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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Marc Wanner Laura Lewis Chiranjib Bhattacharyya Devdatt Dubhashi Alexandru Gheorghiu Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Computer Science and Automation Indian Institute of Science Bangalore India

ISBN: (纸本)9798331314385

A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, Huang et al. in [1], introduced an approach for learning properties of the ground state of an n-qubit gapped local Hamiltonian H from only nO(1) data points sampled from Hamiltonians in the same phase of matter. This was subsequently improved by Lewis et al. in [2], to O(log n) samples when the geometry of the n-qubit system is known. In this work, we introduce two approaches that achieve a constant sample complexity, independent of system size n, for learning ground state properties. Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known in advance. Our second algorithm, which applies even if a description of the property is not known, is a deep neural network model. While empirical results showing the performance of neural networks have been demonstrated, to our knowledge, this is the first rigorous sample complexity bound on a neural network model for predicting ground state properties. We also perform numerical experiments on systems of up to 45 qubits that confirm the improved scaling of our approach compared to [1, 2].

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An Almost Singularly Optimal Asynchronous Distributed MST Algorithm 36

An Almost Singularly Optimal Asynchronous Distributed MST Al...

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36th International Symposium on Distributed Computing, DISC 2022

作者： Dufoulon, Fabien Kutten, Shay Moses, William K. Pandurangan, Gopal Peleg, David Department of Computer Science University of Houston HoustonTX United States Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9783959772556

A singularly (near) optimal distributed algorithm is one that is (near) optimal in two criteria, namely, its time and message complexities. For synchronous CONGEST networks, such algorithms are known for fundamental distributed computing problems such as leader election [Kutten et al., JACM 2015] and Minimum Spanning Tree (MST) construction [Pandurangan et al., STOC 2017, Elkin, PODC 2017]. However, it is open whether a singularly (near) optimal bound can be obtained for the MST construction problem in general asynchronous CONGEST networks. In this paper, we present a randomized distributed MST algorithm that, with high probability, computes an MST in asynchronous CONGEST networks and takes Õ(D1+Ε + √n) time and Õ(m) messages1, where n is the number of nodes, m the number of edges, D is the diameter of the network, and Ε > 0 is an arbitrarily small constant (both time and message bounds hold with high probability). Since (Equation presented)(D + √n) and Ω(m) are respective time and message lower bounds for distributed MST construction in the standard KT0 model, our algorithm is message optimal (up to a polylog(n) factor) and almost time optimal (except for a DΕ factor). Our result answers an open question raised in Mashregi and King [DISC 2019] by giving the first known asynchronous MST algorithm that has sublinear time (for all D = O(n1-Ε)) and uses Õ(m) messages. Using a result of Mashregi and King [DISC 2019], this also yields the first asynchronous MST algorithm that is sublinear in both time and messages in the KT1 CONGEST model. A key tool in our algorithm is the construction of a low diameter rooted spanning tree in asynchronous CONGEST that has depth Õ(D1+Ε) (for an arbitrarily small constant Ε > 0) in Õ(D1+Ε) time and Õ(m) messages. To the best of our knowledge, this is the first such construction that is almost singularly optimal in the asynchronous setting. This tree construction may be of independent interest as it can also be used for efficiently perfor

关键词： Distributed computer systems

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An Adaptive Streaming Feature Selection Technique for Classifying Non-Stationary Data Streams 7

An Adaptive Streaming Feature Selection Technique for Classi...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Arya, Monika Dewangan, Bhupesh Kumar Choudhury, Tanupriya Kotecha, Ketan Dewangan, Sanjana Gaidhane, Akhilesh Bhilai Institute of Technology Department of Computer Science and Engineering Durg India Op Jindal University Department of Computer Science and Engineering Raigarh496109 India Pune411045 India Symbiosis Centre for Applied Artificial Intelligence Pune411045 India School of Science Op Jindal University Raigarh India Sages M.G. Ward Department of Mathematics Kondagaon India

ISBN: (纸本)9798350342154

In today's digital era, various real-world applications generate data in streams, and these data streams are of two types stationary data streams, which are static, and non-stationary data streams that are dynamic in nature. Non-stationary data streams are pro-to-concept drifts. To overcome this issue, an adaptable streaming feature selection technique for non-stationary data streams is proposed in this paper. The selection of relevant feature space from non-stationary data streams is crucial for enabling intelligence through various data mining tasks, specifically using data classification. Feature selection improves the overall performance of the classifier while increasing the computational overhead. The proposed approach aims to enable streaming feature selection whenever the classification accuracy is underperforming to enhance the classification accuracy, while the model remains stable if there is no change in the concepts. The proposed approach is adaptive and ameliorates the classifier performance by reducing the error rate during changes in the concept with time. This adaptability to concept drift improves the accuracy by approximately 16% and reduces the computation time by 50%. The proposed model avoids overfitting of the model. © 2023 IEEE.

