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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Prediction of enhancer-promoter interaction based on ResNeXt

Prediction of enhancer-promoter interaction based on ResNeXt

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2022 International Conference on Advanced Algorithms and Neural Networks, AANN 2022

作者： Gong, KeCun Zhou, MengLin Rui, AiSha The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Sichuan Chengdu610041 China School of Computer Science and Engineering Southwest Minzu University Sichuan Chengdu610041 China School of Software Engineering Chengdu University of Information Technology Sichuan Chengdu610225 China

ISBN: (纸本)9781510655751

Aiming at the low accuracy of traditional algorithms in predicting Enhancer-Promoter Interactions (EPIs) in the human genome, an EPIs prediction network based on ResNeXt and attention mechanism was proposed. In the data processing stage, the gene sequence data of a small number of positive samples in the data set is expanded to be consistent with the number of negative samples;then an EPIRNX model is constructed for feature selection and extraction for a given gene sequence, and long-distance features are mined for use in This cell line prediction;the transfer learning model EPIRNX-Transfer was also trained for cross cell line prediction. Using AUROC and AUPRC as evaluation indicators, EPIRNX can better predict EPIs in this cell line than traditional models, and EPIRNX-Transfer can better predict EPIs across cell lines. © 2022 SPIE.

关键词： Forecasting

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Evaluating the usage of fault localization in automated program repair:an empirical study

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Frontiers of computer Science 2021年第1期15卷 53-67页

作者： Deheng YANG Yuhua QI Xiaoguang MAO Yan LEI College of Computer National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems Changsha 410073China Beijing Institute of Tracking and Communication Technology Beijing 100094China School of Big Data&Software Engineering Chongqing UniversityChongqing 400044China

Fault localization techniques are originally proposed to assist in manual debugging by generally producing a rank list of suspicious *** the increasing popularity of automated program repair,the fault localization techniques have been introduced to effectively reduce the search space of automated program *** developers who mainly focus on the rank information,current automated program repair has two strategies to use the fault localization information:suspiciousness-first algorithm(SFA)based on the suspiciousness accuracy and rank-first algorithm(RFA)relying on the rank ***,despite the fact that the two different usages are widely adopted by current automated program repair and may result in different repair results,little is known about the impacts of the two strategies on automated program *** this paper we empirically compare the performance of SFA and RFA in the context of automated program ***,we implement the two strategies and six well-studied fault localization techniques into four state-of-the-art automated program repair tools,and then use these tools to perform repair experiments on 60 real-world bugs from *** study presents a number of interesting findings:RFA outperforms SFA in 70.02%of cases when measured by the number of candidate patches generated before a valid patch is found(NCP),while SFA performs better in parallel repair and patch diversity;the performance of SFA can be improved by increasing the suspiciousness accuracy of fault localization techniques;finally,we use SimFix that deploys SFA to successfully repair four extra Defects4J bugs which cannot be repaired by SimFix originally using *** observations provide a new perspective for future research on the usage and improvement of fault localization in automated program repair.

关键词： automated program repair fault localization empirical study

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 24

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhou, Hang Yu, Wentao Wan, Sheng Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Jinan University Engineering Research Center of Trustworthy AI Ministry of Education China

ISBN: (纸本)9798331506681

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed 'personalized Federated learning with Traffic Pattern Sharing' (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Highway administration

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Joint Optimization for Cooperative Service-Caching, Computation-Offloading, and Resource-Allocations Over EH/MEC 6G Ultra-Dense Mobile Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Zhian Wang, Fei Zhang, Xi Southwest University College of Electronic and Information Engineering Chongqing400715 China Texas A&M University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

Service-caching, computation-offloading, and mobile edge-computing (MEC) have been widely recognized as three key 6G wireless technologies which can efficiently support implementing the ultra-dense networks (UDNs) with massive small-cell base stations (SBSs). But, these impose the new challenges for the UDNs to solely rely on grid power for energy supplying and to jointly optimize service-caching, computation-offloading, and resource-allocations. To overcome the above described difficulties, integrating energy-harvesting (EH) techniques with MEC-enabled 6G UDNs, we propose to develop the joint optimization schemes for cooperative service-caching, computation-offloading, and resource-allocations. In our considered UDNs, there exist a large number of EH-based stationary users (SUs) or mobile users (MUs), and a mixture of on-grid SBSs powered by electric grid and off-grid SBSs power-supplied by solar, radio frequency (RF) energy, etc. Specifically, first we formulate an energy minimization problem under a non-linear RF-energy EH model to minimize the sum of weighted energy consumption of users and off-grid SBSs. Second, for scenarios with SUs, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocations scheme using the hierarchical multi-agent deep reinforcement learning. We derive cooperative service-caching in each time frame, and then derive computation-offloading and resource-allocations in each time slot. Third, we extend our work to scenarios with MUs, where MUs can move with certain trajectories at low speeds. Finally, we validate and evaluate the performances of our proposed schemes through the extensive simulations. © 2002-2012 IEEE.

