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arXiv 2022年

作者： Zhu, Xiutao Chen, Yaojun Gerbner, Dániel Győri, Ervin Karim, Hilal Hama Department of Mathematics Nanjing University Nanjing210093 China Alfréd Rényi Institute of Mathematics Budapest Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

The generalized Turán number ex(n, Ks, H) is the maximum number of complete graph Ks in an H-free graph on n vertices. Let Fk be the friendship graph consisting of k triangles. Erdős and Sós (1976) determined the value of ex(n, K3, F2). Alon and Shikhelman (2016) proved that ex(n, K3, Fk) ≤ (9k−15)(k+ 1)n. In this paper, by using a method developed by Chung and Frankl in hypergraph theory, we determine the exact value of ex(n, K3, Fk) and the extremal graph for any Fk when n ≥ 4k3 Copyright © 2022, The Authors. All rights reserved.

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A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

TechRxiv

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TechRxiv 2025年

作者： Hadi, Muhammad Usman Al-Tashi, Qasem Qureshi, Rizwan Shah, Abbas Muneer, Amgad Irfan, Muhammad Zafar, Anas Shaikh, Muhammad Bilal Akhtar, Naveed Al-Garadi, Mohammed Ali Hassan, Syed Zohaib Shoman, Maged Wu, Jia Mirjalili, Seyedali Shah, Mubarak School of Engineering Ulster University BelfastBT15 1AP United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Electronics Engineering Mehran University of Engineering and Technology Jamshoro76062 Pakistan of Engineering Sciences and Technology Swabi23460 Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Edith Cowan University 270 Joondalup Drive Joondalup PerthWA6027 Australia Computing and Information Systems The University of Melbourne 700 Swanston Street CarltonVIC3010 Australia Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Civil Environmental and Construction Engineering The University of Central Florida OrlandoFL United States Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest1034 Hungary Center for Research in Computer Vision The University of Central Florida OrlandoFL United States

Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. LLMs are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, vision applications, and question-answering. This survey provides a comprehensive overview of LLMs, including their history, architecture, datasets, training methods, applications, challenges, and future prospects. We begin by discussing the fundamental concepts of generative AI and the architecture of generative pre-trained transformers (GPT). We then provide an overview of the history of LLMs, their evolution over time, and the different training methods. We also present benchmark dataset for training and fine-tuning and evaluating LLMs. We then discuss the wide range of tasks where they are used and also discuss applications of LLMs in different domains, including medicine, education, finance, engineering, agriculture, media, entertainment, politics, and law. We also discuss how LLMs are shaping the future of AI and their increasing role in scientific discovery, and how they can be used to solve real-world problems. Next, we explore the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, privacy concerns, and computational resource requirements. This survey also highlights techniques for enhancing the robustness and controllability of LLMs and addressing bias, fairness, and quality issues in Generative AI. Finally, we conclude by highlighting the future of LLM research and the challenges that need to be addressed in order to make this technology more reliable and useful. This survey is intended to provide researchers, practitioners, and ent

关键词： Natural language processing systems

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Design of smart system for fruit packinghouse management in supply chain

Design of smart system for fruit packinghouse management in ...

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2020 International Conference on computer Science and Its Application in Agriculture, ICOSICA 2020

作者： Khumaidi, Ali Purwanto, Y. Aris Sukoco, Heru Wijaya, Sony Hartono Darmawan, Risanto University of Krisnadwipayana Department of Informatics Jakarta Indonesia Bogor Agricultural University Department of Mechanical and Biosystem Engineering Bogor Indonesia Bogor Agricultural University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781728169071

Packinghouse management is an integration of a series of business processes and good handling practices. Many previous studies have discussed using technology and methods in each business process at the packinghouse but have not been integrated and are still focused on data input, data processing, and reporting. This research proposes the design of technology and computational integration in a business process called the Smart System for Fruit Packinghouse Management (SPHM) with the aim of accurate fruit classification, product selection, and delivery model to be sent appropriately, selection of appropriate packaging, stock optimization, reduce costs, increase sales and profits, and customer loyalty. This system consists of four sub-systems are Smart Logistic System, Smart Indexing System, Smart Packaging System, and Smart Storage System. Integration of technology and computing proposed are RFID, computer Vision, Vis / NIR, IoT, WSN, AHP, GA, Topsis, RNN, LS-SVM, ABC, HOG, SPA, CARS, SVM, SVR, Fuzzy Logic Control, Time Series Analysis Techniques, and MLFANN. © 2020 IEEE.

