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arXiv 2024年

作者： Te’eni, Amit Schwartzman-Nowik, Zohar Nowakowski, Marcin Horodecki, Pawel Cohen, Eliahu Faculty of Engineering The Institute of Nanotechnology and Advanced Materials Bar Ilan University Ramat Gan Israel Department of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel Faculty of Applied Physics and Mathematics Gdansk University of Technology Gdansk80-952 Poland National Quantum Information Center Gdansk80-309 Poland ObserveQ Gdansk80-890 Poland

Quantum query complexity mainly studies the number of queries needed to learn some property of a black box with high probability. A closely related question is how well an algorithm can succeed with this learning task using only a fixed number of queries. In this work, we propose measuring an algorithm’s performance using the mutual information between the output and the actual value. A key observation is that if an algorithm is only allowed to make a single query and the goal is to optimize this mutual information, then we obtain a task which is similar to a basic task of quantum communication, where one attempts to maximize the mutual information of the sender and receiver. We make this analogy precise by formally considering the oracle as a separate subsystem, whose state records the unknown oracle identity. The oracle query prepares a state, which is then measured;and the target property of the oracle plays the role of a message that should be deduced from the measurement outcome. Thus we obtain a link between the optimal single-query algorithm and minimization of the extent of quantum correlations between the oracle and the computer subsystems. We also find a lower bound on this mutual information, which is related to quantum coherence. These results extend to multiple-query non-adaptive algorithms. As a result, we gain insight into the task of finding the optimal non-adaptive algorithm that uses at most a fixed number of queries, for any oracle problem. © 2024, CC BY.

关键词： Quantum communication

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Nonlinear reconfiguration of network edges, topology and information content during an artificial learning task

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Brain Informatics 2021年第1期8卷 26页

作者： Shine, James M. Li, Mike Koyejo, Oluwasanmi Fulcher, Ben Lizier, Joseph T. Centre for Complex Systems The University of Sydney CamperdownNSW2006 Australia Brain and Mind Centre The University of Sydney CamperdownNSW2050 Australia Beckman Institute for Advanced Science and Technology University of Illinois Champaign Champaign United States Department of Computer Science University of Illinois at Urbana-Champaign Champaign United States School of Physics The University of Sydney CamperdownNSW2006 Australia Complex Systems Research Group Faculty of Engineering The University of Sydney CamperdownNSW2006 Australia

Here, we combine network neuroscience and machine learning to reveal connections between the brain’s network structure and the emerging network structure of an artificial neural network. Specifically, we train a shallow, feedforward neural network to classify hand-written digits and then used a combination of systems neuroscience and information-theoretic tools to perform ‘virtual brain analytics’ on the resultant edge weights and activity patterns of each node. We identify three distinct phases of network reconfiguration across learning, each of which are characterized by unique topological and information-theoretic signatures. Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding layers (as relevant). We also observe a process of low-dimensional category separation in the network as a function of learning. Our results offer a systems-level perspective of how artificial neural networks function—in terms of multi-stage reorganization of edge weights and activity patterns to effectively exploit the information content of input data during edge-weight training—while simultaneously enriching our understanding of the methods used by systems neuroscience. © 2021, The Author(s).

关键词： Brain

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Classification of Ecommerce Customer Reviews Using DistilBERT with Support Vector Machine

