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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Andrea Lucaroni Andrea Bottino Fabrizio Lamberti Giulia Mariani Andrea Marinelli Nicolò Boccardo Emanuele Gruppioni Lorenzo De Michieli Matteo Laffranchi Giacinto Barresi Rehab Technologies Lab Istituto Italiano di Tecnologia Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering Università degli Studi di Genova Genoa Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT) Genoa Italy Centro Protesi INAIL Istituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro Bologna Italy

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The embodiment (the sensation that an external object is part of one’s body scheme) is one of the processes targeted by the research in bionic prosthetics as a potential strategy to increase the acceptance of artificial limbs. This study investigates how one of the components of the embodiment, the body ownership (the sensation of possessing the external object), may be affected by a secondary task (possibly competing for cognitive resources requested for the embodiment itself) in a virtual exergame for prosthetic training. In this preliminary study participants, without impairments, are asked to perform activities of pick-and-place in two conditions by using a digital model of the Hannes bionic hand: (i) one is cognitively challenging, requiring to memorize the sequence of objects to move before it disappears; (ii) the other is non-challenging for the subject’s memory, with a constantly visible sequence. In both conditions, each subject is asked to catch objects according to the presented sequence. According to questionnaire data, we noticed how this procedure is feasible. Interestingly, the proprioceptive drift (the person is asked to indicate where they perceive their own real limb without looking at it) in a subsequent Rubber Hand Illusion session with the real bionic hand seems highlighting that the working memory-engaging secondary task can impair the embodiment process in terms of body ownership of the robotic hand. Probably, the memory task actually requires a type of cognitive resources that are necessary for the body ownership process itself during the multisensory stimulation. This allows for envisioning further investigations and novel design concepts for prosthetic training paradigms.

关键词： Training Solid modeling Visualization Biological system modeling Memory management Propioception Virtual environments

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Anomaly Detection for Internet of Things (IoT) Using an Artificial Immune System 12th

Anomaly Detection for Internet of Things (IoT) Using an Arti...

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12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

作者： Elisa, Noe Yang, Longzhi Chao, Fei Naik, Nitin Department of Computer and Information Sciences Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of AI Xiamen University Xiamen China School of Informatics and Digital Engineering Aston University BirminghamB4 7ET United Kingdom

ISBN: (纸本)9783030736880

Internet of Things (IoT) have demonstrated significant impact on all aspects of human daily lives due to their pervasive applications in areas such as telehealth, home appliances, surveillance, and wearable devices. The number of IoT devices and sensors connected to the Internet across the world is expected to reach over 50 billion by the end of 2020. The connection of such rapidly increasing number of IoT devices to the Internet leads to concerns in cyber-attacks such as malware, worms, denial of service attack (DoS) and distributed DoS attack (DDoS). To prevent these attacks from compromising the performance of IoT devices, various approaches for detecting and mitigating cyber security threats have been developed. This paper reports an IoT attack and anomaly detection approach by using the dendritic cell algorithm (DCA). In particular, DCA is an artificial immune system (AIS), which is developed from the inspiration of the working principles and characteristic behaviours of the human immune system (HIS), specifically for the purpose of detecting anomalies in networked systems. The performance of the DCA on detecting IoT attacks is evaluated using publicly available IoT datasets, including DoS, DDoS, Reconnaissance, Keylogging, and Data exfiltration. The experimental results show that, the DCA achieved a comparable detection performance to some of the commonly used classifiers, such as decision trees, random forests, support vector machines, artificial neural network and naïve Bayes, but with reasonably high computational efficiency. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Software Architecture Reconstruction via a Genetic Algorithm: Applying the Move Class Refactoring 24

Software Architecture Reconstruction via a Genetic Algorithm...

