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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Saha, Sourav Roy, Dwaipayan Goud, B. Yuvaraj Reddy, Chethan S. Basu, Tanmay Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computational and Data Sciences Indian Institute of Science Education and Research Kolkata India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

CLEF SimpleText 2022 lab focuses on developing effective systems to identify relevant passages from a given set of scientific articles. The lab has organized three tasks this year. Task 1 is focused on passage retrieval from the given data for a query text. These passages can be complex and hence require further simplification to be carried out in tasks 2 and 3. The BioNLP research group at the Indian Institute of science Education and Research Bhopal (IISERB) in collaboration with two different information retrieval research groups at IISER Kolkata and ISI Kolkata participated only in Task 1 of this challenge and submitted three runs using three different retrieval models. The paper explores the performance of these retrieval models for the given task. We used a standard BM25 model as our first run to identify 1000 relevant passages for each query. Moreover, the passages for each query were ranked based on their similarity scores generated by the BM25 model. For our second run, we used a BERT (Bidirectional Encoder Representations from Transformers) based re-ranking method, called as Mono-BERT to further rank the 1000 passages retrieved by our first run for each query. A pre-trained sequence to sequence model based re-ranking method, called MonoT5 was used as our third run to reorder the 1000 passages retrieved by the Mono-BERT model for each query. As the official results of this task are not yet announced, we cannot explore the performance of our submissions. However, we have manually checked the retrieved results of many queries for each run, which indicate that the performance improved from run 1 to run 2 and further to run 3. © 2022 Copyright for this paper by its authors.

关键词： Information retrieval

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Extensive Analysis of IoT Assisted Fake Currency Detection using Novel Learning Scheme 2

Extensive Analysis of IoT Assisted Fake Currency Detection u...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Sundravadivelu, K. Senthilvel, P. Gururama Duraimutharasan, N. Esther T, Hannah Rose Kumar. K, Rajesh Madurai Kamaraj University School of Information Technology Department of Computer Science Tamil Nadu Madurai625021 India Saveetha School of Engineering Department of Computer Science and Engineering Chennai602 105 India Amet University Department of Information Technology Tamil Nadu Kanathur603112 India Velammal Engineering College Department of Artificial Intelligence and Data Science Chennai600066 India Chendu College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurantakam603311 India

ISBN: (纸本)9798350325799

A person with normal vision can readily read and differentiate between different banknotes, while a person with visual impairment or blindness would have a far more difficult time doing the same. Any person who is blind or visually impaired must have the ability to recognize and identify banknotes in real time since money is so central to our daily lives and is necessary for any business transaction. To do this, deep learning systems were integrated with the Internet of Things (IoT) model. In particular, this study has investigated the feasibility of applying pre-trained deep learning models, namely CNN and CNNXGB for currency categorization and fake currency detection. Pre-trained deep learning models perform well as they require less data to run when compared to newly-trained models. After testing the method on about 4002 photos representing four different denominations of Indian rupees (10, 50, 100, and 500), this study found that the proposed method has performed well. Deep Learning (DL) has recently shown remarkable performance in solving the image classification challenges. Several performance metrics are analyzed to determine the accuracy of the proposed method. The experimental findings result in a training accuracy of 97.12% and a validation accuracy of 96.34%. © 2023 IEEE.

关键词： Internet of things

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Semantic/Bit Communication Based Networking Problem Optimization

Hybrid Semantic/Bit Communication Based Networking Problem O...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Zhang, Lei Imran, Muhammad Ali University of Glasgow James Watt School of Engineering Glasgow United Kingdom Nanyang Technological University College of Computing and Data Science Singapore Clemson University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350351255

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks. © 2024 IEEE.

关键词： Frequency allocation

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology China Department of Computer Science Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus’ ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Automation Scientific computing Instruments Laboratories Process control computer architecture Organizations Dynamic scheduling Fabrics Reliability

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LexC-Gen: Generating data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons

arXiv

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arXiv 2024年

作者： Yong, Zheng-Xin Menghini, Cristina Bach, Stephen H. Department of Computer Science Brown University United States Data Science Institute Brown University United States

data scarcity in low-resource languages can be addressed with word-to-word translations from labeled task data in high-resource languages using bilingual lexicons. However, bilingual lexicons often have limited lexical overlap with task data, which results in poor translation coverage and lexicon utilization. We propose lexicon-conditioned data generation (LexC-Gen), a method that generates low-resource-language classification task data at scale. Specifically, LexC-Gen first uses high-resource-language words from bilingual lexicons to generate lexicon-compatible task data, and then it translates them into low-resource languages with bilingual lexicons via word translation. Across 17 extremely low-resource languages, LexC-Gen generated data is competitive with expert-translated gold data, and yields on average 5.6 and 8.9 points improvement over existing lexicon-based word translation methods on sentiment analysis and topic classification tasks respectively. Through ablation study, we show that conditioning on bilingual lexicons is the key component of LexC-Gen. LexC-Gen serves as a potential solution to close the performance gap between open-source multilingual models such as BLOOMZ and state-of-the-art commercial models like GPT-4o on low-resource-language tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： data assimilation

