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Multi-Sensor Fusion for Fall Detection and Early Warning: Overcoming Camera Blind Spots 24

Multi-Sensor Fusion for Fall Detection and Early Warning: Ov...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Zhao, Yehong Deng, Junwen Zhang, Mingyue Liu, Huan Liu, Mingtao Li, Jinmei College of Electrical and Information Engineering Hunan Institute of Traffic Engineering Hunan Hengyang China School of Computing / Software University of South China Hunan Hengyang China

ISBN: (纸本)9798400710094

The aging population has led to an increased need for reliable fall detection systems that can provide timely assistance to individuals who have fallen. This paper presents a multi-sensor fusion system designed to detect falls and issue early warnings, overcoming the limitations of camera-based systems, particularly in areas where camera surveillance is not feasible. We utilize a combination of an MPU6050 six-axis pose sensor and a MAX30102 heart rate and blood oxygen sensor to achieve high accuracy in fall detection. The system is designed to differentiate between falling and normal physical activities, reducing false positives. When a fall is detected, the system triggers an alert and sends real-time health data to a TCP server for monitoring on a mobile terminal. Our approach addresses camera blind spots and provides a comprehensive solution for fall detection in non-visual environments. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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Ethical Implications of WannaCry: A Cybersecurity Dilemma

Ethical Implications of WannaCry: A Cybersecurity Dilemma

引用

20th international conference on Cyber Warfare and Security, ICCWS 2025

作者： Osamor, Jude Odum, Jane Iwendi, Celestine Olajide, Funminiyi Peter-Osamor, Isaac Onyenagubom, Victor Ayodele, Innocent Bristol United Kingdom School of Computing University of Georgia United States School of Creative Technologies University of Bolton United Kingdom School of Computer Science & Engineering University of Westminster London United Kingdom University of Lagos Akoka Nigeria Department of Computing Teeside University United Kingdom The Department of Engineering & Computer Science University of Hertfordshire United Kingdom

ISBN: (纸本)9781917204316

The WannaCry ransomware attack of May 2017 marked a critical turning point in cybersecurity history, prompting profound ethical discussions about software vulnerability management. This comprehensive analysis examines the ethical dimensions of the WannaCry incident, focusing on the responsibilities of government agencies, technology companies, and security professionals in handling zero-day vulnerabilities. The study investigates the complex balance between national security interests and global cybersecurity while proposing ethical frameworks for future practice. Through a detailed examination of the attack's global impact and subsequent incidents, we demonstrate the ongoing relevance of lessons learned from WannaCry to contemporary cybersecurity challenges. © 2025 Proceedings of the international conference on Information Warfare and Security (ICCWS). All rights reserved.

关键词： Zero-day attack

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Holistic Cyber Threat Modeling for Machine Learning-Based Systems: A Case Study in Healthcare

Holistic Cyber Threat Modeling for Machine Learning-Based Sy...

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18th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Jaal, Janno Bahsi, Hayretdin Department of Software Science Tallinn University of Technology Tallinn Estonia Cybernetica AS Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

Considering the immense pace in machine learning (ML) technology and related products, it may be difficult to imagine a software system, including healthcare systems, without any subsystem containing an ML model in the near future. However, ensuring the resiliency of these ML-based systems against cyber attacks is vital for more seamless and widespread technology usage. The secure-by-design principle, considering security from the early stages of development, is a cornerstone to achieving sufficient security at a reasonable cost. The realization of this principle starts with conducting threat modeling to understand the relevant security posture and identify cyber security requirements before system design. Although threat modeling of software systems is widely known, it is unclear how to apply it to software systems with machine learning models. Although adversarial machine learning is a widely studied research topic, it has yet to be thoroughly researched how adversarial and conventional cybersecurity attacks can be holistically considered to identify applicable cyber threats at the early stage of a software development life cycle. This paper adapts STRIDE, a widely-known threat modeling method, for the holistic cyber threat analysis of an ML-based healthcare system. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Comparative Analysis of software Reliability Prediction and Optimization using Machine Learning Algorithms

Comparative Analysis of Software Reliability Prediction and ...

