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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： H. Alsheakh Tareq Nasser Mahdi Mustafa S. Kadhm Abeer Al Shammari Fatimah I. Abdulsahib Hazeem B Taher Waleed Algriree Marwa Q. Alobaidi Hayder Dhafer Mohammed Department of Computer Science College of Science Mustansiriyah University Baghdad Iraq Clinical Laboratory Science College of Pharmacy Mustansiriyah University Baghdad Iraq IT Directorate Ministry of Science and Technology Baghdad Iraq Department of Computer Science Szechenyi Istvan Egyetem Gyor Hungary Department of Computer Science University of Thi-Qar Thi-Qar Iraq Dep. Of Wireless Communication and Network Engineering University Putra Malaysia Selangor Malaysia National Engineering School of Sfax Sfax University Sfax Tunisia College of Pharmacy Mustansiriyah University Baghdad Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerges various security, privacy, and trust issues, such as vulnerabilities, threats, and attacks. Addressing these challenges requires innovative solutions. New controls (solutions) are needed. Trust by design can be one of these controls, supporting security and privacy (among other system components). This paper proposes a Trusted loT (TloT), a trust-by-design model, and highlights the author's principal contributions. First, it advocates for implementing Tlo T through trust services. Second, it outlines an architecture for using trust by design in loT, including the design of its top-level structure and activities. Minor contributions include overviews of loT and trust, as well as looking at security and privacy in terms of security services and privacy services, respectively.

关键词： Privacy Ecosystems Security Internet of Things Arrays

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Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India

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VirusDisease 2023年第4期34卷 514-525页

作者： Suresh, Kuralayanapalya Puttahonnappa Barman, Nagendra Nath Bari, Tarushree Jagadish, Dikshitha Sushma, Bylaiah Darshan, H.V. Patil, Sharanagouda S. Bora, Mousumi Deka, Abhijit Spatial Epidemiology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Microbiology College of Veterinary Science Assam Agricultural University Guwahati India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Matthikere Karnataka Bengaluru India Virology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Pathology College of Veterinary Science Assam Agricultural University Guwahati India

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R₀) was calculated after the predicted results were superimposed onto the risk maps. The R₀ value ranged from 1.04 to 2.07, implying that the eastern and western regions of Assam are more susceptible to CSF. Machine learning models were implemented using R statistical software version 3.1.3. The random forest, classification tree analysis and gradient boosting machine were found to be the best-fitted models for the study group. The models’ performance was measured using the Receiving Operating Characteristic (ROC) curve, Cohen’s Kappa, True Skill Statistics, Area Under ROC Curve, ACCURACY, ERROR RATE, F1 SCORE, and Logistic Loss. As a part of the suggested study, these models will help us to understand the disease transmission dynamics, risk factors and spatio-temporal pattern of spread and evaluate the efficacy of control measures to battle the economic losses caused by CSF outbreaks. © 2023, The Author(s), under exclusive licence to Indian V

关键词： Assam Classical swine fever Incidence Machine learning Risk modelling Swine

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VibraForge: A Scalable Prototyping Toolkit for Creating Spatialized Vibrotactile Feedback Systems 25

VibraForge: A Scalable Prototyping Toolkit for Creating Spat...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Huang, Bingjian Ren, Siyi Luo, Yuewen Cheng, Qilong Cai, Hanfeng Sang, Yeqi Sousa, Mauricio Dietz, Paul H Wigdor, Daniel Dynamic Graphics Project Lab University of Toronto Toronto ON Canada Division of Engineering Science University of Toronto Toronto ON Canada Mechanical Engineering University of Toronto Toronto ON Canada Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto ON Canada Reality Labs Research at Meta Toronto ON Canada DGP The University of Toronto Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada

