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Towards an Intelligent Industrial Environment: Blockchain-Empowering Human-Centered Artificial Intelligence for Risk Assessment and Strategic Evaluation

SSRN

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SSRN 2023年

作者： Khan, Abdullah Ayub Chen, Yen-Lin Hajjej, Fahima Shaikh, Aftab Ahmed Yang, Jing Ku, Chin Soon Por, Lip Yee Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

With the recent introduction of high-level human control and high-level IoT control in intelligent transportation systems, their association with blockchain increases the performance of the Internet of Vehicles (IoVs) and E-Healthcare, which leads to wider adaptation in the future as well. In this paper, the contributions of this systematic article are divided into three different folds: first, this paper evaluates more than a hundred previously published articles and highlights the gaps in futuristic concerns. Second, the paper presents a novel and secure human-centered AI-enabled risk assessment framework for the IoVs with blockchain distributed ledger technology (DLT). Third, a distributed public permissionless network with two operational channels of inter-communication for vehicle-to-vehicle (V2V) is proposed. It is considered an extension in the Vehicle-to-Everything (V2X) environment. Along with that, the pseudo-development of chaincodes to automate human-centered IoV node transactions. In addition, a Practical Byzantine Fault Tolerance (PBFT) consensus protocol and related controlled executions are programmed for fast scheduling, offloading, managing, and optimizing with reliability. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Machine Learning for Traffic-Driven Services Provisioning in Optical Networks

Machine Learning for Traffic-Driven Services Provisioning in...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： L. Sanker Magadevan N. Shanmugalakshmi M. Giridhar M. Balaji R. Saravanakumar S. Jeya Lakshmi Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi Tamil Nadu India Department of Artificial Intelligence & Data Science Vel Tech High Tech Dr. Rangarajan and Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The increasing demand for high-capacity and dynamic services in optical networks necessitates intelligent and adaptive provisioning mechanisms. This study investigates the application of machine learning (ML) techniques for traffic-driven service provisioning in optical networks, addressing challenges such as traffic prediction, resource allocation, and fault management. The proposed framework employs supervised and unsupervised learning models, including Long Short-Term Memory (LSTM) networks, Random Forests, and K-Means clustering, to analyze traffic patterns and optimize service deployment. The system leverages real-time and historical traffic data to predict demand, enabling proactive resource allocation and dynamic wavelength assignment. Furthermore, reinforcement learning is explored to automate decision- making for adaptive routing and spectrum management under varying network conditions. Performance evaluation on simulated and real-world datasets demonstrates that ML-based approaches significantly improve network efficiency, reduce latency, and enhance the quality of service (QoS). This research highlights the potential of machine learning in revolutionizing service provisioning in optical networks, offering scalable, data-driven solutions to meet the growing demands of next-generation communication systems.

关键词： Reviews Buildings Machine learning Quality of service Optical fiber networks Elastic optical networks Resource management Monitoring Long short term memory Random forests

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A Novel Steganography Algorithm Based on Instance Segmentation

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computers, Materials & Continua 2020年第4期63卷 183-196页

作者： Ruohan Meng Qi Cui Zhili Zhou Chengsheng Yuan Xingming Sun School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Centre of Network Monitoring Nanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorCanada

Information hiding tends to hide secret information in image area where is rich texture or high frequency,so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing *** take advantage of the complexity of the object texture and consider that under certain circumstances,the object texture is more complex than the background of the image,so the foreground object is more suitable for steganography than the *** the basis of instance segmentation,such as Mask R-CNN,the proposed method hides secret information into each object's region by using the masks of instance segmentation,thus realizing the information hiding of the foreground object without *** method not only makes it more efficient for the receiver to extract information,but also proves to be more secure and robust by experiments.

关键词： Steganography object detection mask R-CNN irregular region

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Symphony: Localizing Multiple Acoustic Sources with a Single Microphone Array

arXiv

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arXiv 2022年

作者： Wang, Weiguo Li, Jinming He, Yuan Liu, Yunhao School of Software and BNRist Tsinghua University China Department of Computer Science and Engineering Michigan State University United States

Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device’s interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source’s location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach. © 2022, CC BY.

关键词： Microphone array

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Two quantum algorithms for communication between spacelike separated locations

arXiv

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arXiv 2022年

作者： Datta, Amitava Department of Computer Science and Software Engineering University of Western Australia 35 Stirling Highway PerthWA6009 Australia

The ‘no communication’ theorem prohibits superluminal communication by showing that any measurement by Alice on an entangled system cannot change the reduced density matrix of Bob’s state, and hence the expectation value of any measurement operator that Bob uses remains the same. We argue that the proof of the ‘no communication’ theorem is incomplete and superluminal communication is possible through state discrimination in a higher-dimensional Hilbert space using ancilla qubits. We propose two quantum algorithms through state discrimantion for communication between two observers Alice and Bob, situated at spacelike separated locations. Alice and Bob share one qubit each of a Bell state √12 (|00i + |11i). While sending classical information, Alice measures her qubit and collapses the state of Bob’s qubit in two different ways depending on whether she wants to send 0 or 1. Alice’s first measurement is in the computational basis, and the second measurement is again in the computational basis after applying Hadamard transform to her qubit. Bob’s first algorithm detects the classical bit with probability of error 21k , but Alice and Bob need to share k Bell states for communicating a single classical bit. Bob’s second algorithm is more complex, but Bob can detect the classical bit deterministically using four ancilla qubits. We also discuss possible applications of our algorithms. © 2022, CC BY.

