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arXiv 2024年

作者： Xiong, Zong-Xing Li, Mao-Sheng Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China School of Mathematics South China University of Technology Guangzhou510641 China

The notion of strong nonlocality, which refers to local irreducibility of a set of orthogonal multipartite quantum states across each bipartition of the subsystems, was put forward by Halder et al. in [Phys. Rev. Lett. 122, 040403 (2019)]. Here, we show the existence of three orthogonal quantum states in (C2)⊗N that cannot be perfectly distinguished locally across any bipartition of the subsystems. Specifically, all these three states are genuinely entangled, among which two are the N-qubit GHZ pairs. Since any three locally indistinguishable states are always locally irreducible, the three N-partite orthogonal states we present are strongly nonlocal. Thus, the caridnality of strongly nonlocal sets here is dramatically smaller than all known ones. © 2024, CC BY.

关键词： Quantum entanglement

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An Ensemble of Machine Learning Models for Classifying Bangla-English Code-Mixed SMS as Spam or Ham

An Ensemble of Machine Learning Models for Classifying Bangl...

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International Conference on computer and Information technology (ICCIT)

作者： Babe Sultana Mahmuda Rahman Md Gulzar Hussain Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of ICT Mohammadpur Preparatory School and College Dhaka Bangladesh School of Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In the ever-changing world of digital communication, the proliferation of code-mixed languages raises new challenges for content classification, particularly in the context of spam identification. SMS ( Short Message Service) is a popular form of communication, but there are security flaws in it, such as an increase in spam from online fraudsters. This research aims to develop an ensemble of machine-learning models that can classify Bangla-English code-mixed SMS as spam or ham. Our method, which uses a large dataset of code-mixed SMS, includes several classification algorithms, including Logistic Regression, Support Vector Machines, Random Forests, and Multi-Layer Perceptron Classifier (MLPC). To increase the robustness of our model, we employ feature extraction techniques such as TF-IDF for code-mixing scenarios. The ensemble technique employs a voting procedure to integrate individual model predictions, enhancing accuracy while minimizing the risk of misclassification. The experimental results reveal that our ensemble model outperforms single classifiers, with a 96.92% accuracy and high recall and precision metrics. This study enhances the science of natural language processing and has practical implications for SMS filtering systems in multilingual settings, hence boosting user experience and security in digital communications.

关键词： Support vector machines Accuracy Predictive models Message services Digital communication User experience Robustness Natural language processing Multilingual Random forests

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Attention Augmented Convolutional Neural Network for Fine-Grained Plant Disease Classification and Visualization Using Stochastic Sample Transformations 21

Attention Augmented Convolutional Neural Network for Fine-Gr...

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Proceedings of the 5th International Conference on Advances in Image Processing

作者： Getinet Yilma Zhiguang Qin Maregu Assefa Gedamu Alemu Melese Ayalew Software Engineering University of Electronic Science and Technology of China China Computer Science and Technology University of Electronic Science and Technology of China China

ISBN: (纸本)9781450385183

Deep convolutional neural network (DCNN) image analysis improved plant health management. DCNN methods require train samples with diverse feature distribution and discriminative features for the models to generalize well on unseen categories. The limited train sample coined with the locality nature of the convolution operator and commonly shared kernel weights led CNN models to a sub-optimal solution. To address these challenges, we trained a multi-head self-attention augmented CNN (AAC) and explored other attention augmented networks to jointly exploit global salient and local shared features for fine-grained plant disease classification and visualization. We further proposed stochastic train sample transformations to enlarge and improve train sample diversity and feature distribution. The experiment evaluations on the PlantVillage dataset confirmed that the attention augmentation methods demonstrated robust performance in disease classification and visualization. Specifically, the AAC network scored 99.56% with stochastic transformed train samples, 98.01% with simple geometric transformed samples, and 97.34% without transformation. Therefore, attention augmentation in general, AAC in particular, and stochastic sample transformation can address discriminative plant disease classification and visualization.