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Task Allocation in Heterogeneous Multi-Robot Systems Based on Preference-Driven Hedonic Game

Task Allocation in Heterogeneous Multi-Robot Systems Based o...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Liwang Zhang Minglong Li Wenjing Yang Shaowu Yang Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Multiple preferences between robots and tasks have been largely overlooked in previous research on Multi-Robot Task Allocation (MRTA) problems. In this paper, we propose a preference-driven approach based on hedonic game to address the task allocation problem of muti-robot systems in emergency rescue scenarios. We present a distributed framework considering various preferences between robots and tasks to determine the division of coalitions in such problems and evaluate the scalability and adaptability of our algorithm through relevant experiments. Furthermore, considering the strict communication limitations in emergency rescue scenarios, we have verified that our algorithm can efficiently converge to a Nash-stable coalition partition even in conditions of insufficient communication distance.

关键词： Scalability Games Robustness Partitioning algorithms Resource management Multi-robot systems Task analysis

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Yoga Pose Classification and Correction using PoseNet

Yoga Pose Classification and Correction using PoseNet

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Aditya Potdar Jay Bhanushali Sarvagya Singh Kanchan Dabre Department of Computer Science & Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

Yoga is practiced by people throughout. Many people are participating on their own, either by coaching one another or watching TV or videos. Beginners may find it difficult to recognize the erroneous components of their yoga poses on their own, though. A position correction approach is proposed for assessing yoga poses to support self-learning yoga. KNN is utilized to identify the yoga position, and the PoseNet model is used to detect body points. In order to initially recognize a yoga stance in many circumstances, including brightness level and distance of the yoga practitioner from the camera, the system uses multi-part detection using merely a camera. After that, it establishes how dissimilar the poses of an instructor and a user are. The model was successful in classifying the yoga pose with an overall accuracy of 99.6% in classifying the yoga pose even through a lightweight PoseNet model. The PoseNet model also helped in correcting that yoga pose through angle calculation.

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Online Generalized Method of Moments for Time Series

arXiv

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arXiv 2025年

作者： Leung, Man Fung Chan, Kin Wai Shao, Xiaofeng Department of Statistics University of Illinois at Urbana-Champaign United States Department of Statistics The Chinese University of Hong Kong Hong Kong Department of Statistics and Data Science Department of Economics Washington University in St. Louis United States

Online learning has gained popularity in recent years due to the urgent need to analyse large-scale streaming data, which can be collected in perpetuity and serially dependent. This motivates us to develop the online generalized method of moments (OGMM), an explicitly updated estimation and inference framework in the time series setting. The OGMM inherits many properties of offline GMM, such as its broad applicability to many problems in econometrics and statistics, natural accommodation for over-identification, and achievement of semiparametric efficiency under temporal dependence. As an online method, the key gain relative to offline GMM is the vast improvement in time complexity and memory requirement. Building on the OGMM framework, we propose improved versions of online Sargan–Hansen and structural stability tests following recent work in econometrics and statistics. Through Monte Carlo simulations, we observe encouraging finite-sample performance in online instrumental variables regression, online overidentifying restrictions test, online quantile regression, and online anomaly detection. Interesting applications of OGMM to stochastic volatility modelling and inertial sensor calibration are presented to demonstrate the effectiveness of *** Codes 62L12, 62M15 (Primary) 62J20 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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A permutation-free kernel two-sample test 22

A permutation-free kernel two-sample test

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shubhanshu Shekhar Ilmun Kim Aaditya Ramdas Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA Department of Statistics and Data Science Department of Applied Statistics Yonsei University Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University

ISBN: (纸本)9781713871088

The kernel Maximum Mean Discrepancy (MMD) is a popular multivariate distance metric between distributions that has found utility in two-sample testing. The usual kernel-MMD test statistic is a degenerate U-statistic under the null, and thus it has an intractable limiting distribution. Hence, to design a level-α test, one usually selects the rejection threshold as the (1 — α)-quantile of the permutation distribution. The resulting nonparametric test has finite-sample validity but suffers from large computational cost, since every permutation takes quadratic time. We propose the cross-MMD, a new quadratic-time MMD test statistic based on sample-splitting and studentization. We prove that under mild assumptions, the cross-MMD has a limiting standard Gaussian distribution under the null. Importantly, we also show that the resulting test is consistent against any fixed alternative, and when using the Gaussian kernel, it has minimax rate-optimal power against local alternatives. For large sample sizes, our new cross-MMD provides a significant speedup over the MMD, for only a slight loss in power.

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