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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Cai, Zeyu Qu, Yuben Dong, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665494571

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. The algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5%, and its calculation time has been reduced by 20%. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. The module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20%. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6. © 2021 IEEE.

关键词： Target recognition Smart cities Convolution Face recognition Image edge detection Computational modeling Object detection

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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking

arXiv

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arXiv 2023年

作者： Van Ma, Linh Hussain, Muhammad Ishfaq Park, JongHyun Kim, Jeongbae Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of Department of Mechanical-IT Convergence System Engineering Pusan National University Busan Korea Republic of

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack. © 2023, CC BY.

关键词： Object detection

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Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation

Interpretable Skill Learning for Dynamic Treatment Regimes t...

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Annual Conference on Information sciences and systems (CISS)

作者： Yushan Jiang Wenchao Yu Dongjin Song Wei Cheng Haifeng Chen Department of Computer Science and Engineering University of Connecticut Storrs CT USA NEC Laboratories American Data Science & System Security Princeton NJ USA

Imitation learning that mimics experts' skills from their demonstrations has shown great success in discovering dynamic treatment regimes, i.e., the optimal decision rules to treat an individual patient based on related evolving treatment and covariate history. Existing imitation learning methods, however, still lack the capability to interpret the underlying rationales of the learned policy in a faithful way. Moreover, since dynamic treatment regimes for patients often exhibit varying patterns, i.e., symptoms that transit from one to another, the flat policy learned by a vanilla imitation learning method is typically undesired. To this end, we propose an Interpretable Skill Learning (ISL) framework to resolve the aforementioned challenges for dynamic treatment regimes through imitation. The key idea is to model each segment of experts' demonstrations with a prototype layer and integrate it with the imitation learning layer to enhance the interpretation capability. On one hand, the ISL framework is able to provide interpretable explanations by matching the prototype to exemplar segments during the inference stage, which enables doctors to perform reasoning of the learned demonstrations based on human-understandable patient symptoms and lab results. On the other hand, the obtained skill embedding consisting of prototypes serves as conditional information to the imitation learning layer, which implicitly guides the policy network to provide a more accurate demonstration when the patients' state switches from one stage to another. Thoroughly empirical studies demonstrate that our proposed ISL technique can achieve better performance than state-of-the-art methods. Moreover, the proposed ISL framework also exhibits good interpretability which cannot be observed in existing methods.

关键词： Learning systems Visualization MIMICs Decision making Prototypes Medical services Cognition

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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking

Adaptive Confidence Threshold for ByteTrack in Multi-Object ...

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International Conference on Control, Automation and Information sciences (ICCAIS)

作者： Linh Van Ma Muhammad Ishfaq Hussain JongHyun Park Jeongbae Kim Moongu Jeon School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of Korea Department of Mechanical-IT Convergence System Engineering Pusan National University Busan Republic of Korea

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

arXiv

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arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural Computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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Expectation-Maximization clustering of states based on crime cases against children 14

Expectation-Maximization clustering of states based on crime...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Thota, Lalitha Saroja Sultana, Isharath Shravani, Yerraginnela Changalasetty, Suresh Babu Asif Basha, Syed Changalasetty, Sreelasya Satyanarayana, Pulime Banu, Firdouse Shadan Womens College of Engineering and Technology Department of Cse Hyderabad India Sri Indu College of Engineering Department of Cse Hyderabad India King Khalid University College of Computer Science Abha Saudi Arabia Vellore Institute of Technology Department of Cse Vellore India Samskruthi College of Engineering and Technology Department of Cse Hyderabad India Applied College King Khalid University Dept of Applied Information System Khamis Mushaiyt Saudi Arabia

ISBN: (纸本)9798350335095

A child is the utmost innocent and vulnerable human being. Yet, children fall easy target to many types of violence, all over the place and each single day. Globally, one billion children face certain form of emotional, physical or sexual crimes each single year. To prevent and address crimes against children, a multifaceted approach combining law enforcement, healthcare workers, educators, researchers and social service providers is necessary. Modern technology and advanced tools are widely in use for criminal investigations from crime data. Although crime data set solely comprises the number of crimes reported in each state for various years, clustering can still be used to group states in India according to the crime cases against children. In this paper, we apply Expectation-Maximization (EM) cluster methodology in WEKA platform to find patterns and trends in child crime rate data and cluster states in India based on crime cases against children which may help to create efficient interventions to stop and deal with crimes against children. © 2023 IEEE.

关键词： Crime

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Warped-Compaction: Maximizing GPU Register File Bandwidth Utilization via Operand Compaction

Warped-Compaction: Maximizing GPU Register File Bandwidth Ut...