关键词： Feature Selection

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Leveraging Semantic Parsing using Text Embeddings and Reinforcement Learning

Leveraging Semantic Parsing using Text Embeddings and Reinfo...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Jessenia Piza-Londoño Edgar J. Andrade-Lotero Applied Mathematics and Computer Science Universidad del Rosario Bogotá Colombia School of Engineering Science and Technology Universidad del Rosario Bogotá Colombia

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Representing natural language information is a key challenge in Artificial Intelligence and Cognitive science, requiring the transformation of unstructured data into formats suitable for computational tasks. While logical formalisms offer robust methods for information representation, their complexity often limits widespread adoption. Conversely, transformer architectures provide strong generalization capabilities but struggle with logical inference tasks. To address both the need for generalization and reliable logical inference, we propose a novel approach using deep reinforcement learning, enabling agents to autonomously learn the rules of semantic parsing. Our preliminary results indicate successful generation of appropriate representations for simple queries. Future work will extend the environment to handle a wider range of real-world sentences.

关键词： Translation Semantics computer architecture Information representation Linguistics Transformers Stability analysis Complexity theory Reliability Thermal stability

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Predicting the Geometric Location of Critical Edges in Adaptive GDSW Overlapping Domain Decomposition Methods Using Deep Learning 26th

Predicting the Geometric Location of Critical Edges in Adapt...

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26th International Conference on Domain Decomposition Methods, 2020

作者： Heinlein, Alexander Klawonn, Axel Lanser, Martin Weber, Janine Faculty of Electrical Engineering Mathematics & Computer Science Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 CD Delft72628 Netherlands Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Köln50931 Germany Center for Data and Simulation Science University of Cologne Köln Germany

ISBN: (纸本)9783030950248

For complex model problems with coefficient or material distributions with large jumps along or across the domain decomposition interface, the convergence rate of classic domain decomposition methods for scalar elliptic problems usually deteriorates. In particular, the classic condition number bounds [1, 12] will depend on the contrast of the coefficient function. As a remedy, different adaptive coarse spaces, e.g. [4, 13], have been developed which are obtained by solving certain generalized eigenvalue problems on local parts of the interface, i.e., edges and/or faces. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Domain decomposition methods

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An Artificial Immune System Algorithm for the Permutation Flow Shop Scheduling Problem with Sequence Dependent Setup Times 10

An Artificial Immune System Algorithm for the Permutation Fl...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Mesmar, Khadija Lebbar, Maria Aqil, Said Asebriy, Zahra Allali, Karam National School of Mines of Rabat Rosdm Research Team Lmaid Laboratory of Applied Mathematics and Business Intelligence Rabat Morocco Ensam Casablanca Hassan Ii University Laboratory Artificial Intelligence and Complex Systems Engineering Casablanca Morocco Est Essaouira Cadi Ayyad University Laboratory mathematics Computer sciences and modeling of complex systems Essaouira Morocco Fst Mohammadia Hassan Ii University Laboratory of Mathematics Computer Science and Applications Mohammadia Morocco

ISBN: (纸本)9798350387353

This paper is concerned with a set of independent jobs in a permutation flow shop that have setup times on a set of machines. The M-IAIS algorithm, which is based on immunoglobulins and has been modified, is designed to reduce the makespan. A unique procedure known as B cell repertoire updating, which comprises size C of jobs that are taken out of the sequence and reinserted into all possible positions of the remaining sequences of jobs, has an impact on the M-IAIS algorithm's performance. To increase the convergence velocity, three iterations of the algorithm are suggested. Thorough testing was done, and the three versions' performances on a set (benchmark) are compared. Problem instances are described in Sistemas de Optimizacion Aplicada (http://***/problem-instances) In the paper, percentage deviation is considered to evaluate the performance of the proposed algorithms. Furthermore, the ensuing outcomes demonstrate its suitability for addressing for the Permutation Flow Shop Scheduling Problem with Sequence Dependent Setup Times. © 2024 IEEE.