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24th Pan-Hellenic Conference on informatics, PCI 2020

作者： Maikantis, Theodoros Tsintzira, Angeliki-Agathi Ampatzoglou, Apostolos Arvanitou, Elvira-Maria Chatzigeorgiou, Alexander Stamelos, Ioannis Bibi, Stamatia Deligiannis, Ignatios Apostolos Ampatzoglou Elvira-Maria Ar-Vanitou Alexander Chatzigeorgiou Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (纸本)9781450388979

Modularity is one of the four key principles of software design and architecture. According to this principle, software should be organized into modules that are tightly linked internally (high cohesion), whereas at the same time as independent from other modules as possible (low coupling). However, in practice, this principle is violated due to poor architecting design decisions, lack of time, or coding shortcuts, leading to a phenomenon termed as architectural technical debt (ATD). To alleviate this problem (lack of architectural modularity), the most common solution is the application of a software refactoring, namely Move Class - i.e., moving classes (the core artifact in object-oriented systems) from one module to another. To identify Move Class refactoring opportunities, we employ a search-based optimization process, relying on optimization metrics, through which optimal moves are derived. Given the extensive search space required for applying a brute-force search strategy, in this paper, we propose the use of a genetic algorithm that re-arranges existing software classes into existing or new modules (software packages in Java, or folders in C++). To validate the usefulness of the proposed refactorings, we performed an industrial case study on three projects (from the Aviation, Healthcare, and Manufacturing application domains). The results of the study indicate that the proposed architecture reconstruction is able to improve modularity, improving both coupling and cohesion. The obtained results can be useful to practitioners through an open source tool;whereas at the same point, they open interesting future work directions. © 2020 ACM.

关键词： Genetic algorithms

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Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

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Computational Intelligence and Neuroscience 2022年第0期2022卷 3564482页

作者： Dhanke, Jyoti Rathee, Naveen Vinmathi, M.S. Janu Priya, S. Abidin, Shafiqul Tesfamariam, Mikiale Bharati Vidyapeeth's College of Engineering Lavale Maharashtra Pune412115 India Department of Electronics and Communication Engineering IIMT College of Engineering Uttar Pradesh Greater Noida201310 India Department of CSE Panimalar Engineering College Bangalore Trunk Road Nazarethpet Poonamallee Tamilnadu Chennai600123 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Samayapuram Tamilnadu Tiruchirappalli621112 India Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa Ethiopia

It is essential to change health services from a hospital to a patient-centric platform since medical costs are steadily growing and new illnesses are emerging on a worldwide scale. This study provides an optimal decision support system based on the cloud and Internet of Things (IoT) for identifying Chronic Kidney Disease (CKD) to provide patients with efficient remote healthcare services. To identify the presence of medical data for CKD, the proposed technique uses an algorithm named Improved Simulated Annealing-Root Mean Square -Logistic Regression (ISA-RMS-LR). The four subprocesses that make up the proposed model are a collection of data, preprocessing, feature selection, and classification. The incorporation of Simulated Annealing (SA) during Feature Selection (FS) enhances the ISA-RMS-LR model's classifier outputs. Using the CKD benchmark dataset, the ISA-RMS-LR model's efficacy has been verified. According to the experimental findings, the proposed ISA-RMS-LR model effectively classifies patients with CKD, with high sensitivity at 99.46%, accuracy at 99.26%, Specificity at 98%, F-score at 99.63%, and kappa value at 98.29%. The proposed system has many benefits including the fast transmission of medical data to the medical personnel, real-time tracking, and registration condition of the patient through a medical record. Potential enhancement of the performance measures the provider system's hospital capacity and monitoring of a significant number of patients with a concentrated average delay. © 2022 Jyoti Dhanke et al.