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Intelligent Systems and Computational Networks (ICISCN), international conference on

作者： Prashanth Kumar Bolisetty Midhunchakkaravarthy Faculty of AI AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia Faculty of AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

software reliability plays a crucial role in assessing the overall quality of software systems. Developing highly reliable software presents challenges, including ensuring usability, maintaining consistent performance, offering reliable service, and facilitating smooth maintenance. To overcome these limitations, researchers have turned to intelligent prediction and optimization algorithms. This paper presents a comprehensive review of software reliability prediction, emphasizing the application of machine learning techniques. It also provides an analytical evaluation of existing methods to identify areas for improvement and future research opportunities in software reliability prediction.

关键词： Machine learning algorithms Reviews software algorithms Machine learning software systems Prediction algorithms software reliability Maintenance Usability Optimization

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NBS-Based Mobile Edge computing: A Joint Optimization of QoS and Energy in Computation Offloading 20th

NBS-Based Mobile Edge Computing: A Joint Optimization of Qo...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Peng, Zhihao Liao, Guocheng School of Software Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789819628636

Integrating Mobile Edge computing (MEC) into the Internet of Things (IoT) enables IoT devices with limited computational power and energy to offload their compute-intensive and latency-sensitive tasks to the edge of the network, thus providing high-quality services to the users. In this paper, we utilize Nash Bargaining Solution (NBS) to jointly optimize computation power scheduling and benefit sharing among edge devices efficiently and fairly. We decouple the NBS into task scheduling, computation power allocation and benefit sharing problem, and solve them with Alternating Direction Method of Multipliers (ADMM) algorithm and subgradient descent method. Numerical results validate our analysis and show that our proposed scheme significantly improves the energy efficiency of MEC in IoT networks compared to existing benchmarks. © IFIP international Federation for Information Processing 2025.

关键词： Mobile edge computing

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Duan, Chiming Jia, Tong Yang, Yong Liu, Guiyang Liu, Jinbu Zhang, Huxing Zhou, Qi Li, Ying Huang, Gang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9798350368741

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%. © 2025 IEEE.

关键词： Active Learning Anomaly Detection Log Analysis Retrieval Augmented Generation

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Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming 25

Palantir: Towards Efficient Super Resolution for Ultra-high-...

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16th ACM multimedia Systems conference, MMSys 2025

作者： Jin, Xinqi Zhu, Zhui Sun, Xikai Dang, Fan Liu, Jiangchuan Xu, Jingao Liu, Kebin Chen, Xinlei Liu, Yunhao School of Software & BNRist Tsinghua University Beijing China Department of Automation & BNRist Tsinghua University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Computing Science Simon Fraser University Burnaby Canada Computer Science Department Carnegie Mellon University Pittsburgh United States Global Innovation Exchange Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798400714672

Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on carefully selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantír, the first neural-enhanced UHD live streaming system with fine-grained patch-level *** the core of Palantír is its SR video quality estimation strategy which guides the low-delay selection of the most beneficial anchor patches. Although existing systems propose estimation strategies for anchor frame selection, these strategies heavily rely on empirical insights that cannot be transferred to our context, making fine-grained scheduling a challenging problem that requires a fundamentally new solution. Facing the challenge, we follow the first-principles approach and derive a directed acyclic graph (DAG) model to address the problem. The model can also be generalized to various settings due to its first-principles nature. Compared to the state-of-the-art real-time frame-level scheduling strategy for live streaming, Palantír reduces the anchor size by 80.1% at most and 38.4% on average without compromising the quality gain. Furthermore, Palantír incurs a scheduling latency accounting for only 0.6-3.9% of the end-to-end latency requirement for UHD live streaming. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep neural networks

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Design and Implementation of Multifunctional Logic Circuits Using SRAM Cells Utilizing In-Memory computing Concept 3rd

Design and Implementation of Multifunctional Logic Circuits ...