关键词： Scheduling algorithms

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Certifying IoT Data with Verifiable Credentials

Certifying IoT Data with Verifiable Credentials

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IEEE International Symposium on Network Computing and Applications

作者： Carlo Mazzocca Stefano Allevi Rebecca Montanari Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Salerno Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

The Internet of Things (IoT) is a major contributor to the vast amount of data generated worldwide, significantly impacting the big data market. However, this data holds value only when utilized for insights and applications. Many organizations hesitate to use third-party data due to concerns about accuracy, reliability, and integrity. Enhancing trustworthiness in data is crucial to unlock their full potential, especially in critical domains such as healthcare where erroneous data can have severe consequences. The Identity of Things (IDoT) paradigm addresses this need by identifying trustworthy devices using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), offering promising solutions for digital identification. Although DIDs and VCs have primarily been used for mutual trust and access control, their potential for data certification in IoT remains underexplored. This paper is the first to investigate the feasibility of IoT devices to use VCs for certifying data. We evaluated devices with varying capabilities, focusing on the latency, computing, and storage requirements for issuing VCs. Our findings demonstrate that IoT devices can issue VCs with up to 100 claims in less than 90 ms, with storage requirements growing linearly and remaining below 4 KB. Using VCs for data certification does not introduce significant computational overhead, suggesting its practicality for IoT environments.

关键词： Accuracy Focusing Organizations Medical services Big Data Internet of Things Object recognition Stakeholders Reliability Certification

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A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles 41

A Universal Transfer Theorem for Convex Optimization Algorit...

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41st International Conference on Machine Learning, ICML 2024

作者： Kerger, Phillip Molinaro, Marco Jiang, Hongyi Basu, Amitabh Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore United States Microsoft Research Redmond United States Department of Computer Science PUC-Rio Rio de Janeiro Brazil Department of Civil and Environmental Engineering Cornell University Ithaca United States

Given any algorithm for convex optimization that uses exact first-order information (i.e., function values and subgradients), we show how to use such an algorithm to solve the problem with access to inexact first-order information. This is done in a "black-box" manner without knowledge of the internal workings of the algorithm. This complements previous work that consider the performance of specific algorithms like (accelerated) gradient descent with inexact information. In particular, our results apply to a wider range of algorithms beyond variants of gradient descent, e.g., projection-free methods, cutting-plane methods, or any other first-order methods formulated in the future. Further, they also apply to algorithms that handle structured nonconvexities like mixed-integer decision variables. Copyright 2024 by the author(s)

关键词： Optimization algorithms

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Investigation of Credit Card Fraud detection under the lens of Comparative Machine Learning models

Investigation of Credit Card Fraud detection under the lens ...

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2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Tahsin, Mohammad Sadman Firoz, Neda Chowdhury, Shah Abul Hasnat Wasei, S.M.A. Karim, Musaddiq Al Ara, Mst Yeasmin Brac University Department of Computer Science and Engineering Dhaka Bangladesh Tomsk State University Institute of Applied Mathematics and Computer Science Tomsk Russia American International University-Bangladesh Dept. of Electrical and Electronics Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Fraudulent use of credit cards is common and may result in significant financial losses. Using Trojans or Phishing, criminals may get the credit card numbers of innocent victims for their criminal acts. As a result, it is vital to have fraud detection technology that can catch a thief in the act of purchasing using a stolen credit card. One solution is to utilize machine learning algorithms to construct normal/fraud behavior characteristics from all historical transaction data, including legitimate and fraudulent ones, and then use these features to identify whether a transaction is fraudulent or not. In this paper, SVC, Multinomial Naïve Bayes, K-Neighbors Classifier, Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM algorithms were used for credit card fraud detection. The detailed results of all seven algorithms were studied. Then a comparison between these seven algorithms with their precise accuracy, ROC curve, train-test, validation scores, and the learning curve was discussed. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM showed 100% accuracy in training dataset. While Random Forest algorithm outdid every other algorithm by scoring 100% for both train and test data, the result of the remaining algorithms for the train and test data were also satisfactorily ranging from 75.3% to 99.6%. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM all 4 scored 100% in both train and test data, and Random Forest scored 100% leaving behind all the algorithms. Logistic Regression, Bernoulli Naïve Bayes and SVM scored 99.6% in test. The best-performing algorithm was selected according to its performance and discussed its AUC score and confusion matrix. © 2022 IEEE.

关键词： Crime

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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