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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Zejun Wang, Ye Du, Mengfei Liu, Qingwen Wu, Binhao Zhang, Jiwen Zhou, Chengxing Fan, Zhihao Fu, Jie Chen, Jingjing Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Software College Northeastern University Liaoning Shenyang China Alibaba Group Zhejiang Hangzhou China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities to perceive visual signals and perform visually grounded reasoning. However, the capabilities of LVLMs have not been comprehensively and quantitatively evaluated. Most existing multi-modal benchmarks require task-oriented input-output formats, posing great challenges to automatically assess the free-form text output of LVLMs. To effectively leverage the annotations available and reduce the manual efforts required for constructing new benchmarks, we propose to re-formulate existing benchmarks into unified LVLM-compatible formats. Through systematic data collection and reformulation, we present ReForm-Eval benchmark, offering substantial data for evaluating various capabilities of LVLMs. Through extensive experiments and analysis in ReForm-Eval, we demonstrate the comprehensiveness and reliability of ReForm-Eval in assessing various LVLMs. Our benchmark and evaluation framework is now available at https://***/FudanDISC/ReForm-Eval © 2024 ACM.

关键词： Visual languages

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knowledge-augmented Self-training of A Question Rewriter for Conversational knowledge Base Question Answering

Knowledge-augmented Self-training of A Question Rewriter for...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Ke, Xirui Zhang, Jing Lv, Xin Xu, Yiqi Cao, Shulin Li, Cuiping Chen, Hong Li, Juanzi School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Department of Computer Science and Technology Tsinghua University Beijing China

The recent rise of conversational applications such as online customer service systems and intelligent personal assistants has promoted the development of conversational knowledge base question answering (ConvKBQA). Different from the traditional single-turn KBQA, ConvKBQA usually explores multi-turn questions around a topic, where ellipsis and coreference pose great challenges to the single-turn KBQA systems which require self-contained questions. In this paper, we propose a rewrite- and-reason framework to first produce a full-fledged rewritten question based on the conversation history and then reason the answer by existing single-turn KBQA models. To overcome the absence of the rewritten supervision signals, we introduce a knowledge-augmented self-training mechanism to transfer the question rewriter from another dataset to adapt to the current knowledge base. Our question rewriter is decoupled from the subsequent QA process, which makes it easy to be united with either retrieval-based or semantic parsing-based KBQA models. Experiment results demonstrate the effectiveness of our method and a new state-of-the-art result is achieved. The code and dataset are available online now. © 2022 Association for Computational Linguistics.

关键词： knowledge based systems

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Dynamic Resource Allocation in Edge Computing via Deep Deterministic Policy Gradient Reinforcement Learning 3

Dynamic Resource Allocation in Edge Computing via Deep Deter...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Gopal, Vidhya Surendar, U. Bajulunisha, A. Tamilselvi, M. Sathiyapriya, V. Saranya, N. Sona College of Technology Department of Computer Science and Engineering Salem India Srm institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331508456

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST-WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment. © 2025 IEEE.

关键词： Reinforcement learning

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Dynamic Resource Allocation in Edge Computing via Deep Deterministic Policy Gradient Reinforcement Learning

Dynamic Resource Allocation in Edge Computing via Deep Deter...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Vidhya Gopal U Surendar A Bajulunisha M Tamilselvi V Sathiyapriya N Saranya Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering SRM institute of Science and Technology Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Department of Computer Science and Engineering Roever Engineering College Perambalur India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST–WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment.

关键词： Computational modeling Simulation Markov decision processes Neural networks Reinforcement learning Quality of service Dynamic scheduling Resource management Time factors Edge computing

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Ciao AI: the Italian adaptation and validation of the Chatbot Usability Scale

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Personal and Ubiquitous Computing 2023年第6期27卷 2161-2170页

作者： Borsci, Simone Prati, Elisa Malizia, Alessio Schmettow, Martin Chamberlain, Alan Federici, Stefano Faculty of Behavioural Management and Social Sciences Department of Learning Data Analysis and Technology Group of Cognition Data and Education University of Twente Enschede Netherlands Department of Surgery and Cancer Faculty of Medicine NIHR London IVD Imperial College of London London United Kingdom Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Modena Italy Computer Science Department University of Pisa Pisa Italy Molde University College Molde Norway School of Computer Science University of Nottingham Nottingham United Kingdom Department of Philosophy Social and Human Sciences and Education University of Perugia Perugia Italy

Chatbot-based tools are becoming pervasive in multiple domains from commercial websites to rehabilitation applications. Only recently, an eleven-item satisfaction inventory was developed (the ChatBot Usability Scale, BUS-11) to help designers in the assessment process of their systems. The BUS-11 has been validated in multiple contexts and languages, i.e., English, German, Dutch, and Spanish. This scale forms a solid platform enabling designers to rapidly assess chatbots both during and after the design process. The present work aims to adapt and validate the BUS-11 inventory in Italian. A total of 1360 questionnaires were collected which related to a total of 10 Italian chatbot-based systems using the BUS-11 inventory and also using the lite version of the Usability Metrics for User eXperience for convergent validity purposes. The Italian version of the BUS-11 was adapted in terms of the wording of one item, and a Multi-group Confirmatory Factorial Analysis was performed to establish the factorial structure of the scale and compare the effects of the wording adaptation. Results indicate that the adapted Italian version of the scale matches the expected factorial structure of the original scale. The Italian BUS-11 is highly reliable (Cronbach alpha: 0.921), and it correlates to other measures of satisfaction (e.g., UMUX-Lite, τb = 0.67;p © 2023, The Author(s).

