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International Conference on Machine Learning and Applications (ICMLA)

作者： Ajibola Obayemi Khuong Nguyen School of Architecture Technology and Engineering University of Brighton Brighton UK Department of Computer Science Royal Holloway University of London London UK

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Multimodal models can experience multimodal collapse, leading to sub-optimal performance on tasks like fine-grained e-commerce product classification. To address this, we introduce an approach that leverages multimodal Shapley values (MM-SHAP) to quantify the individual contributions of each modality to the model's predictions. By employing weighted stacked ensembles of unimodal and multimodal models, with weights derived from these Shapley values (MM-SHAP), we enhance the overall performance and mitigate the effects of multimodal collapse. Using this approach we improve previous results(f1-score) from 0.67 to 0.79.

关键词： Machine learning Predictive models Electronic commerce

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Markov chain importance sampling for minibatches

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Machine Learning 2024年第2期113卷 789-814页

作者： Fuh, Cheng-Der Wang, Chuan-Ju Pai, Chen-Hung Graduate Institute of Statistics National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan320317 Taiwan Research Center for Information Technology Innovation Academia Sinica No. 128 Academia Road Section 2 Nankang Taipei115 Taiwan Department of Computer Science National Chengchi University No. 64 Sec. 2 ZhiNan Road Wenshan Taipei116 Taiwan

This study investigates importance sampling under the scheme of minibatch stochastic gradient descent, under which the contributions are twofold. First, theoretically, we develop a neat tilting formula, which can be regarded as a general device for asymptotically optimal importance sampling. Second, practically, guided by the formula, we present an effective algorithm for importance sampling which accounts for the effects of minibatches and leverages the Markovian property of the gradients between iterations. Experiments conducted on artificial data confirm that our algorithm consistently delivers superior performance in terms of variance reduction. Furthermore, experiments carried out on real-world data demonstrate that our method, when paired with relatively straightforward models like multilayer perceptron and convolutional neural networks, outperforms in terms of training loss and testing error. © 2023, The Author(s), under exclusive licence to Springer science+Business Media LLC, part of Springer Nature.

关键词： Importance sampling

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Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

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Bone Research 2024年第1期12卷 1-19页

作者： Xiwei Fan Antonia Rujia Sun Reuben S.E.Young Isaac O.Afara Brett R.Hamilton Louis Jun Ye Ong Ross Crawford Indira Prasadam Centre for Biomedical Technologies Queensland University of TechnologyBrisbaneQLDAustralia School of Mechanical Medical&Process EngineeringQueensland University of TechnologyBrisbaneQLDAustralia Central Analytical Research Facility Queensland University of TechnologyBrisbaneQLDAustralia Molecular Horizons University of WollongongWollongongNSWAustralia Department of Technical Physics University of Eastern FinlandKuopioFinland School of Electrical Engineering and Computer Science Faculty of EngineeringArchitecture and Information TechnologyUniversity of QueenslandBrisbaneQLDAustralia Centre for Microscopy and Microanalysis University of QueenslandBrisbaneQLDAustralia The Prince Charles Hospital BrisbaneQLDAustralia

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose *** presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial ***,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic *** analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint *** issue has led to standardization difficulties and hindered the success of clinical *** insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease *** resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular *** advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial *** fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial ***,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and *** these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease *** review summarizes the rec

关键词： insight Spatial enable

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Enhanced Federated Learning for Iot Systems

SSRN

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SSRN 2023年

作者： Mora-Gimeno, Francisco José Elouali, Aya Mora, Higinio Mora Szymanski, Julian Department of Computer Science Technology and Computation University of Alicante Alicante Spain Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

Federated learning consists on training several local models on edge devices where data is collected. These models are then shared with the cloud where the general model is calculated and transmitted back to the edge. This process is repeated several times until the general model achieves the desired accuracy. This technique permits training a model on the largest possible quantity of data without having to share the device’s private data with other devices or the cloud. However, the main drawback of this method is transmitting big size models multiple time on the network from and to every edge device participating in the training. In this paper, we propose a data transmission reduction method in federated learning context which, based on data similarity, reduces the amount of weights to be transmitted to the cloud. Several experiments has been performed to prove the effectiveness of the method and define the best components to use it such as data representation method and change calculation method. The performed experiments revealed that the proposed solution reduces data transmission up to 75% while having only 0.02% accuracy deterioration © 2023, The Authors. All rights reserved.

