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IEEE International Conference on Industrial Informatics (INDIN)

作者： Padma Iyenghar Michael Kieviet Elke Pulvermueller Juergen Wuebbelmann innotec GmbH Erlenweg 12 Melle Germany Faculty of Engineering and Computer Science Osnabrueck University of Applied Sciences Germany Software Engineering Research Group University of Osnabrueck Germany

In this paper, a novel chatbot for risk reduction as an aid during machinery design is presented. The general workflow of the chatbot involves the identification of the hazard described by the user using a neural network model followed by an interactive dialog based conversation, in which the risk reduction measures are outlined. A prototype implementation of the chatbot presents the steps to generate and pre-process the training data for Artificial Intelligence (AI) based models. Different neural network models are trained and evaluated for the proposed risk reduction chatbot. A comparative study is presented by employing an in-depth qualitative and quantitative evaluation. The work presented in this paper shows significant promise in ensuring safety awareness, thereby aiding in implementing functional safety in the early stages of machinery design and development.

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An adaptive scaling cuckoo search algorithm 4

An adaptive scaling cuckoo search algorithm

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4th International Conference on Advanced Electronic Materials, computers and software engineering, AEMCSE 2021

作者： Song, Zhenyu Song, Shuangyu Zhang, Bin Qian, Jin Tang, Cheng Ji, Junkai Taizhou University College of Computer Science and Technology Taizhou China Jiangsu University of Technology School of Computer Engineering Changzhou China University of Toyama Faculty of Engineering Toyama Japan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781665415965

The cuckoo search algorithm (CSA) is a natureinspired metaheuristic algorithm based on the parasitic behaviour of the cuckoo species, and it has received much attention for solving optimization problems. Nevertheless, the CSA still has some limitations when applied to optimization problems, such as easily falling into local optima and converging slowly. In this paper, we propose a modified local search method with an adaptive boundary scaling mechanism using a chaotic map, which calculates the search radius depending on the position of the current population. Thus, the search ability of the traditional CSA is improved for optimization purposes. To further verify the stability and effectiveness of the proposed adaptive boundary scaling algorithm (ACSA), we compare it with the traditional CSA and nonmodified chaos-embedded CSA (CCSA) on 25 test benchmark functions. The experimental and statistical test results show that the ACSA outperforms its competitors, which means that the proposed algorithm can perform better than these other approaches in terms of their search capabilities for optimization problems. © 2021 IEEE.

关键词： Optimization

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

Transformer-based Text Classification on Unified Bangla Mult...

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Information Technology (ACIT)

作者： Md Sakib Ullah Sourav Mohammad Sultan Mahmud Hua Zheng Mohammad Aljaidi Md. Simul Hasan Talukder Rejwan Bin Sulaiman Abdullah Hafez Nur Ahmad Al-Qerem School of Management Science and Engineering Shandong University of Finance and Economics Jinan China College of Computer Science and Software Engineering Shenzhen University Senzhen China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Dhaka School of Computer Science & Technology Northumbria University Newcastle Tayne United Kingdom Northumbria UK Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Due to its importance in studying people’s thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world’s most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.

关键词： Measurement Human computer interaction Sentiment analysis Analytical models Codes Accuracy Web 2.0 Text categorization Transformers Information technology

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Quantum Circuit Optimisation and MBQC Scheduling with a Pauli Tracking Library

arXiv

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arXiv 2024年

作者： Ruh, Jannis Devitt, Simon Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney NSW2007 Australia InstituteQ Aalto University Espoo02150 Finland

We present a software library for the commutation of Pauli operators through quantum Clifford circuits, which is called Pauli tracking. Tracking Pauli operators allows one to reduce the number of Pauli gates that must be executed on quantum hardware. This is relevant for measurement-based quantum computing and for error-corrected circuits that are implemented through Clifford circuits. Furthermore, we investigate the problem of qubit scheduling in measurement-based quantum computing and how Pauli tracking can be used to capture the constraints on the order of measurements. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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A Novel Approach Detection for False Data Injection, and Man in the Middle Attacks in IoT and IIoT

A Novel Approach Detection for False Data Injection, and Man...

