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arXiv 2024年

作者： Yan, Yuling Wainwright, Martin J. Institute for Data Systems and Society Massachusetts Institute of Technology United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology United States Department of Mathematics Massachusetts Institute of Technology United States

Longitudinal or panel data can be represented as a matrix with rows indexed by units and columns indexed by time. We consider inferential questions associated with the missing data version of panel data induced by staggered adoption. We propose a computationally efficient procedure for estimation, involving only simple matrix algebra and singular value decomposition, and prove non-asymptotic and high-probability bounds on its error in estimating each missing entry. By controlling proximity to a suitably scaled Gaussian variable, we develop and analyze a data-driven procedure for constructing entrywise confidence intervals with pre-specified coverage. Despite its simplicity, our procedure turns out to be instance-optimal: we prove that the width of our confidence intervals match a non-asymptotic instance-wise lower bound derived via a Bayesian Cramér-Rao argument. We illustrate the sharpness of our theoretical characterization on a variety of numerical examples. Our analysis is based on a general inferential toolbox for SVD-based algorithm applied to the matrix denoising model, which might be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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Quantum data Management in the NISQ Era: Extended Version

arXiv

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arXiv 2024年

作者： Hai, Rihan Hung, Shih-Han Coopmans, Tim Littau, Tim Geerts, Floris Web Information Systems Group Delft University of Technology Netherlands Department of Electrical Engineering National Taiwan University Taiwan QuTech Delft University of Technology Netherlands Adrem Data Lab University of Antwerp Belgium

Quantum computing has emerged as a transformative force in the evolution of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the noisy intermediate-scale quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era. © 2024, CC BY.

关键词： Qubits

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Unifying Large Language Models and knowledge Graphs: A Roadmap

arXiv

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arXiv 2023年

作者： Pan, Shirui Luo, Linhao Wang, Yufei Chen, Chen Wang, Jiapu Wu, Xindong Griffith University QLD Australia The Department of Data Science and AI Monash University Melbourne Australia The Nanyang Technological University Singapore The Faculty of Information Technology Beijing University of Technology Beijing China The Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei China The Research Center for Knowledge Engineering Zhejiang Lab Hangzhou China

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolve by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs;2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering;and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： knowledge graph

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Absolute Face Recognition System using Machine Learning Approach from Blurred Images

Absolute Face Recognition System using Machine Learning Appr...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： D. Hemavathi Utkarsh Yashwant Tambe Pao-Ann Hsiung Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur (Chennai) India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan R. O. C.

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Face Recognition is an important task in many domains to obtain the exact image from the pool of images. In general, blurred images are extremely challenging for the sensitive areas like law and order, defense, etc. Many face recognition techniques work well in normal images with various dimensions. In sensitive domains, the blurred image act as key evidence for the entire scenario. So, it is very important to find the original and accurate image from the blurred images. Representation of face, extraction of features and classification are the important steps in face recognition process. In existing models, Linear Binary Pattern (LBP) methods are used to recognize accurate images. LBP with Histogram (LBPH) is used to improve the detection performance of original images. Image sorting is done using the Point Spread Function Estimation on the blurred region and it helped to recognize faces with more accuracy. Extended Uniform Linear Binary Pattern method is used to reduce the dimensions to concentrate more on center pixels, with the use of the Viola-Jones algorithm and K-nearest neighbor (KNN) classifier for *** proposed enhanced LBP approach assisted in achieving 94.7% accuracy in recognizing human faces from blurry images.

关键词： Image recognition Accuracy Machine learning algorithms Face recognition Nearest neighbor methods Prediction algorithms Feature extraction

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A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

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Computer Modeling in engineering & Sciences 2021年第7期128卷 223-245页

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Yihua Gao Xueheng Wang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China Department of Network and Finance Agricultural Bank of China Limited Xuchang BranchXuchang461000China School of Software Pingdingshan UniversityPingdingshan467000China Department of Network and Finance Agricultural Bank of China Limited Nanyang BranchNanyang473000China

As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various ***,data on a blockchain cannot be *** a result,it is impossible to clean invalid and sensitive data and correct erroneous ***,to a certain extent,hinders the application of blockchains in supply chains and Internet of *** address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block *** RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also *** mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature *** analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.

关键词： Blockchain record verification trees multisignature DMBlockChain

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Multiclass Classification and Defect Detection of Steel Tube Using Modified YOLO 30th

Multiclass Classification and Defect Detection of Steel Tu...