关键词： Quantum entanglement

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End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes

arXiv

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arXiv 2022年

作者： Chen, Qiao Poullis, Charalambos Immersive and Creative Technologies Lab Department of Computer Science and Software Engineering Concordia University Quebec CA Montreal Canada

Image-based 3D reconstruction is one of the most important tasks in computer Vision with many solutions proposed over the last few decades. The objective is to extract metric information i.e. the geometry of scene objects directly from images. These can then be used in a wide range of applications such as film, games, virtual reality, etc. Recently, deep learning techniques have been proposed to tackle this problem. They rely on training on vast amounts of data to learn to associate features between images through deep convolutional neural networks and have been shown to outperform traditional procedural techniques. In this paper, we improve on the state-of-the-art two-view structure-from-motion(SfM) approach of [11] by incorporating 4D correlation volume for more accurate feature matching and reconstruction. Furthermore, we extend it to the general multi-view case and evaluate it on the complex benchmark dataset DTU [5]. Quantitative evaluations and comparisons with state-of-the-art multi-view 3D reconstruction methods demonstrate its superiority in terms of the accuracy of reconstructions. © 2022, CC BY-NC-SA.

关键词： Virtual reality

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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The Tower of Babel Game Developers Built: A Tertiary Literature Review into Why Multidisciplinary Teams Struggle to Collaborate in the Games Industry

SSRN

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SSRN 2023年

作者： McKenzie, Timothy Hoermann, Simon Lukosch, Stephan Morales-Trujillo, Miguel Department of Computer Science and Software Engineering New Zealand School of Product Design University of Canterbury 20 Kirkwood Avenue Upper Riccarton Christchurch Canterbury8041 New Zealand

In size, revenue, and global impact, video game development (VGD) is a `tower' in the entertainment industry. Yet most studios struggle to succeed because of challenges in multidisciplinary team dynamics, the production process, and project management. The key reason(s) for why, however, remains not fully explained. This study investigated the critical reasons behind the multidisciplinary VGD team collaboration and leadership problems in the industry despite their reported adoption of agile/lean software engineering practices. A tertiary literature review was undertaken on the empirical academic and industry literature that described VGD studio context and success factors, practices, and challenges. 22 relevant texts were identified and summarised. A root-cause analysis revealed that VGD has five contributing factors to its challenges: its `wicked' nature of continually evolving complexity, the immaturity of its practices, the largely hidden knowledge of industry practice, the incoherent understanding within diverse teams, and the generally confused terminology among developers. The lack of a coherent/shared language among the industry's key stakeholders is a `Tower of Babel' problem because it makes it difficult to clearly understand, unify, and communicate the core concepts of VGD, or to improve studio practices. Guidance is given for future research directions that may help address these issues. © 2023, The Authors. All rights reserved.

关键词： Studios

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Model Ownership Protection for Healthcare Consumer Electronics in Federated Edge Learning

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IEEE Transactions on Consumer Electronics 2024年

作者： Wang, Rong Liang, Junchuan Feng, Chaosheng Chakraborty, Chinmay Alqahtani, Fayez Yu, Keping Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu610068 China Birla Institute of Technology Mesra India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

With the rising demand for intelligent services and privacy protection in consumer artificial intelligence (AI), federated edge learning has emerged as a beacon for privacy-preserving distributed machine learning. This non-centralized approach offers distinct data privacy advantages, making it especially suitable for applications with strict privacy policies, such as consumer-centric healthcare systems. However, consumer electronics/devices in federated edge learning might unlawfully violate model ownership by duplicating or distributing models. Another concern is that a central server, if compromised, can become a conduit for model leakage, whether for profit motives or due to external intrusions, thus exacerbating the model ownership violation. To our knowledge, few works have focused on model ownership protection for federated edge learning in the consumer-centric healthcare domain. In this paper, we introduce a federated model ownership protection (FedMOP) approach. FedMOP is a model ownership safeguard and a formidable fortress built upon two core innovations. First, FedMOP can swiftly embed and replace watermarks during local training, allowing for the accurate identification and subsequent flagging of deceitful consumer devices. Second, FedMOP ensures that the server only accesses encrypted versions of the federated model, keeping the model a secret. Extensive evaluations demonstrate the robustness and effectiveness of FedMOP. We hope that FedMOP can light the way for surmounting similar ethical challenges in the broader realm of consumer-edge AI. © 1975-2011 IEEE.

关键词： ethical AI Federated edge learning healthcare consumer electronicss model ownership protection

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Energy Storage and Forecasting Error Impact Analysis In Photovoltaic Equipped Residential Nano-Grids

Energy Storage and Forecasting Error Impact Analysis In Phot...

引用

Compatibility in Power Electronics (CPE)

作者： Hossein Nourollahi Hokmabad Oleksandr Husev Juri Belikov Dmitri Vinnikov Eduard Petlenkov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

Battery energy storage systems (BESS) enable many applications for photovoltaic (PV) equipped nano-grids. Stored excessive energy is utilized for energy arbitrage, demand response during blackouts, and peak shaving. This technology helped utility service providers deal with the duck-curve-effect and intermittency of renewable energy systems. In this paper, we investigate the benefits of using energy storage systems in PV nano-grids for residential sectors and evaluate the relationship between battery presence and system performance. We also analyze the effects of battery degradation on energy independence and self-sufficiency and investigate the relationship between simple payback time and state of health (SoH) levels of nano-grid components. Additionally, we propose a simple rule-based energy supervision strategy and evaluate its performance under various forecasting error levels, highlighting the importance of high-performance forecasting methods for proper energy management systems. Our contributions will help select and optimize PV-battery solutions and energy management algorithms to achieve the highest benefit and minimize investment risks.

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