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IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia)

作者： Serkan Gönen Mehmet Ali Barişkan Derya Yıltaş Kaplan Ercan Nurcan Yilmaz Aydın Çetin Department of Computer & Software Engineering Istanbul Gelişim University Istanbul Türkiye Department of Computer Engineering Istanbul University-Cerrahpasa Istanbul Türkiye Faculty of Technology Gazi University Ankara Türkiye

Human beings have gone through stages that have made significant contributions to their lives with technological developments. One of the most important of these close to the present day is the introduction of IoTs, and IIoTs into our lives with the industry 4.0 process. From smart homes to smart grids, we are faced with a world that is getting smarter in every aspect of our lives. In this cyber environment where our data, including our personal data, is transferred to the virtual environment, the biggest threat is the attacks that can be made on this existing environment. The biggest methods used by attackers are to exploit the vulnerabilities that the existing system brings in addition to the old vulnerabilities. With the IoT process, there have been very important developments, and all operations can be carried out remotely via mobile phone or computer, such as checking the missing needs in the refrigerator, washing, drying clothes, and even in the most advanced dimension, reducing the temperature if it is too high, or increasing it if it is low, without people coming to their homes, and with these renovations, new types of threads become part of our lives. With this study we focus on one of more dangerous part of these attacks, False Data Injection (FDI), and Men in the Middle (MitM) attacks. With this study we detected MitM, and FDI attacks with success rate of %95 percent.

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Latency and Secrecy Performance Analysis in Backscatter-Enabled Vehicular Cloudlet Networks

Latency and Secrecy Performance Analysis in Backscatter-Enab...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Nguyen, Van-Long Tien Bui, Thi My Phan, Van-Binh Ha, Dac-Binh School of Computer Science Duy Tan University Da Nang Viet Nam Quy Nhon AI Center FPT Software Science Avenue Binh Dinh Province Quy Nhon City Viet Nam Kien Giang Vocational College Faculty of Basic Sciences Viet Nam School of Engineering and Technology Duy Tan University Da Nang Viet Nam

ISBN: (纸本)9798331530839

In this work, we investigate the joint secrecy and latency performance of a multi-user backscatter communication (BC)-enabled vehicular network in the presence of a passive eavesdropper. Specifically, a number of vehicles intend to offload their tasks to a vehicle equipped with a mobile edge computing (MEC) server by utilizing dedicated BC communication and time division multiple access (TDMA) schemes. Meanwhile, a passive mobile eavesdropper attempts to intercept information from each user without any attacks. In order to investigate the performance of this system, we derive the closed-form expressions of the successful computation probability (SCP) and secrecy successful computation probability (SSCP) in two corresponding scenarios: with and without eavesdropper. In addition, the impacts of key parameters on the system's behavior to gain insight into its performance are also examined. Finally, the numerical results using Monte-Carlo simulations are provided to validate our analysis. © 2024 IEEE.

关键词： Time division multiple access

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Experimentation on NN Models for Hazard Identification in Machinery Functional Safety

Experimentation on NN Models for Hazard Identification in Ma...

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IEEE International Conference on Industrial Informatics (INDIN)

The use of Artificial Intelligence (AI) in machinery functional safety can enhance efficiency and accuracy by automating tasks previously carried out by humans. This paper presents an experimental evaluation of Neural Network (NN) models for hazard identification in machinery functional safety. The systematic study includes own implementations of NN models using open source building blocks and the use of an open source conversational AI framework with various pipeline configurations. The paper provides a comparative analysis of the qualitative and quantitative parameters for the models and configurations.