关键词： Energy utilization

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Sentiment Analysis to manage Health Crisis in Smart Cities 18

Sentiment Analysis to manage Health Crisis in Smart Cities

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18th IEEE International Conference on computer Science and information Technologies, CSIT 2023

作者： Bouhissi, Houda El Itmacene, Ouardia Oubekkou, Milissa Pavlova, Olga Hovorushchenko, Tetiana Shestakevych, Tetiana University of Bejaia LMED Laboratory Faculty of Exact Sciences Bejaia06000 Algeria University of Bejaia Faculty of Exact Sciences Bejaia06000 Algeria Khmelnytskyi National University Department of Computer Engineering & Information Systems Khmelnytskyi Ukraine Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine

ISBN: (纸本)9798350360462

Sentiment analysis is a branch of natural language processing that analyzes textual data and provides insights into users' opinions. In the context of smart cities, sentiment analysis seeks to seamlessly integrate technological innovations into people's daily lives, fostering sustainability and enhancing overall quality of life while monitoring citizen satisfaction levels. This approach allows for more precise analysis and facilitates the implementation of strategies geared towards improving services and the general quality of life. The aim of this paper is to propose an approach for managing healthcare crises in smart cities. The proposal relies on a hybrid approach that combines LSTM for tweet classification with EHO for optimizing classification parameters. Experimental results have demonstrated the effectiveness of this proposed technique. © 2023 IEEE.

关键词： Smart city

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Learning Transferable Negative Prompts for Out-of-Distribution Detection

Learning Transferable Negative Prompts for Out-of-Distributi...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Tianqi Li Guansong Pang Xiao Bai Wenjun Miao Jin Zheng State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external out-lier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://***/mala-lab/negprompt.

关键词： Training Learning systems computer vision Codes Computational modeling Semantics Lead

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Uncertainty-Aware Contrastive Learning With Hard Negative Sampling for Code Search Tasks 39

Uncertainty-Aware Contrastive Learning With Hard Negative Sa...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Han Zhan, Jiaqing Zhang, Qin College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University China

ISBN: (纸本)157735897X

Code search is a highly required technique for software development. In recent years, the rapid development of transformer-based language models has made it increasingly more popular to adapt a pre-trained language model to a code search task, where contrastive learning is typically adopted to semantically align user queries and codes in an embedding space. Considering that the same semantic meaning can be presented using diverse language styles in user queries and codes, the representation of queries and codes in an embedding space may thus be non-deterministic. To address the above-specified point, this paper proposes an uncertainty-aware contrastive learning approach for code search. Specifically, for both queries and codes, we design an uncertainty learning strategy to produce diverse embeddings by learning to transform the original inputs into Gaussian distributions and then taking a reparameterization trick. We also design a hard negative sampling strategy to construct query-code pairs for improving the effectiveness of uncertainty-aware contrastive learning. The experimental results indicate that our approach outperforms 10 baseline methods on a large code search dataset with six programming languages. The results also show that our strategies of uncertainty learning and hard negative sampling can really help enhance the representation of queries and codes leading to an improvement of the code search performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Multi-agent policy transfer via task relationship modeling

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Science China(information Sciences) 2024年第8期67卷 102-114页

作者： Rongjun QIN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

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Using Supervised Machine Learning Approaches to Detect Fraud in the Banking Transaction Network 9

Using Supervised Machine Learning Approaches to Detect Fraud...

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9th International Conference on Web Research, ICWR 2023

作者： Khosravi, Samiyeh Kargari, Mehrdad Teimourpour, Babak Eshghi, Abdollah Aliabdi, Amin Tarbiat Modares University Industrial and Systems Engineering Tehran Iran Tarbiat Modares University Information Technology Tehran Iran Amirkabir University of Technology Computer Science Algorithm and Computational Geometry Laboratory Tehran Iran

ISBN: (纸本)9798350399691

Nowadays, criminal frauds occur in an organized manner in the banking sector. This issue is challenging since the number of organized frauds associated with such areas is estimated to range from 2% to 5% of the global gross domestic product (GDP). The people committing organized fraud use Internet-based financial services and conventional financial services. Accordingly, they use more complex plans and maps to avoid being recognized through organized fraud fighting systems. Due to the complexity and variety of fraud methods, the transaction may not seem suspicious initially. Hence, it is crucial to consider the interactions between the cards. For this purpose, the use of network theory is recommended. The current paper aims to classify each transaction as illegal or legal correctly. Therefore, extensive data analysis is used to organized fraud in the bank transaction network. Besides, a comparison between supervised learning algorithms is presented on a dataset with 46,316 transactions related to customers' card activities to distinguish between illegal and legal transactions. According to the Accuracy, Precision, Recall, and F1-Score criteria values, random forest and XGBoost could be considered suitable predictive models for fraud detection. © 2023 IEEE.

关键词： Complex networks

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