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Research Square 2023年

作者： Gebremeskel, Hagos Gebremedhin Chong, Feng Heyan, Huang Beijing Engineering Research Center of High Volume Language Information Processing Cloud Computing Applications Laboratory School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science Mekelle University Tigray Mekelle231 Ethiopia

Morphological analyzer is the base for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs. © 2023, CC BY.

关键词： Machine learning

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A Novel Smartphone Recommendation System Using Ensemble Machine Learning

A Novel Smartphone Recommendation System Using Ensemble Mach...

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2023 IEEE Asia-Pacific Conference on computer science and Data Engineering, CSDE 2023

作者： Almadhor, Ahmad Abbas, Sidra Sampedro, Gabriel Avelino Abisado, Mideth Gadekallu, Thippa Reddy College of Computer and Information Sciences Jouf University Sakaka72388 Saudi Arabia Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of the Philippines Open University Faculty of Information and Communication Studies Los Baños4031 Philippines De la Salle University Center for Computational Imaging and Visual Innovations 2401 Taft Ave. Manila1004 Philippines College of Computing and Information Technologies National University Manila Philippines Zhongda Group Jiaxing City Zhejiang Province Haiyan County314312 China Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing314001 China Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798350341072

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences. © 2023 IEEE.

关键词： Machine learning

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Learning to Parallelize in a Shared-Memory Environment with Transformers

arXiv

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arXiv 2022年

作者： Harel, Re'em Pinter, Yuval Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Department of Physics Nuclear Research Center - Negev Israel Scientific Computing Center Nuclear Research Center - Negev Israel Department of Computer Science Technion - Israel Institute of Technology Israel

In past years, the world has switched to many-core and multi-core shared memory architectures. As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes to software applications. OpenMP is the most comprehensive API that implements such schemes, characterized by a readable interface. Nevertheless, introducing OpenMP into code, especially legacy code, is challenging due to pervasive pitfalls in management of parallel shared memory. To facilitate the performance of this task, many source-to-source (S2S) compilers have been created over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. In this work, we propose leveraging recent advances in machine learning techniques, specifically in natural language processing (NLP), to suggest the need for an OpenMP directive. The model also suggests directives complementary to S2S automatic parallelization compilers or provides immediate on-the-fly advice to the developer without compiling or executing the code. We create a database, Open-OMP, specifically for this goal. Open-OMP contains over 17,000 unique code snippets from different domains, half of which contain OpenMP directives while the other half do not need parallelization with high probability. We use the corpus to train systems to automatically classify code segments in need of parallelization, as well as suggest individual OpenMP clauses. We train several transformer models, named PragFormer, for these tasks and show that they outperform statistically-trained baselines and automatic S2S parallelization compilers in both classifying the overall need for an OpenMP directive and the introduction of private and reduction clauses. In the future, our corpus can be used for additional t

关键词： Learning algorithms

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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无人飞行器在不确定扰动下的动态目标跟踪

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Engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle visual servoing Velocity observer Target tracking

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Private Data Manipulation in Sponsored Search Auctions

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CAAI Artificial Intelligence Research 2023年第1期2卷 114-122页

作者： Xiaotie Deng Tao Lin Tao Xiao Center on Frontiers of Computing Studies Department of Computer SciencePeking UniversityBeijing 100871China School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Huawei TCS Lab Shanghai 201206China

The repeated nature of sponsored search auctions allows the seller to implement Myerson’s auction to maximize revenue using past *** since these data are provided by strategic buyers in the auctions,they can be manipulated,which may hurt the seller’s *** model this problem as a Private Data Manipulation(PDM)game:the seller first announces an auction(such as Myerson’s)whose allocation and payment rules depend on the value distributions of buyers;the buyers then submit fake value distributions to the seller to implement the *** seller’s expected revenue and the buyers’expected utilities depend on the auction rule and the game played among the buyers in their choices of the submitted *** the PDM game,we show that Myerson’s auction is equivalent to the generalized first-price auction,and under further assumptions equivalent to the Vickrey-Clarke-Groves(VCG)auction and the generalized second-price *** results partially explain why Myerson’s auction is not as popular as the generalized second-price auction in the practice of sponsored search auctions,and provide new perspectives into data-driven decision making in mechanism design.

