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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Subramanian, Malliga Chakravarthi, Bharathi Raja Shanmugavadivel, Kogilavani Pandiyan, Santhiya Kumaresan, Prasanna Kumar Palani, Balasubramanian Singh, Muskaan Raja, Sandhiya Vanaja Mithunajha, S. Kongu Engineering College Tamil Nadu India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland Indian Institute of Information Technology Kottayam India Ulster University United Kingdom

ISBN: (纸本)9789544520854

The rapid proliferation of fake news has emerged as a significant challenge to the credibility of online information, largely due to the swift dissemination of content on social media platforms. This article provides a concise summary of the findings of the shared task on"Fake News Detection in Dravidian Languages1 - DravidianLangTech@RANLP 2023". The aim of this shared task is to categorize social media posts as either fake or original, specifically focusing on content in Malayalam. The shared task garnered participation from 8 teams who presented their systems. These systems encompassed a spectrum of methodologies including machine learning techniques and transformer-based models like MuRIL, XLMRoBERTa, and Indic BERT. Notably, the XLMRoBERTa-based model demonstrated exceptional performance, achieving a macro F1-score of 0.90. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.

关键词： Learning systems

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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Information entropy peaks clustering using dynamic reverse nearest neighbor sequence and 3D decision graph

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Expert Systems with Applications 2025年 288卷

作者： Lu, Jianyun Chen, Zhong Shao, Junming Wu, Chunling School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Artificial Intelligence and Big Data Chongqing Polytechnic University of Electronic Technology Chongqing China School of Computer Science Peking University Beijing China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China University of Electronic Science and Technology of China China

Density Peaks Clustering has been widely studied due to its ability to detect clusters with arbitrary shapes and sizes. However, inappropriate density measures can result in the detection of multiple density peaks within a single cluster, leading to a degeneration of the availability of density peaks clustering. In this paper, we propose a new concept, information entropy peaks, which is robust to variable densities and noise and can accurately identify cluster representatives. To achieve this, we introduce a dynamic reverse nearest neighbor sequence to compute information entropy for each data point. We then develop a 3D decision graph that utilizes information entropy, Euclidean distance, and cosine distance to identify potential cluster representatives. The cosine distance is calculated based on the dynamic reverse nearest neighbor sequence and can measure distance in the direction of space. Finally, a nearest neighbor propagation algorithm is applied to form clusters, initialized with cluster representatives. Our method is effective in identifying cluster representatives and is robust to variable densities and noise. Comprehensive experiments on both synthetic and real-world datasets have demonstrated that our method produces better clustering results compared to many state-of-the-art clustering algorithms. © 2025 Elsevier Ltd

关键词： Nearest neighbor search

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Fortaleza: The emergence of a network hub

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 272-279页

作者： Bragion, Eric Akter, Habiba Kumar, Mohit Xu, Minxian Abdelmoniem, Ahmed M. Gill, Sukhpal Singh School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology National Institute of Technology Jalandhar India Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies. © 2023 The Authors

关键词： Cloud data centre Latency Network hubs Fortaleza Roubaix Networking

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A Hybrid Deep Learning Framework for Stock Price Forecasting with Sentimental Analysis 13

A Hybrid Deep Learning Framework for Stock Price Forecasting...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Banoth, Shobhan Yadala, Sucharitha Vice President-Data/Information Management Lead Engineer Citi Bank United States School of Computer Science and Engineering Vnr Vignana Jyothi Institute of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9798350380583

