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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Hemalatha Jayabharathi Ramasamy K. Selvi P. Anandan R. Meenakshi S. P. Maniraj Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Faculty of Management Studies S. A. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management.

关键词： Cloud computing Hospitals Process control User interfaces Control systems HTTP Real-time systems

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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Negative Sample Mining based Deep Feature Learning for Kinship Verification 21

Negative Sample Mining based Deep Feature Learning for Kinsh...

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Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Xiaoke Zhu Changlong Li Xiaopan Chen Minghao Zheng Yunwei Li Jihui Hu Henan Key Laboratory of Big Data Analysis and Processing Henan University China School of Computer and Information Engineering Henan University China

ISBN: (纸本)9781450385893

Kinship verification is an important and challenging task. The aim of it is to find out whether there is a kin relationship between one pair of facial images. However, there are many challenges for kinship verification. One of the most important challenge is the problem of data imbalance. Specifically, if there exists N positive kinship pairs, we can get N(N-1) negative pairs. Obviously, the number of negative kinship pairs is much larger than the number of positive kinship pairs. How to make full use of positive and negative samples for training is a valuable problem. But most of the existing methods just randomly select the same number of negative sample pairs as the positive sample pairs. What is more, different negative pairs contain different discrimination information. Therefore, finding discriminative sample is also an important problem. In this paper, we propose a simple and effective method, named Negative sample Mining based Deep Feature Learning (NMDFL) to solve the above problems. Specifically, different with most existing methods, we sample N positive kinship pairs and T*N negative pairs, where T>1. Then, they are sent to the train net, and their impacts on network updating are adjusted in proportion to the number of positive and negative samples. At the same time, the corresponding weight of each negative sample is dynamically adjusted according to the training results of each time. Experimental results on the KinFaceW-I and KinFaceW-II datasets proves the effectiveness of our method.

关键词： Deep Feature Learning Image-based Kinship Verification Mining Negative Samples Unbalanced data

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Percept U-Net: Percept Attention-based Convolutional Neural Network for Atrial Fibrillation Episode Localization

Percept U-Net: Percept Attention-based Convolutional Neural ...

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International Conference on data Science and Advanced Analytics (DSAA)

作者： Ying An Lebing Pan Lin Guo Xianlai Chen Yu Shen Big Data Institute Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665473316

Early detection of paroxysmal atrial fibrillation (AF) is valuable in determining treatment options and diagnosing complications, and identifying the number and duration of AF episodes in the dynamic electrocardiogram (ECG) may help advance the study of pathological AF mechanisms. Current computer-aided ECG signal diagnosis methods can be divided into two categories: (a) methods based on every single heartbeat split from ECG records, which cannot use information from adjacent heartbeats, and (b) methods based on a segment of ECG records that contain multiple heartbeats, which can only give a classification of the whole segment but cannot identify the categories of each heartbeat in the record at a fine granularity. Few methods can exploit the relationship between heartbeats as well as identify the onset and end of disease episodes. To fill this gap, we designed an improved U-Net model named Percept U-Net to detect AF episodes from the ECG records. In this model, we propose the percept attention to replace skip connections in U-Net to integrate low-level features with high-level features, thereby enhancing the ability to detect and discriminate between normal and abnormal heartbeats. Experimental results show that Percept U-Net achieves higher classification and localization accuracy with less computational overhead and paraments than other comparative methods.

关键词： Location awareness Pathology Heart beat Computational modeling Atrial fibrillation Medical services Electrocardiography

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Improving Software Defined Network Controllers in a Multi-Vendor Environment

SSRN

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SSRN 2023年

作者： Deneke, Bereketeab Berhanu Beyene, Asrat Mulatu Haile, Endalew Ayenew Department of Computer Science School of Graduate Studies St. Mary’s University Addis Ababa Ethiopia High Performance Computing & Big Data Analytics CoE Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Adama Science and Technology University School of Electrical Engineering and Computing Electrical Power and Control Engineering Adama Ethiopia

Software Defined Networking (SDN) is an emerging networking paradigm with the potential to foster innovation through programmable networks. SDNs are characterized by the separation of control and data planes where, in a logically centralized controller, it’s possible to make routing decisions on behalf of forwarding elements. For this, there are different protocols used like Open Flow, Forward and Control Element Separation (FORCES), Path Computation Element Protocol (PCEP), Network Configuration Protocol (NETCONF), and Interface to Routing System (IRS). Border Gateway Protocol (BGP) is one such protocol that can enable the network programmability promised by SDNs. The controllers used in BGP as control plane protocol leverages RESTCONF (RFC 8040) as management plane protocol to interact with physical routers, switches, and network service firewalls. Therefore, the aim of this work is to improve the performance of SDN controllers using BGP enabling a multi-vendor environment without requiring infrastructure upgrade showcasing how interoperability can be achieved. The proposed system is implemented using OpenDaylight controllers. The performance of the prototype is evaluated using Latency, Throughput, CPU consumption, and TTL (Time to Live). Based on the simulation results it is observed that the SDN controllers that use BGP show improvements in terms of Latency and Throughput. © 2023, The Authors. All rights reserved.

关键词： Controllers

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Energy-Efficient User Allocation and Content Updating in Mobile Edge Computing Networks

Energy-Efficient User Allocation and Content Updating in Mob...