Classification of Ecommerce Customer Reviews Using DistilBER...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Supriya P Belkar Haider Mohammed Abbas K J Hemalatha P. Merlin Sheela Shilpa Ajay School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Commerce and Management Seshadripuram College Bangalore India Department of English New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Management Studies Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In the rapid evolving market, the reviews of product and services can influence the purchasing decision of a customer. Therefore, manually evaluating the reviews is the most challenging tasks to make the decision and observe the business model. The Machine Learning (ML) and Deep Learning (DL) model are introduced for the customer reviews classification. However, the existing models such as Bidirectional Encoder Representations from Transformers (BERT) suffered with computational complexity and slow interference. In this paper, distilBERT with Support Vector Machine (distilBERT-SVM) is proposed for the effective classification of ecommerce customer reviews. Initially, women's ecommerce clothing reviews dataset is considered and then preprocessed with punctuation, stop word removal and lemmatization. Then, dB model is introduced for extracting the features using lexical and transformer encoders for converting the vectors into word, segment and a positional embedding's. After that, linear layer is served as a feature extraction layer and these features are further processed into classification with SVM classifier to classify the reviews into binary labels as positive and negative reviews. From the results, the proposed distilBERT-SVM achieved better results than the existing systems in terms of accuracy, precision, recall and Fl-score of 98.2%, 98.5%, 92.3% and 94.5% respectively.

关键词： Support vector machines Accuracy Reviews Computational modeling Clothing Feature extraction Transformers Vectors Electronic commerce Videos

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Chimera state in neural network with the Proportional-Integral-Derivative coupling

arXiv

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arXiv 2024年

作者： Abdoulaye, Adamdine M. Meli, Venceslas Nguefoue Kongni, Steve J. Njougouo, Thierry Louodop, Patrick Research Unit Condensed Matter Electronics and Signal Processing University of Dschang P.O. Box 67 Dschang Cameroon MoCLiS Research Group Dschang Cameroon IMT School for Advanced Studies Lucca Italy Faculty of Computer Science naXys Institute University of Namur Namur Belgium Member of the Leibniz Association P.O. Box 60 12 03 PotsdamD-14412 Germany

This study delves into the emergence of collective behaviors within a network comprising interacting cells. Each cell integrates a fixed number of neurons governed by an activation gradient based on Hopfield’s model. The intra-cell interactions among neurons are local and directed, while inter-cell connections are facilitated through a PID (Proportional-Integral-Derivative) coupling mechanism. This coupling introduces an adaptable environmental variable, influencing the network dynamics significantly. Numerical simulations employing three neurons per cell across a network of fifty cells reveal diverse dynamics, including incoherence, coherence, synchronization, chimera states, and traveling wave. These phenomena are quantitatively assessed using statistical measures such as the order parameter, strength of incoherence, and discontinuity measure. Variations of the resistive, inductive, or capacitive couplings of the inter-cell environment are explored and their effects are analysed. Furthermore, the study identifies multistability in network dynamics, characterized by the coexistence of multiple stable states for the same set of parameters but with different initial conditions. A linear augmentation strategy is employed for its control. © 2024, CC BY-NC-SA.

关键词： Choquet integral

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advanced Urban Air Quality Control and Management Using XM and RFE Models for Enhanced Predictive Analysis

Advanced Urban Air Quality Control and Management Using XM a...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Bolisetty Sreelatha V. Bhoopathy Saravanan B Tulasirao Vattikolla Deepak Asrani S. Kaliappan Department of Electronics and Communication Engineering Geethanjali College of Engineering and Technology Medchal India Department of CSE Sree Rama Engineering College Tirupati India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Englis Malla Reddy Engineering College (A) Hyderabad India School of Information Technology Institute of Management Studies Noida India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Urban air pollution, particularly in large cities, has historically been exacerbated by construction activity, resulting in significant surges. Pollutants such as Sulphur dioxide and soot have, however, been significantly reduced in the industrialized Western world as a result of developments in abatement technologies. As a result, the attention has shifted to pollutants related with mobility, such as nitrogen oxides, organic compounds, and fine particulate matter. This work uses a machine learning approach to analyze urban air quality, with a focus on feature selection and data preparation. Data preprocessing, feature selection using Principal Component Analysis (PCA), and model training are all important steps. Normalization of Life Cycle Assessment (LCA) results was also added as a preprocessing step. The model had a maximum accuracy of 91.93% in categorizing and predicting urban air quality. This outcome was greatly influenced by the selection of important features using PCA and model refining by feature deletion. The findings highlight the efficacy of sophisticated machine learning approaches, specifically PCA and normalization, in improving air quality prediction models. The findings show that, while traditional pollutants have been significantly reduced, new concerns in urban air pollution necessitate specific solutions centered on transportation-related particles and other minute pollutants. This paper establishes a paradigm for better urban air quality management and emphasizes the value of adaptive approaches in environmental monitoring.

