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Examining the Effectiveness of Academic Title Verification: A Case Study 24th

Examining the Effectiveness of Academic Title Verification: ...

24th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication, RelStat 2024

作者： Panges, Ioannis Liagkou, Vasiliki Stamatiou, Yannis Stylios, Chrysostomos Department of Informatics and Telecommunications of the University of Ioannina Kostakoi Arta Greece Computer Technology Institute and Press "Diophantus" Rio Greece Business Administration Department of the University of Patras Patras Greece ATHENA Research Center Institute of Industrial System Patras Patras Greece

ISBN: (纸本)9783031875311

In the ever-changing realm of identity management, the incorporation of privacy-preserving technologies has become increasingly vital. Despite significant advancements in identity management systems and access control mechanisms designed to protect user data in accordance with recent GDPR guidelines, their implementation faces challenges due to complexity and non-compliance with existing identity management standards. Furthermore, the absence of user-friendly interfaces in these systems hinders widespread adoption, highlighting a critical flaw in their design and usability. In this paper, we present a use-case scenario for postgraduate and doctoral applications that utilize a highly efficient, scalable, and user-friendly identity management system that gives formal security, and privacy guarantees to all parties. The goal is to enhance the user experience and promote the adoption of best practices in the field. This system empowers users by allowing them to control their digital identities through secure, decentralized storage of verifiable credentials on their mobile devices. We hope this work will be a significant step forward in the evolution of identity management, offering a more secure and user-friendly alternative to existing systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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An automated speech analysis system for the detection of cognitive decline in elderly

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International Journal of Speech technology 2023年第2期26卷 337-353页

作者： Loizou, Christos P. Pantzaris, Marios Faculty of Engineering & Technology Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology 30 Arch. Kyprianos Str. Office 420 Limassol3036 Cyprus Cyprus Institute of Neurology and Genetics P. O. Box 23462 Nicosia1683 Cyprus

The goal of this study is to develop and test an automated integrated speech analysis system for detecting mild cognitive impairment (MCI) and dementia in spontaneous free speech. During the years 2010–2016, speech recordings (N = 2800) were obtained from 200 Greek Cypriots over the age of 65. These were divided into three groups (G1, G2, and G3) based on the results of their Mini-mental state examination (MMSE): G1:95 normal (NOR) individuals with an MMSE greater than 26;G2:65 MCI subjects with 20 ≤ MMSE ≤ 26;G3:40 dementia subjects with 0 ≤ MMSE © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Neurodegenerative diseases

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Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

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Complex System Modeling and Simulation 2023年第3期3卷 169-190页

作者： Yinan Guo Yao Huang Shirong Ge Yizhe Zhang Ersong Jiang Bin Cheng Shengxiang Yang School of Mechanical Electronic and Information Engineering China University of Mining and Technology(Beijing)Beijing 100083China Inner Mongolia Research Institute China University of Mining and Technology(Beijing)Ordos 017010China Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityLeicesterLE19BHUK.

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines,and the rationality of their routes plays the direct impact on operation safety and energy *** studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil,however,less works are for electric trackless rubber-tyred ***,energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving,especially the limited cruising ability of electric trackless rubber-tyred vehichles(TRVs).To address this issue,an energy consumption model of an electric trackless rubber-tyred vehicle is formulated,in which the effects from total mass,speed profiles,slope of roadways,and energy management mode are all *** that,a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance,allowable load,and endurance *** a problem-solver,an improved artificial bee colony algorithm is put *** especially,an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific *** order to assign onlookers to some promising food sources reasonably,their selection probability is adaptively *** a stagnant food source,a knowledge-driven initialization is developed to generate a feasible *** experimental results on four real-world instances indicate that improved artificial bee colony algorithm(IABC)outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

关键词： electric trackless rubber-tyred vehicles low-carbon routing artificial bee colony algorithm

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Inference Helps PLMs' Conceptual Understanding: Improving the Abstract Inference Ability with Hierarchical Conceptual Entailment Graphs

Inference Helps PLMs' Conceptual Understanding: Improving th...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Juncai Li, Ru Li, Xiaoli Chai, Qinghua Pan, Jeff Z. School of Computer and Information Technology Shanxi University China Institute for Infocomm Research A*STAR Singapore A*STAR Centre for Frontier AI Research Singapore ILCC School of Informatics University of Edinburgh United Kingdom