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30th International Conference on Neural information Processing, ICONIP 2023

作者： G, Deepti Raj B, Prabadevi Gadekallu, Thippa Reddy Bhatia Khan, Surbhi Saraee, Mohammed School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Information Science and Engineering Jiaxing University Jiaxing314001 China Department of Data Science School of Science Engineering and Environement University of Salford Manchester United Kingdom School of science engineering and environment University of Salford Salford United Kingdom

ISBN: (纸本)9789819981830

Steel tubes are widely used in hazardous high pressure environments such as petroleum, chemicals, natural gas and shale gas. Defects in steel tubes have serious negative consequences. Using deep learning object recognition to identify and detect defects can greatly improve inspection efficiency and drive industrial automation. In this work, we use a well-known YOLOv7(You Only Look Once version7) deep learning model and propose to improve it to achieve accurate defects detection of steel tube images. First, the classification of the dataset is checked using a sequential model and AlexNet. A Coordinate Attention (CA) mechanism is then integrated into the YOLOv7 backbone network to improve the expressive power of the feature graph. Additionally, the SIoU (SCYLLA-Intersection over Union) loss function is used to speed up convergence due to class imbalance in the dataset. Experimental results show that the evaluation index of the optimized and modified YOLOv7 algorithm outperforms other models. This study demonstrates the effectiveness of using this method in improving the model’s detection performance and providing a more effective solution to steel tube defects. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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CovKG: A Covid-19 knowledge Graph for enabling multidimensional analytics on Covid-19 epidemiological data considering spatiotemporal, environmental, health, and socioeconomic aspects

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International Journal of information Management data Insights 2025年第1期5卷

作者： Deb Nath, Rudra Pratap Raihan, S.M. Shafkat Das, Tonmoy Chandro Pedersen, Torben Bach Ghose, Debasish Big Data Information and Knowledge Engineering Lab Department of Computer Science and Engineering University of Chittagong Chattogram 4331 Bangladesh Department of Computer Science Aalborg University Aalborg 9220 Denmark School of Economics Innovation and Technology Kristiania University College State Three Bergen 0153 Norway

The Covid-19 pandemic is influenced by many environmental, health, and socioeconomic aspects such as air pollution, comorbidity, occupation, etc. To better manage future pandemics, decision-makers need comprehensive data on Covid-19 mortality and morbidity. Most Covid-19 data sources focus on spatiotemporal aspects, and existing research often overlook the combined impact of multiple interconnected factors. This study introduces a Covid-19 knowledge Graph (CovKG) derived from 20 data sources, enabling multidimensional analysis of epidemiological data, including time, location, temperature, comorbidity, occupation, and others. CovKG is modeled using RDF, connected to 10,951 external resources, and semantically enriched with data Cube (QB) and QB for OLAP (QB4OLAP) vocabularies to adhere to the FAIR principles and ensure OLAP compatibility. Finally, we perform a qualitative and comparative evaluation and extract statistical insights across multiple dimensions of Covid-19 epidemiology. When assessed, CovKG answers 100% of competency queries, outperforming other data stores that only answer 39%. CovKG and its analytical interface are available at https://***/datasets/CovKG/. © 2025 The Authors

关键词： Covid-19 FAIR principles knowledge Graph Linked data Multidimensional analysis Semantic technology

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Taming "data-hungry" reinforcement learning? stability in continuous state-action spaces 24

Taming "data-hungry" reinforcement learning? stability in co...

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Proceedings of the 38th International Conference on Neural information Processing systems

作者： Yaqi Duan Martin J. Wainwright Department of Technology Operations and Statistics Stern School of Business New York University New York NY Laboratory for Information and Decision Systems Statistics and Data Science Center and Department of Electrical Engineering and Computer Science and Department of Mathematics Massachusetts Institute of Technology Cambridge MA

ISBN: (纸本)9798331314385

We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both off-line and on-line settings. Our analysis highlights two key stability properties, relating to how changes in value functions and/or policies affect the Bellman operator and occupation measures. We argue that these properties are satisfied in many continuous state-action Markov decision processes. Our analysis also offers fresh perspectives on the roles of pessimism and optimism in off-line and on-line RL.

关键词：

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Modelling Air Pollution and Traffic Congestion Problem Through Mobile Application 1st

Modelling Air Pollution and Traffic Congestion Problem Throu...

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1st International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2022

作者： Amudha, S. Shobana, J. Satheesh Kumar, M. Chitra, P. Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Information Technology K.L.N. College of Engineering Tamil Nadu Madurai India Department of Electrical and Electronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031276217

As we know, air pollution is one of the major problems in the metropolis. Due to this a lot of consequences have to be faced by all people. One of the main reasons for this are the vehicles. Most of the time people use their vehicles unnecessarily without any certain reasons. Due to the same reason another problem of traffic congestion arises. And this can be reduced if people become a bit more serious about it. They should prefer using public transport rather than personal vehicles for travelling large distances. By this the number of vehicles on the road will be turned down and it might solve the above two problems directly. So, the main approach is based on this theory itself to reduce the number of transports on the road. The solution is an application which runs on phone and keep track of the fuel used by the vehicle. A certain fuel limit shall be set based on vehicle type, its fuel consumption and other constraints. Once the limit got exceeded, fuel price shall differ and automated calculation would get you actual fuel amount. This aims at reducing the unnecessary usage of vehicles. There is a CO2 emission model in an application which can predict the amount of CO2 produced by the vehicle. The objective of the proposed work is to control the usage of vehicles based on the fuel used by a particular vehicle for a period of time. As the module has been tested with ML model for mileage prediction and CO2 emission with different algorithms and got the best accuracy of around 85%. There will be no hardware in this work, if any hardware implemented all over India then that prototype model will produce good impact in the vehicle fuel usage prediction and CO2 emissions. Hardware based projects using IOT devices, VTRS etc. is something which is very hard to implement in India as CCTV are still not installed and used properly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic congestion

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Brain computer interface

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