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IoST: Internet of Softwarized Things Networks, Security Challenges and Future Research Directions

IoST: Internet of Softwarized Things Networks, Security Chal...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Adil, Muhammad Attique, Muhammad Wang, Jian Alrefaei, Faisal Song, Houbing Farouk, Ahmed University at Buffalo Computer Science & Engineering New York United States Sejong University Department of Software Seoul05006 Korea Republic of University of Tennessee Department of Computer Science MartinTN39238 United States Embry Riddle Aeronautical Universit Electrical Engineering & Computer Science Florida United States South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt

ISBN: (纸本)9781665459754

Internet of Softwarized Things (IoST) is a promising and dynamic programmable technology that has the capability to interconnect sensor devices with an objective to share the accumulated data in the network without the intervention of human beings. Despite its advantages, this technology suffers from numerous security vulnerability threats, which can damage the trust of clients. Therefore, it is very important to familiarize the readers, students, experts, and industry stakeholders working in this domain with the existing counteraction schemes followed by potential security threats. For redressal of identified security threats, the literature presents various schemes, but somehow they are unable to provide fool-proof security infrastructure for these networks, because the attackers work restlessly to compromise the new security countermeasure schemes. To explore this discussion and evaluate the open security challenges with future research directions, in this paper, we present a survey regarding the most recent published literature from 2020 to 2022. Following this, we have covered the architectural and interoperability literature in the context of security threats and their countermeasure techniques to identify the weak aspects of them. Based on underscored problems, we have highlighted the unresolved security challenges to set a footstep for the future research work that could be useful for all stakeholders associated with IoST technology. © 2022 IEEE.

关键词： Interoperability

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From hippocampal neurons to broad spiking neural networks

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Neurocomputing 2025年 647卷

作者： Wang, Yaodong Zuo, Yiping Chen, Dan Tu, Weiping Zomaya, Albert Y. Li, Xiaoli National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Computer Science The University of Sydney SydneyNSW Australia State Key Laboratory of Cognitive Neuroscience and Learning Faculty of Psychology Beijing Normal University Beijing100875 China

Spiking Neural Network (SNN) provides an irreplaceable mechanism for series prediction, particularly in scenarios where time and computing resources are both critical, exhibiting superiority over other deep learning models with sparse and spike-based communication inherent to SNN methods. However, as the depth of the network increases, a critical challenge arises: excessive diffusion of signals in the deeper network can seriously affect its accuracy and efficiency. Inspired by the organizational principles of hippocampal circuits, this study proposes a broad learning framework inspired by the brain (B2L), offering an alternative SNN architecture to mitigate the problem of excessive diffusion. It adopts a broad, incrementally extendable structure while adopting sparse coding for data representation, dedicated to: (1) Construction of the B2L SNN: Combining preliminary feature extraction with enhancement processing, a dual-layer broad structure establishes random vector functional-link (RVFL) mapping between rate-coded spike sequences and output labels, effectively preventing excessive diffusion to upstream neurons;(2) Extendable Optimization of B2L SNN: Drawing from the concept of incremental learning, B2L SNN employs a weight optimization method to search for optimal configurations by expanding RVFL mappings from initial architecture to static datasets through incremental neuron integration. Experiments across six benchmarks (MNIST, CIFAR-10, CIFAR-100, NORB, N-MNIST, and BCI IV 2b) demonstrate: (1) B2L SNN achieves 20.3×faster training time than four SNN variants (4058 seconds→197 s) via random vector functional-link mapping;(2) 96.97% NORB accuracy (+3.64% over SNN2ANN) with 2.79 ms inference time (3.06×speedup), also achieving SOTA 89.52% accuracy on BCI IV 2b and leading N-MNIST inference speed (3.85ms);(3) Scalability: With 1.5×expansion in feature layers (PF: 60→100, EF: 7k→11k), B2L SNN achieves 1.04% accuracy improvement on MNIST alongside 12.3% faster traini

关键词： Deep learning

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