ISBN: (纸本)9798891761643

The abstract inference capability of the Language Model plays a pivotal role in boosting its generalization and reasoning prowess in Natural Language Inference (NLI). Entailment graphs are crafted precisely for this purpose, focusing on learning entailment relations among predicates. Yet, prevailing approaches overlook the polysemy and hierarchical nature of concepts during entity conceptualization. This oversight disregards how arguments might entail differently across various concept levels, thereby missing potential entailment connections. To tackle this hurdle, we introduce the concept pyramid and propose the HiCon-EG (Hierarchical Conceptual Entailment Graph) framework, which organizes arguments hierarchically, delving into entailment relations at diverse concept levels. By learning entailment relationships at different concept levels, the model is guided to better understand concepts so as to improve its abstract inference capabilities. Our method enhances scalability and efficiency in acquiring common-sense knowledge through leveraging statistical language distribution instead of manual labeling, Experimental results show that entailment relations derived from HiCon-EG significantly bolster abstract detection tasks. Our code is available at https://***/SXUCFN/HiCon-EG. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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CURRICULUM REINFORCEMENT LEARNING FOR QUANTUM ARCHITECTURE SEARCH UNDER HARDWARE ERRORS 12

CURRICULUM REINFORCEMENT LEARNING FOR QUANTUM ARCHITECTURE S...

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12th International Conference on Learning Representations, ICLR 2024

作者： Patel, Yash J. Kundu, Akash Ostaszewski, Mateusz Bonet-Monroig, Xavier Dunjko, Vedran Danaci, Onur 〈aQaL〉 Applied Quantum Algorithms Leiden University Netherlands Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Joint Doctoral School Silesian University of Technology Poland Warsaw University of Technology Institute of Computer Science Poland Lorentz Institute Leiden University Netherlands LIACS Leiden University Netherlands

The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a potential solution by fixing the circuit architecture and optimizing individual gate parameters in an external loop. However, parameter optimization can become intractable, and the overall performance of the algorithm depends heavily on the initially chosen circuit architecture. Several quantum architecture search (QAS) algorithms have been developed to design useful circuit architectures automatically. In the case of parameter optimization alone, noise effects have been observed to dramatically influence the performance of the optimizer and final outcomes, which is a key line of study. However, the effects of noise on the architecture search, which could be just as critical, are poorly understood. This work addresses this gap by introducing a curriculum-based reinforcement learning QAS (CRLQAS) algorithm designed to tackle challenges in realistic VQA deployment. The algorithm incorporates (i) a 3D architecture encoding and restrictions on environment dynamics to explore the search space of possible circuits efficiently, (ii) an episode halting scheme to steer the agent to find shorter circuits, and (iii) a novel variant of simultaneous perturbation stochastic approximation as an optimizer for faster convergence. To facilitate studies, we developed an optimized simulator for our algorithm, significantly improving computational efficiency in simulating noisy quantum circuits by employing the Pauli-transfer matrix formalism in the Pauli-Liouville basis. Numerical experiments focusing on quantum chemistry tasks demonstrate that CRLQAS outperforms existing QAS algorithms across several metrics in both noiseless and noisy environments. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Explainable Emotion Recognition in Social Networks with Transformers

Explainable Emotion Recognition in Social Networks with Tran...

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International Conference on computer and Information Science (ACIS)

作者： Isidoros Perikos Chara Pylarinou Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press “Diophantus” Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9798350376647

ISBN: (纸本)9798350376654

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences.

关键词： Human computer interaction Emotion recognition Analytical models Sentiment analysis Social networking (online) Computational modeling Predictive models Transformers Data models Artificial intelligence

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Hierarchical facial expression recognition 22

Hierarchical facial expression recognition

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2022 Conference on Research in Adaptive and Convergent Systems, RACS 2022

作者： Zhang, Xiang Gu, Yu Yan, Huan Wang, Yantong Ren, Fuji Ji, Yusheng Hefei University of Technology National Institute of Informatics Tokyo Japan School of Computer and Information Hefei University of Technology Anhui Hefei China Tokushima University Tokushima Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781450393980

Learning facial expressions constitutes a challenging job due to the uncertainties caused by the ambiguity of facial expressions. To address this issue, we propose a simple yet efficient Coarse-to-Fine Network (CFNet) inspired by human being's cognitive mode for suppressing such uncertainties. A child learns quickly whether his behavior is allowed by reading adults' coarse facial expressions like positive or negative, and then adjusts accordingly via further interpreting its fine meaning like happy or angry. Similarly, CFNet first aggregates basic facial expressions into 3 coarse categories based on their distributions in the Valence-Arousal (VA) emotion space. Then, CFNet leverages fine labels with the coarse classification results for fine-grained facial expression recognition with discriminative loss handling high intra-class variations within coarse categories. Experiments on benchmark datasets demonstrate the superiority of our method over the state-of-art rivals. © 2022 ACM.

关键词： Emotion Recognition

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Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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RecStitchNet:Learning to stitch images with rectangular boundaries

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Computational Visual Media 2024年第4期10卷 687-703页

作者： Yun Zhang Yu-Kun Lai Lang Nie Fang-Lue Zhang Lin Xu Key Lab of Film and TV Media Technology of Zhejiang Province College of Media EngineeringCommunication University of ZhejiangHangzhou 310018China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK Institute of Information Science Beijing Jiaotong UniversityBeijing 100091China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand STEM University of South AustraliaAdelaide 5095Australia

Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.

关键词： image stitching boundaries convolutional neural network

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