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arXiv 2024年

作者： Li, Changhao Li, Haoling Xue, Mengqi Fang, Gongfan Zhou, Sheng Feng, Zunlei Wang, Huiqiong Song, Mingli Song, Jie School of Software Technology Zhejiang University Hangzhou China School of Computer and Computing Science Hangzhou City University Hangzhou China College of Design and Engineering National University of Singapore Singapore Ningbo Innovation Center Zhejiang University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China

Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics, leaving the progress in this area not fully comprehended. To fill this gap, we present the first comprehensive benchmark, termed PruningBench, for structural pruning. PruningBench showcases the following three characteristics: 1) PruningBench employs a unified and consistent framework for evaluating the effectiveness of diverse structural pruning techniques;2) PruningBench systematically evaluates 16 existing pruning methods, encompassing a wide array of models (e.g., CNNs and ViTs) and tasks (e.g., classification and detection);3) PruningBench provides easily implementable interfaces to facilitate the implementation of future pruning methods, and enables the subsequent researchers to incorporate their work into our leaderboards. We provide an online pruning platform http://*** for customizing pruning tasks and reproducing all results in this paper. Leaderboard results can be available on https://***/HollyLee2000/PruningBench. © 2024, CC BY.

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Adaptive watermark generation mechanism based on time series prediction for stream processing

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Frontiers of computer science 2021年第6期15卷 59-73页

作者： Yang SONG Yunchun LI Hailong YANG Jun XU Zerong LUAN Wei LI Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China College of Life Sciences and Bioengineering Beijing University of TechnologyBeijing 100083China

The data stream processing framework processes the stream data based on event-time to ensure that the request can be responded to in *** reality,streaming data usually arrives out-of-order due to factors such as network *** data stream processing framework commonly adopts the watermark mechanism to address the data *** is a special kind of data inserted into the data stream with a timestamp,which helps the framework to decide whether the data received is late and thus be *** watermark generation strategies are periodic;they cannot dynamically adjust the watermark distribution to balance the responsiveness and *** paper proposes an adaptive watermark generation mechanism based on the time series prediction model to address the above *** mechanism dynamically adjusts the frequency and timing of watermark distribution using the disordered data ratio and other lateness properties of the data stream to improve the system responsiveness while ensuring acceptable result *** implement the proposed mechanism on top of Flink and evaluate it with realworld *** experiment results show that our mechanism is superior to the existing watermark distribution strategies in terms of both system responsiveness and result accuracy.

关键词： data stream processing watermark time series based prediction dynamic adjustment

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Pre-training Point Cloud Compact Model with Partial-aware Reconstruction

arXiv

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arXiv 2024年

作者： Zha, Yaohua Wang, Yanzi Dai, Tao Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Research Center of Artificial Intelligence Peng Cheng Laboratory China College of Computer Science and Software Engineering Shenzhen University China

The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, two drawbacks hinder their practical application. Firstly, the positional embedding of masked patches in the decoder results in the leakage of their central coordinates, leading to limited 3D representations. Secondly, the excessive model size of existing MPM methods results in higher demands for devices. To address these, we propose to pre-train Point cloud Compact Model with Partial-aware Reconstruction, named Point-CPR. Specifically, in the decoder, we couple the vanilla masked tokens with their positional embeddings as randomly masked queries and introduce a partial-aware prediction module before each decoder layer to predict them from the unmasked partial. It prevents the decoder from creating a shortcut between the central coordinates of masked patches and their reconstructed coordinates, enhancing the robustness of models. We also devise a compact encoder composed of local aggregation and MLPs, reducing the parameters and computational requirements compared to existing Transformer-based encoders. Extensive experiments demonstrate that our model exhibits strong performance across various tasks, especially surpassing the leading MPM-based model PointGPT-B with only 2% of its parameters. The code will be released. © 2024, CC BY.

关键词： Embeddings

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Resource Aware Multi-User Task Offloading In Mobile Edge Computing

Resource Aware Multi-User Task Offloading In Mobile Edge Com...

