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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Intent-Driven Distributed Applications Management Over Compute and Network Resources in the computing Continuum 19

Intent-Driven Distributed Applications Management Over Compu...

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19th Annual International Conference on Distributed computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Zafeiropoulos, Anastasios Fotopoulou, Eleni Vassilakis, Constantinos Tzanettis, Ioannis Lombardo, Chiara Carrega, Alesssandro Bruschi, Roberto National Technical University of Athens Institute of Communication and Computer Systems Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Cnit S2N National Laboratory Genoa Italy

ISBN: (纸本)9798350346497

Intent-driven orchestration enables organizations to achieve greater automation, integration, and intelligence of their compute and network resources by defining their objectives and policies in a more abstract and holistic way, while letting the orchestration system handle the coordination, optimization, and adaptation of the resources in response to changing needs and conditions. The trend for the development of intent-driven orchestration mechanisms has been growing in recent years as the deployment of distributed applications in the computing continuum has become more complex and diverse. In this manuscript we detail a novel approach for the management of distributed applications in the computing continuum. A hierarchical decision making scheme is proposed where an entity in a specific level of the hierarchy has the responsibility for the management of the overall application or a part of it, without having the control of all the management actions. The control is distributed across various entities that have to collaborate towards some joint objectives. Both compute and network resources management is considered that may be provided by the same or different infrastructure providers. © 2023 IEEE.

关键词： Decision making

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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Assessing the Impact of Circular Economy Projects: The DEFINITE-CCRI Approach 15

Assessing the Impact of Circular Economy Projects: The DEFIN...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Makri, Evi Kanellou, Eleni Jacobi, Nikolai Nikas, Alexandors Doukas, Haris School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens15780 Greece Energy Material & Built Infrastructure Sustainable Resources Climate & Resilience Iclei Iclei European Secretariat Freiburg79098 Germany

ISBN: (纸本)9798350368833

This paper presents the technical and impact potential assessment provided for selected circular economy projects by DEFINITE-CCRI. Among the main goals of the methodology employed in the DEFINITE-CCRI project are the support of the selected projects enabling them to become more attractive to investors and gain funding and the overall bridging of the gap between the circularity-based innovation and the financing available for its development. The DEFINITE-CCRI impact assessment methodology has been developed based on the 9R strategies, according to which specific indicators have been chosen and tailored to fit the particularities of the projects under assessment and to shed light on their environmental, social, and macro-economic impacts. © 2024 IEEE.

关键词： Circular economy

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Illusion Spaces in VR: The Interplay Between Size and Taper Angle Perception in Grasping 25

Illusion Spaces in VR: The Interplay Between Size and Taper ...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Jian Zhang Wafa Johal Jarrod Knibbe School of Computing and Information Systems University of Melbourne Melbourne VIC Australia School of Electrical Engineering and Computer Science The University of Queensland St Lucia QLD Australia

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Secure and Reliable 6G Communications with BCH-Based GLDPC and Kyber

Secure and Reliable 6G Communications with BCH-Based GLDPC a...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Linh Nguyen Cheol-Hong Min Hanho Lee Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA Department of Electrical. and Computer Engineering University of St. Thomas USA Department of Electrical and Computer Engineering Inha University Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10– 6 to 10– 9 , to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized low-density parity check (GLDPC) codes offer a more advantageous tradeoff between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new security-based GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reli-ability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security.

关键词： 6G mobile communication Quantum computing computer network reliability System performance Bit error rate Parity check codes Error correction codes Decoding Reliability Channel coding

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A Classification and Pathology Study on Schizophrenia Based on Self-Attention Model 3

A Classification and Pathology Study on Schizophrenia Based ...