ISBN: (纸本)9798400713941

Spatialized vibrotactile feedback systems deliver tactile information by placing multiple vibrotactile actuators on the body. As increasing numbers of actuators are required to adequately convey information in complicated applications, haptic designers find it difficult to create such systems due to limited scalability of existing toolkits. We propose VibraForge, an open-source vibrotactile toolkit that supports up to 128 vibrotactile actuators. Each actuator is encapsulated within a self-contained vibration unit and driven by its own microcontroller. By leveraging a chain-connection method, each unit receives independent vibration commands from a control unit, with fine-grained control over intensity and frequency. We also designed a GUI Editor to expedite the authoring of spatial vibrotactile patterns. Technical evaluation showed that vibration units reliably reproduced audio waveforms with low-latency and high-bandwidth data communication. Case studies of a phonemic tactile display, virtual reality fitness training, and drone teleoperation demonstrated the potential usage of VibraForge within different domains. A usability study with non-expert users highlighted the low technical barrier and customizability of the toolkit. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Haptic Toolkit Haptics Multi-Actuator Devices Vibrotactile Feedback Wearable Devices

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Two routers network architecture and scheduling algorithms under packet category classification constraint 5

Two routers network architecture and scheduling algorithms u...

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5th International Conference on Future Networks and Distributed Systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Sarhan, Akram Jemmali, Mahdi Ben Hmida, Abir Department of Information Systems College of Computing and Information Technology University of Jeddah Jeddah Saudi Arabia Department of Computer Science and Information College of Science at Zulfi Majmaah University Majmaah 11952 Saudi Arabia Mars Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia

ISBN: (纸本)9781450387347

The sending and exchanging of data have become essential in the current revolution of industry 4.0 and digital transformation and all aspects of life. Ensuring the security of transmitted data continues to be a critical issue due to the fast advancement in technology. The studied problem is proven to be NP-hard in the strong sense. The contribution of this paper is twofold. First, we propose a network architecture that enables multilevel data sharing through two routers based on a data category classification constraint. Second, we propose a set of heuristic algorithms for an NP-Hard problem. The constraint of the studied problem is described as follows. Two files belonging to the same category of confidential level should not be transmitted simultaneously through the routers. The proposed architecture involves a scheduler component responsible for finding a schedule that minimizes the maximum estimated transmission time respecting the data category classification constraint. An experimental result is carried out to show the performance of the proposed algorithms. Several indicators are used to measure the performance of algorithms. The best algorithm is the Modified decreasing estimated-transmission time algorithm in 91.6% of cases. The experimental results confirm the practicality of the proposed scheme in terms of two-machines scheduling problems. © 2021 ACM.

关键词： Network architecture

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Vision Transformer Based Hash Coding for Efficient Image and Audio Retrieval with Global and Local Equilibrium Distance Constraints

Vision Transformer Based Hash Coding for Efficient Image and...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Ye Pan, Yan Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Lizhi Inc. Artificial Intelligence and Big Data Department Guangzhou Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665410205

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets. © 2024 IEEE.

关键词： Image coding

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Decentralized Service Discovery and Composition in Dynamic IoT Systems 27

Decentralized Service Discovery and Composition in Dynamic I...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Moeini, Hessam Yen, I-Ling Bastani, Farokh University of Illinois at Urbana-Champaign Coordinated Science Laboratory United States University of Texas at Dallas Department of Computer Science United States

ISBN: (纸本)9781665408783

Internet of Things (IoT) interconnects billions of smart sensors, devices, actuators, as well as people, over a distributed environment. To make further use of these things towards a better and smarter physical world, it is necessary to dynamically compose the IoT services to handle complex, dynamically arising tasks. In this paper, we consider how to efficiently discover and compose IoT services to achieve some given goals. When a dynamic task arises, a distributed planning is taken place over the IoT network to discover the needed available IoT services and how to compose them. To ensure planning efficiency, a Logic-based Overlay Network of Services (LONS) is maintained and updated periodically. We then develop a decentralized Graphplan based algorithm to traverse through the LONS to derive the desired service composition. An intelligent mutual exclusion consideration model in our algorithm results in an efficient backward search procedure for solution extraction. We consider different distributed planning variations and compare their tradeoffs in terms of network traffic and composition efficiency experimentally. © 2021 IEEE.

