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Proceedings - 1st international symposium on computer network and multimedia technology, CNMT 2009

Proceedings - 1st International Symposium on Computer Networ...

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1st international symposium on computer network and multimedia technology, CNMT 2009

ISBN: (纸本)9781424452736

The proceedings contain 307 papers. The topics discussed include: SSIFT: an improved SIFT descriptor for Chinese character recognition in complex images;feature extraction of plant leaf based on visual consistency;multi-resolution remote sensing image registration based on tensor voting;an improved uneven clustering routing algorithm for sensor networks;risk analysis of construction schedule based on Monte Carlo simulation;chain routing based on coordinates-oriented clustering strategy in WSNs;a new trusted application platform architecture based on TPM;an algorithm of adaptive deformation estimation of moving object in the mean shift algorithm;research on real-time object tracking by improved camshift;a virtual MIMO transmission scheme in wireless video sensor network;a text clustering method based on two-dimensional OTSU and PSO algorithm;and a fuzzy preferences method of QoS-awared grid workflow schedule.

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PhD symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

PhD Symposium, Posters, Demos, and A Web for more inclusive,...

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PhD symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

ISBN: (纸本)9789819614820

The proceedings contain 36 papers. The special focus in this conference is on Web Information Systems Engineering. The topics include: Effective Transparent Monitoring of Personal Data;iCNN-LstM: An Incremental CNN-LstM Based Ransomware Detection System;a Mini Review on Purchaser Security in the Metaverse: Challenges and Solutions;low-Resource Dataset Synthetic Generation for Hate Speech Detection;Web Open Data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution;leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval;time Distance Aware for Multi-component Graph Collaborative Filtering;scientific Documents Recommendation Based on Graph Convolutional network;semantic Communication of Images Using Image Generation and Image Captioning Models;leveraging Optimization Techniques for Effective Arabic Query Expansion;DURLLCON: Deep Reinforcement Learning for URLLC Optimization in Multi-edge networks;FMM-RNS: A Fast HMM Map Matching Method Based on Road network Simplification;Context-Aware Selection of Machine Learning as a Service (MLaaS) in IoT Environments;GraphTFD: A Fraud Detection System Based on Graph Transformer;MLGE-AC-UFD: Multi-level Graph Embedding and Approximate Computation for Unsupervised Fraud Detection;SeCORE: Quantitative Security Assurance and Evaluation Platform;developing Geospatial Web Applications Using Question Answering Engines, Knowledge Graphs and Linked Data Tools;A Visual Query Builder for DBpedia;FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated Learning;ORCPM: An Online Regional Core Pattern Mining System;MTRM: A Web-Miner Multi-Threshold Mining Co-location Patterns to Mitigate Redundancy;constrained Path Optimization on Time-Dependent Road networks;prompt strategies for Sarcastic Meme Detection: A Comparative Analysis;Deepfake Detection in Cancer Medical Imaging Using CNN Architectures;strengthening Cybersecurity: The Influence o

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Scientific Documents Recommendation Based on Graph Convolutional network

Scientific Documents Recommendation Based on Graph Convolut...

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作者： Li, Jianmin School of Cyber Security and Computer Hebei University Baoding071002 China Hebei Machine Vision Engineering Research Center Hebei University Baoding071002 China

ISBN: (纸本)9789819614820

The rapid growth in the number of scientific documents makes it increasingly necessary to provide researchers with efficient paper recommendation services. Since scientific documents contain many mathematical expressions and rich text information, effectively mining this information to improve recommendation quality is a challenging and necessary task. Therefore, this paper proposes a method that fuses content-based and Graph Convolutional network (GCN) introducing the multi-head attention mechanism for scientific document recommendation. The method employs Hesitant Fuzzy Set (HFS) and Bidirectional Encoder Representations from Transformers (BERT) to calculate the similarity between mathematical expressions and their context, respectively, and utilizes GCN to learn the complex relationships between nodes and mine the potential similarity between nodes. Then, the multi-head attention mechanism is introduced to dynamically assign importance weights to neighbor nodes to enhance the expression of embedding vectors, thereby generating more accurate and personalized recommendation lists. Experimental results show that the method performs well on both Ntcir and extended Chinese datasets, achieving maximum F1_Score vales of 0.817 and 0.837 at Top-50 and Top-30, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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FMM-RNS: A Fast HMM Map Matching Method Based on Road network Simplification

FMM-RNS: A Fast HMM Map Matching Method Based on Road Netwo...