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IEEE Symposium on High-Performance computer Architecture

作者： Eunbi Jeong Ipoom Jeong Myung Kuk Yoon Nam Sung Kim Department of Computer Science and Engineering Ewha Womans University Seoul Korea Department of System Semiconductor Engineering Yonsei University Seoul Korea Department of Electrical and Electronic Engineering Yonsei University Seoul Korea University of Illinois Urbana-Champaign Urbana U.S.A

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

The GPU has been successfully used for diverse emerging compute-intensive applications, including imaging, computer vision, and more recently, deep learning, to name a few. To offer high performance for such applications, it is provisioned with massive Register Files (RFs) to exploit high Thread-Level Parallelism (TLP). As RFs are designed to store thousands of contexts and provide very high bandwidth for uninterrupted supply of operands to hundreds of compute units for high TLP, they have become one of the most power-hungry components in the GPU. Meanwhile, faced with the end of Dennard scaling, it is more important than ever to innovate the microarchitecture of (power-constrained) GPUs to continuously improve the performance of future compute-intensive *** this work, we propose a GPU microarchitecture, WarpedCompaction, designed to utilize given RFs more efficiently, instead of relying on larger-capacity and/or higher-bandwidth RFs to further improve GPU performance. Specifically, first, we reverse-engineer the latest GPU’s RF organization through microbenchmarking. This uncovers that each sub-core within a streaming multiprocessor contains only two dual-ported RF banks, and accesses to these banks are arbitrated solely based on register IDs. We also reveal that, despite the modest configurations, RF banks are largely underutilized, staying inactive for 33.5% of the time. Second, we observe that previously proposed RF optimization techniques, data forwarding and dead register elimination, cannot address this underutilization problem. This is mainly due to the (R1) insufficient RF access requests from a limited number of Operand Collector Units (OCUs) and (R2) inefficient operand distribution by the conventional RF bank arbitration. Third, we present two architectural solutions to tackle the observed inefficiency: (S1) OCU sharing and (S2) skewed arbitrator. Building on enhanced OCU early allocation for partially ready instructions and operand forwardin

关键词： Radio frequency Microarchitecture Graphics processing units Bandwidth Organizations Parallel processing Hardware Energy efficiency Registers Resource management

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Automating smart Internet of Things devices in modern homes using context-based fuzzy logic

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Computational Intelligence 2024年第1期40卷 e12370-e12370页

作者： Ming, Chen Kadry, Seifedine Dasel, A. Antony Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang China School of Automotive and Traffic Engineering Hubei University of Arts and Science Xiangyang China Department of Mathematics and Computer Science Beirut Arab University Beirut Lebanon Department of Mechanical Engineering VIT University Vellore India

Internet of Things (IoT) is mainly used to connect different embedded objects over the internet to make communication between them possible. With the help of IoT, devices find a way to interact, work together, and study from each other's experiences just like humans do. IoT finds its way in applications such as smart home, smart city, healthcare, agriculture, and so on. The name smart home arises due to the automation of the normal home appliances to make it smart. When the devices of the normal smart home are connected via the internet, they become a part of the IoT. The smart home should ensure the following characteristics such as security, comfort, convenience, and energy saving. The article presents a technique for IoT controlled devices in a smart home using context-based fuzzy logic. Fuzzy logic is mainly used to monitor and analyze the real-time data collected from the sensors in the smart homes from various environments. Context-based fuzzy logic uses a multivalued logic principle which differs from the normal Boolean logic, where the truth value lies between only zero and one (ie, true or false). The proposed smart home is implemented in a real case scenario where it yields an accuracy of 90.5%, response time of 6.41 milliseconds, and an F-measure of 97%. © 2020 Wiley Periodicals LLC.

关键词： Internet of things

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Efficient IOT based COVID patient healthcare control system using arduino with mobile app

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Measurement: Sensors 2024年 33卷

作者： sudha, K. Mercy Theresa, M. Vinayagam, P. Suganthi, R. Sripriya, T. uthayakumar, G.S. Computer Science and Business System R. M. D Engineering College India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of ECE Saveetha Engineering College India Department of ECE Panimalar Engineering College India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamilnadu Chennai India Department of ECE St.Joseph's Institute of Technology India

Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. Sensors, an information acquisition unit, a microcontroller (e.g., Arduino), and a product modification were used to construct a mobile phone-based remote healthcare monitoring framework that may provide continuous web-based data about a patient's physiological states (i.e., JAVA). The framework checks, displays, and stores the patient's temperature, heart rate, and EEG data before sending it out on the specialist's portable device with the application. In this way, an IoT-based patient monitoring framework may effectively monitor a patient's health state and save their life on time. © 2024

关键词： Data acquisition

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