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Proceedings of the 41st International Conference on Machine Learning

作者： Tong Wei Zhen Mao Zi-Hao Zhou Yuanyu Wan Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China School of Software Technology Zhejiang University Ningbo China

Long-tail learning primarily focuses on mitigating the label distribution shift between longtailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing longtail learning approaches, consistently improving their performance. The source code is available at https://***/Stomach-ache/label-shift-correction.

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Analysis of MDE Based Gamification Framework for Cognitive Evaluation 34

Analysis of MDE Based Gamification Framework for Cognitive E...

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34th International Conference on computer Theory and Applications, ICCTA 2024

作者： Islam, Mahbubul Biswas, Swapnil Ahmed, Md. Shakil Rahman, Md. Jobayer Hasan, Ahmed Rafi Amin, Al Mukta, Md Saddam Hossain Southern Illinois University Department of Electrical and Computer Engineering Edwardsville 62026 IL United States University of Maryland Department of Information Systems Baltimore 21250 MD United States United International University (UIU) Department of CSE Dhaka 1212 Bangladesh LUT University Department of Software Engineering Lappeenranta 53850 Finland

ISBN: (纸本)9798331529673

Gamification is a promising approach with expanding applications in academia and industry. Gamification techniques can increase psychological and behavioral engagement with a particular domain with the help of game elements. This study examines gamification within the MDE framework, focusing on its dynamics, mechanics, and emotional components, with a particular emphasis on its relation to cognitive evaluation. We conduct a comparative analysis of the MDE framework to align game elements with user segments that respond best to them. Additionally, we explore the cognitive outcomes of the MDE-based gamification framework, highlighting its relevance and impact. Here, we compare the results of past research on various gamified platforms, tasks, and strategies, which analyzed their effects on user behavior and cognitive reactions. We also identify the published correlations between game elements and cognitive outcomes. We synthesize these results into a model demonstrating which game elements can enhance user experience and performance and outlining future research directions. © 2024 IEEE.

关键词： Cognitive assessment Game elements Gamification Human behavior analysis MDE framework

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization software defined networking

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Improving students’programming quality with the continuous inspection process:a social coding perspective

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Frontiers of computer Science 2020年第5期14卷 43-60页

作者： Yao Lu Xinjun Mao Tao Wang Gang Yin Zude Li College of Computer National University of Defense TechnologyChangsha410073China Key Laboratory of Software Engineering for Complex Systems Changsha410073China School of Information Science and Engineering Central South UniversityChangsha410012China

College students majoring in computer science and software engineering need to master skills for high-quality ***,rich research has shown that both the teaching and learning of high-quality programming are challenging and deficient in most college education ***,the continuous inspection paradigm has been widely used by developers on social coding sites(e.g.,GitHub)as an important method to ensure the internal quality of massive code *** paper presents a case where continuous inspection is introduced into the classroom setting to improve students’programming *** the study,we first designed a specific continuous inspection process for students’collaborative projects and built an execution environment for the *** then conducted a controlled experiment with 48 students from the same course during two school years to evaluate how the process affects their programming *** results show that continuous inspection can help students in identifying their bad coding habits,mastering a set of good coding rules and significantly reducing the density of code quality issues introduced in the ***,we describe the lessons learned during the study and propose ideas to replicate and improve the process and its execution platform.

关键词： continuous inspection programming quality SonarQube

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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A Deep Learning-Based Model for Edible Mushroom Greenhouse Prediction 11

A Deep Learning-Based Model for Edible Mushroom Greenhouse P...

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11th International Conference on information systems and Computing Technology, ISCTech 2023

作者： Huang, Shuanggen Liu, Quanyao Wu, Yan Hu, Meifu Zhu, Yutao Jiangxi Agriculture University Jiangxi Agricultural University Agricultural Equipment Key Laboratory of Jiangxi Provincial Nanchang China Jiangxi Agricultural University Jiangxi Agricultural University School of Software Nanchang China Jiangxi Agricultural University Jiangxi Agricultural University School of Computer and Information Engineering Nanchang China

