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5th International Conference on Information Technology and Education Technology, ITET 2024

作者： Vogel, Joyce Bouhnik, Dan Jerusalem College of Technology Computer Science and Software Engineering Jerusalem Israel

ISBN: (纸本)9798331540425

The course Introduction to computer Networks (ICN) has become one of the most vital courses in computer science and software engineering degrees and clearly is an imperative course for a degree in computer networking. However, despite its importance, the course suffers from many issues which lead to a high failure rate and low success. Even students who pass the course have problems understanding key concepts in data communication. One of the key issues is the theoretical nature of the material of the course. Many of the concepts are abstract and dry, which the students find hard to understand and boring. At the Jerusalem college of Technology (JCT), we encountered such problems with our networking course. We therefore decided to suspend the course for a year, research successful networking courses, and apply our findings to a revised networking course. One of the strategic changes that was made was to introduce more practical material into the course. Top-down approach to networking replaced a bottom-up approach, hands-on lab were added to the course, and online quizzes were included. This paper presents some of our findings and changes that were made to the networking course. The problems that were present in our original version of the ICN course are not unique to the JCT but are common to networking courses throughout higher education institutions. We therefore believe that the findings of our research can benefit other faculty who wish to improve their networking courses. © 2024 IEEE.

关键词： Students

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Research on CUDA-based Multidimensional Time Series Motif Mining 5

Research on CUDA-based Multidimensional Time Series Motif Mi...

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5th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Wang, Jimin Wang, Shuaiwei Wang, Junru Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331533991

Traditional serial motif mining methods struggle to quickly identify motif information in large-scale time series data. A CUDA-based multidimensional motif mining algorithm is proposed to discover motifs in multidimensional time series. The algorithm introduces a multidimensional kNN Matrix Profile (kNN mMP) structure to address the limitations of the multidimensional mMP. Parallel computation of kNN mMP using the CUDA architecture enables rapid motif discovery in massive datasets and facilitates detection of additional motif features, such as strong and weak motifs, as well as identification of anomalous motifs. Experiments on public datasets demonstrate that using CUDA for kNN mMP structure achieves a significant speedup;on NVIDIA GeForce RTX 4060, the kNN-mSTOMP-GPU algorithm exhibits a speedup of over 220 times compared to the STOMP algorithm utilizing only kNN mMP when the sequence length is 200,000. © 2024 IEEE.

关键词： Time series

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A Purified Stacking Ensemble Framework for Cytology Classification 30th

A Purified Stacking Ensemble Framework for Cytology Classif...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Qian, Linyi Huang, Qian Chen, Yulin Chen, Junzhou College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031533075

Cancer is one of the fatal threats to human beings. However, early detection and diagnosis can significantly reduce death risk, in which cytology classification is indispensable. Researchers have proposed many deep learning-based methods for automated cancer diagnosis. Nevertheless, due to the similarity of pathological features in cytology images and the scarcity of high-quality datasets, neither the limited accuracy of single networks nor the complex architectures of ensemble methods can meet practical application needs. To address the issue, we propose a purified Stacking ensemble framework, which employs three homogeneous convolutional neural networks (CNNs) as base learners and integrates their outputs to generate a new dataset by a k-fold split and concatenation strategy. Then a distance weighted voting technique is applied to purify the dataset, on which a multinomial logistic regression model with a designed loss function is trained as the meta-learner and performs the final predictions. The method is evaluated on the FNAC, Ascites, and SIPaKMeD datasets, achieving accuracies of 99.85%, 99.24%, and 99.75%, respectively. The experimental results outperform the current state-of-the-art (SOTA) methods, demonstrating its potential for reducing screening workload and helping pathologists detect cancer. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cytology

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Deadline-aware Load Allocation for Coded Computation over Heterogeneous Clusters 5

Deadline-aware Load Allocation for Coded Computation over He...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Cheng, Shiying Lin, Yuxuan Tang, Bin Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331506612

In large-scale distributed systems, the performance of computation tasks is often significantly degraded by straggling nodes. Recently, coded computation has emerged as a promising approach to mitigate the effect of stragglers. However, the performance of coded computation is still significantly affected by node workloads, particularly in heterogeneous clusters. Some load allocation schemes have been proposed to minimize the expected computation latency, but for time-sensitive tasks with strict deadlines, they often result in high failure probabilities. In this paper, we examine the fundamental matrix-vector multiplication tasks and focus on the deadline-aware load allocation problem under two typical runtime models, aiming to minimize the task failure probability. We firstly propose a simple yet effective normal approximation method based on the central limit theorem to approximate the failure probability, transforming the problem into a non-convex multivariate optimization, and then present an efficient iterative load allocation algorithm. Extensive simulations demonstrate the effectiveness of our proposed scheme. © 2024 IEEE.

