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arXiv 2024年

作者： Li, Guanzhong Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Shenzhen518045 China

The original Grover's algorithm suffers from the souffle problem, which means that the success probability of quantum search decreases dramatically if the iteration time is too small or too large from the right time. To overcome the souffle problem, the fixed-point quantum search with an optimal number of queries was proposed [Phys. Rev. Lett. 113, 210501 (2014)], which always finds a marked state with a high probability when a lower bound of the proportion of marked states is given. The fixed-point quantum search relies on a key lemma regarding the explicit formula of recursive quasi-Chebyshev polynomials, but its proof is not given explicitly. In this work, we give a detailed proof of this lemma, thus providing a sound foundation for the correctness of the fixed-point quantum search. This lemma may be of independent interest as well, since it expands the mathematical form of the recursive relation of Chebyshev polynomials of the first kind, and it also constitutes a key component in overcoming the souffle problem of quantum walk-based search algorithms, for example, robust quantum walk search on complete bipartite graphs [Phys. Rev. A 106, 052207 (2022)]. Hopefully, more applications of the lemma will be found in the future. © 2024, CC BY.

关键词： Iterative methods

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EdgeMob: A Context-Aware Dynamic Service Migration for Wearable IoT in Healthcare

EdgeMob: A Context-Aware Dynamic Service Migration for Weara...

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Computing, Internet of Things and Microwave Systems (ICCIMS), International Conference on

作者： Imran Raza Syed Asad Hussain Sana Latif Department of Computer Science COMSATS University Islamabad Lahore Lahore Pakistan Department of Software Engineering University of Central Punjab (UCP) Lahore Pakistan

ISBN: (数字)9798350351736

ISBN: (纸本)9798350351743

Edge Computing (EC) is a distributed network architecture offering computation and storage resources at the network edge, addressing cloud computing limitations by optimizing latency and enhancing data safety and privacy for real-time services. It also supports intelligent solutions for IoT-based smart healthcare systems through Deep Learning (DL). DL aids in resource allocation optimization and disease prediction. However, the surge in wearable device usage introduces challenges in user mobility and migration, leading to increased latency and service inconsistencies. To tackle this, we propose EdgeMob, an intelligent and context-aware scheme deployed as a federation of edge servers. EdgeMob utilizes Q-learning-based reinforcement learning for personalized service migration decisions based on user mobility and Quality of Service (QoS). We evaluate our system against existing solutions, considering parameters such as user-perceived latency, migration cost, and network bandwidth utilization.

关键词： Quality of service Medical services Reinforcement learning Real-time systems Safety Internet of Things Servers Resource management Wearable devices Surges

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Research on the Numerical Representation of Stock Linkage Prediction 2nd

Research on the Numerical Representation of Stock Linkage Pr...

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2nd International Symposium on Simulation and Process Modelling, ISSPM 2020

作者： Ma, Chi Wang, Shaofan Lu, Shengliang Han, Guojing School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Meteorological Administration Anshan China

ISBN: (纸本)9789813345744

In this paper, time series of numerical correlations and morphological similarities are analyzed. It is proposed to combine the correlation coefficient with a time-weighted dynamic time warping (DTW) distance to emphasize timeliness as a stock linkage numerical formula. Therefore, the problem of finding the connection relationship between stocks can be converted into a numerical representation problem of stock linkage, and a stock linkage prediction optimized model based on long short-term memory (LSTM) can be established. At the same time, in order to improve the prediction performance of the LSTM model for the time series of stock interconnection values, wavelet transform and denoising autoencoder are used to denoise and reconstruct the input samples, thereby achieving more accurate prediction and analysis of stock linkage. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Unveiling Crime Patterns and Empowering Investigative Insights Through Machine Learning and NLP

Unveiling Crime Patterns and Empowering Investigative Insigh...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Prashanth Ragam H.V. Mallesh L. Pavithra Suma L. Yasaswini M. Varun Kumar N. Siri Varsha Sankati Jyothi School of Computer Science & Engineering VIT-AP UNIVERSITY Vijayawada AP Software Development Cell (SDC) VIT-AP UNIVERSITY Vijayawada AP

