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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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In situ construction of PtSe_(2)/Ge Schottky junction array with interface passivation for broadband infrared photodetection and imaging

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InfoMat 2024年第4期6卷 37-46页

作者： Xue Li Shuo-En Wu Di Wu Tianxiang Zhao Pei Lin Zhifeng Shi Yongtao Tian Xinjian Li Longhui Zeng Xuechao Yu School of Physics and Microelectronics Key Laboratory of Material Physics Ministry of EducationZhengzhou UniversityZhengzhouHenanthe People's Republic of China Department of Electrical and Computer Engineering University of CaliforniaLa JollaCaliforniaUSA National Research Center for Optical Sensors/Communications Integrated Networks School of Electronic Science and EngineeringSoutheast UniversityNanjingJiangsuthe People's Republic of China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhouJiangsuthe People's Republic of China

Infrared(IR)detection is vital for various military and civilian *** research has highlighted the potential of two-dimensional(2D)topological semimetals in IR detection due to their distinctive advantages,including van der Waals(vdW)stacking,gapless electronic structure,and Van Hove singularities in the electronic density of ***,challenges such as large-scale patterning,poor photoresponsivity,and high dark current of photodetectors based on 2D topological semimetals significantly impede their wider applications in low-energy photon ***,we demonstrate the in situ fabrication of PtSe_(2)/Ge Schottky junction by directly depositing 2D PtSe_(2) films with a vertical layer structure on a Ge substrate with an ultrathin AlOx *** to high quality junction,the photodetector features a broadband response of up to 4.6μm,along with a high specific detectivity of�1012 Jones,and operates with remarkable stability in ambient conditions as ***,the highly integrated device arrays based on PtSe_(2)/AlOx/Ge Schottky junction showcases excellent Mid-IR(MIR)imaging capability at room *** findings highlight the promising prospects of 2D topological semimetals for uncooled IR photodetection and imaging applications.

关键词： broadband photodetection imaging platinum diselenide Schottky junction van der Waals integration

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Analysis and Control for Synchronization of Coupled Reaction-Diffusion Neural Networks with Multistate Couplings Subject to Recoverable Attacks 13

Analysis and Control for Synchronization of Coupled Reaction...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Zhao, Xin-Yu Ren, Shun-Yan Wang, Jin-Liang School of Computer Science and Technology Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Sichuan611731 China

ISBN: (纸本)9798350315196

This paper addresses the synchronization for coupled reaction-diffusion neural networks (CRNNs) with multi-state couplings suffering attacks. By utilizing the Kronecker product, a network synchronization criterion is formulated. Moreover, since networks suffering attacks may not be synchronized in some circumstances, a simple controller is also developed. Finally, the effectiveness of the devised control schemes is verified through a numerical example. © 2023 IEEE.

关键词： Synchronization

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Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

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computers, Materials & Continua 2022年第5期71卷 2497-2513页

作者： Mohammad Alamgeer Fahd N.Al-Wesabi Huda G.Iskandar Imran Khan Nadhem Nemri Mohammad Medani Mohammed Abdullah Al-Hagery Ali Mohammed Al-Sharafi Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication *** this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed *** researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English ***,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any *** has been implemented using PHP with VS code *** robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and *** SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

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A Scenario-Oriented Approach to Energy-Reserve Joint Procurement and Pricing

A Scenario-Oriented Approach to Energy-Reserve Joint Procure...

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作者： Shi, Jiantao Guo, Ye Tong, Lang Wu, Wenchuan Sun, Hongbin Tsinghua University Smart Grid and Renewable Energy Laboratory Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Shenzhen518071 China Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Cornell University School of Electrical and Computer Engineering IthacaNY14853 United States

We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources. These issues have so far been largely addressed empirically. In this paper, we first develop a scenario-oriented energy-reserve co-optimization model, which directly connects reserve procurement with possible outages and load/renewable power fluctuations without the need for empirical reserve requirements. Accordingly, reserve can be optimally procured system-wide to handle all possible future uncertainties with the minimum expected system total cost. Based on the proposed model, marginal pricing approaches are developed for energy and reserve, respectively. Locational uniform pricing is established for energy, and the similar property is also established for the combination of reserve and re-dispatch. In addition, properties of cost recovery for generators and revenue adequacy for the system operator (SO) are also proven. Simulations validate the effectiveness of the proposed mechanism. © 1969-2012 IEEE.

关键词： Costs

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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Derivation of Aggregated Equivalent Models for the Steady-State Analysis of Active Distribution Networks Operated Under Voltage Control Schemes

SSRN

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SSRN 2024年

作者： Pasiopoulou, Ioanna D. Kontis, Eleftherios O. Papadopoulos, Theofilos A. Papagiannis, Grigoris K. Power Systems Laboratory School of Electrical and Computer Engineering Aristotle University of Thessaloniki GR Thessaloniki54124 Greece Department of Electrical and Computer Engineering University of Thessaly Volos Greece Power Systems Laboratory Dept. of Electrical and Computer Engineering Democritus University of Thrace XanthiGR 67100 Greece

To facilitate the coordination between transmission and distribution system operators, accurate equivalent models of power system components are required. Towards this objective, in this paper, a blackbox model is proposed for developing steady-state reduced-order equivalents of active distribution networks (ADNs) that host converter-interfaced renewable energy sources (CI-RES) and operate under various voltage control schemes. Droop-based and traditional voltage control schemes, such as on-load tap changer (OLTC) control strategies and the use of shunt capacitors, are considered. The performance of the proposed model is evaluated via simulations considering various load types and CI-RES penetration levels. Comparisons against the conventional polynomial and exponential models are also conducted, demonstrating the superior performance of the proposed black-box model. © 2024, The Authors. All rights reserved.

关键词： Electric power distribution

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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Raising Awareness of Location Information Vulnerabilities in Social Media Photos using LLMs

arXiv

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arXiv 2025年

作者： Ma, Ying Zhang, Shiquan Yang, Dongju Sarsenbayeva, Zhanna Knibbe, Jarrod Goncalves, Jorge School of Computing and Information Systems University of Melbourne Melbourne Australia School of Computer Science University of Sydney Sydney Australia School of Electrical Engineering and Computer Science University of Queensland St Lucia Australia

Location privacy leaks can lead to unauthorised tracking, identity theft, and targeted attacks, compromising personal security and privacy. This study explores LLM-powered location privacy leaks associated with photo sharing on social media, focusing on user awareness, attitudes, and opinions. We developed and introduced an LLM-powered location privacy intervention app to 19 participants, who used it over a two-week period. The app prompted users to reflect on potential privacy leaks that a widely available LLM could easily detect, such as visual landmarks & cues that could reveal their location, and provided ways to conceal this information. Through in-depth interviews, we found that our intervention effectively increased users’ awareness of location privacy and the risks posed by LLMs. It also encouraged users to consider the importance of maintaining control over their privacy data and sparked discussions about the future of location privacy-preserving technologies. Based on these insights, we offer design implications to support the development of future user-centred, location privacy-preserving technologies for social media photos. © 2025, CC BY.

关键词： Differential privacy

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