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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Cenic, Aleksandar Jovanovic, Martin Stojkovic, Suzana University of Niš Faculty of Electronic Engineering Computer Science Department Niš Serbia

ISBN: (纸本)9798350353907

This paper presents a hybrid search based retrieval-augmented generation (RAG) system in the domain of history, in Serbian language. The system was implemented in Python programming language, and is based on Google BERT embedding model, OpenSearch hybrid search, OpenAI GPT 3.5 large language model, and two original Serbian datasets for augmentation and system evaluation. The system performance was evaluated using ROGUE-1, cosine similarity and human evaluation, and its performance was compared to the purely semantic search based version. The system performance was satisfactory in human measurement, but estimated as low in automated metrics. In both metrics the hybrid search showed significantly better performance. © 2024 IEEE.

关键词： Question answering

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic Systems

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IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Implications of the forthcoming forensic sciences standard ISO/IEC 21043 for forensic biometrics 12

Implications of the forthcoming forensic sciences standard I...

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12th International Workshop on Biometrics and Forensics, IWBF 2024

作者： Meuwly, Didier University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9798350354478

The forthcoming forensic sciences standard ISO/IEC 21043 is a methodological and technical standard, currently at the stage of Draft International Standard. When adopted, it will apply to all forensic disciplines, including forensic biometrics. This standard has been designed by forensic, legal, and standardisation professionals to ensure that forthcoming forensic services, products, and systems are safe, reliable, and consistently perform as intended. The aim of this contribution is to present to the forensic biometric community some of the implications of adopting ISO/IEC 21043 for the forensic biometric *** an introduction (§ I) and a review of the existing international standards applied in forensic science (§ II), the five-part structure of ISO/IEC 21043 is described: 1. Terms and definitions, 2. Recovery, 3. Analysis, 4. Interpretation and 5. Reporting, (§ III). Then a selection of requirements and recommendations of this five-part standard are highlighted, and their implication for forensic biometric research, development, implementation, and practice is discussed (§ IV).The conclusion is that the adoption and use of ISO/IEC 21043 will help forensic laboratories to reach accreditation and to enhance their existing quality management systems. It will also make it easier for forensic biometric researchers to develop, deliver, and implement more relevant, more reliable, and safer services, products, and systems to the forensic biometric examiners performing casework (§ V). © 2024 IEEE.

关键词： Forensic science

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Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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computer Modeling in engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

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Textual Emotion Recognition: Advancements and Insights 6

Textual Emotion Recognition: Advancements and Insights

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Esmail, Ahmed A. Abdallah, Sayed M. Elsayed, Mohamed A. Ghaith, Amr E. Elbanhawy, Magy Al-Zoghby, Aya M. Moawad, Ibrahim F. Artificial Intelligence Science New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Mansoura University Faculty of Computers and Information Dakhlia Egypt New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Portsaid University Faculty of Engineering Egypt Damietta University Faculty of Computers and Artificial Intelligence Computer Science Department Damietta Egypt Ain Shams University Information Systems Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m

关键词： Emotion Recognition

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Implementation of human-following robot system applying virtual delay to posture of a leader 3

Implementation of human-following robot system applying virt...

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Takemori, Fumiaki Tottori University Faculty of Engineering Dept. of Electrical Engineering and Computer Science Tottori Japan

ISBN: (纸本)9798350374766

This paper develops an implementation of a measurement and control system in which a vehicle follows its predecessor while maintaining a certain distance. First, we construct a model that virtually delays the reference coordinates of the leading target backwards, and propose a control law that follows the steering angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the position and orientation of the leader, and the position and orientation is estimated using a template matching method based on the person's body contour point group measured by the sensor. Finally, the effectiveness (including robustness) of the proposed system is demonstrated in some simulations and human tracking experiments. © 2024 IEEE.

关键词： Template matching

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Roadmap for Software engineering Education using ChatGPT

Roadmap for Software Engineering Education using ChatGPT

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2023 International Conference on Artificial Intelligence science and Applications in Industry and Society, CAISAIS 2023

作者： Abdelfattah, Aly Maher Ali, Nabila Ahmed Elaziz, Mohamed Abd Ammar, Hany H Galala University Faculty of Computer Science and Engineering Egypt

ISBN: (纸本)9798350314786

This paper presents a novel method for teaching software engineering using the AI tool, ChatGPT, to create an engaging and immersive learning platform. The technique emphasizes understanding requirements engineering principles via interactive exercises and hands-on examples. The approach involves ChatGPT assisting in collecting user stories, creating a use case and class diagrams, and formulating sequence diagrams. This method employs an agile strategy focusing on select user stories and encourages student interaction with ChatGPT for a deeper understanding of the subject. The goal is to demonstrate the potential of AI tools to transform software engineering education by providing practical applications and real-life scenarios. The research outlines a comprehensive plan for integrating ChatGPT into a software engineering syllabus, focusing on requirements engineering. The findings could significantly influence the teaching and understanding of software engineering principles, benefiting educators and students. © 2023 IEEE.

关键词： Requirements engineering

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Reinforcement Learning Approach for Improving Platform Performance in Two-Sided Mobility Markets 27

Reinforcement Learning Approach for Improving Platform Perfo...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Ghasemi, Farnoud Tabatabaei, Seyed Hassan Ghanadbashi, Saeedeh Kucharski, Rafal Golpayegani, Fatemeh Jagiellonian University Faculty of Mathematics and Computer Science Poland School of Computer Engineering Iran University of Science and Technology Tehran Iran School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9798331505929

Two-sided mobility markets, with platforms like Uber and Lyft, are complex systems by nature due to intricate, non-linear interactions between the platform and the involved parties including travelers and drivers. These interactions give rise to phenomena underlying market evolution, mainly cross-side network effects. Currently, such platforms rely on rule-based (RB) strategies with a constant commission rate to grow and achieve sustainability in terms of market share and profitability. However, the constant commission rate significantly constrains the platform's ability to leverage network effects, leading to inefficient growth. In this study, a Reinforcement Learning-based (RLB) strategy is proposed to improve the platform performance through strategic levers. We employ a Deep Q-Network (DQN) within an agent-based framework, enabling the platform to adjust the commission rate on a day-to-day basis while learning the complex, non-linear interactions in the market. The results show that the RL-based strategies successfully generate and control the essential cross-side network effects in the market enhancing the platform performance via dynamic commission rate. Our results indicate 12% improvement in the platform revenue with the RL-based strategy in comparison to the rule-based strategy without significantly compromising the platform market share which can essentially impact the platform's viability in the long term. © 2024 IEEE.

关键词： Deep reinforcement learning

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Continuous and Discrete Similarity Coefficient for Identifying Essential Proteins Using Gene Expression Data

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Big Data Mining and Analytics 2023年第2期6卷 185-200页

作者： Jiancheng Zhong Zuohang Qu Ying Zhong Chao Tang Yi Pan College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences ShenzhenGuangzhou 518055China

Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential ***,gene expression data are prone to significant fluctuations due to noise interference in topological *** this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise *** then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression *** the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential *** strategy exhibited a high recognition rate and good *** validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy *** with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and *** also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene *** experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

关键词： Protein-Protein Interaction(PPI)network continuous and discrete similarity coefficient essential proteins

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