Social media electronic word-of-mouth data affects stock market confidence and trading. As a result, stock market sentiment research of comments is essential for stock market forecasting. Unfortunately, English is becoming the de facto language for sentiment analysis. This study translates writings from places where English is not the local language into English so that multilingual sentiment analysis can be performed. In order to predict stock market movements, this research combined social media's unstructured data with structured data, such as transaction records and technical indicators. Parameter determination has a significant impact on the effectiveness of forecasting models;in recent years, deep-learning(DL) and machine-learning(ML) models have developed as effective means of dealing with anticipating issues. In order to anticipate stock market indices and business stock prices using hybrid data in places where English is not the native language, this research utilized Long-Short-Term Memory(LSTM) models that utilized the geneticalgorithm(GA) to choose parameters. The created LSTMGA model outperformed the other ML-strategies with different types of data in terms of accuracy when it came to generating forecasts using hybrid multilingual sentiment data. As a result, the stock market forecasting method that combines the suggested LSTMGA model with hybrid sentiment analysis is both practical and encouraging. © 2024 IEEE.

关键词： Marketplaces

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Studying the Travel Direction Patterns in Urban Mobility: A Case Study of Taxis 20

Studying the Travel Direction Patterns in Urban Mobility: A ...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Weipeng, Nie Zetao, Li Zhidan, Zhao Shimin, Cai Compleχ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Complexity Computation Laboratory School of Engineering Shantou University Department of Computer Science Shantou515063 China Shantou515063 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Travel direction is important information in urban mobility and reflects the trend of urban travel. Understanding travel direction patterns in urban mobility is crucial for urban planning, transportation management, and resource allocation. Through analyzing the travel direction distribution and direction entropy of three taxi trip datasets from Chengdu, New York City and Beijing, this research examines the relationship between travel behavior and city structure by analyzing travel direction patterns in urban mobility. The analysis reveals that travel direction distributions are influenced by city morphology, indicating a connection with urban shape and temporal factors. Moreover, spatial heterogeneity in travel direction entropy is across different city regions, highlighting varying levels of mobility chaos. Additionally, the analysis reveals temporal variations in travel direction entropy during weekdays, and weekends, reflecting temporal regularities in mobility. These findings contribute to a better understanding of population movement dynamics and inform urban planning and transportation management strategies. © 2023 IEEE.

关键词： Entropy

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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Big data Mining and analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

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Overview of the Second Shared Task on Fake News Detection in Dravidian Languages: DravidianLangTech@EACL 2024 4

Overview of the Second Shared Task on Fake News Detection in...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Subramanian, Malliga Chakravarthi, Bharathi Raja Shanmugavadivel, Kogilavani Pandiyan, Santhiya Kumaresan, Prasanna Kumar Palani, Balasubramanian Premjith, B. Vanaja Mithunajha, S. Devika, K. Hariprasath, S.B. Haripriya, B. Vigneshwar, E. Kongu Engineering College Tamil Nadu India School of Computer Science University of Galway Ireland Data Science Institute University of Galway Ireland National Institute of Technology Tamil Nadu India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798891760783

The rise of online social media has revolutionized communication, offering users a convenient way to share information and stay updated on current events. However, this surge in connectivity has also led to the proliferation of misinformation, commonly known as fake news. This misleading content, often disguised as legitimate news, poses a significant challenge as it can distort public perception and erode trust in reliable sources. This shared task consists of two subtasks such as task 1 and task 2. Task 1 aims to classify a given social media text into original or fake. The goal of the FakeDetect-Malayalam task2 is to encourage participants to develop effective models capable of accurately detecting and classifying fake news articles in the Malayalam language into different categories like False, Half True, Mostly False, Partly False, and Mostly True. For this shared task, 33 participants submitted their results. © 2024 Association for Computational Linguistics.

关键词： Fake detection

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Cooperative Multi-source data Trading

Cooperative Multi-source Data Trading

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Cheng, Jin Ding, Ningning Lui, John C. S. Huang, Jianwei The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Hong Kong University of Science and Technology Data Science and Analytics Thrust Information Hub Guangzhou China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen China

ISBN: (纸本)9798350351255

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers' cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data's utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers' revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller's profit by up to 46.32% compared to traditional data trading methods in the current data market. © 2024 IEEE.

关键词： Marketplaces

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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