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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Noelle R. Benavides Hayley E. Rutkey Courteney M. DiDomenico Tin Wong Joe Martel-Foley Chen-Hsiang Yu Ali Kiapour Wentworth Institute of Technology School of Computing and Data Science Wentworth Institute of Technology

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

Over-exposure to harmful radiation that can damage the skin is a common issue in today's world. In particular excessive exposure to ultraviolet (UV) radiation affects mast cells in the epidermis that can drive skin aging, eye damage, immune suppression, and melanoma, a type of skin cancer that causes over 75% of all skin cancer deaths. UV exposure can occur through various sources and during certain activities including tanning beds and lamps. However, the most common form of exposure is through the natural sunlight in numerous climates. In this paper, we proposed a new wearable medical device to quickly and conveniently monitor ultraviolet exposure, hydration levels, and location tracking for the users. The device is dedicated to ensuring the individuals will avoid overexposure to the UV radiation and build healthy habits. Partnered with a mobile application, the device not only allows the individuals to track their sun exposure history, but it also provides a history of an individual's hydration levels and group tracking for multiple users. Our proposed wearable device will allow users to be more aware and responsive to the level of sun exposure and dehydration so that the outdoors can be experienced in a safe way and any increased risk of skin cancer may be avoided.

关键词： Wearable computers Conferences Mobile applications History Biomedical monitoring Sun Monitoring

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FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment

arXiv

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arXiv 2023年

作者： Liu, Jiahao Wu, Jiang Chen, Jinyu Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others’ models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and guidance models, so as to customize aggregation weights for each client. Guidance models are obtained by the local one-step ahead adaptation on individual clients. Finally, we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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Estimating China's poverty reduction efficiency by integrating multi-source geospatial data and deep learning techniques

引用

地球空间信息科学学报（英文版） 2024年第4期27卷 1000-1016页

作者： Yao Yao Jianfeng Zhou Zhenhui Sun Qingfeng Guan Zhiqiang Guo Yin Xu Jinbao Zhang Ye Hong Yuyang Cai Ruoyu Wang School of Geography and Information Engineering China University of GeosciencesWuhanChina Center for Spatial Information Science The University of TokyoChibaJapan Alibaba Group HangzhouChina Foshan Surveying Mapping and Geoinformation Research Institute FoshanChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities(Ministry of Natural Resources) School of Geographic SciencesEast China Normal UniversityShanghaChina School of Geography and Information Engineering China University of GeosciencesWuhanChina Institute of International Rivers and Eco-Security Yunnan UniversityKunmingChina Institute of Geography and Spatial Information School of Earth SciencesZhejiang UniversityHangzhouChina School of Geography and Planning Sun Yat-Sen UniversityGuangzhouChina Institute of Cartography and Geoinformation ETH ZurichZürichSwitzerland Department of Civil Environmental and Geomatic Engineering School of EngineeringUniversity College LondonLondonUK Centre for Public Health Queen's University BelfastBelfastNorthern IrelandUK

Poverty threatens human development especially for developing countries,so ending poverty has become one of the most important United Nations Sustainable Development Goals(SDGs).This study aims to explore China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning *** poverty reduction efficiency(PRE)is measured by the difference in the out-of-poverty rates(which measures the probability of being not poor)of 2016 and *** study shows that the probability of poverty in all regions of China has shown an overall decreasing trend(PRE=0.264),which indicates that the progress in poverty reduction during this period is *** Hu Huanyong Line(Hu Line)shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest *** 2016 to 2019,the centroid of China's out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line,indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to *** results imply that the government's future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor *** study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government's poverty reduction policy.

关键词： Poverty reduction efficiency(PRE) driving forces multisource big data deep learning random forest(RF)

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BotsideP2P: A Peer-to-Peer Botnet Testbed

BotsideP2P: A Peer-to-Peer Botnet Testbed

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Adam Beauchaine Orion Collins Mira Yun School of Computing and Data Science Wentworth Institute of Technology Boston MA

ISBN: (纸本)9781665406918

The number of botnet attacks has been rapidly increasing in recent years. The threat of botnets constitutes a major security consideration for institutions and organizations concerned with the prevention of cybercrime. Among the most severe of these concerns are those regarding peer-to-peer (P2P) botnet attacks. These botnets present difficulties in local detection, as well as direct challenges to traditional network security practices due to their decentralized nature. We propose a testbed, named BotsideP2P, specialized in the deployment of a P2P botnet with built-in network and endpoint monitoring, so that researchers and industry professionals may gain a better understanding of these botnets in a lab environment. The botnet we have deployed implements a distributed hash table (DHT) to provide bot-to-bot encryption using the Kademlia DHT, and the Asyncio network framework is used for process handling. Emphasis is placed on documenting both the structure of the botnet deployed as well as the logging procedures implemented, and flow data and logs are included in results.

