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检索条件"机构=Dept. Software Engineering and Computer Systems"

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Rule-Augmented Neural Networks for Improved Answer Validation in Question Answering systems

Rule-Augmented Neural Networks for Improved Answer Validatio...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Nisreen Otoum dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Software Engineering University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.

关键词： Accuracy Computational modeling Neural networks computer architecture Question answering (information retrieval) Cognition Reliability Artificial intelligence Computational intelligence

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

引用

IEEE Workshop on Applications of computer Vision (WACV)

作者： Mahdi Alehdaghi Pourya Shamsolmoali Rafael M. O. Cruz Eric Granger Dept. of Systems Engineering ETS LIVIA ILLS Montreal Canada Dept. of Computer Science University of York UK

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments 1 1 Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance.

关键词： Training computer vision Accuracy Codes Prototypes Feature extraction Identification of persons

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Blockchain-based Reputation System For IoT Data Ecosystem: A Utility Maximization Approach 19

Blockchain-based Reputation System For IoT Data Ecosystem: A...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Khezr, Seyednima Yassine, Abdulsalam Benlamri, Rachid Lakehead University Dept. of Electrical and Computer Engineering Thunder BayON Canada Lakehead University Dept. of Software Engineering Thunder BayON Canada University of Doha for Science and Technology Dept. of Information Technology Doha Qatar

ISBN: (纸本)9781665461979

Recently, blockchain technology has been used to build private and secure internet of things (IoT) data markets and trading systems. Reputation of the trading parties is an important attribute that affect their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation. Such an issue can be avoided by registering all reputation scores in the blockchain which guarantees trans-parency, encryption, privacy, and security. To this end, this paper proposes a blockchain-based reputation system where market players can maximize their utility while engaged in the IoT data ecosystem. We also propose an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. We provide extensive experiments to ensure our system satisfies the feasibility, usability, and availability properties. These properties are essential for functional deployment in real IoT data markets. © 2022 IEEE.

关键词： Blockchain

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Adaptive and Lightweight Cyber-Attack Detection in Modern Automotive Cyber-Physical systems

Adaptive and Lightweight Cyber-Attack Detection in Modern Au...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Youngmi Baek Seo-Hee Park Dept. of Computer Software Engineering Changshin University Changewon Korea

Modern automotive cyber-physical systems (CPS) may no longer refer to the means of traditional automobiles over time. As the automotive CPS are connected to everything in the real world, they will be transformed into a personalized entertainment space for a more comfortable and enjoyable trip. The fundamental for serving all these changes should be that automotive CPS are safely controlled during traveling. In the automotive CPS, a controller area network is well known as a reliable communication protocol for real-time control applications. However, due to the lack of vehicular security functionality, they expose critical vulnerabilities to cyber-attacks, which are injected from external links, targeting the modern CPS. In this paper, to detect these cyber-attacks, we develop a noble message authentication mechanism to be implemented in the existing electronic control units. It does not require any hardware security module to increase production costs. Our preliminary result demonstrates that the proposed mechanism can protect the vehicular system against fuzzy attacks.

关键词： Adaptive systems Protocols Production Cyber-physical systems Real-time systems Space exploration Reliability

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Synthetic Data Generation: An Evaluation of the Saving Images Pipeline in Unity

Synthetic Data Generation: An Evaluation of the Saving Image...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Sankar, Kevin Pooransingh, Akash Ramroop, Sarika The University of the West Indies Dept. of Electrical and Computer Eng. St Augustine Trinidad and Tobago Software Engineering Virtana Tt Ltd. St Augustine Trinidad and Tobago

ISBN: (纸本)9798350327595

In this paper, we propose a new framework, CUDA DirectX 11 Interop, for the Saving Images Pipeline that can exceed current performance standards. Unity generates synthetic data to supplement the large amounts of training data required by neural networks. The Saving Images Pipeline saves synthetic data to disk, but is not optimized due to overhead costs. Current (Linear and Coroutines) and alternative (DirectX 11 and CUDA OpenGL Interop) solutions were evaluated in terms of execution times, image resolutions, and JPEG qualities. The DirectX 11 solution exhibited the best performance as it met the 40 ms baseline. Using CUDA Interoperability with the DirectX 11 solution creates a new framework, CUDA DirectX 11 Interop, that exceeds the performance of all solutions examined. © 2023 IEEE.

