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TSCCL: A Contrastive Learning Framework for Time Series Clustering with Hurst Exponent

TSCCL: A Contrastive Learning Framework for Time Series Clus...

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International conference on

作者： Yan Zhou Xiaodong Li Feng Li Kedong Zhu Huibiao Yang Yong Ren College of Computer Science and Software Engineering Hohai University Nanjing China Power Automation Department China Electric Power Research Institute Nanjing China Ningxia Electric Power Research Institute Yinchuan China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Time series clustering plays a vital role in various fields, including power grids and finance. In recent years, contrastive learning has been widely adopted to improve the performance of clustering algorithms. However, these methods were initially designed for image data. When applied to time series, which exhibit unique characteristics such as mean reversion, the labels of generated samples often do not match the labels of the original samples. this misalignment occurs because the nature of time series data is not sufficiently preserved during augmentation. therefore, ensuring that the inherent properties of time series data are maintained during augmentation has become a critical issue. In this paper, we firstly introduce a novel data augmentation method called HTGAN (Hurst-Time series Generative Adversarial Network), which is integrated into a contrastive learning framework. Moreover, we propose a new clustering framework, TSCCL (Contrastive Learning Framework for Time Series Clustering), specifically designed for financial time series data clustering. Experimental results demonstrate that TSCCL outperforms all baseline methods, highlighting the effectiveness of HTGAN in financial time series data augmentation.

关键词： Learning systems Time series analysis Finance Clustering algorithms Contrastive learning Data augmentation Generative adversarial networks Power grids Consumer electronics

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RANGER: Context-Aware Service Unit of Work Recommendation for Incremental Scientific Workflow Composition 25th

RANGER: Context-Aware Service Unit of Work Recommendation f...

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25th International conference on Web Information systems engineering, WISE 2024

作者： Xie, Xihao Liu, Chang Zhang, Jia Ramachandran, Rahul Lee, Tsengdar Lee, Seungwon Department of Computer Science Southern Methodist University Dallas United States Marshall Space Flight Center NASA Huntsville United States Science Mission Directorate NASA Headquarters Washington United States Jet Propulsion Laboratory NASA Pasadena United States

ISBN: (纸本)9789819605699

Service discovery and recommendation has been considered an effective technique for freeing workflow developers out from time-consuming work of manually selecting suitable software services from a sea of service candidates. Facing complex scientific workflow development, recommending multiple chainable services (service unit of work or UoW in short) instead of individual services shall not only increase workflow composition productivity but also address the data shimming problem. Unfortunately, the problem of UoW recommendation is NP-hard. this study presents a novel workflow UoW recommendation engine called RANGER that comprises two integral components: a RANker network and a UoW merGER. the RANker network is trained to rank its comprising edges against a query. At each step during a workflow composition, the RANker consumes the embeddings of the composition context as input. the RANker network mines past service collaboration networks and ranks chained service pairs as "seed" UoWs. the UoW merGER then tries to further chain close-by seed UoWs in the ranked list into larger UoWs as candidate UoWs. Finally, the top K candidate UoWs are suggested to the developers. Extensive experiments on a real-world dataset have demonstrated that the RANGER technique saves development efforts when composing scientific workflows. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mergers and acquisitions

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Session-Based Recommendation with Graph Neural Networks with an Examination of the Impact of Local and Global Vectors 5th

Session-Based Recommendation with Graph Neural Networks wit...

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5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender systems, PERS 2024 held in conjunction with 28th European conference on Advances in Databases and Information systems, ADBIS 2024

作者： Glogowska, Justyna Kobiela, Dariusz Mielewczyk, Szymon Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Multimedia Systems and Department of Software Engineering Gabriela Narutowicza 11/12 Gdańsk80-233 Poland

ISBN: (纸本)9783031704208

this study investigates the application of graph neural networks (GNN) in session-based recommendation systems (SR), focusing on a key modification involving the use of a global vector. Session-based recommendation systems often face challenges in accurately capturing user behavior due to the limited data available within individual sessions. the SR-GNN model, originally designed for automatic feature extraction from session graphs by leveraging rich connections between nodes, addresses these challenges effectively. In our experiments, we replaced the local vector with a global vector representing the entire session sequence, not just the last element. Our results show that both local and global vectors perform comparably, suggesting that the global vector is sufficient to capture the session context. Additionally, our study indicate that the SR-GNN algorithm maintains consistent performance across various datasets, with minor fluctuations depending on the dataset characteristics. the conducted experiments highlight the resilience and adaptability of the SR-GNN model in diverse scenarios, demonstrating its potential for use in session-based recommendation systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Design of a Scaled-Vehicle Research Platform for software-Defined Vehicles

Design of a Scaled-Vehicle Research Platform for Software-De...