关键词： Botnet Scalability Organizations Network analyzers Inspection Network security Mobile communication

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An Efficient and Reliable Multi-Robot Navigation Platform with Collision Avoidance

An Efficient and Reliable Multi-Robot Navigation Platform wi...

引用

IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： David Crafts Jake Sousa William Mclellan Nathan Robson Cuong Pham School of Computing and Data Science Wenworth Institute of Technology Boston USA

ISBN: (纸本)9781665405966

With the ever-increasing demand for consumer goods, companies are pivoting towards semi or fully autonomous robotic warehouses to efficiently fulfill orders. Large companies have become increasingly reliant on autonomous robotic warehouse solutions, however, the demand from small to medium-sized companies has increased dramatically [8]. These companies often rely on manual solutions, such as forklifts, to traverse their warehouses. In 2019, forklifts accounted for 79 work related deaths and 8,140 nonfatal injuries [9]. Warehouse accidents are the direct result of their fast paced, ever-changing nature. With warehouses containing a multitude of static and dynamic variables, an open-source robotics platform is needed that can safely traverse a warehouse, preventing unnecessary death and injury. The proposed solution allows for the safe and effective navigation of these environments utilizing a path-planning algorithm in tandem with a dynamic obstacle avoidance algorithm. The dynamic obstacle avoidance algorithm uses LiDAR sensor data to detect and avoid obstacles along a path. The path-planning algorithm uses a map of a warehouse environment that accounts for static objects, such as shelves, and generates an efficient path from a starting point to a goal point. These algorithms are simulated in Gazebo allowing for a detailed visual of the platform's performance. These algorithms, together, create a platform that allows for robots to autonomously navigate to desired destinations while safely avoiding a myriad of obstacles.

关键词： Laser radar Navigation Educational robots Heuristic algorithms Companies Robot sensing systems Reliability

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ShiftwiseConv: Small Convolutional Kernel with Large Kernel Effect

arXiv

引用

arXiv 2024年

作者： Li, Dachong Li, Li Chen, Zhuangzhuang Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Mathematical Sciences Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology China

Large kernels make standard convolutional neural networks (CNNs) great again over transformer architectures in various vision tasks. Nonetheless, recent studies meticulously designed around increasing kernel size have shown diminishing returns or stagnation in performance. Thus, the hidden factors of large kernel convolution that affect model performance remain unexplored. In this paper, we reveal that the key hidden factors of large kernels can be summarized as two separate components: extracting features at a certain granularity and fusing features by multiple pathways. To this end, we leverage the multi-path long-distance sparse dependency relationship to enhance feature utilization via the proposed Shiftwise (SW) convolution operator with a pure CNN architecture. In a wide range of vision tasks such as classification, segmentation, and detection, SW surpasses state-of-the-art transformers and CNN architectures, including SLaK and UniRepLKNet. More importantly, our experiments demonstrate that 3 × 3 convolutions can replace large convolutions in existing large kernel CNNs to achieve comparable effects, which may inspire follow-up works. Code and all the models at https://***/lidc54/shift-wiseConv. © 2024, CC BY-NC-SA.

关键词： Convolution

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DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation 41

DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Est...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Liu, Qinshuo Wang, Zixin Li, Xi-An Zhang, Xinyao Ji Lei Liu, Lin Liu, Zhonghua Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong School of Life Sciences Shanghai Jiao Tong University Shanghai China Institute of Natural Sciences MOE-LSC School of Mathematical Sciences CMA-Shanghai Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University Shanghai China Department of Biostatistics Columbia University New YorkNY United States