ISBN: (纸本)9798350342406

Edible mushrooms are gaining increasing popularity among consumers due to their high nutritional value. However, large-scale mushroom production heavily relies on greenhouse environments. Traditional mushroom greenhouses can only monitor the current greenhouse environment, and environmental control devices such as air conditioners and humidifiers require time to take effect, resulting in a lagging effect in environmental control. The main environmental factors that affect mushroom growth include temperature, humidity, and carbon dioxide, and current mainstream mushroom greenhouses mainly regulate temperature and *** this paper, we propose a deep learning method combined with environmental data from both indoor and outdoor sensors, as well as data on the on/off status of humidifiers and air conditioners, to predict changes in temperature, humidity, and carbon dioxide in the greenhouse environment. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) network, and CNN-LSTM models with attention mechanisms (A- CNN-LSTM) were tested, and the results show that the actual environmental changes in the greenhouse align with the neural network prediction model. The A-CNN-LSTM model achieved the best performance with an RMSE of 10.76 and an R2 of 0.9067 for a one-minute prediction time. This study's prediction model can improve management strategies and address the environmental control lag issue in edible mushroom greenhouses. © 2023 IEEE.

关键词： Greenhouses

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Multi-Stage Guided Code Generation for Large Language Models

SSRN

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SSRN 2024年

作者： Han, Mr Yewei Lyu, Chen Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University University Road 1 Jinan250014 China

Currently, although Large Language Models have shown significant performance in the field of code generation, their effectiveness in handling complex programming tasks remains limited. This is primarily due to the substantial distance between the problem description and the correct code, making it difficult to ensure accuracy when directly generating code. Human programmers, when faced with a complex programming problem, usually use multiple stages to solve it in order to reduce the difficulty of development. First, they analyze the problem and think about a solution plan, then they design a code architecture based on that plan, and finally they finish writing the detailed code. Based on this, we propose a multi-stage guided code generation strategy that aims to gradually shorten the transformation distance between the problem description and the correct code, thus improving the accuracy of code generation. Specifically, the approach consists of three stages: planning, design and implementation. In the planning phase, the Large Language Model generates a solution plan based on the problem description;in the design phase, the code architecture is further designed based on the solution plan;and in the implementation phase, the previous solution plan and code architecture are utilized to guide the Large Language Model in generating the final code. Additionally, we found that existing competition-level code generation benchmarks may overlap with the training data of the Chat Generative Pre-trained Transformer, posing a risk of data leakage. To validate the above findings and circumvent this risk, we created a competition-level code generation dataset. © 2024, The Authors. All rights reserved.

关键词： Architecture

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Resilience-oriented Operation of Power Distribution Networks with Line Hardening and Comprehensive Reconfiguration Measures 14

Resilience-oriented Operation of Power Distribution Networks...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Li, Zhengmao Shi, Zhao Ruan, Guangchun Lin, Yuzhang Zhao, Jin Aalto University School of Electrical Engineering Espoo Finland Nanyang Technological University School of Electrical and Electronic Engineering Singapore Massachusetts Institute of Technology Laboratory for Information & Decision Systems Boston United States New York University Department of Electrical and Computer Engineering New York United States Trinity College Dublin Department of Electronic and Electrical Engineering Dublin Ireland

ISBN: (纸本)9781665455541

With the frequent occurrence of extreme events like natural disasters and man-made attacks, the resilience concept is attracting worldwide research attention. Thus, this paper proposes a resilient operation model for the power distribution network (PDN) to recover load and limit economic loss to the greatest possible extent. First, the line hardening measure is applied to strengthen the PDN to avoid the serious breakdown of distribution lines. In addition, a comprehensive single commodity flow-based reconfiguration approach is used which not only sets the buses connecting generation sources but also those at the end of broken lines as the slack buses. Then, the radiality of PDNs can still be guaranteed after reconfiguration. The resilience-oriented operation problem can be originally formulated as a nonlinear programming one while efficient linearization methods are applied to reduce computational burdens. Hence, the operation problem is reformulated as a mixed-integer linear programming one which will enjoy favorable solution performances. Finally, case studies on an IEEE 33 PDN including comparison cases with traditional benchmarks are done to show the effectiveness of our method. © 2023 IEEE.

关键词： Hardening

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification

Domain Adaptation Random Vector Functional Link for White Bl...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qin Wang Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells.

关键词： White blood cells Training Deep learning Adaptation models Neural networks Focusing Vectors Data models Medical diagnosis Medical diagnostic imaging

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