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作者： Meng, Fei Chen, Chao Guo, Shangzhi Li, Ruiyuan Zhao, Jiale College of Computer Science Chongqing University Chongqing400044 China Chongqing Institute of Technology Chongqing400056 China

ISBN: (纸本)9789819614820

Map matching serves as a fundamental component of various location-based services and trajectory mining applications, as it aligns GPS trajectory points with actual road networks. This process enhances the understanding of traffic flow patterns, provides insights into human behavior, and supports initiatives aimed at improving traffic conditions, thereby underscoring its scientific and practical significance. Among the various map matching methodologies, the Hidden-Markov Model (HMM) has emerged as a predominant approach, effectively addressing the challenge of matching accuracy by integrating historical vehicle travel data and utilizing observation and hidden state probabilities within a probabilistic framework. Nevertheless, despite the advancements in accuracy achieved through HMM, there remains potential for further enhancement of its computational efficiency to better accommodate large volumes of trajectory data. To address this issue, we propose the Fast HMM Map Matching Method Based on Road network Simplification (FMM-RNS). Our method simplifies road network nodes to decrease query computations and employs bidirectional search along with segmentation of GPS trajectories, thereby significantly enhancing the computational efficiency of HMM-based map matching without compromising accuracy. Comprehensive experiments conducted on real road network and trajectory datasets demonstrate that FMM-RNS effectively reduces both the road search workload and the traversal of trajectory points, resulting in a substantial improvement in map matching efficiency when compared to state-of-the-art algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Modeling Interactions Between stocks Using LLM-Enhanced Graphs for Volume Prediction 9

Modeling Interactions Between Stocks Using LLM-Enhanced Grap...

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Joint Workshop of the 9th Financial technology and Natural Language Processing, FinNLP 2025, the 6th Financial Narrative Processing, FNP 2025, and the 1st Workshop on Large Language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international Conference on Computational Linguistics, COLING 2025

作者： Xu, Zhiyu Liu, Yi Wang, Yuchi Bao, Ruihan Harimoto, Keiko Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Mizuho Securities Co. Ltd. Japan

ISBN: (纸本)9798891762091

Accurate trading volume prediction is essential for portfolio optimization, market regulation, and financial risk control. An effective method for predicting trading volume involves building a graph to model relations between stocks. Recent research has enhanced these models by integrating stock news to improve forecasting ability. However, existing approaches primarily integrate news data as auxiliary features for nodes in Graph Neural networks (GNNs), often overlooking the relational information between stocks embedded in news. To address this, we propose LLM-Enhanced Dynamic Graph Neural network (LED-GNN), a framework that constructs dynamic graphs using inter-stock relationships extracted from news via a large language model (LLM)-centered pipeline. The news graph is then combined with graphs learned from historical price-volume data and fed into a dynamic GNN to generate predictions. Evaluated on a real-world dataset, TOPIX, with Reuters Financial News, LED-GNN consistently outperformed all baseline models, achieving a 2% improvement over the strongest baseline. © 2025 Association for Computational Linguistics.

关键词： Prediction models

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Deepfake Detection in Cancer Medical Imaging Using CNN Architectures

Deepfake Detection in Cancer Medical Imaging Using CNN Arch...