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The well-being of society and public safety are severely threatened by crime. Policymakers, law enforcement agencies, and community stakeholders must comprehend the distribution of crimes, their patterns, and the variables that drive criminal activity. Stakeholders may identify regional patterns in crime by visualising state-level crime data, which facilitates the allocation of resources to high-risk locations and supports targeted actions. Furthermore, content recommendation systems provide relevant information about crimes, enabling stakeholders to design successful crime prevention plans and back community engagement programmes with the knowledge and tools they need. These actual criminal scenarios may be effectively addressed with the help of the models used in this study. With the use of regression models, law enforcement organisations and legislators may forecast future trends and effectively deploy resources by doing predictive analysis of crime rates based on previous data. Additionally, the powerful natural language processing techniques used by the content recommendation system make it easier for stakeholders to receive pertinent information about crimes. The technology improves accessibility to actionable ideas and best practices in crime prevention and intervention by using machine learning algorithms to evaluate user searches and article content. In general, the incorporation of these models provides stakeholders with data-driven decision-making abilities, enabling them to successfully tackle criminal activity and protect communities.

关键词： Analytical models Law enforcement Prevention and mitigation Computational modeling Market research Natural language processing Public security Stakeholders Resource management Recommender systems

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A Brief Review on Prediction Methods for Cloud Resource Management

A Brief Review on Prediction Methods for Cloud Resource Mana...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Chenxing Kuang Yunyun Qiu Weipeng Cao Zhijiao Xiao Zhong Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China

ISBN: (数字)9798350389500

ISBN: (纸本)9798350389517

The complexity of apps running on cloud platforms is evident in their nature. Every application has distinct needs for processing power and memory at various times. In order to effectively cater to tenants’ varied computing requirements while reducing inefficiencies in resource usage, cloud service providers need to be able to predict workload variations with high accuracy and evaluate how cloud apps use resources. In the recent past, there has been a significant increase in research and engineering efforts in cloud computing. These efforts are focused on developing methods to address important issues such as predicting changes in workloads, assessing resource demands, perceiving quality of service (QoS), and detecting anomalies. By reviewing these algorithms in depth, this paper aims to provide academics a better grasp of the most recent developments in this area.

关键词： Surveys Measurement Cloud computing Reviews Computational modeling Quality of service Prediction methods

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Enhanced Fourier Transform Using Wavelet Packet Decomposition

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Journal of Sensor Technology 2024年第1期14卷 1-15页

作者： Wouladje Cabrel Golden Tendekai Mumanikidzwa Jianguo Shen Yutong Yan Department of Physics and Electronic Information Engineering Optical Engineering Zhejiang Normal University Jinhua China Department of Computer Science and Technology Software Engineering Zhejiang Normal University Jinhua China Department of Physics and Electronic Information Engineering Electronic Engineering Zhejiang Normal University Jinhua China

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

关键词： Fourier Transform Wavelet Packet Decomposition Time-Frequency Analysis Non-Stationary Signals

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A computer Vision-Based Vehicle Detection System Leveraging Deep Learning

A Computer Vision-Based Vehicle Detection System Leveraging ...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Saadullah Khan Sanayna Vohra Sultan Abdullah Siddique Abdul Ahad Abro Mansoor Ebrahim Department of Software Engineering Iqra University Main Campus Karachi Pakistan Department of Computer Science Iqra University Main Campus Karachi Pakistan