关键词： Simulated annealing

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Wearable motion capture: Reconstructing and predicting 3D human poses from wearable sensors

TechRxiv

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TechRxiv 2021年

作者： Moniruzzaman, Md. Yin, Zhaozheng Hossain, Md Sanzid Bin Guo, Zhishan Choi, Hwan Department of Computer Science Stony Brook University United States Department of Computer Science Department of Biomedical Informatics Stony Brook University United States Department of Electrical and Computer Engineering University of Central FL United States Department of Electrical and Computer Engineering University of Central FL United States Department of Mechanical and Aerospace Engineering University of Central FL United States

Identifying 3D human walking poses in unconstrained environments has many applications such as enabling prosthetists and clinicians to access the amputees’ walking functions outside clinics and helping amputees obtain an optimal walking condition with predictive control. Thus, we propose the wearable motion capture problem of reconstructing and predicting 3D human poses from the wearable IMU sensors and wearable cameras. To solve this challenging problem, we introduce a novel Attention-Oriented Recurrent Neural Network (AttRNet) that contains a sensor-wise attention-oriented recurrent encoder, a reconstruction module, and a dynamic temporal attention-oriented recurrent decoder, to reconstruct the current pose and predict the future poses. To evaluate our approach, we collected a new WearableMotionCapture dataset using wearable IMUs and wearable video cameras, along with the musculoskeletal joint angle ground truth. The proposed AttRNet shows high accuracy on the WearableMotionCapture dataset, and it also outperforms the current best methods on two public pose prediction datasets with IMU-only data: DIP-IMU and TotalCaputre. The source codes and the new dataset will be publicly available on https://***/MoniruzzamanMd/Wearable-Motion-Capture. © 2021, CC BY.

关键词： Recurrent neural networks

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Game-Theoretic Incentive Mechanism for Blockchain-Based Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Tang, Wenzheng Liu, Erwu Ni, Wei Qu, Xinyu Huang, Butian Li, Kezhi Niyato, Dusit Jamalipour, Abbas Tongji University College of Electronics and Information Engineering Shanghai201804 China Tongji University Department of Ophthalmology Tongji Hospital School of Medicine China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia Hangzhou Yunphant Network Technology Co. Ltd. Hangzhou China University College London Institute of Health Informatics LondonWC1E 6BT United Kingdom College of Computing and Data Science 639798 Singapore University of Sydney School of Electrical and Computer Engineering NSW2006 Australia

Blockchain-based federated learning (BFL) has gained attention for its potential to establish decentralized trust. While existing research primarily focuses on personalized frameworks for various applications, essential aspects including incentive mechanisms—critical for ensuring stable system operation—remain under-explored. To bridge this gap, we propose a game-theoretic incentive mechanism designed to foster active participation in BFL tasks. Specifically, we model a BFL system comprising a model owner (MO), i.e., task publisher, multiple miners, and training terminals, framing their interactions through two-tier Stackelberg games. In the first-tier game, the MO designs reward strategies to incentivize training terminals to contribute more data, enhancing model accuracy. The second-tier game introduces a multi-leader multi-follower Stackelberg game, enabling miners to set model packaging prices based on competitors' strategies and anticipated user behavior. By deriving the Stackelberg equilibrium, we identify optimal strategies for all participants, leading to an incentive mechanism balancing individual interests with overall performance. Compared to its benchmarks, our incentive mechanism offers 5.8% and 53.4% higher utilities in the two games compared to its alternatives, accelerating convergence and improving accuracy. © 2002-2012 IEEE.

关键词： Federated learning

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An Exploratory Investigation into Sustaining Cybersecurity Protection Through the Implementation of SETA

