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Hybrid Profile based Multi-document Text Summarisation

Procedia computer Science 2025年 252卷 862-872页

作者： Jean Louis K.E. Fendji Dounia Donatien Marcellin Atemkeng Department of Computer Engineering University Institute of Technology University of Ngaoundere 455 Ngaoundere Cameroon Stellenbosch Institute for Advanced Study (STIAS) Wallenberg Research Centre at Stellenbosch University Stellenbosch South Africa Department Mathematics and Computer Science Faculty of Science University of Ngaoundere 454 Ngaoundere Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

The internet has become a crucial component of the daily routines, offering numerous resources and documents for a variety of tasks and information retrieval. However, the large volume of available information often leads to ”information saturation,” posing a challenge to efficient processing and extraction of relevant information. To mitigate this issue, extensive research has been conducted exploring a range of methods, including machine learning and deep learning techniques. A significant advancement in this field is automatic text summarisation, which employs Natural Language Processing (NLP). Despite their efficacy, traditional summarisation methods typically fall short as they fail to consider the unique needs and preferences of individual users. This study introduces a novel, hybrid and profile-based multi-document summarisation method that selects relevant documents according to user queries and preferences, as defined in a user profile. By leveraging NLP algorithms, the proposed system creates personalised summaries by initially extracting sentences from documents that closely match the user’s profile, followed by the generation of a concise abstract summary. The model, specifically developed for French, results in a success rate of 87.5%, and delivering semantically coherent summaries for up to three documents concurrently. This method enhances the user experience by providing succinct and customised information.

关键词： Text Summarisation User Profile Multi-document preferences

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Deep Reinforcement Learning-based Physical Layer Security Framework for Internet of Medical Things

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IEEE Transactions on Consumer Electronics 2024年

作者： Razaq, Mian Muaz Jiao, Yan Peng, Limei Ho, Pin-Han Chen, Yuguang Dong, Fangjie Kyungpook National University School of Computer Science and Engineering Daegu41566 Korea Republic of University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Guangdong China Department of Electrical and Computer Engineering University of Waterloo ON Canada Shenzhen Unicom Innovation Business Capability Center China National Health Commission's Center for Statistics and Information China

The Internet of Medical Things (IoMT) is transforming modern healthcare information systems by connecting a diverse array of medical devices and sensors. However, significant security and privacy challenges arise when handling confidential medical data during transmission. This paper addresses these challenges by proposing a Physical Layer Security (PLS) framework integrated with Cell-Free Massive Multiple Input Multiple Output (CF-mMIMO) to enhance security in IoMT environments. The framework introduces a safe zone (SZ), a protected area surrounding legitimate healthcare devices to prevent access by eavesdroppers. This spatial segmentation enables precise beamforming within the SZ while amplifying artificial noise (AN) outside it, significantly boosting the secrecy rate. Additionally, the framework dynamically selects communication devices based on channel quality and orthogonality, optimizing network resources, reducing inter-user interference, and ensuring high-quality communication in densely deployed healthcare settings. Simulation results confirm that our approach adapts to and leverages the spatial dynamics of eavesdroppers, maintaining high secrecy rates even in scenarios with increased eavesdropper presence, thus keeping sensitive medical data secure and unreadable to unauthorized entities. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Ultra-compact and broadband polarization-insensitive mode-order converting power splitter

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Chinese Optics Letters 2024年第3期22卷 36-41页

作者：陈浩琪姚润葵汪鹏君符强陈伟伟戴世勋孔德军林健金涛李军戴庭舸杨建义 Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou 325035China College of Science and Technology Ningbo UniversityNingbo 315211China Laboratory of Infrared Materials and Devices The Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Department of Information Science and Electronics Engineering Zhejiang UniversityHangzhou 310027China

A polarization-insensitive mode-order converting power splitter using a pixelated region is presented and investigated in this *** TE_(0)and TM_(0)modes are injected into the input port,they are converted into TE_(1)and TM_(1)modes,which evenly come out from the two output *** finite-difference time-domain method and direct-binary-search optimization algorithm are utilized to optimize structural parameters of the pixelated region to attain small insertion loss,low crosstalk,wide bandwidth,excellent power uniformity,polarization-insensitive property,and compact *** results reveal that the insertion loss,crosstalk,and power uniformity of the fabricated device at 1550 nm are 0.57,-19.67,and 0.094 d B in the case of TE polarization,while in the TM polarization,the relevant insertion loss,crosstalk,and power uniformity are 0.57,-19.40,and 0.11 d *** a wavelength range from 1520 to 1600 nm,for the fabricated device working at TE polarization,the insertion loss,crosstalk,and power uniformity are lower than 1.39,-17.64,and 0.14 *** the case of TM polarization,we achieved an insertion loss,crosstalk,and power uniformity less than 1.23,-17.62,and 0.14 dB.

关键词： integrated optics optical waveguide polarization-insensitive property mode-order converting power splitter

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Large-scale phase retrieval

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Light(Science & Applications) 2021年第10期10卷 1814-1815页

作者： Gabriel Popescu Quantitative Light Imaging Laboratory Department of Electrical and Computer EngineeringBeckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA

The coherent interaction between an electromagnetic field and a 3D weakly scattering medium results in a simple Fourier transform relationship between the object’s structure and the complex scattered *** a result,knowledge about the phase of the scattered field is necessary for solving this inverse problem with a unique ***,in applications,such as X-ray crystallography,typically one only has experimental access to the amplitude of the diffracted field,which results in ambiguities of the *** century-old challenge,known as“the phase problem”2 motivated the development of computational algorithms combined with a priori knowledge about the object of interest to limit the solution *** generally successful,this approach sometimes led to multiple solutions,i.e.,different inferred object structures for the same set of data:“Not all the guesses have been *** is clear,for example,from the following:Two different structures were predicted for the mineral bixbyite,one by ***,the other by *** is not known which,if either,is correct.”(Chapter 7 in ref.2).