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IEEE International Conference on Communications (ICC)

作者： Jingchao Tan Tiancheng Zhang Chenyang Wang Xiuhua Li Xiaofei Wang College of Intelligence and Computing Tianjin University Tianjin China College of Computer and Software Engineering Shenzhen University Shenzhen Guangdong China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Guangdong China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

As a robust platform for mobile edge computing, 5G networks, while delivering high data rates and low latency, face a pressing concern with the escalating energy consumption of 5G Base Stations (BSs). To address this issue, we propose an algorithm called the Environmental Protection Prophet (EPP), based on the clustered and geographically inclined user request patterns. The EPP algorithm groups users according to their proximity to BSs and utilizes edge caching to reduce response times for user requests. This clustering strategy optimizes user allocation while minimizing BSs' energy consumption, all while meeting user Quality of Service (QoS) requirements. Simulation experiments illustrate the potential for energy savings and latency reduction, particularly in densely populated urban areas. The findings provide valuable insights for the design of energy-efficient 5G networks, concurrently addressing environmental concerns and meeting user performance expectations.

关键词： Energy consumption Multi-access edge computing 5G mobile communication Urban areas Clustering algorithms Quality of service Energy efficiency

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GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

arXiv

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a "teacher" to create guidance prompts, paired with a smaller "student" model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation. Our code is publicly available at https://***/Zoeyyao27/GKT © 2024, CC BY.

关键词： Distillation

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Double-Active-IRS Aided Wireless Communication: Deployment Optimization and Capacity Scaling

arXiv

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arXiv 2023年

作者： Kang, Zhenyu You, Changsheng Zhang, Rui The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Shenzhen518055 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs are properly deployed to assist the communication from a base station (BS) to multiple users located in a given zone via the double-reflection links. Under the assumption of fixed per-element amplification power for each active-IRS element, we formulate a rate maximization problem subject to practical constraints on the reflection design, elements allocation, and placement of active IRSs. To solve this non-convex problem, we first obtain the optimal active-IRS reflections and BS beamforming, based on which we then jointly optimize the active-IRS elements allocation and placement by using the alternating optimization (AO) method. Moreover, we show that given the fixed per-element amplification power, the received signal-to-noise ratio (SNR) at the user increases asymptotically with the square of the number of reflecting elements;while given the fixed number of reflecting elements, the SNR does not increase with the per-element amplification power when it is asymptotically large. Last, numerical results are presented to validate the effectiveness of the proposed AO-based algorithm and compare the rate performance of the considered double-active-IRS aided wireless system with various benchmark systems. © 2023, CC BY.

关键词： Amplification

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What-if Analysis Framework for Digital Twins in 6G Wireless Network Management

What-if Analysis Framework for Digital Twins in 6G Wireless ...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Elif Ak Berk Canberk Vishal Sharma Octavia A. Dobre Trung Q. Duong Department of Computer Engineering Istanbul Technical University Istanbul Turkey School of Engineering and Built Environment Edinburgh Napier University Edinburgh UK Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey Queen’s University Belfast Belfast UK Memorial University St. Johns Canada

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

This study explores implementing a digital twin network (DTN) for efficient 6 G wireless network management, aligning with the fault, configuration, accounting, performance, and security (FCAPS) model. The DTN architecture comprises the Physical Twin Layer, implemented using NS-3, and the Service Layer, featuring machine learning and reinforcement learning for optimizing carrier sensitivity threshold and transmit power control in wireless networks. We introduce a robust “What-if Analysis” module, utilizing conditional tabular generative adversarial network for synthetic data generation to mimic various network scenarios. These scenarios assess four network performance metrics: throughput, latency, packet loss, and coverage. Our findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker and the impact of twinning intervals on network performance.

关键词： 6G mobile communication Wireless sensor networks Sensitivity Wireless networks Power control Reinforcement learning Throughput

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Topological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia

Topological Characteristics of 5d Spatially Dynamic Brain Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Mustafa S. Salman Armin Iraji Noah Lewis Vince D. Calhoun School of Electrical and Computer Engineering Georgia Institute of Technology Tri-Institutional Center for Translational Research in Neuroimaging & Data Science (TReNDS) Georgia Institute of Technology Georgia State University & Emory University School of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA

The last decade of rich data-driven research on functional magnetic resonance imaging (fMRI) has provided novel insights into human brain function and aberrant behavior in brain disorders. Independent component analysis (ICA) is a widely-used technique for data-driven analysis of fMRI data. Spatial ICA is the most prominent variation of ICA and provides replicable and interpretable intrinsic connectivity network (ICN). It assumes common spatial activation across time. However, very recent studies indicate that there is utility in adopting a dynamic spatial activation modeling approach. Characterizing dynamics for both temporal and spatial domains means we have a multitude of decompositions of the already high-dimensional, multi-dataset, and multi-subject fMRI data. Hence making sense of the derived data becomes a significant issue. Here we use topological data analysis (TDA) to identify topological descriptors of the windowed spatially dynamic components of fMRI data. We discover and summarize differences in the spatial dynamics of controls and schizophrenia patients (SZs). We discover that SZs generally have lower Betti numbers and higher Wasserstein distance between spatiotemporal brain states, which provide intuitive summaries of the reduced dynamism SZs exhibit in resting-state fMRI studies.

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