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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Karan, Mladen Vulić, Ivan Korhonen, Anna Glavaš, Goran TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Language Technology Lab TAL University of Cambridge Data and Web Science Group University of Mannheim

ISBN: (纸本)9781952148255

Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present CLASSYMAP, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs. Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process. We show the benefits of CLASSYMAP for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs. © 2020 Association for Computational Linguistics

关键词： Iterative methods

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Acoustic XR Technology Merging Real and Virtual Sounds

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NTT Technical Review 2024年第6期22卷 30-34页

作者： Noguchi, Kenichi Chiba, Hironobu Kako, Tatsuya Kozuka, Shihori Kurokawa, Yoshiaki Watanabe, Yuki Nakayama, Akira Ultra-Reality Computing Group NTT Computer and Data Science Laboratories

With the spread of open-ear earphones that do not cover the ear, new listening experiences are being proposed that combines real ambient sounds with virtual sounds heard from earphones. At NTT, we call this merging of the real and virtual sounds through open-ear earphones "acoustic XR (extended reality) technology," which we are now developing with a view to actual services. In this article, we describe this technology with a focus on actual trials and touch upon future developments. © 2024 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Merging

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Descriptive AI ethics: Collecting and understanding the public opinion

arXiv

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arXiv 2021年

作者： Lima, Gabriel Cha, Meeyoung School of Computing KAIST and Data Science Group IBS Korea Republic of Data Science Group IBS and School of Computing KAIST Korea Republic of

There is a growing need for data-driven research efforts on how the public perceives the ethical, moral, and legal issues of autonomous AI systems. The current debate on the responsibility gap posed by these systems is one such example. This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other, by aiding scholarly discussion with data gathered from the public. We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems’ deployment. Copyright © 2021, The Authors. All rights reserved.

关键词： Social aspects

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A Bias-Free Revenue-Maximizing Bidding Strategy for data Consumers in Auction-based Federated Learning 33

A Bias-Free Revenue-Maximizing Bidding Strategy for Data Con...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Tang, Xiaoli Yu, Han Li, Zengxiang Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore ENN Group Beijing China Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (纸本)9781956792041

Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leading to biased estimates by overlooking information from losing bids. To address these issues, we propose a Bias-free Revenue-maximizing Federated bidding strategy for DCs in AFL (BR-FEDBIDDER). Our theoretical exploration of the relationships between Return on Investment (ROI), bid costs, and utility, and their impact on overall revenue underscores the complexity of maximizing revenue solely by prioritizing ROI enhancement. Leveraging these insights, BR-FEDBIDDER optimizes bid costs with any given ROI constraint. In addition, we incorporate an auxiliary task of winning probability estimation into the framework to achieve bias-free learning by leveraging bid records from historical bid requests, including both winning and losing ones. Extensive experiments on six widely used benchmark datasets show that BR-FEDBIDDER outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 5.66%, 6.08% and 2.44% in terms of the total revenue, ROI, and test accuracy of the resulting FL models, respectively. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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DBMiner: A System for Mining Knowledge in Large Relational databases 2

DBMiner: A System for Mining Knowledge in Large Relational D...

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2nd International Conference on Knowledge Discovery and data Mining, KDD 1996

作者： Han, Jiawei Fu, Yongjian Wang, Wei Chiang, Jenny Gong, Wan Koperski, Krzysztof Li, Deyi Lu, Yijun Rajan, Amynmohamed Stefanovic, Nebojsa Xia, Betty Zaiane, Osmar R. Data Mining Research Group School of Computing Science Simon Fraser University British ColumbiaV5A 1S6 Canada

ISBN: (纸本)1577350049

A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational *** system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and *** incorporating several interesting data mining techniques, including attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive data mining environment with good performance. © 1996 AAAI (***). All Rights Reserved.

