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Dynamic control and manipulation of near-fields using direct feedback

Light(science & Applications) 2024年第12期13卷 3151-3157页

作者： Jacob Kher-Aldeen Kobi Cohen Stav Lotan Kobi Frischwasser Bergin Gjonaj Shai Tsesses Guy Bartal The Andrew&Erna Viterbi Faculty of Electrical&Computer Engineering Technion—Israel Institute of TechnologyHaifa 3200003Israel Department of Physical Engineering Polytechnic University of Tirana—Faculty of Physical&Math EngineeringTirana 1000Albania Faculty of Medical Sciences Albanian UniversityDurrës StreetTirana 1000Albania Department of Physics MIT—Harvard Center for Ultracold Atoms and Research Laboratory of ElectronicsMassachusetts Institute of TechnologyCambridgeMAUSA

Shaping and controlling electromagnetic fields at the nanoscale is vital for advancing efficient and compact devices used in optical communications,sensing and metrology,as well as for the exploration of fundamental properties of light-matter interaction and optical ***-time feedback for active control over light can provide a significant advantage in these endeavors,compensating for ever-changing experimental conditions and inherent or accumulated device *** nearfield microscopy,being slow in essence,cannot provide such a real-time feedback that was thus far possible only by scattering-based ***,we present active control over nanophotonic near-fields with direct feedback facilitated by real-time near-field *** use far-field wavefront shaping to control nanophotonic patterns in surface waves,demonstrating translation and splitting of near-field focal spots at nanometer-scale precision,active toggling of different near-field angular momenta and correction of patterns damaged by structural defects using feedback enabled by the real-time *** ability to simultaneously shape and observe nanophotonic fields can significantly impact various applications such as nanoscale optical manipulation,optical addressing of integrated quantum emitters and near-field adaptive optics.

关键词： scattering metrology feedback

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Securing IoT Systems with AI-Infused Software and Virtual Replica Models 2

Securing IoT Systems with AI-Infused Software and Virtual Re...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Supriya, K. Sangeetha Jeno Lovesum, S.P. Arora, Renuka Bhatia, Rishu Yadwad, Sunita Natrayan, L. Department of Computer Science & Engineering Christ University Bengaluru India Department of Computer Science & Engineering Jagannath University NCR Bahadurgarh India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Jhajjar India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges due to the increased attack surface and the inherent vulnerabilities of interconnected systems. This paper proposes a novel security framework that integrates Artificial Intelligence (AI)-infused software and Virtual Replica Models, or Digital Twins, to enhance the robustness of IoT systems. The framework leverages AI-driven anomaly detection and predictive analysis to identify potential threats in real-time, while Digital Twins provide a virtual replica of the IoT ecosystem for continuous monitoring and simulation-based threat assessment. By fusing these technologies, the proposed solution enables proactive security measures, ensuring rapid response to cyberattacks and continuous system optimization. Extensive simulations and experimental results demonstrate the system’s ability to reduce attack detection times by 30%, while improving overall system resilience and minimizing false positive rates. This approach provides a scalable and adaptable solution for securing IoT infrastructures in various industries. © 2024 IEEE.

关键词： Risk assessment

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Energy Preserving Techniques for CPS in Smart Healthcare System 13

Energy Preserving Techniques for CPS in Smart Healthcare Sys...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Srivastava, Jyoti Routray, Sidheswar School of Engineering Indrashil University Department of Computer Science and Engineering Gujarat Rajpur Mehsana India Ganpat University Faculty of Engineering and Technology Department of Computer Engineering Gujarat Kherwa Mehsana India School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar382007 India

ISBN: (纸本)9798350380583

Cyber Physical System (CPS) enhances the functionality of various cyber and physical equipment of Smart Healthcare System (SHS) and provides automation in the healthcare sector using Artificial Intelligence (AI) techniques. SHS collects medical data with the help of sensors or actuators equipped with smart medical devices that act as the physical component of CPS, then processes and analyzes these medical data with the help of cyber components of CPS to provide automation in health monitoring, doctor consultancy, medical prescriptions, medicine delivery and various other perspectives of the healthcare sector. These advancements in the healthcare sector result in great assistance for healthcare individuals like doctors, patients, nursing staff, etc, but at the same time deal with very sensitive parameters like time, efficiency, reliability, security, etc, that need to be managed with utmost accuracy for preserving each life in this global smart healthcare environment. There can be various efficiency parameters in CPS enabled SHS namely the number of hops, average energy consumption, resource utilization, etc, This work elaborately defines various characteristics of CPS in SHS, efficiency parameters, and major challenges for the successful implementation of CPS enabled SHS. © 2024 IEEE.

关键词： Electronic health record

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Paillier Cryptography Based Message Authentication Code for IoMT Security

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computer Systems science & engineering 2023年第3期44卷 2209-2223页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of *** digital technology-based communication system is widely used in all *** of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human *** smart medical devices may consume with limited resources and also the data generated by these devices are large in *** IoMT based applications suffer from the issues such as security,anonymity,privacy,and *** overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and *** article proposed a well-organized data aggregation and secured transmission *** data generated by the sensor are collected and *** nodes(AN)received the compressed data and concatenate *** concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with *** proposed system ensures data integrity,security and also protects from security *** proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication *** node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage *** proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy *** of communica

关键词： Fog IoMT wireless sensor network cloud aggregation encryption decryption energy

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Optimizing Algorithms for Human Behavior Recognition: Single-Modality Spatiotemporal Convolutional Networks 11

Optimizing Algorithms for Human Behavior Recognition: Single...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Zhao, Rui Hao, Jutao Feng, Zhikun Wang, Xianzhi Prasad, Mukesh Huo, Huan School of Computer Science University of Technology Sydney Faculty of Engineering and IT Sydney Australia School of Electric Information Engineering Shanghai Dianji University Shanghai China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798331531904

Behavior recognition constitutes a pivotal area of research within computer vision and serves as an integral technology in video surveillance and related domains, thereby possessing substantial research value. Convolutional neural networks (CNNs) have been proven to possess robust learning capabilities. However, due to the high complexity and variability of human behavior, recognizing human behaviors in videos remains challenging due to the difficulty of capturing useful features over long time series and the redundancy of multi-frame information. To address these issues and improve the utilization of continuous frame information, we have designed a cross-frame multiscale information fusion module. This module allows the backbone network to acquire multiscale temporal features from different frames, thereby reducing information redundancy across multiple frames while minimizing additional computational costs. Furthermore, to mitigate the problem of video temporal feature loss, we introduce an easily integrable attention mechanism module, the Easy-Attention Block. This module facilitates rapid capture of long-range dependencies, enabling the model to focus on the most relative parts of continuous video sequences and suppress irrelevant temporal information. Consequently, the model can extract more discriminative spatial-temporal features, thereby enhancing its efficiency in handling long sequence data. Experimental results express that this model can achieve high recognition accuracy and effectively extract the temporal features of video, suitable for real-time applications in critical infrastructure surveillance, which enhancing safety and reducing potential risks in energy power systems. © 2024 IEEE.

关键词： Spatio-temporal data

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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