关键词： Buses

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Ripple Sparse Self-Attention for Monaural Speech Enhancement 48

Ripple Sparse Self-Attention for Monaural Speech Enhancement

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Qiquan Zhu, Hongxu Song, Qi Qian, Xinyuan Ni, Zhaoheng Li, Haizhou University of New South Wales School of Electrical Engineering and Telecommunications Australia National University of Singapore Department of Electrical and Computer Engineering Singapore Alibaba Group China Meta United States The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility. © 2023 IEEE.

关键词： Speech enhancement

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Brain Tumor Detection using Deep Features

Brain Tumor Detection using Deep Features

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Nijhawan, Rahul Bisht, Rajendra Singh Pandey, Mukesh Kumar, Sanyam Mendirtta, Neha Awasthi, Poojita Sagar Thapar Institute of Engg. & Technology Computer Science Engineering Patiala India Graphic Era Hill University Department of Cse Bhimtal India University of Petroleum & Energy Studies School of Computer Science Dehradun India Medical Knowledge Group New York City United States Chandigarh University Punjab India University of Petroleum & Energy Studies Computer Science Engineering Dehradun India

ISBN: (纸本)9798350313413

An excessive proliferation of aberrant cells in the brain, which may result in a brain tumor. Moreover, it is very difficult for doctors to detect brain tumors at an early stage. However, with the help of MRI images, they can now monitor the condition. Due to the complexity of the disease, it is very difficult for doctors to determine its exact causes. With the help of brain tumor images, we can now monitor the condition and identify the possible cause of the disease. In this paper, we have proposed a deep learning technique for brain tumor detection. We employed a pre-trained VGG-16 architecture for detection of brain tumour. Our proposed approach outperformed the state-of-the-art algorithms with an accuracy of 86.6%. We compared the results by computing different statistical measures such as AUC, precision, recall, and F1 score. © 2023 IEEE.

关键词： deep learning Detection MRI Tumor

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Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

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computers, Materials & Continua 2022年第9期72卷 4675-4689页

作者： Mohammed Hadwan Mohammed A.Al-Hagery Mohammed Al-Sarem Faisal Saeed Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Inteligent Analytic Group College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Institute for Artificial Intelligence and Big Data Universiti Malaysia KelantanPengkalan ChepaKota Bharu16100KelantanMalaysia

Different types of pandemics that have appeared from time to time have changed many aspects of daily *** governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during *** Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store.A huge number of reviews are written daily by users to express their opinions,which include significant information to improve these *** manual processing and extracting of information from users’reviews is an extremely difficult and time-consuming ***,the use of intelligent methods is necessary to analyse users’reviews and extract issues that can help in improving these *** research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning *** the best of our knowledge,this is the first study to explore users’opinions about governmental apps in Saudi *** findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases.A new dataset used for this research includes 8000 user reviews gathered from social media,Google Play and the app *** methods are applied to the dataset,and the results show that the k nearest neighbourhood(KNN)method generates the highest accuracy compared to other implemented methods.

关键词： Arabic sentiment analysis software quality user satisfaction improving online governmental services machine learning intelligent systems mobile app

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Towards Redefining the Reproducibility in Quantum Computing: A data Analysis Approach on NISQ Devices 4

Towards Redefining the Reproducibility in Quantum Computing:...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Senapati, Priyabrata Wang, Zhepeng Jiang, Weiwen Humble, Travis S Fang, Bo Xu, Shuai Guan, Qiang Kent State University Department of Computer Science United States George Mason University Department of Electrical and Computer Engineering United States George Mason University Quantum Science and Engineering Center United States Quantum Science Center Oak Ridge National Laboratory United States Hpc Group Pacific Northwest National Laboratory United States Case Western Reserve University Department of Computer and Data Sciences United States

ISBN: (纸本)9798350343236

Although the building of quantum computers has kept making rapid progress in recent years, noise is still the main challenge for any application to leverage the power of quantum computing. Existing works addressing noise in quantum devices proposed noise reduction when deploying a quantum algorithm to a specified quantum computer. The reproducibility issue of quantum algorithms has been raised since the noise levels vary on different quantum computers. Importantly, existing works largely ignore the fact that the noise of quantum devices varies as time goes by. Therefore, reproducing the results on the same hardware will even become a problem. We analyze the reproducibility of quantum machine learning (QML) algorithms based on daily model training and execution data collection. Our analysis shows a correlation between our QML models' test accuracy and quantum computer hardware's calibration features. We also demonstrate that noisy simulators for quantum computers are not a reliable tool for quantum machine learning applications. © 2023 IEEE.

关键词： Machine learning

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