关键词： phase scattered typically

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Ground-State Energy and Related Properties Estimation in Quantum Chemistry with Linear Dependence on the Number of Atoms

Ground-State Energy and Related Properties Estimation in Qua...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Taehee Ko Xiantao Li Chunhao Wang School of Computational sciences Korea Institute for Advanced Study South Korea Department of Mathematics Pennsylvania State University USA Department of Computer Science and Engineering Pennsylvania State University USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

At the heart of quantum chemistry and materials science lies the critical task of estimating ground-state properties. We present a quantum algorithm for this task by quantizing the density-functional theory (DFT). A key aspect of imple-menting DFT faithfully is the requirement for self-consistent calculations, which involve repeated di-agonalizations of the Hamiltonian. This procedure, however, creates a significant bottleneck, as a classical algorithm generally demands a computational complexity that grows cubically with the number of electrons, restricting the scalability of DFT for tackling large-scale problems that involve complex chemical environments and microstructures. This article presents the first quantum algorithm that has provided substantial speedup for the ground state computation, by improving the complexity to one with a linear scaling with the number of atoms. The algorithm leverages the exponential speedup by the quantum singular value transformation to generate a quantum circuit to encode the density-matrix, followed by an efficient estimation method for the output electron density, which constitutes a simple hybrid approach for achieving self-consistency. Moreover, the algorithm produces the ground state Hamiltonian, from which the ground state energy and band structures can be efficiently computed. The proposed framework is accompanied by a rigorous error analysis that establishes the convergence and quantifies various sources of error and the overall computational complexity. The combination of effi-ciency and precision opens new avenues for exploring large-scale physical systems.

关键词： Quantum algorithm Quantum chemistry Scalability Stationary state Discrete Fourier transforms Estimation Atoms Computational complexity Quantum circuit Electrons

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Enhancing Early Diagnosis of Autism Spectrum Disorder Using a Bayesian-Optimized Random Forest Algorithm

Enhancing Early Diagnosis of Autism Spectrum Disorder Using ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Babu, Tina Nair, Rekha R Kishore, S. Mahapatra, Sheila Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD. © 2025 The Author(s).

关键词： Random forests

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Temporal-guided Mixture-of-Experts for Zero-Shot Video Question Answering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qin, Yixin Zhao, Lei Gao, Lianli Zhang, Haonan Zeng, Pengpeng Shen, Heng Tao University of Electronic Science and Technology of China Center for Future Media School of Computer Science and Engineering Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Tongji University Department of Information Engineering and Computer Science China

Video Question Answering (VideoQA) is a challenging task in the vision-language field. Due to the time-consuming and labor-intensive labeling process of the question-answer pairs, fully supervised methods are no longer suitable for the current increasing demand for data. This has led to the rise of zero-shot VideoQA, and some works propose to adapt large language models (LLMs) to assist zero-shot learning. Despite recent progress, the inadequacy of LLMs in comprehending temporal information in videos and the neglect of temporal differences, e.g., the different dynamic changes between scenes or objects, remain insufficiently addressed by existing attempts in zero-shot VideoQA. In light of these challenges, a novel Temporal-guided Mixture-of-Experts Network (T-MoENet) for zero-shot video question answering is proposed in this paper. Specifically, we apply a temporal module to imbue language models with the capacity to perceive temporal information. Then a temporal-guided mixture-of-experts module is proposed to further learn the temporal differences presented in different videos. It enables the model to effectively improve the capacity of generalization. Our proposed method achieves state-of-the-art performance on multiple zero-shot VideoQA benchmarks, notably improving accuracy by 5.6% on TGIF-FrameQA and 2.3% on MSRVTT-QA while remaining competitive with other methods in the fully supervised setting. The codes and models developed in this study will be made publicly available at https://***/qyx1121/T-MoENet. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Extending LLMs to New Languages: A Case study of Llama and Persian Adaptation

arXiv

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arXiv 2024年

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model’s performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model’s initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Translation (languages)

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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Measurement of Absorption Current of Polyester Coated Enameled Wires with Q(t) meter

Measurement of Absorption Current of Polyester Coated Enamel...

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2023 International Symposium on electrical Insulating Materials, ISEIM 2023

作者： Watanabe, Akinao Fukuma, Masumi Sekiguchi, Yoitsu Advanced Electronic and Information Systems Course National Institute of Technology Matsue College Matsue Japan Department of Electrical Engineering and Computer Science National Institute of Technology Matsue College Matsue Japan Energy and Electronics Materials R&D Laboratories Sumitomo Electric Industries Osaka Japan

ISBN: (纸本)9784886864376

A Q(t) meter is used to measure electrical properties in insulating materials. Here, Q(t) is the integral value of circuit current with time t. It is possible to measure the Q(t) of the insulation material of a capacitor and a cable by using Q(t) meter. The current through an insulating material is divided into an instantaneous charging current, an absorption current and an equilibrium leakage current. It is considered that the absorption current is related to charge accumulation in the insulating materials. In this paper, an enameled wire (1.6 PEW) is used to prepare a cable sample, the Q(t) of the coated polyester (PEs) is measured, and extraction of absorption current components from measured Q(t) are reported. © 2023 The institute of electrical Engineers of Japan.

关键词： Insulating materials

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