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AI-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

The rising use of IoT devices in Public Health information systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare information systems Testing

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NCA: Accelerating Network Caching with eXpress Data Path

NCA: Accelerating Network Caching with eXpress Data Path

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IEEE International Conference on Hot information-Centric Networking (HotICN)

作者： Peng Wu Shuo Wang Tao Feng Yudong Huang Tao Huang Yunjie Liu State Key Laboratory of Networking and Switching Technology BUPT China Purple Mountain Laboratories Nanjing China Nanjing Yanhang Information Technology Nanjing National Key Laboratory of Science and Technology on Information System Security Institute of System Engineering Chinese Academy of Military Science China

ISBN: (纸本)9781665405508

In-memory network caching is a common solution to relieve pressure on databases and applications. However, the in-memory caching applications such as redis and memcached cannot achieve maximum performance due to the bottleneck inherent in the linux network stack. While most of the solutions today rely on kernel-bypass approaches or reengineering of applications, little attention has been given to in-kernel processing. In this article, we present NCA (Network Caching Accelerator), an in-kernel cache to release overhead brought by the linux network stack, enabling flexibility and customization without bypassing the kernel that was unthinkable before. NCA can process the cache requests as early as possible in the linux network, bringing significant performance gains without modifying the cache application itself. It works in concert with the linux kernel, enabling reuse of the security model of linux kernel. Additionally, NCA uses little CPU resources, leaving valuable CPU cycles for other applications and processes on the host. We evaluate NCA using redis as an example, and find that redis with NCA can achieve three times the throughput of the original redis. Besides, other caching applications can also use NCA to improve their performance.

关键词： Databases Linux information-centric networking Performance gain Throughput Security Kernel

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Accelerating Surrogate Assisted Evolutionary Algorithms Via Explainable Machine Learning

SSRN

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SSRN 2024年

作者： Li, Bingdong Yang, Yanting Liu, Dacheng Zhang, Yan Zhou, Aimin Yao, Xin Shanghai Institute of AI for Education East China Normal University Shanghai200062 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Space Microwave Remote Sensing System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100090 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100039 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the quality of the solution. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the DTLZ test suite and three real-world optimization problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals

arXiv

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arXiv 2025年

作者： Liu, Fan Xiong, Yifeng Lu, Shihang Li, Shuangyang Yuan, Weijie Masouros, Christos Jin, Shi Caire, Giuseppe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China The School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Chair of Communications and Information Theory Technical University of Berlin Berlin10623 Germany The Department of Electrical and Electronic Engineering University College London LondonWC1E 7JE United Kingdom

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an "iceberg hidden in the sea", where the "iceberg" represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the "sea level" characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Tea Wing Bug Inspired Flexible Deep Brain Probe Minimally Invasive Implantation

Tea Wing Bug Inspired Flexible Deep Brain Probe Minimally In...

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IEEE International Conference on Micro Electro Mechanical systems

作者： Dujuan Zou Xueying Wang Huiran Yang Wanqi Jiang Ziyi Zhu Chen Tao Zhitao Zhou Liuyang Sun Tiger H. Tao Xiaoling Wei 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Physical Science and Technology Shanghai Tech University Shanghai China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Neuroxess Co. Ltd. (Jiangxi) Nanchang China Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

This paper reports a bionic silk-based neural probe implantation method which inspired by the anatomy of the mouth parts of the tea wing bug. Remarkably, compared to the latest polymer-coated reinforcement, the implanted footprint area was reduced by 2.17 times [1] to only 3450 μm 2 . In terms of structural design, we incorporated a Tshaped structure, effectively doubling the yield strength within the same area; Furthermore, through the utilization of riboflavin-ultraviolet crosslinking, we significantly enhanced the Young's modulus of the protein reinforcement layer to approximately ~2.77 GPa, representing an impressive 20~30 times increase compared to conventional polymer like PEG. In addition, the protein was regulated to dissolve after 120 s, which ensured the strength of probe during the deep brain implantation and eliminated the presence of foreign bodies in tissues after the implantation, which further reduced inflammation. Moreover, the riboflavin-ultraviolet cross-linked protein matrix offers distinct advantages with controllable degradation. It retains the ability to carry active factors and drugs, enabling concurrent disease treatment while collecting neural signals.

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Impact of Black Swan Events on Ethereum Blockchain ERC20 Token Transaction Networks

arXiv

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arXiv 2023年

作者： Pradeep, Moturi Dyapa, Uday Kumar Reddy Jalan, Sarika Pradhan, Priodyuti Department of Physics & Astrophysics University of Delhi India Department of Computer Science & Engineering Indian Institute of Information Technology Karnataka Raichur584135 India Complex Systems Lab Department of Physics Indian Institute of Technology Indore Khandwa Road Simrol Indore453552 India

The Ethereum blockchain and its ERC20 token standard have revolutionized the landscape of digital assets and decentralized applications. ERC20 tokens developed on the Ethereum blockchain have gained significant attention since their introduction. They are programmable and interoperable tokens, enabling various applications and token economies. Transaction graphs, representing the flow of the value between wallets within the Ethereum network, have played a crucial role in understanding the system’s dynamics, such as token transfers and the behavior of traders. Here, we explore the evolution of daily transaction graphs of ERC20 token transactions, which sheds light on the trader’s behavior during the Black Swan Events – 2018 crypto crash and the COVID-19 pandemic. By using the tools from network science and differential geometry, we analyze 0.98 billion of ERC20 token transaction data from November 2015 to January 2023. Our analysis reveals that ERC20 financial ecosystem has evolved from a localized wealth formation period to a more mature financial ecosystem where wealth has dispersed among the traders in the network after the crypto crash and during the pandemic period. Before the crash, most sellers only sell the tokens, and buyers only buy the tokens. However, after the crash and during the pandemic period, sellers and buyers both performed buying and selling activities. In addition, we observe no significant negative impact of the COVID-19 pandemic on user behavior in the financial ecosystem. © 2023, CC BY.