关键词： Internet of things

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FastCat Catalogues: Interactive Entity-based Exploratory Analysis of Archival Documents

arXiv

引用

arXiv 2023年

作者： Rinakakis, Georgios Petrakis, Kostas Tzitzikas, Yannis Fafalios, Pavlos Information Systems Laboratory ICS-FORTH Department of Computer Science University of Crete Heraklion Greece Information Systems Laboratory ICS-FORTH Heraklion Greece

We describe FastCat Catalogues, a web application that supports researchers studying archival material, such as historians, in exploring and quantitatively analysing the data (transcripts) of archival documents. The application was designed based on real information needs provided by a large group of researchers, makes use of JSON technology, and is configurable for use over any type of archival documents whose contents have been transcribed and exported in JSON format. The supported functionalities include a) source- or record-specific entity browsing, b) source-independent entity browsing, c) data filtering, d) inspection of provenance information, e) data aggregation and visualisation in charts, f) table and chart data export for further (external) analysis. The application is provided as open source and is currently used by historians in maritime history research. © 2023, CC BY.

关键词： History

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Riemannian Complex Hermit Positive Definite Convolution Network for Polarimetric SAR Image Classification

arXiv

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arXiv 2025年

作者： Shi, Junfei Nie, Mengmeng Li, Yuke Jin, Haiyan Lin, Weisi Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China College of Computing and Data Science Nanyang Technological University 639798 Singapore

Deep learning can learn high-level semantic features in Euclidean space effectively for PolSAR images, while they need to covert the complex covariance matrix into a feature vector or complex-valued vector as the network input. However, the complex covariance matrices are essentially a complex Hermit positive definite (HPD) matrix endowed in Riemannian manifold rather than Euclidean space. The matrix’s real and imagery parts are with the same significance, as the imagery part represents the phase information. The matrix vectorization will destroy the geometric structure and manifold characteristics of complex covariance matrices. To learn complex HPD matrices directly, we propose a Riemannian complex HPD convolution network(HPD CNN) for PolSAR images. This method consists of a complex HPD unfolding network(HPDnet) and a CV-3DCNN enhanced network. The proposed complex HPDnet defines the HPD mapping, rectifying and the logEig layers to learn geometric features of complex matrices. In addition, a fast eigenvalue decomposition method is designed to reduce computation burden. Finally, a Riemannian-to-Euclidean enhanced network is defined to enhance contextual information for classification. Experimental results on two real PolSSAR datasets demonstrate the proposed method can achieve superior performance than the state-of-the-art methods especially in heterogeneous regions. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Advanced Privacy-Preserving Federated Relationship Recommendation 5

Advanced Privacy-Preserving Federated Relationship Recommend...

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2023 5th International Conference on Artificial Intelligence and computer Applications, ICAICA 2023

作者： Xue, Bin Zheng, Qinghua Zhao, Weihu Li, Zhinan Cui, Tianrun Feng, Xue School of Information and Communications National University of Defense Technology Wuhan China Xi'an Jiaotong University Department of Computer Science and Technology Xi'an China University Office National University of Defense Changsha China Tsinghua University Laboratory of Flexible Electronics Technology Beijing China

ISBN: (纸本)9798350323313

Federated relationship recommendation is a growing technology trend in future intelligent system. Over the recent years, federated recommendation has recorded an exponential growth, leading to millions of intelligent equipments, and still increasing. However, privacy-preserving is an intractable problem in modern digital systems. In this paper, a comprehensive review of privacy-preserving federated recommendation is presented. Particularly, three kinds of federated recommendation methods are reviewed based on matrix factorization, neural network and other algorithms. Moreover, five authoritative datasets and three general evaluation metrics are mainly described. Simultaneously, a serious of outstanding federated recommender systems with related research discussions are provided. Finally, some potential future works will be tried to improve the application performance. © 2023 IEEE.

关键词： Intelligent systems

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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IEEE International Symposium on Biomedical Imaging

作者： Eugenia Mylona Dimitrios I. Zaridis Charalampos N. Kalantzopoulos Vasileios C. Pezoulas Nikolaos S. Tachos George K. Matsopoulos Daniele Regge Nikolaos Papanikolaou Manolis Tsiknakis Kostas Marias Dimitrios I. Fotiadis Biomedical Research Institute FORTH Ioannina Greece Unit of Medical Technology Intelligent Information Systems University of Ioannina Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Computer Science FORTH Heraklion Greece Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale Candiolo Italy Computational Clinical Imaging Group Champalimaud Foundation Portugal Computational Biomedicine Laboratory Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

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