This study uses advanced computer vision (CV) and deep learning (DL) techniques to present a combined approach for extensive vehicle detection and number plate recognition. Improving the effectiveness and precision of traffic surveillance and administration systems is the main goal. The two primary parts of the suggested system are identification of number plates recognition and vehicle detection. The vehicle detection module uses rigid images or video streams to recognize along with monitor vehicles in real-time using innovative DL algorithms. Such limitations of system reliability are overcome through the application of CNN and object detection models in an integrated computer vision system that allows for reliable performance under a variety of environmental conditions including unfavorable lighting and weather situations. The goal of an identification plate recognition feature is to read and extract alphanumeric characters from license plates that have been detected. To precisely identify and interpret license plate data, this stage combines DL models with the use of optical character recognition (OCR) techniques. The system can adapt to different geographical locations by supporting changes in plate layouts, fonts, and positions. By combining these two modules, a complete intelligent transportation system solution is produced, offering useful information for applications such as congestion examination, safety, and assurance. Because of its modular architecture, the system is easily scalable and customizable, enabling it to be adjusted to various hardware configurations and surveillance scenarios. Using real-world scenarios and benchmark datasets, extensive experiments are conducted to assess the effectiveness of the proposed system. The outcomes show excellent efficiency and accuracy in tasks involving the detection of vehicles and number plate recognition. By promoting enhanced security, integrity, and general traffic management, the suggested integrated syste

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Genealogical Relationship Extraction from Unstructured Text Using Fine-Tuned Transformer Models

Genealogical Relationship Extraction from Unstructured Text ...

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International Conference on Semantic Computing

作者： Carloangello Parrolivelli Lubomir Stanchev Computer Science and Software Engineering Department California Polytechnic State University San Luis Obispo CA USA

The paper tackles the task of extracting genealogical relationships, such as "sibling-of", "parent-of", "child-of", and "spouse-of", from unstructured, free-form text. In order ...

The paper tackles the task of extracting genealogical relationships, such as "sibling-of", "parent-of", "child-of", and "spouse-of", from unstructured, free-form text. In order to solve the problem, we propose a three-stage pipeline consisting of Named Entity Recognition (NER), Coreference Resolution (CR), and Relationship Classification (RC). NER identifies tokens in the text that refer to people, such as proper nouns or nicknames, using the SpaCy software. CR maps multiple tokens representing pronouns to their antecedents. For example, CR could map "She", "His sister", and "Maria" to the antecedent "Maria Johnson". CR allows us to transform a genealogical relationship between two tokens, such as the sibling relationship between "him" and "his sister", to a relationship between the corresponding antecedents, for example, "Bob Johnson" and "Maria Johnson". Our novel algorithm for coreference resolution is based on the AllenNLP software. The last step is the RC, which classifies the relationship between two sets of tokens given adjacent context. We use the LUKE transformer deep-learning model to extract the genealogical relationships. The end-to-end pipeline can extract and correctly classify genealogical relationships from our hand-labeled testing set of Wikipedia documents with macro precision, recall, and F1 scores of 0.794, 0.616, and 0.676, respectively.

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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OMLog: Online Log Anomaly Detection for Evolving System with Meta-learning

arXiv

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arXiv 2024年

作者： Tian, Jiyu Li, Mingchu Wang, Zumin Chen, Liming Qin, Jing Zhang, Runfa The School of Software Technology Dalian University of Technology Dalian China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang China The College of Information Engineering Dalian University Dalian China The School of Computer Science and Technology Dalian University of Technology Dalian China The School of Software Engineering Dalian University Dalian China The School of Automation and Software Engineering Shanxi University Taiyuan China

Log anomaly detection (LAD) is essential to ensure safe and stable operation of software systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with evolving systems. To construct a real-time and reliable online log anomaly detection model, we propose OMLog, a semi-supervised online meta-learning method, to effectively tackle the distribution shift issue caused by changes in log event types and frequencies. Specifically, we introduce a maximum mean discrepancy-based distribution shift detection method to identify distribution changes in unseen log sequences. Depending on the identified distribution gap, the method can automatically trigger online fine-grained detection or offline fast inference. Furthermore, we design an online learning mechanism based on meta-learning, which can effectively learn the highly repetitive patterns of log sequences in the feature space, thereby enhancing the generalization ability of the model to evolving data. Extensive experiments conducted on two publicly available log datasets, HDFS and BGL, validate the effectiveness of the OMLog approach. When trained using only normal log sequences, the proposed approach achieves the F1-Score of 93.7% and 64.9%, respectively, surpassing the performance of the state-of-the-art (SOTA) LAD methods and demonstrating superior detection efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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