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IEEE International Conference on Web Services (ICWS)

作者： Shunhui Ji Jiajia Li Huiying Jin Hai Dong Zhiyuan Ge Shuhan Yang Pengcheng Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Mobile edge computing (MEC) relies on offloading tasks to edge nodes to avoid delays and failures caused by local computing. However, developing efficient offloading decisions is challenging, as it involves addressing the intricacies of tasks and the instability of edge node resources(e.g. available computer resources, memory, and bandwidth). In this paper, we propose a novel approach to tackle the problem of task offloading. Our approach involves dividing tasks into smaller units and considering the correlations between these sub-tasks. To make optimal offloading decisions, we employ a deep reinforcement learning algorithm that takes into account user movement patterns and the availability of resources at edge nodes. Through simulations, we demonstrate that our proposed algorithm outperforms several existing algorithms in terms of offloading decisions. It effectively reduces task execution delays and energy costs. These findings highlight the potential of our approach in improving the performance of task offloading in MEC systems.

关键词： Multi-access edge computing Costs Correlation Web services Heuristic algorithms Image edge detection Prediction algorithms Dynamic scheduling Deep reinforcement learning Delays

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Machine Learning-Based Analysis of Tennis Match Processes

Machine Learning-Based Analysis of Tennis Match Processes

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Robotics & Intelligent Manufacturing Technology (ISRIMT), International Symposium on

作者： Ke Li Nuo Yang Yan Yu School of Economics and Finance Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Geography and Remote Sensing Hohai University Nanjing China

ISBN: (数字)9798331528942

ISBN: (纸本)9798331528959

Prediction is an important topic in machine learning. Given the random nature of sports events, they are meaningful experimental subjects for conducting research. In this paper, we focused on answering three key questions: (1) How to determine which player is performing better at a specific time? (2) What is the impact of so-called “momentum” on the outcome of a match? And (3) How to determine which player has the advantage at a certain point in the match. Firstly, we construct the wavelet neural network, in which Spearman's correlation coefficient is 0.7069, to decompose the performance of a player in real-time into relevant quantifiable features, evaluating which player is performing better at the moment. Then, we identified the main features affecting momentum and proposed the Additive-Multiplicative Model. The results confirm the momentum role in determining the outcome of a player's match. Finally, we established a random forest model with 30 decision trees. The accuracy and recall rates are 83.3% and 89.0%, respectively. Our study shows high accuracy and robustness in the outcome of sports events prediction and demonstrates promising potential for further applications.

关键词： Accuracy Forestry Predictive models Wavelet analysis Robustness Real-time systems Decision trees Random forests Tuning Sports

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Examining the Effects of Layout and Working Memory on UML Class Diagram Defect Identification

Examining the Effects of Layout and Working Memory on UML Cl...

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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Bonita Sharif Kang-il Park Michael P. DeJournett Isaac Baysinger Mohammed Aly Jonathan I. Maletic School of Computing University of Nebraska - Lincoln Lincoln Nebraska USA Software Engineering Department Assiut University Assiut Egypt Department of Computer Science Kent State University Kent Ohio USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

A controlled experiment investigating the effect layout has on how students find defects in UML class diagrams with respect to requirements is presented. Two layout schemes from prior literature namely, multi-cluster and orthogonal layouts, are compared with respect to two open source systems, Doxygen and Qt. The experiment is conducted with 89 students from two universities in a classroom lab setting. Each participant is placed in one of two groups where each group are given 2 defect detection tasks (with five sub-parts) with each task using one of the two layouts in each subject system. The only difference between groups is that the layouts were flipped between the two tasks. Feedback is collected after each task. A mental rotation and object memory task is conducted at the end of the two tasks to correlate their spatial and working memory skills to the task performance. Results indicate that the multi-cluster layout performed better in terms of accuracy of finding defects, but not significantly. There is also not much difference in time to find them. Furthermore, it is found that the object memory skills are sometimes correlated with the performance of the defect detection tasks. These results can be used to help improve the teaching of UML class diagram defect detection skills by incorporating clustered layouts and object memory tasks. In addition, they can help identify people who are best suited for finding critical defects in design.

关键词： Visualization Accuracy Layout Unified modeling language Education software Defect detection

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M3bunet:Mobile Mean Max Unet for Pancreas Segmentation on Ct-Scans

SSRN

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SSRN 2024年

作者： Juwita, Juwita Hassan, Ghulam Mubashar Akhtar, Naveed Datta, Amitava Department of Computer Science and Software Engineering The University of Western Australia Australia School of Computing and Information Systems The University of Melbourne Australia Department of Informatics Syiah Kuala University Banda Aceh Indonesia

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on NIH and MSD pancreas datasets. Our approach demonstrates performance improvement, achieving an average DSC value of up to 89.53±1.82 and IOU score of up to 81.16±0.03 for the NIH, and 88.60 ±1.48 DSC and 79.90±2.19 IOU for the MSD. © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Beam Splitting Sensing for Terahertz Near-Field Channel Estimation

Beam Splitting Sensing for Terahertz Near-Field Channel Esti...