关键词： Life cycle assessment Training Analytical models Atmospheric measurements Atmospheric modeling Machine learning Predictive models Air pollution Pollution measurement Principal component analysis

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Artificial Intelligence-Enhanced Health Monitoring System via with Disease Prediction

Artificial Intelligence-Enhanced Health Monitoring System vi...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Aathithya J Mohammed Basil Abdulkareem Abdulnaser A. Hagar Saliha Bathoo l Dexter Woodward MaajidMohiUd Din Malik Research Officer-Medicine Siddha Central Research Institute Central Council for Research in Siddha (Ministry of Ayush) Government of India Chennai Tamil Nadu India Department of Business Administration College of Administration and Economic University of Anbar Iraq Faculty of Administrative and Computer Sciences Albaydha University Albaydha Yemen Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bangalore India Institute of Management and Rural Development Administration Sangli Maharashtra Dr. D.Y. Patil School of Allied Health Sciences Dr.D.Y Patil Vidyapeeth Pimpri Pune Maharashtra

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Internationally, coronary infection is the main source of mortality. This technique's results incorporate the capacity to anticipate earlier cardiovascular illness. The task utilizes an Arduino to control an IOT sensor (a heartbeat sensor to screen beat), and the outcomes might be seen on a successive screen. The therapeutic parameters that are also used for information preparation and testing are used to categorise the datasets. This approach uses the information preparation order technique to evaluate those parameters. with the aid of categorization and AI computations. The dataset is first separated, inspected, and screened. The data that is then accumulated is then handled in Python programming utilizing AI calculations, to be specific Choice Tree and Arbitrary Boondocks Classifier calculations. Concerning exactness, SVM (Backing Vector Machine) delivers improved results when used to analyze coronary illness. It is consequently shown that the recommended system is a solid one for foreseeing cardiovascular sickness previously. The proposed equipment and programming arrangement helps patients in the early identification of heart illness.

关键词： Support vector machines Heart Mortality Programming Solids Vectors Internet Random forests Monitoring Testing

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Resnet Based Blockchain Architecture for The Detection of Plant Leaf Disease in Agriculture Field

Resnet Based Blockchain Architecture for The Detection of Pl...

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Disruptive Technologies (ICDT), International Conference on

作者： B. Aruna Devi M. Praveen Kumar Lakshmana Phaneendra Maguluri P. Tamilselvan Department of Electronics and Communication Engineering Dr.NGP Institute of Technology Coimbatore Tamil Nadu India Department of CSE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP India Department of Management Studies Mahendra Engineering College Namakkal Tamil Nadu India

The only way to get better crop yields is to find and treat crop diseases quickly. Deep learning models diagnoses the plant diseases by looking at the leaves. A residual neural network is developed for the detection of disease in maize leaf. The leaves are collected from the available dataset, where the detection architecture is decentralized using blockchain architecture. The residual neural network with decentralized blockchain enables an optimal classification of instances. The model is implemented with improved disease detection accuracy with reduced training time in a python simulator with keras library. The results of simulation show an improved rate of classification accuracy, precision, recall land f-measure in detecting the leaf disease than the existing convolutional neural network models.