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作者： Alhalabi, Dima Talal Alawida, Moatsum Chikhaoui, Belkacem Hamadeh, Hala Samer Department of Computer Sciences Abu Dhabi University Abu Dhabi59911 United Arab Emirates Applied Artificial Intelligence Institute TELUQ University Montreal Canada

ISBN: (纸本)9789819614820

Deepfake technology poses an emerging threat in the medical field, as its potential to manipulate medical scans could result in misdiagnoses, fraudulent claims, and serious health risks. This study addresses the urgent need for robust detection methods to safeguard medical imaging systems and patient safety. We developed a deepfake detection model by evaluating four Convolutional Neural network (CNN) architectures: VGG16, EfficientNetV2, InceptionV3, and Sequential. These models were assessed using Precision, Recall, F1-Score, and Accuracy metrics to determine their effectiveness. CT scan images were resized to 256×256 pixels, and the models were trained for 100 epochs with early stopping to enhance training efficiency. The EfficientNetV2 architecture initially achieved the highest accuracy of 92% without data augmentation. However, following data augmentation, both EfficientNetV2 and VGG16 reached an accuracy of 93%, with VGG16 being selected due to its superior recall rate of 98%, effectively minimizing false negatives and improving deepfake detection reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Comprehensive Exploration of Deepfake Detection Using Deep Learning 8th

Comprehensive Exploration of Deepfake Detection Using Deep ...

引用

8th international symposium on Intelligent Informatics, ISI 2023

作者： Agrawal, Pratham Jha, Anchalaa Bhute, Avinash SCTR’s Pune Institute of Computer Technology 411046 Pune India Pimpri Chinchwad College of Engineering Pimpri-Chinchwad411044 India

ISBN: (纸本)9789819721467

The proliferation of deepfake technology poses a significant threat to the veracity of multimedia content, necessitating robust countermeasures for detection. This research explores the efficacy of three distinct deep learning architectures—Long Short-Term Memory (LstM), Gated Recurrent Unit (GRU), and Convolutional Neural network (CNN)—in discerning deepfake manipulations within video data. The study encompasses a diverse dataset encompassing real and manipulated videos, ensuring comprehensive evaluation. Each model is meticulously trained and rigorously tested, with performance assessed in terms of accuracy, precision, recall, and F1-score. The results underscore the CNN’s exceptional prowess in spatial feature extraction, yielding superior accuracy compared to its temporal counterparts. A comprehensive discussion delves into the strengths and limitations of each model, providing valuable insights for future research in this critical domain. This research not only contributes to the arsenal of techniques for deepfake detection but also underscores the dynamic landscape of multimedia forensics, necessitating continuous innovation to safeguard the integrity of visual content in an era of increasingly sophisticated manipulations. The findings herein offer a foundational framework for further advancements in deepfake detection methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Semantic Communication of Images Using Image Generation and Image Captioning Models

Semantic Communication of Images Using Image Generation and ...

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作者： Mahgoub, Asma Yaacoub, Elias Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (纸本)9789819614820

With the emergence of 6G networks and the new networking requirements, the existing networks are approaching the Shannon capacity limit. Hence, a new paradigm called ‘semantic communication’ is proposed in the literature. Semantic communication is the transmission of the meaning of a message between two intelligent agents. In this work, the problem of image semantic communication is considered. Most of the existing works in literature focus on the exact reconstruction of the transmitted image. Yet, to achieve full semantic communication, it is possible to focus on transmitting a similar image with the same meaning as the original image. This can be achieved by transmitting a text description of the image. An image captioning model, namely, the one-for-all (OFA) model is used to transform an image into its caption. The caption is transmitted through the network and at the receiver an image-to-text diffusion model, stability diffusion XL, is used to reconstruct a semantically similar image. The similarity between the transmitted and the received images is assessed using the textual similarity between the corresponding captions. The captions of the original and the generated images had a similarity above 0.7 using five text similarity measures, namely, ANLS, METEOR, ROUGE, BLEU and BERT similarity. This approach is shown to achieve a 99% reduction in the message size. The results emphasize the effect of using AI models to extract a general representation of a message and in turn consume less communication resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chatbots

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1st international symposium on Intelligent technology for Future Transportation, ITFT 2024

1st International Symposium on Intelligent Technology for Fu...