关键词： disease classification Attention augmentation stochastic transformation

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M-MNFT: A Novel Modified (m, n)-Fault Tolerance Approach for Service Migration in Vehicular Edge Computing

M-MNFT: A Novel Modified (m, n)-Fault Tolerance Approach for...

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software Services engineering (SSE) IEEE International Conference on

作者： YaWen Li Xiaoning Sun Yunni Xia Peng Chen Yin Li Qinglan Peng College of Computer Science Chongqing University Chongqing China College of Computer Science Chongqing Normal University Chongqing China School of Computer and Software Engineering Xihua University Chengdu China Guangzhou Institute of Software Application Technology Guangzhou China School of Artificial Intelligence Henan University Zhengzhou China

Vehicle Edge Computing (VEC) is the deployment of applications close to edge servers to provide low latency and highly responsive services to users. However, due to the complexity and dynamics of the VEC environment, it is prone to errors and failures, and the reliability of edge service migration may be compromised if no measures are taken to cope with different levels of failures. To address this issue, this paper proposes an modified (m, n)-fault tolerance strategy (M-MNFT). Unlike the traditional one, which only considers ES failures, M-MNFT additionally selects redundant edge base stations to ensure task reliability during task migration, and takes into account the fact that the relative distance between the request and the base station is as small as possible when the request is sent, so as to avoid the impact of the edge base station failure on the Quality of Service (QoS) during task migration. In addition, we have performed extensive simulations to show that M-MNFT outperforms existing methods in terms of the number of delayed requests, on-time finish rate, and average waiting time.

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software Testing Issues Improvement in Quality Assurance 2

Software Testing Issues Improvement in Quality Assurance

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Salahat, Mohammed Said, Raed A. Hamid, Khalid Haseeb, Usama Abdel Maguid Abdel Ghani, Elsaid Abualkishik, Abedallah Iqbal, Muhammad Waseem Inairat, Mohammad University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Canadian University Dubai Faculty of Management Dubai United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Superior University Department of Software Engineering Lahore Pakistan Canadian University Dubai Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai United Arab Emirates

ISBN: (纸本)9798350335644

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above. © 2023 IEEE.

关键词： Quality control

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Using DSCB: A Depthwise Separable Convolution Block Rebuild MTCNN for Face Detection 2022

Using DSCB: A Depthwise Separable Convolution Block Rebuild ...

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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Wang, Qiang Cui, Jingru Qin, Zunying An, Ninggang Ma, Xiaofei Li, Guodong Network Information Center Xi'an Jiaotong University China School of Computer Science and Technology Xi'an Jiaotong University China Network Information Center Xi'an Jiaotong University China School of Software Engineering Xi'an Jiaotong University China

ISBN: (纸本)9781450395465

Nowadays, there are huge demands of face detection in images and videos for surveillance, education, autonomous driving and health care. These application scenarios need high accuracy and efficiency of face detection. However, in some scene, unconstrained pose variation, occlusion, large number of faces and illumination bring great challenges to existing face detection methods. In view of above problems, we propose a depthwise separable convolution block (DSCB) which can maintain the speed of training and improve the accuracy at the same time. Then, using the proposed DSCB, we design a face detection model based on MTCNN (Multi-task Convolution Neural Network) to improve performance of occlusion, unconstrained pose variation, large numbers of small targets. In order to better evaluate the proposed method, we built a new dataset which is derived from the classroom teaching scene for training and evaluating. Our dataset consists of 7168 images and 294924 face bounding boxes with occlusion, unconstrained pose variation, and large numbers of small targets. The comparative experiments on our dataset show that the proposed method is superior to other state-of-the-art methods in accuracy and speed of face detection. Compared with the original MTCNN, the face detection method we proposed can bring about 3.9%, 8.66% and 1.39 times overall performance improvement on precision, recall and detection speed respectively. © 2022 ACM.