关键词： Edge computing

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A comprehensive study on Frequent Pattern Mining and Clustering categories for topic detection in Persian text stream

arXiv

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arXiv 2024年

作者： Zafarani-Moattar, Elnaz Kangavari, Mohammad Reza Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Iran University of Science and Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Topic detection in social networks is extracting and summarizing frequent posts about an event or subject. Topic detection is a complex process and depends on language because it somehow needs to analyze text. There have been few studies on topic detection in Persian, and the existing algorithms are not remarkable. Therefore, we aimed to study topic detection in Persian. The objectives of this study are: 1) to conduct an extensive study on the best algorithms for topic detection, 2) to identify necessary adaptations to make these algorithms suitable for the Persian language, and 3) to evaluate their performance on Persian social network texts. To achieve these objectives, we have formulated two research questions: First, considering the lack of research in Persian, what modifications should be made to existing frameworks, especially those developed in English, to make them compatible with Persian? Second, how do these algorithms perform, and which one is superior? There are various topic detection methods that can be categorized into different categories. Frequent pattern and clustering are selected for this research, and a hybrid of both is proposed as a new category. Then, ten methods from these three categories are selected. All of them are re-implemented from scratch, changed, and adapted with Persian. These ten methods encompass different types of topic detection methods and have shown good performance in English. The text of Persian social network posts is used as the dataset. Additionally, a new multiclass evaluation criterion, called FS, is used in this paper for the first time in the field of topic detection. Approximately 1.4 billion tokens are processed during experiments. The results indicate that if we are searching for keyword-topics that are easily understandable by humans, the hybrid category is better. However, if the aim is to cluster posts for further analysis, the frequent pattern category is more suitable. © 2024, CC BY-NC-ND.

关键词： Data mining

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

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International Journal of Information technology and Decision Making 2024年第1期23卷 197-268页

作者： AlSereidi, Abeer Salih, Sarah Qahtan M. Mohammed, R.T. Zaidan, A.A. Albayati, Hassan Pamucar, Dragan Albahri, A.S. Zaidan, B.B. Shaalan, Khaled Al-Obaidi, Jameel Albahri, O.S. Alamoodi, Abdulah Majid, Nazia Abdul Garfan, Salem Al-Samarraay, M.S. Jasim, A.N. Baqer, M.J. Faculty of Engineering & IT The British university in Dubia United Arab Emirates United Arab Emirates Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq University of Defence in Belgrade Department of Logistic Pavla Jurisica Sturma 33 Belgrade11000 Serbia Baghdad Iraq Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu64002 Taiwan Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Institute of Biological Sciences Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Foundation of Alshuhda Baghdad Iraq

Context: When the epidemic ¯rst broke out, no speci¯c treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment e®orts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The ¯rst is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes ¯ve sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates

关键词： Coronavirus

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PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks

arXiv

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arXiv 2023年

作者： Lin, Nan Orfanoudakis, Stavros Cardenas, Nathan Ordonez Giraldo, Juan S. Vergara, Pedro P. Section Delft University of Technology Delft Netherlands Computer Science Department Delft University of Technology Delft Netherlands Amsterdam Netherlands

Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks' operation and planning. Therefore, there is a need for scalable algorithms that can provide accurate and fast solutions for both small and large scale power networks. As the power network can be interpreted as a graph, Graph Neural Networks (GNNs) have emerged as a promising approach for improving the accuracy and speed of PF approximations by exploiting information sharing via the underlying graph structure. In this study, we introduce PowerFlowNet, a novel GNN architecture for PF approximation that showcases similar performance with the traditional Newton-Raphson method but achieves it 4 times faster in the IEEE 14-bus system and 145 times faster in the realistic case of the French high voltage network (6470rte). Meanwhile, it significantly outperforms other traditional approximation methods, such as the DC power flow, in terms of performance and execution time;therefore, making PowerFlowNet a highly promising solution for real-world PF analysis. Furthermore, we verify the efficacy of our approach by conducting an in-depth experimental evaluation, thoroughly examining the performance, scalability, interpretability, and architectural dependability of PowerFlowNet. The evaluation provides insights into the behavior and potential applications of GNNs in power system analysis. © 2023, CC BY.

关键词： Newton-Raphson method

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Investigating Context-Aware Collaborative Text Entry on Smartphones using Large Language Models 25

Investigating Context-Aware Collaborative Text Entry on Smar...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Chen, Weihao Shi, Yuanchun Wang, Yukun Shi, Weinan Chen, Meizhu Gao, Cheng Mei, Yu Zhu, Yeshuang Zhang, Jinchao Yu, Chun Department of Computer Science and Technology Tsinghua University Beijing China Qinghai University Qinghai Xining China School of Architecture Tsinghua University Beijing China Pattern Recognition Center WeChat AI Tencent Inc. Beijing China

ISBN: (纸本)9798400713941

Text entry is a fundamental and ubiquitous task, but users often face challenges such as situational impairments or difficulties in sentence formulation. Motivated by this, we explore the potential of large language models (LLMs) to assist with text entry in real-world contexts. We propose a collaborative smartphone-based text entry system, CATIA, that leverages LLMs to provide text suggestions based on contextual factors, including screen content, time, location, activity, and more. In a 7-day in-the-wild study with 36 participants, the system offered appropriate text suggestions in over 80% of cases. Users exhibited different collaborative behaviors depending on whether they were composing text for interpersonal communication or information services. Additionally, the relevance of contextual factors beyond screen content varied across scenarios. We identified two distinct mental models: AI as a supportive facilitator or as a more equal collaborator. These findings outline the design space for human-AI collaborative text entry on smartphones. © 2025 Copyright held by the owner/author(s).

关键词： Ambient intelligence

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OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks

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International Journal of Information technology (Singapore) 2024年第6期16卷 3913-3921页

作者： Marriwala, Nikhil Kumar Shukla, Vinod Kumar Raju, Ayalapogu Ratna Panda, Sunita S, Suma Purad, Hemavati C. Electronics and Communication Engineering Department University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Information Technology Head of Academics–School of Engineering Architecture Interior Design Amity University Dubai Dubai International Academic City Dubai United Arab Emirates Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India CMR Technical Campus Hyderabad India Department of AIML BITM Karnataka Ballari India

CR-IoT networks can benefit from the idea of blockchain technology to achieve accurate decisions in order to provide very high-accuracy spectrum sensing in the network. It allows for multi-user cooperation as the sensing readings are being exchanged in the spectrum. It leads to security issues in CR-IoT networks. Thus, in order to increase the quality of decision-making in spectrum sensing, the given paper introduces an ontological framework with a blockchain-enabled system to detect malicious users in networks. The proposed framework first captures network packets from available Wi-Fi, followed by the creation of blocks with private, public, and hash keys. The keys are used to encode information about the nodes, such as confidential information and their location, data relative to users, their position where packets are delivered, etc. This is subsequently followed by the generation of network ontology, which includes parsing, computation of similarity, and reduction in clustered packets. Finally, the performance of the suggested mechanism is validated using a set of evaluation measures such as authentication rate, intrusion rate, average network throughput time consumption, and energy efficiency. As seen from the findings, it is evident that the proposed framework performs better than existing recent studies, thereby improving the performance of CR-IoT networks. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Cognitive radio Internet of things (IoT) Ontology Spectrum sensing

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