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Intelligent Communications and Computing (ICC), International Conference on

作者： Shaochen Tang Zhicheng Dong Weixi Zhou Donghong Cai Enzhi Zhou Ping Lan College of Information Science Technology Tibet University Lhasa China School of Computer Science Sichuan Normal University Chengdu China College of Cyber Security Jinan University Guangzhou China School of Computer and Software Engineering Xihua University Chengdu China

Terahertz technology is considered as one of the key techniques for future 6G due to its ultra-wide bandwidth and ultra-high communication rate. However, terahertz communication channel estimation faces several significant challenges, one is that extremely-large-scale MIMO communication typically occurs in the near field, which will result in traditional far-field channel estimation schemes no longer being suitable. The other is the beam splitting effect caused by terahertz wideband communication, which leads to a large impact on the steering vector caused by different subcarrier frequencies. In order to solve these problems, this paper first studied the model of THz near-field beam splitting; secondly discussed the construction method of the THz near-field polar dictionary matrix; and then proposed a two-stage beam splitting sensing (Two-stage-BSS) algorithm to improve the accuracy of near-field channel estimation and reduce the impact of beam splitting. The experimental results validate the effectiveness of the proposed algorithm.

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Security Code Recommendations for Smart Contract

Security Code Recommendations for Smart Contract

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Xiaocong Zhou Yingye Chen Hanyang Guo Xiangping Chen Yuan Huang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China School of Communication and Design Sun Yat-sen University Guangzhou China

A smart contract is a self-executing program that is stored on the blockchain and runs when predetermined conditions are satisfied. Many frequent transactions involving asset transfers rely on smart contracts deployed on the blockchain, making them highly vulnerable to attack, thus it is essential to ensure the security of smart contracts. Since the smart contract is immutable once deployed, developers must try their best to fix existing vulnerabilities in advance to ensure security. Current approaches for automatic program repair on the smart contracts have mainly adopted the heuristic search algorithms or defined patterns to fix several well-defined types of vulnerabilities. They can only provide security code recommendations for developers in specific scenarios. We explore more general automated program repair of smart contracts in software *** pave the way for studying code changes related to bug fix of smart contracts in software history, we present a labeled public dataset for method-level program repair task, containing over 12 typical insecure code patterns. Unlike bugs in traditional software, the vulnerabilities of smart contracts are more associated with access control and conditional statements as smart contracts pertain to financial assets. For this problem, we devise a novel double-encoder network and use a code representation designed for the smart contract based on syntax information to repair program. By implementing and evaluating our approach on new dataset comprised of over 10,000 program pairs, we demonstrate the superiority of our approach in both qualitative and quantitative aspects.

关键词： Codes Smart contracts computer bugs Maintenance engineering Syntactics software Blockchains

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The Optimization of COVID-19 Detection Based on DesNet Model

The Optimization of COVID-19 Detection Based on DesNet Model

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International Conference on computer Research and Development (ICCRD)

作者： Liu Mingchen Fu Jikun Chen Zhongxin Teoh Teik Toe School of Software Engineering HuaZhong University of Science and Technology Wuhan China Microelectronics Science and Engineering HuaZhong University of Science and Technology Wuhan China School of Computer Science and Technology HuaZhong University of Science and Technology Wuhan China NTU Business AI Lab Nanyang Technological University Singapore

In recent years, with the pandemic of COVID-19, how to identify the positive cases of COVID-19 accurately and rapidly from patients has become the key to block the spread of the epidemic and assist clinical diagnosis. In this paper, a COVID-19 detection model was constructed for the purpose to identify the positive cases from patients with other lung diseases as well as the normal using the chest X-ray images. The basic structure of the detection system is a CNN model based on DesNet with some optimization algorithms and the accuracy has reached 94.2%. We also applied three multi-sample data augmentation methods: SMOTE, mixup and CutMix to the model to analyze their performance. By applying these methods, the model finally reached 97.9 % on test set and showed a good generalization on other datasets, which could reach over 80% without extra training. The results show that using transfer learning and some muli-sample data augmentation methods can significantly improve the accuracy and overcome overfitting problem of fewshot learning, while others may not be so effective.

关键词： COVID-19 Training Pandemics Pulmonary diseases Computational modeling Transfer learning Data models

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