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Proceedings of the 7th International Conference on Future Networks and Distributed systems

作者： Qusai Ramadan Zeyd Boukhers Muath Alshaikh Christoph Lange Jan Jurjens Institute of Software Engineering University of Koblenz Germany Fraunhofer Institute for Applied Information Technology FIT Germany and University Hospital of Cologne Germany College of Computing and Informatics Saudi Electronic University Kingdom of Saudi Arabia Fraunhofer Institute for Applied Information Technology FIT Germany and RWTH Aachen University Germany University of Koblenz Germany and Fraunhofer-Institute for Software and Systems Engineering ISST Germany

ISBN: (纸本)9798400709036

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking.

关键词： Data Exchange

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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Advancement in Renewable Energy and Intelligent systems (AREIS), International Conference on

作者： V. Raji S. Maheswari S. Sree Dharinya P. Chinniah Kavin K. S. Kiruba Thangam Raja Dept. of Information Technology S.K.P. Engineering College Tiruvannamalai India Dept. of CSE(IoT) Nandha Engineering College Erode Tamilnadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Dept. of ECE ST. Joseph College of Engineering Sriperumbudur Tamilnadu India Dept. of EEE Government College of Engineering Tirunelveli Tamil Nadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems(SCORE) VIT University Vellore India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Transfer learning Computer architecture Brain modeling Data augmentation Data models Convolutional neural networks

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Federated Graph Learning for Cross-Domain Recommendation 38

Federated Graph Learning for Cross-Domain Recommendation

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. We open source the code at https://***/LafinHana/FedGCDR. © 2024 Neural information processing systems foundation. All rights reserved.

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A Pedestrian Trajectory Prediction Network Based on Trajectory Conditional Visual Attention 23

A Pedestrian Trajectory Prediction Network Based on Trajecto...

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Proceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Xiaojie Tian Ruoxi Lin Shimeng Yang Kaiju Li Guangqiang Yin Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China School of Information and Communication Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China Kashgar Municipal Committee of Xinjiang China

ISBN: (纸本)9798400709203

Pedestrian trajectory prediction task can increase artificial intelligence’s understanding of human behavior perception and intention, and is widely used in traffic management, service robots, automatic driving and other fields, which has great research value and significance. Although there is more and more research on pedestrian trajectory prediction tasks, pedestrian trajectory prediction is still a very challenging task due to the uncertainty of pedestrian will and the complexity of interaction in the complex real scene. Most of the research focus on the interaction between the target pedestrian and other pedestrians, which has great computational cost in the crowded pedestrian scene. We proposed CDCNet, by observing the historical track of pedestrians, and using the Conditional Dual Coordinated Attention to obtain the information that pedestrians pay attention to when moving forward, so that the network pays attention to the information around pedestrians instead of the content in the entire video, so that the network can also have a lower computing cost under the crowded pedestrian scene. Our experiments on five publicly available datasets show that CDCNet has good predictive efficiency and advanced performance.

关键词： attention mechanism

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Leveraging Chat-GPT to Generate Educational Assessment Materials for software engineering 20th

Leveraging Chat-GPT to Generate Educational Assessment Mate...

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20th International Conference on Foundations of Computer Science, FCS 2024, and 20th International Conference on Frontiers in Education, FECS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Seibert, Vasiliy Geger, Tobias Rausch, Andreas Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9783031859298

This paper presents an educational use-case for creating assessment materials with Chat-GPT 3.5 turbo in the domain of software engineering. We attempt to (a) alter specific aspects of a generic domain model (UML class diagram) and (b) generate requirement texts (problem scenarios) that creatively describe the previously altered model. The challenge that comes with these two approaches is (1) to change only the variables contained in the generic domain model in such a way, that the resulting domain model makes sense and (2) to generate natural language requirement texts (scenarios) that creatively and correctly describe the underlying domain model. Results: The first approach (a) yielded satisfactory results. After providing Chat-GPT with examples, it was able to alter all variables in a coherent way according to the topic specified in the prompt. This result indicates potential for other educational use cases and ongoing software engineering research with LLMs. Chat-GPT’s generated requirements texts were useful but required caution from the lecturers before handing them out to students. We reflected on the contribution of this paper (and papers similar to this) and argued, that the provided value lies not only in the quality of the yielded results but much rather in describing educational use cases that are worth following up on with future versions of large language models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： software design