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1st international symposium on Intelligent technology for Future Transportation, ITFT 2024

ISBN: (纸本)9783031841477

The proceedings contain 32 papers. The special focus in this conference is on Intelligent technology for Future Transportation. The topics include: Modeling Autonomous Delivery Robots Under Framework of Automated Driving System Using Dynamic Transport Assignment: A state-of-the-Art;virtual Variable Baseline stereo Vision Algorithm;A Conceptual Framework to Operational Design Domain (ODD)-Based Scenario Generation for Technical Evaluation Obstacle Detection in Automated Shuttle Bus;precise Driver’s Drowsiness Detection Using a Combination of Proven Methods with a Single Layer Neural network;fusionSis: Analysis and Evaluation Framework for Fusion Safety state of Connected and Automated Vehicles Under Cyber Attacks;deep Learning-Based Monocular Depth Estimation Method for Forklift Collision Avoidance;enhancing Multi-user Experience: Optimizing Explanation Timing Through Game Theory;user-Intimate and Trustworthy Ontology-Based Requirement Engineering Methodology: A Case study on Connected and Autonomous Electrical Vehicles;AI and Leadership in startup Innovation and Disruption: The Case of Mobility as a Service (MaaS);research on Traffic Analysis Zones Division Model for New Roads Based on Complex network Theory;forecasting Vehicle Mobility on Various Segments of the Urban Transport network in the Fuzzy Paradigm;The Emerging ICT for Exposure Reduction in Urban Mobility: Transport Models for Risk Cycle;concept of a Virtual Test Field for Inland Waterway Transport;critical Characterization of Three-Phase Traffic Flow in Severe Condition;design and Implementation of Traffic Congestion Relief strategies Based on Multi-objective Optimization Algorithms;wide-Area Ship Movement Prediction Using Random Forests;characteristics of Heterogeneous Traffic Flow Involving Different Intelligent Level Autonomous Vehicles;the Research on Customer Demand of Asia-Europe Liner Shipping Companies Based on Kano Model.

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PIMnet: A Domain-Specific network for Efficient Collective Communication in Scalable PIM 31

PIMnet: A Domain-Specific Network for Efficient Collective C...

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31st IEEE international symposium on High Performance computer Architecture, HPCA 2025

作者： Son, Hyojun Jonatan, Gilbert Wu, Xiangyu Cho, Haeyoon Shivdikar, Kaustubh Abellán, José L. Joshi, Ajay Kaeli, David Kim, John KAIST Korea Republic of Northeastern University United States Universidad de Murcia Spain Boston University United States

ISBN: (纸本)9798331506476

Processing-in-memory (PIM), where compute is moved closer to memory or data, has been explored to accelerate emerging workloads. Different PIM-based systems have been announced, each offering a unique microarchitectural organization of their compute units, ranging from fixed functional units to programmable general-purpose compute cores near memory. However, one fundamental limitation of PIM is that each compute unit can only access its local memory;access to 'remote' memory must occur through the host CPU - potentially limiting application performance scalability. In this work, we first characterize the scalability of real PIM architectures using the UPMEM PIM system. We analyze how the overhead of communicating through the host (instead of providing direct communication between the PIM compute units) can become a bottleneck for collective communications that are commonly used in many workloads. To overcome this inter-PIM bank communication, we propose PIMnet - a PIM interconnection network for PIM banks that provides direct connectivity between compute units and removes the overhead of communicating through the host. PIMnet exploits bandwidth parallelism where communication across the different PIM bank/chips can occur in parallel to maximize communication performance. PIMnet also matches the DRAM packaging hierarchy with a multi-tier network architecture. Unlike traditional interconnection networks, PIMnet is a PIMcontrolled network where communication is managed by the PIM logic, optimizing collective communications and minimizing the hardware overhead of PIMnet. Our evaluation of PIMnet shows that it provides up to 85 × speedup on collective communications and achieves a 11.8 × improvement on real applications compared to the baseline PIM. © 2025 IEEE.

关键词： Interconnection networks (circuit switching)

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