Semiparametric statistics play a pivotal role in a wide range of domains, including but not limited to missing data, causal inference, and transfer learning, to name a few. In many settings, semiparametric theory leads to (nearly) statistically optimal procedures that yet involve numerically solving Fredholm integral equations of the second kind. Traditional numerical methods, such as polynomial or spline approximations, are difficult to scale to multi-dimensional problems. Alternatively, statisticians may choose to approximate the original integral equations by ones with closed-form solutions, resulting in computationally more efficient, but statistically suboptimal or even incorrect procedures. To bridge this gap, we propose a novel framework by formulating the semiparametric estimation problem as a bi-level optimization problem;and then we develop a scalable algorithm called Deep Neural-Nets Assisted Semiparametric Estimation (DNA-SE) by leveraging the universal approximation property of Deep Neural-Nets (DNN) to streamline semiparametric procedures. Through extensive numerical experiments and a real data analysis, we demonstrate the numerical and statistical advantages of DNA-SE over traditional methods. To the best of our knowledge, we are the first to bring DNN into semiparametric statistics as a numerical solver of integral equations in our proposed general framework. Copyright 2024 by the author(s)

关键词： Fredholm integral equations

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Development of ontology on crime investigation process

Development of ontology on crime investigation process

引用

2020 National science, Engineering and Technology Conference, NCSET 2020

作者： Srimukh, P.V. Shridevi, S. School of Computer Science and Engineering Vellore Institute of Technology University Chennai India Research Division of Advanced Data Science Vellore Institute of Technology University Chennai India

This paper is an extended version of the research paper "Ontology based crime investigation process" which deals with the working principle and the construction of an ontology based extensively on organized crime. Ontologies on various domains are created in which their assistance has been widely recognized. They are being developed for providing basis for allocation of knowledge in a domain and imparting reasonable information. In this paper we describe the structure of the ontology we created and also by validating the ontology via an online ontology evaluating tool. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Ontology

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Multi-Strategy Bus Deviation-Driven Layer Assignment Algorithm

Multi-Strategy Bus Deviation-Driven Layer Assignment Algorit...

引用

Electronics Design Automation (ISEDA), International Symposium of

作者： Yantao Yu Zepeng Li Jiarui Chen Xing Huang Genggeng Liu Ning Xu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China School of Computer Science Northwestern Polytechnical University Xian China School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

In modern very large-scale integration (VLSI) design, the solution quality of the bus routing is a crucial factor that determines the timing and power of circuit, and finally affects the performance and yield of chips. Taking bus deviation as the main optimization objective, an effective multi-strategy bus deviation-driven layer assignment algorithm is proposed to solve the timing-matching problem of bus routing. First, a net priority determination method that integrates multiple features is presented to determine the layer assignment order, thus obtaining a routing sequence which can weigh the wirelength and bus deviation well. Second, an effective single net layer assignment algorithm is proposed to assign each net based on dynamic programming, thus reducing the number of vias. Third, a layer shifting strategy based on the bus lookup table is designed to effectively balance total wirelength and bus deviation by sacrificing a certain number of vias. Experimental results, compared to existing work, show that the proposed algorithm can achieve significant optimization on the bus deviation and total wirelength, and finally obtain the best results in terms of the bus deviation, which is the most important optimization objective for bus routing.

关键词： Design automation Heuristic algorithms Very large scale integration Routing Timing Pins Dynamic programming

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Quantum-based Variational Approach for Solving Graph Isomorphism Problems

arXiv

引用

arXiv 2025年

作者： Wang, Yukun Shen, Yingtong Zhang, Zhichao Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China School of Computer and Information Science Southwest University Chongqing400715 China

The graph isomorphism problem remains a fundamental challenge in computer science, driving the search for efficient decision algorithms. Due to its ambiguous computational complexity, heuristic approaches such as simulated annealing are frequently used to explore the solution space selectively. These methods often achieve high probabilities of identifying solutions quickly, avoiding the exhaustive enumeration required by exact algorithms. However, traditional simulated annealing usually struggles with low sampling efficiency and reduced solution-finding probability in complex or large graph problems. In this study, we integrate the principles of quantum technology to address the graph isomorphism problem. By mapping the solution space to a quantum many-body system, we developed a parameterized model for variational simulated annealing. This approach emphasizes the regions of the solution space that are most likely to contain the optimal solution, thereby enhancing the search accuracy. Artificial neural networks were utilized to parameterize the quantum many-body system, leveraging their capacity for efficient function approximation to perform accurate sampling in the intricate energy landscapes of large graph problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic methods

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