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A Way to Protect the Surveillance Data Stored in the Cloud Using FC and SVM Methods 4

A Way to Protect the Surveillance Data Stored in the Cloud U...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Suganthi, D. Raju, M. Mahesh Kumar, N.B. Vasavi, M. Washeel, Oday Faris Abdtawfeeq, Tareq Hafdhi Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai Tamilnadu Thandalam602105 India Sri Krishna Arts and Science College Department of Software Systems Coimbatore India Tamil Nadu Coimbatore641 032 India Cvr College of Engineering Et Department Hyderabad India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq

ISBN: (纸本)9798350360165

Building trust between consumers and service providers is critical to the functioning of cloud computing, a technology that offers a range of services over the Internet. When consumers choose which cloud services to utilise for processing and storing sensitive data, trust values are a major factor. In addition, service providers can employ trust values as a means of granting access to resources, protecting against malevolent users. The subjective trust model category, which evaluates trust based on user and service provider behaviour, is the one that this research suggests for trust models. Because trust is intrinsically ambiguous in our method, trust levels for cloud consumers and service providers are calculated using fuzzy logic and support vector machines. Using triangle membership functions for defuzzification and Gauss membership functions for fuzzification, we use the Mamdani fuzzy approach. In order to determine how trustworthy a resource is, our paradigm for evaluating trust takes performance and flexibility into account. Performance evaluation includes measurements related to workload and reaction time, whereas elasticity evaluation takes into account elements like scalability, availability, security, and usability. We apply fuzzy C-means clustering on pertinent criteria to establish trust levels for users, including faulty requests, fake requests, unauthorised requests, and total requests. © 2024 IEEE.

关键词： Cloud platforms

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Deepfake detection using deep learning methods: A systematic and comprehensive review

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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2024年第2期14卷

作者： Heidari, Arash Jafari Navimipour, Nima Dag, Hasan Unal, Mehmet Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has Universitesi Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Management Information Systems Kadir Has Universitesi Istanbul Turkey Department of Computer Engineering Nisantasi Universitesi Istanbul Turkey

Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human-level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL-powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, w

关键词： National security

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： S. Shanthi K. Nirmaladevi N. Mathiarasi As. Krithika Tharani A. Monisha R. Kuppuchamy N. Krishnamoorthy Department of Computer Science & Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Software Systems and Engineering Vellore Campus Vellore Institute of Technology India

Today’s marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women’s clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation.

关键词： Deep learning Recurrent neural networks Filtering Clothing Informatics Recommender systems Business

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Autonomous Environmental Exploration and Target Tracking Control for Unmanned Surface Vehicles in Uncertain Environment

Autonomous Environmental Exploration and Target Tracking Con...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Kaiwei Zhu Shihan Kong Fang Wu Jinan Sun Junzhi Yu Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China SPIC Nuclear Energy Co. Ltd. China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world.

关键词： Target tracking Observers Path planning software Real-time systems Reliability Vehicle dynamics

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Q2oE: Balancing QoE Fairness and Preference for Video Streaming from Cooperative Edge Servers

Q2oE: Balancing QoE Fairness and Preference for Video Stream...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Xinwei Huang Yin Xu Jinbo Cai He Sun Jie Wu Mingjun Xiao School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Study University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China Department of Computer and Information Sciences Temple University USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Quality of Experience (QoE) is a pivotal factor that defines the overall quality within the realm of video streaming services. To improve users' QoE indicators, existing methods have proposed many QoE models and Adaptive Bitrate (ABR) algorithms. However, these advanced methods do not account for the tradeoff between users' personalized preferences and fairness, leading to resource wastage on the edge server and an inefficient bitrate allocation. Given resource constraints, non-cooperative edge servers frequently request the cloud to enhance user QoE, consequently resulting in high-latency transmissions. In this paper, we address this issue by introducing collaborative edge servers, which alleviate the burden of video chunk transcoding and caching on individual servers. Specifically, we design a novel QoE model named Q 2 0E that incorporates user-level QoE preference and QoE fairness in this scenario. Furthermore, we propose Q2D3, a Q 2 oE-aware reinforcement learning algorithm based on a video chunk transcoding strategy for mobility-aware vehicular networks, to improve Q 2 oE model performance.

关键词： Measurement Simulation Bit rate Transcoding Reinforcement learning Streaming media Smart systems Quality of experience Servers Resource management

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