关键词： computer Vision CUDA DirectX Image Generation JPEG Compression Unity

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Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-Free Massive MIMO

Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-F...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Tran, Thong-Nhat Interdonato, Giovanni Da Costa, Daniel Benevides An, Beongku Hongik University Dept. of Electronics and Computer Engineering Graduate School Korea Republic of Italy Digital Science Research Center Technology Innovation Institute Abu Dhabi Masdar City United Arab Emirates Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350370218

This paper evaluates the uplink spectral efficiency (SE) achieved by a reconfigurable intelligent surface (RIS)-aided cell-free massive MIMO system with max-min fairness (MMF) power allocation. We provide an exact closed-form expression for an achievable SE expression which assumes maximum ratio receive combining at the access points (APs) and centralized large-scale fading decoding at the central processing unit. The derived SE expression also accounts for imperfect channel estimation and spatially correlated Ricean fading channels for both direct (APs-users) and cascaded (APs-RISs-users) links. Alongside this, we provide a generalized SE expression and evaluate the performance achieved by local partial minimum mean-squared error combining. Finally, we optimally solve the RIS phase-shift selection problem in order to maximize the aggregate channel gains, and derive a closed-form solution to the MMF power allocation problem. Simulation results validate the effectiveness of the proposed framework in enhancing the unlink SE with resnect to various benchmarks. © 2023 IEEE.

关键词： Cell-free massive MIMO max-min fairness power control reconfigurable intelligent surface spectral efficiency

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Impact of Artificial Intelligence on Decision-Making in Information systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in Computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models Information systems

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On The Understanding of Customer Purchase Behavior: A Case Study on the computer systems E-Commerce Website 22

On The Understanding of Customer Purchase Behavior: A Case S...

引用

22nd International Conference on Computational Science and Its Applications, ICCSA 2022

作者： Adak, Tahir Enes Sahin, Yunus Zaval, Mounes Aktas, Mehmet S. Casper Computer Systems Research and Development Center Istanbul Turkey Yildiz Technical University Computer Engineering Dept. Istanbul Turkey

ISBN: (纸本)9781665455848

The tremendous growth of the E-commerce industry over the last decade drew attention to the importance of understanding customers' behaviors and modeling user-system interactions to maximize customers' satisfaction by providing them the best user experience and gaining new customers to the e-commerce website. Within the scope of this study, software architecture _that can help us understand customers' purchase behaviors and then make a binary prediction based on their interactions with the system to determine whether a computer configuration will be bought_ is proposed and explained thor-oughly. For this purpose, machine learning algorithms are utilized to correctly model complex user-system interactions. A prototype of the proposed software architecture is developed using the latest technologies, and experimental studies are performed on top of this prototype in order to understand how successful our software architecture is. The conducted experimental results prove the efficiency of the proposed software architecture in real-world data, with 83% and 88% scores for the accuracy and F1-score metrics, respectively. © 2022 IEEE.

关键词： software architecture

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A New Robust Hybrid Active Noise Control System 8

A New Robust Hybrid Active Noise Control System

引用

8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Wang, Z. Xiao, Y. Ma, Y. Ma, L. Khorasani, K. Prefectural University of Hiroshima Dept. of Management & Information Systems Hiroshima734-8558 Japan Institute of Automation Jiangnan University Wuxi214122 China Concordia University Dept. of Electrical & Computer Engineering Montreal Canada

ISBN: (纸本)9798350397932

The conventional hybrid active noise control (ANC, HANC) may significantly degrade if the reference signal for the feedforward ANC (FFANC) subcontroller consists of not only a broadband component but also a low-frequency sinusoidal component that is uncorrelated or partially correlated with the narrowband noise component that is attenuated by the feedback ANC (FBANC) subcontroller. In this paper, a new robust HANC system is proposed to mitigate the performance degradation resulting from the low-frequency sinusoidal component. A band-pass filter bank (BPFB) is applied to the FFANC reference signal to separate the low-frequency sinusoidal component from the broadband one and each of them is fed to an FFANC subcontroller that solely focuses on a single noise component. A supporting filter takes the extracted broadband component and the residual error as its input and desired signal, respectively. The same BPFB is then applied to the supporting filter error in order to separate the remaining low-frequency sinusoidal component from the narrowband component that persists in the residual error. The use of the two BPFBs and the supporting filter allows the three HANC subcontrollers to operate practically independently, each taking care of one of the pre-processed noise components which are uncorrelated with each other irrespective of the relationship between the two original noise sources. Extensive simulations are provided to demonstrate the improved effectiveness and robust capability of the proposed HANC as compared to its counterpart, even in a case that the low-frequency sinusoidal component in the FFANC reference signal is partially corrected with the primary narrowband noise component that is targeted by the FBANC. © 2023 IEEE.

关键词： Filter banks

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Impact of Artificial Intelligence on Decision-Making in Information systems 5

Impact of Artificial Intelligence on Decision-Making in Info...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Rajasinghe, Rwmr Dharani Faculty of Computing Nsbm Green University Dept. of Software Engineering & Information Systems Homagama Sri Lanka

ISBN: (纸本)9798331530983

关键词： Risk perception

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