引用

3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Ansari, Nazida Mushtaq, Umayia Eram, Seema Waseem Akram, Md. Department of Electronics and Communication Engineering Jamia Millia Islamia New Delhi110025 India

ISBN: (纸本)9789819730896

In-memory computing (IMC) is a cutting-edge technology that has disrupted the conventional approach of computing, instead of data is stored separately and processed, in-memory computing eliminates delays, reduces energy consumption, and minimizes resource wastage by addressing the memory bottleneck issue during computation. Leveraging the speed and efficiency of RAM (Random Access Memory), IMC offers fast data access and computation, making it ideal for handling large-scale data-intensive tasks and real-time analytics. This paper consists of design and simulation of 6T SRAM cell array and Multifunctional in memory computing logic circuit to implement the basic two input digital logic gates such as AND, NOR, NAND, OR, XOR and XNOR. Additionally same architecture can perform Half Adder operation and 2-bit binary to gray conversion. The proposed circuit is designed using PTM32nm (Predictive Technology Model) technology node with 1 V operating voltage and implemented in LTSpice software. The circuit performs bitwise operations, enabling simultaneous read and perform each logic operation parallelly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computer circuits

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Investigation of the Architecture for A Application Creation Platform Leveraging Modern Technology on Smart Campus 24

Investigation of the Architecture for A Application Creation...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Huang, Jie Ma, Kai He, Xiaosong He, Hongjing Institute of Big Data Technology and Application Chongqing Institute of Engineering Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Chongqing China

ISBN: (纸本)9798400709999

Intelligent technologies are driving the development of smart campuses, fostering a dynamic and diverse intelligent ***, the current trend of customizing smart campus solutions often positions campus citizens as mere bystanders or end *** enhance participatory engagement in campus development and increase the autonomy of platform construction, this paper leverages the strengths of big data warehouses, data analytics, the Internet of Things (IoT), and cloud computing-specifically their security, concurrency, reliability, redundancy, and scalability-to propose a campus application creation *** platform aims to streamline the software development process, facilitate the publication of software products, and promote functionality sharing and collaborative *** introduce an architectural model comprising four components: the data lake and warehouse layer, the data ferry layer, the resource service layer, and the platform *** internal operational mechanisms of this architecture are discussed from the perspectives of"applications as data, " self-feedback analysis, application evaluation systems, and system *** advantages of the platform are analyzed, along with application cases from both teacher and student *** also explore the platform's significance within the campus context, including its potential to adjust campus functionality, enhance overall innovation capabilities in education, and increase the autonomy of software *** findings suggest that a modern application creation platform can significantly facilitate the self-improvement of smart campus functionalities, thereby further enhancing the intelligence of the campus. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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14th international conference on Frontier computing, FC 2024

14th International Conference on Frontier Computing, FC 2024

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14th international conference on Frontier computing, FC 2024

ISBN: (纸本)9789819627974

The proceedings contain 104 papers. The special focus in this conference is on Frontier computing. The topics include: multimedia Digital AI Painting System Based on Machine Vision Algorithm;intelligent Decision Optimization of Big Data in the Digital Economy Under Deep Reinforcement Learning;development of Virtual Maintenance Training Platform for Electric Motors;design of Automatic Driving and Parking System for New Energy Vehicles Based on Artificial Intelligence;application of Machine Learning Algorithms to Mental Health Data Processing for College Students;on the Translation Strategy of "Intangible Cultural Heritage" Based on Visual Multimodality;research on the Improvement of Human-Machine Collaborative Machine Translation Model Based on Internet;research on Subway Station Space Design from the Perspective of Regional Culture;application of Intelligent Algorithm in Image Recognition Under the Vision of Artificial Intelligence;risk Assessment Model of Electricity Customer Data Based on Dynamic Grid Generation Technology;an Evaluation Model of Equestrian Athletes’ Sense of Speed Based on Inertial Sensor Network;emotion Analysis of Low Resource Domain Based on Transfer Learning and Meta-Learning Methods;digital Technology Integrated with Artificial Intelligence Digital Product Research and Development;research on Artificial Neural Network Prediction of the Strength of Metal-Based Carbon Fiber Reinforced Polymer Layer;Intelligent Recommendation Algorithm for New Media Content Based on AI Technology;design of Daily Life Ability Training System Based on Intelligent Evaluation Algorithm;business Model for Developing Power Load Resources;practice of Artificial Intelligence Technology in Enterprise Human Resource Management;medical Service Management Based on Modern Information Technology;tetris Game System Based on Java.

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