关键词： Convex optimization

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Reducing Age of Collection with Dynamic-Frame Time Division Multiple Access 100

Reducing Age of Collection with Dynamic-Frame Time Division ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Lai, Yurong Han, Xinhui Wang, Xueer Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798331517786

This paper introduces a dynamic-frame time division multiple access (DF-TDMA) scheme aimed at decreasing the age of collection (AoC) in collaborative monitoring scenarios. Unlike the conventional age of information (AoI) metric, AoC decreases only when partial information from multiple sources is aggregated to form a complete observation. Previous studies on AoC predominantly assumed that complete observations are derived from aggregating information from all sources. However, this assumption does not hold in scenarios where sources are correlated, and a complete observation can be achieved with partial information from only a subset of sources. To address this, new channel access protocols are necessary to achieve a low network-wide average peak AoC. DF-TDMA is proposed as a solution, featuring dynamically adjustable TDMA frame sizes to minimize time wastage and thereby reduce AoC. While the dynamic frame size adjustment complicates AoC analysis, we theoretically derive the average peak AoC of DF-TDMA. Simulations show that DF-TDMA maintains a stable average peak AoC across varying numbers of sources N, and notably reduces the average peak AoC compared to a fixed-frame TDMA scheme, particularly under conditions with a large N. © 2024 IEEE.

关键词： Time division multiple access

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A Semi-Supervised image classification algorithm inspired by the Primacy Effect 4

A Semi-Supervised image classification algorithm inspired by...

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4th International Conference on Digital Signal and computer Communications, DSCC 2024

作者： Zhao, Dianqing Li, Chaofan Zhu, Anmin College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781510681538

Semi-supervised-Learning(SSL) providing a solution to leverage vast amounts of unlabeled data. In cognitive psychology, the Primacy-effect refers to the phenomenon where the initial information encountered tends to leave a deeper impression in human cognitive processes, serving as the basis for subsequent judgments. Inspired by the Primacy-effect, this paper proposes a novel semi-supervised image classification algorithm. The core idea of this algorithm is to mimic the beneficial effects of the Primacy effect on human cognitive processes, simulate similar phenomenon on artificial neural networks. In this paper, the initially labeled data is referred to as exemplar. The algorithm includes an exemplar prediction module, whose main function is to accurately identify examples, ensuring that the model forms a "deep impression" of them. We found that due to the scarcity of examples, it is easy to cause model overfitting. Therefore, we proposes the Weighted-Gradient-Chain technique. Additionally, Pseudo-labeling technique was employed, but during model training, we found that generated erroneous Pseudo-labels could introduce errors. To enhance the quality of Pseudo-label generation, this paper proposes a Pre-Pseudo-labeling method. A series of experiments were conducted on multiple datasets. The results indicate that the proposed model performed well. © 2024 SPIE.

关键词： Image classification

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Surtify: A Smart Surface Identification System Based on Multi-dimensional Acoustic Dispersion

Surtify: A Smart Surface Identification System Based on Mult...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Wang, Yunshu Yuan, Baojie Dong, Feihang Hong, Shicong Zou, Yongpan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798331507886

With the continuous advancement of the smart home market, household items have become more intelligent. By identifying the different material attributes of various household tabletops, we can obtain contextual information and engage in further interactions. This paper explores a phenomenon, acoustic dispersion and applies it to surface identification. Integrating mobile deep learning, we propose a method to recognize solid surface materials through tapping and utilize tapping events as triggers for interaction by identifying surface material categories. To achieve the goal of zero training effort and tiny hardware cost for unseen users, we analyze the propagation patterns of different frequency components in tapping sound signals from both temporal and spatial dimensions. Based on this, we propose a time-frequency data augmentation strategy that combines multiple event points to improve the recognition accuracy. The results show that, using the leave-one-user-out cross validation for 24 volunteers trained with simple guidance on tapping intensity and position, our system achieves a recognition accuracy of 98.6% for 11 common material types. For 11 newly recruited volunteers without specific control over force and position, the accuracy also remains at 93.4%. We also conduct a series of cross-environment experiments and user study, all of which indicate the high potential of our system. © 2024 IEEE.

关键词： Smart homes

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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Taming diffusion model for exemplar-based image translation

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Computational Visual Media 2024年第6期10卷 1031-1043页

作者： Ma, Hao Yang, Jingyuan Huang, Hui Shenzhen University Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen China (GRID:grid.263488.3) (ISNI:0000 0001 0472 9649)

Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given ***,most existing GAN-based translation methods fail to produce photorealistic *** this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in *** proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic *** then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically *** demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

关键词： exemplar image translation denoising diffusion probabilistic model(DDPM)

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Aspect Extraction Approach for Sentiment Analysis Using Keywords

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computers, Materials & Continua 2023年第3期74卷 6879-6892页

作者： Nafees Ayub Muhammad Ramzan Talib Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Software Engineering Government College UniversityFaisalabad38000Pakistan

Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp *** online reviews about products are also becoming essential for consumers and companies as *** rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and *** reviews are also a very precious source of information for requirement *** companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer *** to this,many researchers have developed approaches for aspect-based sentiment *** existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit *** paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit *** also captures opinion words and classifies the sentiment about each *** applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect *** used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven *** compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.

关键词： Sentiment analysis aspect extraction keywords-based machine learning

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