关键词： Integration

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Movement Sonification of Familiar Music to Support the Agency of People with Chronic Pain 25

Movement Sonification of Familiar Music to Support the Agenc...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Kyrill Potapov Nicolas Gold Temitayo Olugbade Amanda C de C Williams Christopher Dieter Overbeck Danielle Lynch Minna Nygren Nadia Berthouze UCL Interaction Centre University College London London United Kingdom UCL Computer Science University College London London United Kingdom School of Engineering and Informatics University of Sussex Brighton United Kingdom Department of Clinical Educational & Health Psychology University College London London United Kingdom UCL Computer Science London United Kingdom His Majesty's Civil Service London United Kingdom UCL Interaction Centre UCL London United Kingdom

ISBN: (纸本)9798400713941

FFAME (Filtering Familiar Audio for Movement Exploration) is a novel sonification framework aiming to facilitate movement in individuals with chronic back pain. Our personalised, music-based approach contrasts and extends prior work with predetermined tonal sonification. FFAME progressively filters selected music based on angles of the trunk. Through a qualitative analysis of reported experience of 15 participants with chronic pain and 5 physiotherapists, we identify how sonification parameters and musical characteristics affect movement and meaning-making. Music-based movement sonification proved impactful across multiple dimensions: (1) encouraging movement, (2) escaping pain-related rumination, (3) externalizing pain experiences, and (4) scaffolding physical activities. Drawing on enactivism and related philosophies, the study highlights how the semantic indeterminacy of music, combined with real-time movement sonification, created a rich, open-ended environment that supported user agency and exploration. Sonification for pain management can be creative and expressive, enabling people with pain to extend challenging movements and build movement confidence. © 2025 Copyright held by the owner/author(s).

关键词： movement sonification

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EXPLOITING PRETRAINED BIOCHEMICAL LANGUAGE MODELS FOR TARGETED DRUG DESIGN

arXiv

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arXiv 2022年

作者： Uludoğan, Gökçe Ozkirimli, Elif Ulgen, Kutlu O. Karalı, Nilgün Özgür, Arzucan Department of Computer Engineering Bogazici University Istanbul34342 Turkey Data and Analytics Chapter Pharma International Informatics F. Hoffmann-La Roche AG 4303 Switzerland Department of Pharmaceutical Chemistry Istanbul University Istanbul34116 Turkey

Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target specific training. We also compare two decoding strategies to generate compounds: beam search and sampling. Results: The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality. © 2022, CC BY-NC-SA.

关键词： Molecules

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Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

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Security and Communication Networks 2022年第0期2022卷

作者： Reegu, Faheem Ahmad Abas, Hafiza Hakami, Zaid Tiwari, Sanatan Akmam, Rudzidatul Muda, Iskandar Almashqbeh, Hashem Ali Jain, Rituraj Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur50480 Malaysia Department of Computer Science Jazan University Jazan45142 Saudi Arabia Faculty of Management Studies University of Delhi Delhi India Faculty Economic and Business Universitas Sumatera Utara Medan Indonesia Department of Business Qatar University Doha Qatar Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase's research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges. © 2022 Faheem Ahmad Reegu et al.