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3rd International Symposium on computer Technology and Information Science, ISCTIS 2023

作者： Li, Guibing Jin, Weidong Lin, Rude Tan, Ying Miao, Jiaqing School of Electrical Engineering Southwest Jiaotong University Chengdu China Southwest Minzu University The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China School of Mathematical Southwest Minzu University Chengdu China

ISBN: (纸本)9798350325386

Schizophrenia is a severe mental disorder characterized by symptoms such as hallucinations and delusions that significantly impair patients' daily life. Magnetic resonance imaging has provided high-quality data support for research on psychiatric disorders due to its high imaging resolution and low side effects. However, conventional medical analysis methods based on statistics require a considerable amount of manual feature engineering. Moreover, machine learning and traditional deep learning methods struggle to perform well on high-dimensional MRI data, resulting in a bottleneck in further schizophrenia research. In this study, the self-attention mechanisms are applied to design a more efficient neural network for disease classification and pathological analysis, which does not depend on conventional convolutional approach. One significant advantage of self-attention mechanisms is that they can fully extract global representations, enabling the model to effectively leverage the complex MRI data. Additionally, by extracting the self-attention maps of the model, the proposed approach can be applied to detect critical pathological brain regions related to schizophrenia. It achieved end-to-end, purely data-driven pathological analysis and significantly reduced the cost of feature engineering. Experimental results show that the average classification accuracy of the self-attention-based neural network model is 76.76%, superior to traditional classification methods. It is also revealed that that structural damage to the thalamus, cerebellum, and surrounding regions is the main cause of schizophrenia. This model provides new insights for schizophrenia research using MRI data and for understanding the disease mechanism. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Spatial Attention-Enhanced Encoder-Decoder Network for Accurate Segmentation of the Prostate's Transition Zone 46

Spatial Attention-Enhanced Encoder-Decoder Network for Accur...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos S. Kalantzopoulos, Charalampos Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute Forth Ioannina Greece Computational Biomedicine Laboratory Forth Heraklion Greece University of Ioannina Biomedical Research Institute Unit of Medical Technology and Intelligent Information Systems Forth Ioannina Greece

ISBN: (纸本)9798350371499

Accurate segmentation of the prostate and its substructures consist the most important component for reliable localization and characterization of prostate cancer. In this study a Spatial Attention Residual U-Net (Spatial ResU-Net) deep learning (DL) network is proposed for segmenting the transitional zone of the prostate, by leveraging the learning capacity of spatial attention modules and residual connections. Spatial attention modules efficiently extract features in intra-channel manner and boost the performance of encoder and decoder while residual connections facilitate the information flow within the different network's levels. The proposed model was compared against 8 state-of-the-art DL segmentation models demonstrating a superior performance. The improvement in terms of Sensitivity, Dice Score, Hausdorff distance and Average surface distance was at least 1%, 1%, 0.05 mm and 0.09 mm, respectively. © 2024 IEEE.

关键词： Urology

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Mixture of Experts for Recognizing Depression from Interview and Reading Tasks

arXiv

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arXiv 2025年

作者： Ilias, Loukas Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece

Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an objective marker for the early recognition of depression. For this reason, many studies have been developed aiming to recognize depression through speech. However, existing methods rely on the usage of only the spontaneous speech neglecting information obtained via read speech, use transcripts which are often difficult to obtain (manual) or come with high word-error rates (automatic), and do not focus on input-conditional computation methods. To resolve these limitations, this is the first study in depression recognition task obtaining representations of both spontaneous and read speech, utilizing multimodal fusion methods, and employing Mixture of Experts (MoE) models in a single deep neural network. Specifically, we use audio files corresponding to both interview and reading tasks and convert each audio file into log-Mel spectrogram, delta, and delta-delta. Next, the image representations of the two tasks pass through shared AlexNet models. The outputs of the AlexNet models are given as input to a multimodal fusion method. The resulting vector is passed through a MoE module. In this study, we employ three variants of MoE, namely sparsely-gated MoE and multilinear MoE based on factorization. Findings suggest that our proposed approach yields an Accuracy and F1-score of 87.00% and 86.66% respectively on the Androids corpus. © 2025, CC BY-NC-ND.

关键词： Deep neural networks

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