关键词： data mining

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Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

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Computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad Zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

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Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity 23

Transformer as a hippocampal memory consolidation model base...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Dong-Kyum Kim Jea Kwon Meeyoung Cha C. Justin Lee IBS Data Science Group IBS Center for Cognition and Sociality IBS Data Science Group and KAIST School of Computing

The hippocampus plays a critical role in learning, memory, and spatial representation, processes that depend on the NMDA receptor (NMDAR). Inspired by recent findings that compare deep learning models to the hippocampus, we propose a new nonlinear activation function that mimics NMDAR dynamics. NMDAR-like nonlinearity shifts short-term working memory into long-term reference memory in transformers, thus enhancing a process that is similar to memory consolidation in the mammalian brain. We design a navigation task assessing these two memory functions and show that manipulating the activation function (i.e., mimicking the Mg2+-gating of NMDAR) disrupts long-term memory processes. Our experiments suggest that place cell-like functions and reference memory reside in the feed-forward network layer of transformers and that nonlinearity drives these processes. We discuss the role of NMDAR-like nonlinearity in establishing this striking resemblance between transformer architecture and hippocampal spatial representation.

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Parallel computing TEDA for high frequency streaming data clustering 2nd

Parallel computing TEDA for high frequency streaming data cl...

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2nd International Neural Network Society Conference on Big data, INNS 2016

作者： Gu, Xiaowei Angelov, Plamen P. Gutierrez, German Iglesias, Jose Antonio Sanchis, Araceli Data Science Group School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom CAOS Research Group Computer Science Department Carlos III University of Madrid Leganes28911 Spain

ISBN: (纸本)9783319478975

In this paper, a novel online clustering approach called Parallel TEDA is introduced for processing high frequency streaming data. This newly proposed approach is developed within the recently introduced TEDA theory and inherits all advantages from it. In the proposed approach, a number of data stream processors are involved, which collaborate with each other efficiently to achieve parallel computation as well as a much higher processing speed. A fusion center is involved to gather the key information from the processors which work on chunks of the whole data stream and generate the overall output. The quality of the generated clusters is being monitored within the data processors all the time and stale clusters are being removed to ensure the correctness and timeliness of the overall clustering results. This, in turn, gives the proposed approach a stronger ability of handling shifts/drifts that may take place in live data streams. The numerical experiments performed with the proposed new approach Parallel TEDA on benchmark datasets present higher performance and faster processing speed when compared with the alternative well-known approaches. The processing speed has been demonstrated to fall exponentially with more data processors involved. This new online clustering approach is very suitable and promising for real-time high frequency streaming processing and data analytics. © Springer International Publishing AG 2017.

关键词： Clustering algorithms

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Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

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High-Confidence computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

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Collaboration based multi-label propagation for fraud detection 29

Collaboration based multi-label propagation for fraud detect...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Haobo Li, Zhao Huang, Jiaming Hui, Pengrui Liu, Weiwei Hu, Tianlei Chen, Gang Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang University China Alibaba Group Hangzhou China School of Computer Science Wuhan University China

ISBN: (纸本)9780999241165

Detecting fraud users, who fraudulently promote certain target items, is a challenging issue faced by e-commerce platforms. Generally, many fraud users have different spam behaviors simultaneously, e.g. spam transactions, clicks, reviews and so on. Existing solutions have two main limitations: 1) the correlations among multiple spam behaviors are neglected;2) large-scale computations are intractable when dealing with an enormous user set. To remedy these problems, this work proposes a collaboration based multi-label propagation (CMLP) algorithm. We first introduce a generic version that involves collaboration technique to exploit label correlations. Specifically, it breaks the final prediction into two parts: 1) its own prediction part;2) the prediction of others, i.e. collaborative part. Then, to accelerate it on large-scale e-commerce data, we propose a heterogeneous graph based variant that detects communities on the user-item graph directly. Both theoretical analysis and empirical results clearly validate the effectiveness and scalability of our proposals. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Forecasting

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