关键词： Blockchain

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Design of vehicle management reader based on STM32

Design of vehicle management reader based on STM32

引用

2020 International Conference on Applied Physics and Computing, ICAPC 2020

作者： Ding, Chenglong Liu, Zhifeng Xu, Yuefeng Shu, Zhihao Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang Jiangxi330013 China School of Information Engineering East China University of Technology China

This paper mainly studies the parking lot counting system, which is a modern and new system integrating detection and conversion technology, computer technology, information processing, digital technology and other technologies. This work uses STM32F103 single-chip microcomputer as the MCU processor, and uses 2 infrared modules to detect vehicle access. Through the single-chip microcomputer, information is collected to detect vehicle access. LCD12864 is used to display vehicle parking information for drivers to watch. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Microcomputers

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Attention in Attention: Modeling Context Correlation for Efficient Video Classification

arXiv

引用

arXiv 2022年

作者： Hao, Yanbin Wang, Shuo Cao, Pei Gao, Xinjian Xu, Tong Wu, Jinmeng He, Xiangnan The CCCD Key Lab of Ministry of Culture and Tourism School of Data Science School of Information Science and Technology University of Science and Technology of China Anhui 230026 China The Wuhan Research Institute of Posts and Telecommunications Hubei Wuhan430205 China The School of Computer Science and Information Engineering School of Artificial Intelligence Hefei University of Technology Anhui 230009 China The School of Data Science School of Computer Science and Technology University of Science and Technology of China Anhui 230026 China The Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Hubei Wuhan430070 China

Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current research on video attention generally focuses on adopting a specific aspect of contexts (e.g., channel, spatial/temporal, or global context) to refine the features and neglects their underlying correlation when computing attentions. This leads to incomplete context utilization and hence bears the weakness of limited performance improvement. To tackle the problem, this paper proposes an efficient attention-in-attention (AIA) method for element-wise feature refinement, which investigates the feasibility of inserting the channel context into the spatio-temporal attention learning module, referred to as CinST, and also its reverse variant, referred to as STinC. Specifically, we instantiate the video feature contexts as dynamics aggregated along a specific axis with global average and max pooling operations. The workflow of an AIA module is that the first attention block uses one kind of context information to guide the gating weights calculation of the second attention that targets at the other context. Moreover, all the computational operations in attention units act on the pooled dimension, which results in quite few computational cost increase ( Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context

arXiv

引用

arXiv 2023年

作者： Song, Yicheng Gao, Shuyong Xing, Haozhe Cheng, Yiting Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Keenon Robotics Co. Ltd. Shanghai China Academy for Engineering & Technology Fudan University Shanghai China

Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods for USOD have been proposed as a promising alternative. However, current solutions rely heavily on noisy handcraft labels and fail to mine rich semantic information from deep features. In this paper, we propose a self-supervised end-to-end salient object detection framework via top-down context. Specifically, motivated by contrastive learning, we exploit the self-localization from the deepest feature to construct the location maps which are then leveraged to learn the most instructive segmentation guidance. Further considering the lack of detailed information in deepest features, we exploit the detail-boosting refiner module to enrich the location labels with details. Moreover, we observe that due to lack of supervision, current unsupervised saliency models tend to detect non-salient objects that are salient in some other samples of corresponding scenarios. To address this widespread issue, we design a novel Unsupervised Non-Salient Suppression (UNSS) method developing the ability to ignore non-salient objects. Extensive experiments on benchmark datasets demonstrate that our method achieves leading performance among the recent end-to-end methods and most of the multi-stage solutions. The code is available. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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All-Fiber Mode-Locked Femtosecond Laser Based on Er: YAG Crystal-Derived Silica Fiber

All-Fiber Mode-Locked Femtosecond Laser Based on Er: YAG Cry...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Ying Wan Chen Jiang Yuxia Zheng Yongtao Chen Taximaiti Yusufu Jianxiang Wen The Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean School of Physics and Optoelectronic Engineering Nanjing University of Information Science & Technology Nanjing China Advanced Photonic Technology Lab College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology) Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Specialty Fiber Optics and Optical Access Networks School of Communication and Information Engineering Shanghai University Shanghai China Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials School of Physics and Electronic Engineering Xinjiang Normal University Urumqi China

We demonstrated an all-fiber mode-locked femtosecond laser employing a 7.6 cm long Er: YAG crystal-derived silica fiber. The mode-locked pulses operate with a duration of $\sim 686$ fs and achieve a signal-to-noise ...

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