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Towards Trustworthy Dialogue Systems with advanced Out-of-Scope Intent Detection Model

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Yin, Qing Wang, Zhihua Bai, Liang Song, Yunya Xu, Dongling Yang, Xian University of Manchester Alliance Manchester Business School ManchesterM139PL United Kingdom Shanghai Institute for Advanced Study of Zhejiang University Zhejiang China Shanxi University School of Computer and Information Technology Shanxi Taiyuan China Hong Kong Baptist University Department of Interactive Media Hong Kong Imperial College London Data Science Institute SW7 2AZ United Kingdom

The growing demand for trustworthy dialogue systems emphasizes the need for consistently accurate responses to user inputs. The first step in developing a trustworthy dialogue system is detecting user inputs with the out-of-scope (OOS) intent. advanced research on OOS intent detection enhances effectiveness by using data augmentation to generate numerous artificial OOS samples from a limited set of true OOS data, modelling its distribution for training. However, data augmentation presents challenges, including higher costs, increased time, and a greater risk of overfitting. Additionally, current studies treat the OOS intent as a homogeneous category equivalent to known intents within a classification framework, overlooking the inherent diversity of OOS intents. To tackle these challenges, we introduce a novel method called Anchor-Integrated Dynamic Out-of-scope Intent Learning (AIDOIL), which integrates the selected anchor to represent the OOS intent adapting to diverse inputs dynamically. The intent representations transform the global classification problem into a matching task that determines if a user input aligns with each intent. This eliminates the necessity to augment OOS data and accommodate the diversity of OOS intents through dynamic representation learning. We conducted extensive experiments on three public dialogue datasets, demonstrating that AIDOIL achieves an average 7.21% improvement in OOS detection accuracy, while maintaining an acceptable increase in training time. © 2020 IEEE.

关键词： Anchor-Integrated Dynamic OOS Intent Learning Out-of-scope Intent Detection Trustworthy Dialogue Systems

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DEBM: Differential evolution-based block matching algorithm 2nd

DEBM: Differential evolution-based block matching algorithm

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2nd Doctoral Symposium on Intelligence Enabled Research, DoSIER 2020

作者： Dixit, Abhishek Mani, Ashish Bansal, Rohit Department of Computer Science Amity School of Engineering and Technology Amity University NoidaUttar Pradesh India Amity Innovation & Design Centre Amity University NoidaUttar Pradesh India Department of Management Studies Rajiv Gandhi Institute of Petroleum Technology Harbanshganj Amethi JaisUttar Pradesh India

ISBN: (纸本)9789811592898

Of all the motion estimation approaches, block matching approach is the most powerful and effective method for motion estimation of video sequences. In recent years nature-inspired algorithms are being used in an effective way for motion estimation. This study proposes a novel approach of motion estimation: differential evolution-based block matching algorithm for motion estimation. To showcase the effectiveness of the proposed work, we have used four well-known test video sequences. The video sequences used in the experiments have all the required characteristics like diverse resolutions, formats, and the frame count that are needed in input video sequences. The proposed approach is compared with standard block matching algorithms by considering the parameters like structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The empirical results indicate that our proposed algorithms perform better in comparison to standard block matching algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Motion estimation

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An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction

arXiv

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arXiv 2024年

作者： Jin, Zhenzhou You, Li Wang, Jue Xia, Xiang-Gen Gao, Xiqi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211100 China School of Information Science and Technology Nantong University Nantong226019 China Nantong Research Institute for Advanced Communication Technologies Nantong226019 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this context, a fundamental challenge lies in efficiently constructing the CKM based on a given wireless propagation environment. Most existing methods are based on stochastic modeling and sequence prediction, which do not fully exploit the inherent physical characteristics of the propagation environment, resulting in low accuracy and high computational complexity. To address these limitations, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form structure of the LP, we show its natural suitability for our task in designing a fast I2I mapping network. For different frequency components of LP decomposition, we design tailored networks accordingly. Besides, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Finally, experimental results demonstrate that the proposed scheme outperforms the benchmark, achieving higher reconstruction accuracy while with lower computational complexity. Moreover, the proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios. © 2024, CC BY-NC-ND.

关键词： Location

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