关键词： Internet economics sponsored search auction Myerson’s auction generalized first-price auction data-driven decision making

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An AI-Human Collaboration Chatbot with Digital Representation of Users for Knowledge Sharing

An AI-Human Collaboration Chatbot with Digital Representatio...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Sruthy Anand Adhwaith B Pillai A M Abhishek Sai Sethuraman N Rao Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Software Development Engineer Fridayy AI Noida India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

With the advancement in Artificial Intelligence (AI), especially generative AI, conversational agents are becoming a norm in everyday lives. These agents can become more intelligent if they can collaborate with humans or other agents in analysing the conversation with the user. This work presents the use of AI to connect different sets of users and help them solve their life problems. Currently, sharing the wisdom of the old generation is happening within the close family or the student circle alone. To receive knowledge from a wider community at their fingertips, we designed a chatbot called wisdomBOT which can connect the younger generation users with the older generation users to receive their wisdom through the sharing of life experiences and stories and to learn from them. It also helps to address the loneliness of the older generation and connect them through a new way to make younger friends.

关键词： Pervasive computing Sentiment analysis Social networking (online) Generative AI Digital representation Collaboration Oral communication Chatbots Security Older adults

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An Architecture of Adaptive Cognitive Digital Twins for Resilient Healthcare Infrastructures and Services 2nd

An Architecture of Adaptive Cognitive Digital Twins for Re...

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2nd International Workshop on Secure and Resilient Digital Transformation of Healthcare, SUNRISE 2024

作者： Xu, Shouhuai Pirbhulal, Sandeep Abie, Habtamu Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO80918 United States Norwegian Computing Center/Norsk Regnesentral P.O. Box 114 Blindern OsloN-0314 Norway

ISBN: (纸本)9783031855573

Modern healthcare infrastructures and services are dependent on advanced data analytics, sensing and communication technologies that include 5G/6G networks, Artificial intelligence (AI), Internet of Medical Things (IoMT), Information Technology (IT), and Operational Technology (OT). This integration incurs a large vulnerability surface and cyber attackers can exploit those vulnerabilities to wage successful attacks against modern healthcare infrastructures and services. Therefore, identifying potential vulnerabilities in healthcare infrastructures and services and securing end-to-end monitoring of sensitive healthcare infrastructures and services are crucial for achieving resilient healthcare infrastructures and services. In this paper, we propose an architecture designed to enhance the resilience of healthcare infrastructures and services. This architecture is centered around the concept of Adaptive Cognitive Digital Twins (ACDTs), which are capable of orchestrating adaptive defenses to proactively respond to anticipated cyber attacks. Our architecture has seven layers. We detail the functions at each layer of the architecture to guide the design and development of mechanisms that can be employed at each layer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 5G mobile communication systems

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A preliminary study about gender gap perception in informatics studies in Spain 12

A preliminary study about gender gap perception in informati...

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12th International Conference on Virtual Campus, JICV 2022

作者： Garcia-Holgado, Alicia Gonzalez-Gonzalez, Carina S. Fernandez, Inmaculada Garcia Lopez, Lourdes Moreno Barrenechea, Edurne Rueda, Silvia Navarro, Angeles G. Martinez, Paloma Masia, Belen Navarro, Elena Fornes, Alicia Grial Research Group Universidad DeSalamanca Computer Science and Automation Department Salamanca Spain University of la Laguna Computer Engineering Department San Cristobal de La Laguna Spain Universidad de Málaga Computer Architecture Department Málaga Spain Universidad Carlos Iii de Madrid Computer Science and Engineering Department Madrid Spain Universidad Pública de Navarra Departamento de Estadística Informática y Matemáticas Pamplona Spain Universitat de València Departament of Computer Science Valencia Spain Universidad de Zaragoza Computer Science Department Zaragoza Spain Universidad de Castilla la Mancha Departament of Computing Systems Albacete Spain Computer Vision Center Universitat Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

The gender gap in STEM is an issue that affects regions and countries worldwide. Furthermore, the percentage of women in these areas depends on a range of different factors. In particular, the gender gap is critical in technology in general and, more specifically, in informatics. This problem affects not only the business sector but also society. Informatics is part of our lives;however, 50% of the world population is not represented in the teams that are developing solutions for solving society's problems. In Spain, the number of women with informatics degrees is around 15%. This work describes a case study developed by the committee of Women in Informatics at the Scientific Society of Spanish Informatics (SCIE) to analyse the perception of informatics scientists on the gender gap in informatics. © 2022 IEEE.

关键词： computers

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MetaCity: Data-driven sustainable development of complex cities

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The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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