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Mechatronics and Robotics engineering (ICMRE), International conference on

作者： Sung Bhin Oh Young Soo Do Se Jeong Lim Hyeok Jun Choi Sung Keun Cha Hyeong Keun Hong Seong Hyeon Lim Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Modern vehicles are integrated systems of complex software that support advanced features such as autonomous driving and over-the-air updates. software-defined vehicles (SDVs) are emerging as a future automotive paradigm for managing increasing software complexity by centralizing computing resources and enabling flexible software updates and feature deployment. However, most SDV research is conducted in simulation environments because using real vehicles presents many challenges, including high cost, safety concerns, and implementation difficulties. In this paper, we present the design of an SDV research platform using a 1/5-scale vehicle model to overcome these limitations of SDV research. the platform implements a zone architecture and integrates various embedded systems to replicate the distributed computing environment of a modern vehicle. the platform allows researchers to experiment with different SDV concepts such as distributed computing, OTA updates, middleware, and vehicle software management without the constraints of full-scale vehicle testing. Our design demonstrates the feasibility of using extended vehicles for SDV research and provides insights into platform design for SDV research.

关键词： Mechatronics Embedded systems Wireless networks computer architecture Safety Distributed computing Middleware Robots Automotive engineering Testing

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Parameterized Model for Fork Antenna Design in UWB Band Using Fully Connected Artificial Neural Networks 26th

Parameterized Model for Fork Antenna Design in UWB Band Us...

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26th International conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Adegboye, Olaoluwa Li, Xiangfang Qian, Lijun CREDIT Center and Electrical and Computer Engineering Department Prairie View A&M University Prairie View 77446 TX United States Texas A&M University System College Station United States

ISBN: (纸本)9783031866227

this study introduces a novel approach for designing fork antennas optimized for the Ultra-Wideband (UWB) frequency band using machine learning. the aim is to develop a parameterized model predicting antenna dimensions based on given performance indices. Leveraging parameter sweep techniques, a dataset capturing the relationship between antenna design (dimensions) and the corresponding performance metrics was generated in CST Microwave Studio. then the generated dataset was used to train a fully connected Artificial Neural Network (ANN) model where the inputs are the performance metrics and the outputs are the corresponding antenna dimensions. It is demonstrated that the trained ANN model can determine the optimal antenna dimensions very efficiently given the desired performance indices comparing to existing antenna design software. this approach accelerates innovation in 5G communication systems and contributes to ongoing efforts in 6G communication research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 5G Antenna Design Artificial Neural Network Machine Learning Ultra-Wideband

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Employing Multiple Online Translation Services in a Multilingual Database Design Tool 5th

Employing Multiple Online Translation Services in a Multil...

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作者： Banjac, Danijela Matic, Milica Cvijanovic, Nedeljko Brdjanin, Drazen Banjac, Goran Stojisavljevic, Djordje Faculty of Electrical Engineering University of Banja Luka Patre 5 Banja Luka78000 Bosnia and Herzegovina Codaxy Ltd Bulevar srpske vojske 17 Banja Luka78000 Bosnia and Herzegovina Ongulus Software Development Svetozara Markovica 5 Banja Luka78000 Bosnia and Herzegovina University Computing Center University of Banja Luka Bulevar Vojvode Petra Bojovica 1A Banja Luka78000 Bosnia and Herzegovina

ISBN: (纸本)9783031704208

TexToData is an online tool for automated database design with multilingual support. It enables the automated derivation of an initial conceptual database model from textual specifications in different natural languages, whereby more than a hundred natural languages are supported. the multilingualism is enabled by the translation of a source textual specification into the English language and vice versa. For instance, TexToData first translates the source text into English, then processes the English text, generates the corresponding conceptual model in English, and finally translates the model back into the source natural language. In this paper we analyze the suitability of employing different online translation services in this process. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Specifications

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Efficient Design of Rectifier Circuit for Low Power Applications at 2.45 GHz

Efficient Design of Rectifier Circuit for Low Power Applicat...

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Innovative Research in Applied Science, engineering and Technology (IRASET),conference on

作者： Mohammed Jabbar Walid En-Naghma Mouhcen El-Kahiri Outmane Salah Abdelghani El Ougli Hanan Halaq Computer Science Signal Automation and Cognitivism Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

this paper presents an advanced approach for designing an efficient rectifying circuit for energy harvesting systems aimed at low-power applications around 2.45 GHz. the proposed rectifier based on voltage doubler topology using the SMS7630 Schottky diode is simulated under Advanced Design System (ADS) software using S-parameter and Harmonic Balance simulations. key parameters such as matching network and circuit components, are tuned and optimized to achieve a maximum RF-DC conversion efficiency and highest output voltage value. the simulation results show that this proposed circuit exhibits a wide bandwidth of 208 MHz centered around 2.45 GHz, with a high conversion efficiency of 71.015% and an output voltage of 1.31 V with an input power of 0 dBm at a load charge of 4 kilo-ohms at 2.45 GHz. Compared to previous studies, the suggested rectifying circuit demonstrates higher efficiency making it highly suitable for a variety of wireless power transmission applications such as low-power consumption devices.

关键词： Schottky diodes Simulation Rectifiers Rectifying circuits Voltage software Wireless power transmission Topology Scattering parameters Energy harvesting

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AIDetectVul: software Vulnerability Detection Method Based on Feature Fusion of Pre-trained Models

AIDetectVul: Software Vulnerability Detection Method Based o...