An Exploratory Investigation into Sustaining Cybersecurity P...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Abdullah Mahfuzul Huda Mohammed Brayyich Noor Hanoon Haroon Abdul Azeem Khan Rajan Verma University Institute of Computing Chandigarh University Punjab India College of Computing and Informatics Saudi Electronic University Riyadh KSA College of Technical Engineering National University of Science & Technology Dhi Qar Iraq Department of Computer Technical Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Faculty of Islamic Technology Negara Brunei Darussalam Division of Research and Development Lovely Professional University Phagwara KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Most modern businesses run their competitive operations online. Cybersecurity is crucial to commercial operations. No cybersecurity defensive solution is universal. Standard security strategy involves using hardware and software to counter known security threats that may become stronger over time. Attackers and security professionals fight constantly. A security approach based primarily on tools is untenable. However, some firms skip employee awareness training, resulting in biased cybersecurity breach decisions. This study objectively analyzes key factors that help enterprises and organizations deploy Security Education, Training, and Awareness (SETA) to find ways to maintain such protection. A dataset of UK corporate cybersecurity breach surveys is used to perform performance testing on eight supervised learning models. The entire research uses the model's built characteristics that are excellent at recognizing SETA-adopting enterprises and organizations. The trial shows that awareness is vital when organizations use SETA. Many firms don't know how to safeguard employees from cybersecurity risks when they use online products and services, such as external web services.

关键词： Training Surveys Radio frequency Analytical models Web services Standards organizations Information security computer security Protection Testing

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING 12

MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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The timer compression of data and information 3

The timer compression of data and information

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3rd IEEE International Conference on Data Stream Mining and Processing, DSMP 2020

作者： Skuratovskii, Ruslan Osadchyy, Yevgen Osadchyy, Volodymyr NTU KPI Named by I. Sikorskii and IAPM. Department of Informatics and Computer Science Kiev Ukraine National University Named by Taras Shevchenko Kiev Ukraine Igor Sikorsky Kyiv Polytechnic Institute Faculty of Informatics and Computer Engineering Kyiv Ukraine

ISBN: (纸本)9781728132143

The ability to find short representations, i.e. to compress data, is crucial for many intelligent systems. This paper is devoted to data compression and a transform-based quantitative data compression technique involving quick enumeration in a unary-binary time-based numeral system (NS). The symbols comprising the alphabets of human-computer interaction languages (HCIL), which are used in an informational message (IM), are collected in primary code tables, such as the ASCII table. The statistical-oriented data compression method using unconventional timer encryption and encoding information are proposed by us. It was constructed probability - discrete model of the set of character sequences and characterized some probabilistic algorithms associated with the recovery of text by its public key and its cipher. We find the possibility of parallel implementation of this method by building a block of timer tags. The necessary estimations of complexity are obtained. The method can be used to compress SMS messages. Probabilistic-statistical analysis and evaluation of their effectiveness are obtained. © 2020 IEEE.

关键词： Data compression

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A Reliability-constrained Association Rule Mining Method for Explaining Machine Learning Predictions on Continuity of Asthma Care

A Reliability-constrained Association Rule Mining Method for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yao Tong Yongli Wang Qibin Zhang Zhenxiang Zhang Gang Chen Computer and artificial intelligence school Zhengzhou University Zhengzhou China Biomedical informatics and medical education department University of Washington Seattle USA School of computer science and engineering Nanjing University of Technology Nanjing China School of nursing and health Zhengzhou University Zhengzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Continuity of care has been shown to possess numerous health benefits for asthma. However, the use of methodology to predetermine the level of care continuity has been underinvestigated. We recently built a machine learning model to predict the continuity of care for diagnosed asthma patients at the University of Washington Medicine. As there is a consensus on the un-explainable featured nowadays by black-box machine learning, which impedes the clinical deployment of our model. To tackle this issue, we proposed a reliability-constrained association rule mining method, called RC-ARM, to automatically explain the predictions of any machine learning model. First, we introduced the belief function to build a reliability-constrained rule framework. Then, the two-step reliable association rule mining algorithms were developed to generate reliable explanation rules for the machine learning model’s predictions. At last, clinical intervention suggestions regarding each mined rule were embedded for further understanding. The results showed that the proposed method could explain all (110/110) predictions of our machine learning model for asthma patients with low levels of continuity of care. This semantic-fused method sheds light on black-box models and encourages clinical experts to embrace the benefits of machine learning without any prior concern about its lack of explainability.

关键词： Machine learning algorithms Evidence theory Closed box Machine learning Predictive models Solids Prediction algorithms

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