关键词： Blockchain

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Integrating educational theories with virtual reality: Enhancing engineering education and VR laboratories

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Social Sciences and Humanities Open 2024年 10卷

作者： Abbas Shah, Syed Faisal Mazhar, Tehseen Shahzad, Tariq khan, Muhammad Amir Ghadi, Yazeed Yasin Hamam, Habib Department of Computer Science & Information Technology Virtual University of Pakistan Lahore 54000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 12555 United Arab Emirates Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles BP Libreville 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR), create realistic digital experiences by overlaying digital elements. This study aims to highlight the benefits of utilizing VR technology in education, particularly in universities and higher education institutions, by integrating VR with educational theories to create engaging and interactive learning experiences that enhance understanding and retention. This review includes a comprehensive analysis of 103 works, synthesizing existing literature to evaluate the suitability of various educational approaches and theories for implementation in VR-based educational applications. The findings emphasize VR's potential to transform learning by providing immersive experiences that bridge different domains and encourage engagement. By integrating learning theory into VR development, meaningful learning environments can be created, improving student understanding and retention across diverse learning settings. The study concludes that the combination of technology and educational theories provides a novel approach to motivate students and foster deep learning in VR laboratories. © 2024 The Authors

关键词： Augmented reality Educational theories engineering education Immersive technology Interactive learning VR laboratories

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Mutual information based logistic regression for phishing URL detection

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Ronin Institute Montclair NJ United States Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India

Phishing is a cybersecurity problem that hackers employ to deceive individuals and organizations. Phishing is dynamic in nature;the hackers change several tricks to deceive the victims in multiple ways. It is important to track the tricks of hackers with recent technology. This study makes a notable contribution to enhancing cybersecurity defences by offering insights that aid in the detection and mitigation of phishing threats. Specifically, the study's analysis of URLs using mutual information and logistic regression techniques yielded a remarkably high accuracy rate of 99.97%, surpassing previous efforts. The identification of the most informative features for distinguishing phishing attempts provides valuable intelligence for cybersecurity professionals, enabling them to bolster defenses and stay ahead of evolving phishing tactics. © 2024

关键词： Feature selection Logistic regression Mutual information Phishing URL analysis

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Robustness modularity in complex networks

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Physical Review E 2022年第5期105卷 054308-054308页

作者： Filipi N. Silva Aiiad Albeshri Vijey Thayananthan Wadee Alhalabi Santo Fortunato Indiana University Network Science Institute (IUNI) Bloomington Indiana 47408 USA Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Kingdom of Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.

关键词： Community structure Complex systems Robustness

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StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems

arXiv

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arXiv 2024年

作者： Bouzinis, Pavlos S. Radoglou-Grammatikis, Panagiotis Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th. Sarigiannidis, Panagiotis Karagiannidis, George K. MetaMind Innovation P.C. Kila Kozani Kozani50100 Greece The Department of Electrical and Computer Engineering University of Western Macedonia Campus ZEP Kozani Kozani50100 Greece K3Y Ltd. William Gladstone 31 Sofia1000 Bulgaria The Department of Computer Science Democritus University of Thrace Kavala Campus Kavala65404 Greece The Department of Networks and Digital Media Kingston University London Penrhyn Road Kingston upon Thames Surrey LondonKT1 2EE United Kingdom The Department of Informatics and Telematics Harokopio University of Athens Omirou 9 Tavros AthensGR17778 Greece The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Beirut1102 2801 Lebanon

Federated learning (FL) is a decentralised learning technique that enables participating devices to collaboratively build a shared Machine Learning (ML) or Deep Learning (DL) model without revealing their raw data to a third party. Due to its privacy-preserving nature, FL has sparked widespread attention for building Intrusion Detection Systems (IDS) within the realm of cybersecurity. However, the data heterogeneity across participating domains and entities presents significant challenges for the reliable implementation of an FL-based IDS. In this paper, we propose an effective method called Statistical Averaging (StatAvg) to alleviate non-independently and identically (non-iid) distributed features across local clients’ data in FL. In particular, StatAvg allows the FL clients to share their individual data statistics with the server, which then aggregates this information to produce global statistics. The latter are shared with the clients and used for universal data normalisation. It is worth mentioning that StatAvg can seamlessly integrate with any FL aggregation strategy, as it occurs before the actual FL training process. The proposed method is evaluated against baseline approaches using datasets for network and host Artificial Intelligence (AI)powered IDS. The experimental results demonstrate the efficiency of StatAvg in mitigating non-iid feature distributions across the FL clients compared to the baseline methods. © 2024, CC BY.

关键词： Intrusion detection

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