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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

Frontiers of computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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On Evaluation of Bangla Word Analogies

On Evaluation of Bangla Word Analogies

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Akter, Mousumi Sarkar, Souvika Karmaker, Shubhra Kanti Lab Department of Computer Science & Software Engineering Auburn University AL United States

ISBN: (纸本)9798891760608

This paper presents a benchmark dataset of Bangla word analogies for evaluating the quality of existing Bangla word embeddings. Despite being the 7th largest spoken language in the world, Bangla is still a low-resource language and popular NLP models often struggle to perform well on Bangla data sets. Therefore, developing a robust evaluation set is crucial for benchmarking and guiding future research on improving Bangla word embeddings, which is currently missing. To address this issue, we introduce a new evaluation set of 16,678 unique word analogies in Bangla as well as a translated and curated version of the original Mikolov dataset (10,594 samples) in Bangla. Our experiments with different state-of-the-art embedding models reveal that current Bangla word embeddings struggle to achieve high accuracy on both data sets, demonstrating a significant gap in multilingual NLP research. ©2023 Association for Computational Linguistics.

关键词： Embeddings

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Recommendation systems-based software requirements elicitation process—a systematic literature review

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Journal of Engineering and Applied Science 2024年第1期71卷 29页

作者： Akram, Faiz Ahmad, Tanvir Sadiq, Mohd. Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi110025 India Software Engineering Lab. Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions. © 2024, The Author(s).

关键词： Recommender systems

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Rendering discrete participating media using geometrical optics approximation

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Computational Visual Media 2022年第3期8卷 425-444页

作者： Jie Guo Bingyang Hu Yanjun Chen Yuanqi Li Yanwen Guo Ling-Qi Yan State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science UC Santa BarbaraUSA

We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete *** particle size is expected to range from the scale of the wavelength to several orders of magnitude greater,resulting in an appearance with distinct graininess as opposed to the smooth appearance of continuous *** fundamental issue in the physically-based synthesis of such appearance is to determine the necessary optical properties in every local *** these properties vary spatially,we resort to geometrical optics approximation(GOA),a highly efficient alternative to rigorous Lorenz–Mie theory,to quantitatively represent the scattering of a single *** enables us to quickly compute bulk optical properties for any particle size *** then use a practical Monte Carlo rendering solution to solve energy transfer in the discrete participating *** proposed framework is the first to simulate a wide range of discrete participating media with different levels of graininess,converging to the continuous media case as the particle concentration increases.

关键词： light scattering geometrical optics approximation(GOA) discrete participating media volume rendering

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Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

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Digital Communications and Networks 2024年第2期10卷 328-336页

作者： Bo Yi Jianhui Lv Xingwei Wang Lianbo Ma Min Huang College of Computer Science and Engineering Northeastern UniversityShenyang110169China Pengcheng Lab. Shenzhen518055China College of Software Northeastern UniversityShenyang110819China College of Information Science and Engineering Northeastern UniversityShenyang110819China

The rapid development of 5G/6G and AI enables an environment of Internet of Everything(IoE)which can support millions of connected mobile devices and applications to operate smoothly at high speed and low ***,these massive devices will lead to explosive traffic growth,which in turn cause great burden for the data transmission and content *** challenge can be eased by sinking some critical content from cloud to *** this case,how to determine the critical content,where to sink and how to access the content correctly and efficiently become new *** work focuses on establishing a highly efficient content delivery framework in the IoE *** particular,the IoE environment is re-constructed as an end-edge-cloud collaborative system,in which the concept of digital twin is applied to promote the *** on the digital asset obtained by digital twin from end users,a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention(TPA)enabled Long Short-Term Memory(LSTM)***,the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning(RL)***,a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing *** experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate,the average throughput,the successful content delivery rate and the average routing overhead.

关键词： Digital twin IoE Content delivery Caching Routing

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Privacy by Design Solution for Robust Fall Detection

Privacy by Design Solution for Robust Fall Detection

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17th International Conference of the Association for the Advancement of Assistive Technology in Europe, AAATE 2023

作者： Brandstötter, Michael Lumetzberger, Jennifer Kampel, Martin Planinc, Rainer Cogvis Software and Consulting GmbH Austria Vienna University of Technology Computer Vision Lab Austria

ISBN: (纸本)9781643684222

The majority of falls leading to death occur among the elderly population. The use of fall detection technology can help to ensure quick help for fall victims by automatically informing caretakers. Our fall detection method is based on depth data and has a high level of reliability in detecting falls while maintaining a low false alarm rate. The technology has been deployed in over 1,200 installations, indicating user acceptance and technological maturity. We follow a privacy by design approach by using range maps for the analysis instead of RGB images and process all the data in the sensor. The literature review shows that real-world fall detection evaluation is scarce, and if available, is conducted with a limited amount of participants. To our knowledge, our depth image based fall detection method has achieved the largest field evaluation up to date, with more than 100,000 events manually annotated and an evaluation on a dataset with 2.2 million events. We additionally present an 8-months study with more than 120,000 alarms analysed, provoked by 214 sensors located in 16 care facilities in Austria. We learned that on average 2.3 times more falls happen than are documented. Consequently, the system helps to detect falls that are otherwise overseen. The presented solution has the potential to make a significant impact in reducing the risk of accidental falls. © 2023 IOS Press. All rights reserved.

关键词： Fall detection

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m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt 30

m3P: Towards Multimodal Multilingual Translation with Multim...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yang, Jian Guo, Hongcheng Yin, Yuwei Bai, Jiaqi Wang, Bing Liu, Jiaheng Liang, Xinnian Chai, Linzheng Yang, Liqun Li, Zhoujun State Key Lab of Software Development Environment Beihang University China Department of Computer Science University of British Columbia Canada

ISBN: (纸本)9782493814104

Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between languages in the text-only modality, especially when the number of languages is large. To bridge this gap, we introduce visual context as the universal language-independent representation to facilitate multilingual translation. In this paper, we propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual neural Machine Translation (m3P), which aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. We construct a multilingual multimodal instruction dataset (InstrMulti102) to support 102 languages Our method aims to minimize the representation distance of different languages by regarding the image as a central language. Experimental results show that m3P outperforms previous text-only baselines and multilingual multimodal methods by a large margin. Furthermore, the probing experiments validate the effectiveness of our method in enhancing translation under the low-resource and massively multilingual scenario. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Visual languages

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