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International conference on

作者： Shiying Xue Lin Li Tao Li Haodong Chen Jiapan Li Yangqing Qin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real time Industrial Systems Wuhan University of Science and Technology Wuhan China Wuhan Jiaoyun Huizhi Information Technology Co. Ltd. Wuhan China Guotou Securities Co. Ltd. Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Data-driven deep learning models are constrained by the scale and diversity of training data, making them vulnerable to data bias. While large language models (LLMs) exhibit superior generalization in vulnerability detection, their low inference efficiency and high computational costs hinder practical deployment in industrial settings. To address these limitations, we propose AIDetectVul, a novel vulnerability detection framework leveraging feature fusion from pre-trained models. Our approach concurrently utilizes encoder-only and decoder-only architectures to extract complementary code embeddings, with feature fusion enhancing semantic diversity. these enriched representations are then processed by a Transformer model, where the self-attention mechanism effectively captures long-range code dependencies, ultimately improving both detection accuracy and generalization capability. Comprehensive evaluations on proprietary enterprise datasets and open-source benchmarks demonstrate that AIDetectVul achieves comparable detection accuracy to the state-of-the-art LineVul model while demonstrating measurable improvements in generalization performance. Compared to LLM-based approaches, our solution maintains significantly lower computational overhead and training costs, making it particularly suitable for industrial applications.

关键词： Training Accuracy Codes Computational modeling Large language models Training data computer architecture Feature extraction Transformers Vectors

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A Multi-Sensor-Based System for Cardiovascular Health Monitoring

A Multi-Sensor-Based System for Cardiovascular Health Monito...

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Sensors and Information Technology (ICSI), International conference on

作者： Haicheng Shan Wenguang Jin College of Information Science & Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the rising prevalence of cardiovascular diseases, cardiovascular health monitoring systems have become crucial for personal health management. this paper presents the design and implementation of a multi-sensor-based cardiovascular health monitoring system. the system collects physiological signals, including photoplethysmography (PPG), electrocardiography (ECG), and phonocardiography (PCG) from the forehead and chest/abdomen, while also integrating auxiliary parameters such as body temperature and respiratory rate to provide a comprehensive cardiovascular health assessment. the hardware design consists of signal acquisition circuits for the forehead and chest/abdomen, along with a Bluetooth Low Energy (BLE) wireless transmission circuit. the collected data is transmitted wirelessly via the BLE protocol and subsequently analyzed on a personal computer (PC). Regarding software design, the data acquisition module is optimized for low power consumption and transmission stability. On the PC side, signal quality is enhanced through filtering techniques, heart rate and respiratory rate are determined using peak detection algorithms, and blood oxygen saturation is computed based on the Lambert-Beer law. Experimental results demonstrate that the system effectively monitors cardiovascular health in real-time and exhibits promising potential for practical applications.

关键词： Temperature measurement Heart rate Accuracy Power demand Forehead Electrocardiography Real-time systems Biomedical monitoring Monitoring Phonocardiography

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International conference on computer Safety, Reliability, and Security, SAFECOMP 2017 and 5th International Workshop on Assurance Cases for software-Intensive systems, ASSURE 2017, 12th Workshop on Dependable Embedded and Cyber-physical systems and systems-of systems, DECSoS 2017, 6th International Workshop on Next Generation of System Assurance Approaches for Safety Critical systems, SASSUR 2017, 3rd International Workshop on Technical and Legal Aspects of Data Privacy and Security, TELERISE 2017 and 2nd International Workshop on the Timing Performance in Safety engineering, TIPS 2017

International Conference on Computer Safety, Reliability, an...

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International conference on computer Safety, Reliability, and Security, SAFECOMP 2017 and 5th International Workshop on Assurance Cases for software-Intensive systems, ASSURE 2017, 12th Workshop on Dependable Embedded and Cyber-physical systems and systems-of systems, DECSoS 2017, 6th International Workshop on Next Generation of System Assurance Approaches for Safety Critical systems, SASSUR 2017, 3rd International Workshop on Technical and Legal Aspects of Data Privacy and Security, TELERISE 2017 and 2nd International Workshop on the Timing Performance in Safety engineering, TIPS 2017

ISBN: (纸本)9783319662831

the proceedings contain 39 papers. the special focus in this conference is on computer Safety, Reliability, and Security. the topics include: Making the case for safety of machine learning in highly automated driving;a thought experiment on evolution of assurance cases —from a logical aspect;a thought experiment on evolution of assurance cases from a logical aspect;uniform model interface for assurance case integration with system models;integrated model-based development of system and safety cases;a web based GSN editor for multiple stakeholders;towards combined safety and security constraints analysis;reconciling systems-theoretic and component-centric methods for safety and security co-analysis;reconciling systems-theoretic and component-centric methods for safety and security co-analysis;increasing dependability in safety critical CPSs using reflective statecharts;a survey of hardware technologies for mixed-critical integration explored in the project EMC2;a model-based design approach;GSN support of mixed-criticality systems certification;concepts for reliable communication in a software-defined network architecture and combining safety and security analysis for industrial collaborative automation systems.

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