关键词： Convolution

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E-tutor: Comprehensive Student Productivity Management System for Education

E-tutor: Comprehensive Student Productivity Management Syste...

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Advancements in Computing (ICAC), International Conference on

作者： Kumuditha Silva Rashmin Induwara Malshan Wimukthi Sathsarani Poornika Udara Srimath S. Samaratunge Arachchillage Thilini Jayalath Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

With the advancement of technology, e-learning has emerged as predominant in the education sector. As students, parents, and educators acknowledged, adopting e-learning can offer several benefits over traditional learning techniques. Since more individuals are becoming acclimated to online learning platforms, these online platforms can provide a simple, instructive, and efficient mode of delivery. This novel approach could be improved with the aid of Artificial Intelligence (AI) to comprehend consumers more thoroughly and provide valuable and better-suited services. Most sectors in education, including universities, swiftly adapted to new educational methodologies because of their flexibility and productivity. Nevertheless, there are some downsides that young demography experiences, such as less instructiveness, distraction due to the absence of teachers, and poor IT literacy. Consequently, these drawbacks would recede the capability of students to assimilate content during the lecture. Therefore, the main objective of this research is to implement an E-learning platform with AI learning analytics to enhance students’ performance regularly while reducing the significant drawbacks of the E-learning platforms. This research consists of students’ focus detection, essay-based answer evaluation, note summarization, mind map generation, and personalized guidance facilities.

关键词： Productivity Performance evaluation Visualization computer aided instruction Electronic learning Demography Education

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Clustering With Adaptive Graph Learning and Spectral Rotation 7

Clustering With Adaptive Graph Learning and Spectral Rotatio...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of engineering & technology 2023.

关键词： Fuzzy clustering

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E2VIDiff: Perceptual Events-to-Video Reconstruction using Diffusion Priors

arXiv

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arXiv 2024年

作者： Liang, Jinxiu Yu, Bohan Yang, Yixin Han, Yiming Shi, Boxin National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Software and Microelectronics Peking University China

Event cameras, mimicking the human retina, capture brightness changes with unparalleled temporal resolution and dynamic range. Integrating events into intensities poses a highly ill-posed challenge, marred by initial condition ambiguities. Traditional regression-based deep learning methods fall short in perceptual quality, offering deterministic and often unrealistic reconstructions. In this paper, we introduce diffusion models to events-to-video reconstruction, achieving colorful, realistic, and perceptually superior video generation from achromatic events. Powered by the image generation ability and knowledge of pretrained diffusion models, the proposed method can achieve a better trade-off between the perception and distortion of the reconstructed frame compared to previous solutions. Extensive experiments on benchmark datasets demonstrate that our approach can produce diverse, realistic frames with faithfulness to the given events. © 2024, CC BY-NC-ND.

关键词： Cameras

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Quantitative Trends Analysis on Russian Literature Research in Korean Academia: Insights from KCI Data

Quantitative Trends Analysis on Russian Literature Research ...

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International Conference on Platform technology and Service (PlatCon)

作者： Ten Alexander Iosifovich Sang-Soo Yeo Young-Ae Jung Dept. of Computer Science Sun Moon University Asan South Korea Dept. of Computer Engineering Mokwon University Daejeon South Kora Dept. of AI Software Technology Sun Moon University Asan South Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

This paper presents a quantitative analysis of trends in Russian literature and Korean-Russian translated literature within Korean academia from 1996 to 2024. By analyzing frequency themes and contexts, the study explores evolving research trends, revealing how sociocultural, political, and artistic exchanges between Korea and Russia have influenced each other. Future research using text mining promises to uncover more specific patterns, offering deeper insights into literary and cultural exchanges. This study contributes to understanding the role of literature in expressing and understanding sociopolitical and cultural values in both countries.

关键词： Text mining Translation Statistical analysis Data visualization Curriculum development Market research Cultural differences Indexes

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