models perform exceptionally well in the dream environment of car racing-v2 (OpenAI GYM env), improving the agent's performance by 45%. We then demonstrate that the NVAE-based World Models can be applied to robotic simulation environments like panda-gym, where the agent achieved a 95 % success rate in solving the reach task. © 2023 IEEE.

关键词： Image reconstruction

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Yoga Pose Identification Using Deep Learning 1

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5th International Conference on Data and Information sciences, ICDIS 2023

作者： Verma, Ashutosh Kumar Sharma, Divyanshu Aggarwal, Himanshu Chauhan, Naveen Department of Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Yoga is an ancient practice that has gained popularity all over the world. It is a holistic approach to maintaining physical and mental health. Identifying yoga poses can be a challenging task for beginners and even experienced practitioners. In this research paper, we present a deep learning-based approach for identifying yoga poses from images. We put up a convolutional neural network (CNN) architecture that can appropriately classify 20 distinct yoga poses. Using a dataset of 5000 images, our suggested method had an accuracy of 97.3%. The results show that deep learning techniques can be used to accurately identify yoga poses from images, which can be used to develop an intelligent yoga training system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Convolutional neural networks

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Peer to Peer Communication using Web Sockets and Web RTC

Peer to Peer Communication using Web Sockets and Web RTC

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Sharma, Vineet Pandey, Mridul Kumar Gupta, Jayant Kishan, Hem KIET Group of Institutions Department of Computer Science and Engineering Ghaziabad Delhi-NCR India

ISBN: (纸本)9798350376470

In the rapidly expanding information era, the Internet has become an indispensable part of peoples' lives, and in this rapidly globalizing world, communication amongst people is paramount. Users can now access websites and other online resources because of the HTTP (Hypertext Transfer Protocol) request-response protocol, which is used to transfer data over the internet. However, one of its disadvantages is that clients cannot access servers' real-time data. Therefore, real-time communication is not possible with technologies like HTTP streaming, long polling, and polling. As a result, WebRTC, or Web Real-Time Communication, and the Web Socket Protocol were used. The latter enables browser-to-browser sharing of media and data in real time. With the help of this peer-to-peer technology, web browsers can now communicate in real time. Almost all contemporary browsers have this technology, which allows them to talk to each other rather than just web servers. Thus, this paper covers the importance of communicating in real-time, the limitations of traditional web real-time communication, an overview of web sockets, and the differences between HTTP and Web Socket Protocol. The paper then explains the significance of audio and video conferencing in the modern world and provides an overview of Web Real-Time Communication (WebRTC), including its architecture and the use of a web-RTC-based system. © 2024 IEEE.

关键词： HTTP

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Data-Driven Insights: Leveraging Analytics for Predictive Modeling in Finance 4

Data-Driven Insights: Leveraging Analytics for Predictive Mo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Purwar, Mitali Deka, Upasana Raj, Himadri Ritu Upes Department of Computer Science & Engineering Dehradun India Noida Institute of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Chandigarh College of Engineering Chandigarh Group of Colleges Department of Computer Science & Engineering Jhanjeri140307 India

ISBN: (纸本)9798350387490

A distinct gap in the most recent business intelligence literature is addressed by this research, which discusses the possibility of radical changes in the market based on the application of predictive analytics. The key goal is to evaluate the impact of the prediction models on ethics, expansiveness, precision and reliability. The evaluation is of a qualitative nature, and topic analysis along with case histories is used to acquire specific, contextualised data. Basic findings indicate that employment of market forecasting skills is enhanced through the use of predictive models since decisions are made much more deliberately. The analysis reveals that these models remain prone to change in a variety of contexts and demonstrates their capacity to deal with large data efficiently. Ethical issues is stressed out, which leads to the conclusion that there should be stricter rules for personal data protection and more openness for procedures to gain the compliance and trust. The conclusions underline the major competitive advantages that, if applied line by line, predictive analytics can offer, calling for more adaptability and sensitivity to the market trends. This report which proposes a 50-50 mix of value creation and integrity does, nonetheless;provide notice of potential ethical issues and data privacy. The guidelines for enhancing the accuracy of the prediction, which complies with the ethical standard are strengthening the information management system and developing novel predictive model technologies. Future research must continue to analyze shifts in predictive analytics understanding new technologies and possible changes in the market. © 2024 IEEE.

关键词： Prediction models

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai201804 China Ant Group Shanghai200122 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Fudan University School of Computer Science Shanghai200438 China Tongji University Department of Control Science and Engineering Shanghai201804 China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne3122 Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this article, we propose a hypergraph learning framework named learning from heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterward, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experimental results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Scene Synthesis with Automated Generation of Textual Descriptions

Scene Synthesis with Automated Generation of Textual Descrip...

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43rd Annual Conference on European Association for computer graphics, EUROgraphics 2022

作者： Müller-Huschke, J. Ritter, M. Harders, M. Interactive Graphics and Simulation Group The Department of Computer Science University of Innsbruck Austria

ISBN: (纸本)9783038681694

Most current research on automatically captioning and describing scenes with spatial content focuses on images. We outline that generating descriptive text for a synthesized 3D scene can be achieved via a suitable intermediate representation employed in the synthesis algorithm. As an example, we synthesize scenes of medieval village settings, and generate their descriptions. Our system employs graph grammars, Markov Chain Monte Carlo optimization, and a natural language generation pipeline. Randomly placed objects are evaluated and optimized by a cost function capturing neighborhood relations, path layouts, and collisions. Further, in a pilot study we assess the performance of our framework by comparing the generated descriptions to others provided by human subjects. While the latter were often short and low-effort, the highest-rated ones clearly outperform our generated ones. Nevertheless, the average of all collected human descriptions was indeed rated by the study participants as being less accurate than the automated ones. © 2022 The Author(s).

关键词： Cost functions

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