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arXiv 2023年

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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A Legal Prediction Model Using Support Vector Machine and K-Means Clustering Algorithm for Predicting Judgements and Making Decisions

A Legal Prediction Model Using Support Vector Machine and K-...

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Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： A Jaya Mabel Rani Bharathwaj K S N M Jothi Swaroopan K Hari Kumar Geetha R Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of EEE R.M.K. Engineering College Chennai Department of Computer Science St Jude’s College Thoothoor KanyaKumari District Tamil Nadu India Department of EEE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Chennai Tamil Nadu India

The legal community has displayed significant interest in harnessing predictive models to anticipate case outcomes, providing judges and attorneys with valuable tools to enhance their decision-making processes. These models offer the potential to expedite choices, offer support for arguments, and strengthen defence strategies. However, accurately predicting legal rulings and case results is a complex endeavour, involving multifaceted stages such as sourcing appropriate bulk case documents, extracting and refining data, and navigating the intricate nature of legal paper complexities. In this innovative machine learning-powered legal prediction model is introduced, named "Legal Acumen." Beyond predictions, this model also offers informed decisions based on the appropriate laws, providing an added layer of insight and value. "Legal Acumen" not only generates predictions about judgments from the Supreme Court but also incorporates a feature that aids end users: upon uploading a new case description document to a designated folder, the system rapidly comprehends its contents and generates both a prediction and a legally informed decision. The understanding indicates that "Legal Acumen" stands among the pioneering legal prediction models striving to anticipate decisions from the Indian Supreme Court. For automatic prediction of Legal Acumen used different types of machine learning algorithms such as Support vector machine, Decision Tree algorithm, Etc., But this paper proposed two-step process. First step used Support vector machine for the prediction of judgement. Then the second step explained about the punishment of the criminal based on K-Means Clustering algorithm.

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An Effective Learning Management System for Revealing Student Performance Attributes

arXiv

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arXiv 2024年

作者： Zhang, Xinyu Lee, Vincent C.S. Xu, Duo Chen, Jun Obaidat, Mohammad S. The School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China The Department of Data Science and Artificial Intelligence Faculty of IT Monash University Melbourne3800 Australia Guangyuan Foreign Language School Guangyuan628018 China Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Chongqing400064 China The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India

Contribution: A learning management system (LMS) incorporated with an advanced educational data mining module is proposed, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Background: An LMS streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an elearning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Intended outcomes: The design and application of the LMS enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success. Application design: This study proposes an effective LMS which processes the stored data through an advanced educational data mining module. The mining module utilizes probability-based frequent pattern mining for generating candidate itemsets and updating LMS databases in each iteration for complexity reduction. In addition, the conditional probability increment ratio (CPIR) is deployed as an exceptionality measure to mine effective common and exception rules simultaneously. The iteratively generated rules can reveal academic performance patterns so that have merits in learning personal goal achievement. Findings: Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowl

关键词： data mining

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Aspect-Based Capsule Network With Mutual Attention for Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2021年第3期2卷 228-237页

作者： Yang, Zhenyu Wang, Xuesong Cheng, Yuhu Liu, GuoJing The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Qilu University of Technology Shandong Academy of Sciences Software Integration Institute Jinan250353 China The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. Recently, mainstream recommendation approaches have been using deep learning techniques to utilize review text. These efforts employ an affinity matrix to model the correlations between users and items and in turn aggregate the contextual features of users and items to form latent representations with some interpretability. However, constructing this simple interaction does not capture the complex correlations between users and items. Furthermore, a single representation of latent features is not sufficient to express user preferences and item attributes in reviews with multiple granularities. To this end, we propose an aspect-based capsule network and mutual attention for recommendation systems, named Acapnet. We design a novel contextual mutual attention mechanism for modeling the fine-grained correlations in the contextual features of users and items. We employ reverse dynamic routing to aggregate user and item context features into aspect features (capsules) for rating predictions;the vector length of a capsule can be used as a basis for estimating aspect importance. Extensive experiments are conducted on five real-world datasets with different features. The results show that the proposed Acapnet is superior to recent state-of-the-art models in terms of performance. Further analysis reveals that our model effectively mitigates the homogeneity among various aspect capsules with strong interpretability. © 2021 IEEE.

关键词： Recommender systems

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Cervical Cancer Classification using Multi-Scale, Multiple-Attention-based Deep Learning Approach

Cervical Cancer Classification using Multi-Scale, Multiple-A...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Ritesh Maurya Suvidha Deshwal Lucky Rajput T Gopalakrishnan Raguru Jaya Krishna Amity Centre for Artificial Intelligence Amity University Noida India Amity School of Engineering & Technology Amity University Noida India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Cervical cancer is a widely acknowledged health concern, especially in the areas lacking appropriate medical treatment. The early diagnosis and the explicit categorization of cervical lesions may bring about effective therapy and restrict the expansion of the disease. The novel approach proposed in this work employs a multi-scale, multi-attention based MobileNetV2 network for the categorisation of cervical cancer. The proposed model improves the efficiency of MobileNetV2, a lightweight convolutional neural network (CNN), by incorporating multiscale and multi-attention techniques to improve feature extraction capabilities and categorisation accuracy. By incorporating multiscale component, the suggested model can effectively detect features at different resolutions. Moreover, the integration of multi-attention mechanism allows the model to dynamically highlight salient features over multiple scales. The proposed method has been evaluated using single cell conventional Pap Smear images, exhibiting improved performance compared to existing approaches in terms of accuracy. The results suggest that the Multi-Scale, Multiple-Attention MobileNetV2-based network proves to be a useful tool for cervical cancer categorisation, offering potential interests for clinical diagnosis with an accuracy of 92.4

关键词： Deep learning Accuracy Image resolution Transfer learning Medical treatment Feature extraction Convolutional neural networks Lesions Cervical cancer Knowledge transfer

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TextFuseNet: Scene text detection with richer fused features 29

TextFuseNet: Scene text detection with richer fused features

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29th International Joint Conference on artificial intelligence, IJCAI 2020

作者： Ye, Jian Chen, Zhe Liu, Juhua Du, Bo School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney Australia School of Printing and Packaging Institute of Artificial Intelligence Wuhan University China

ISBN: (纸本)9780999241165

Arbitrary shape text detection in natural scenes is an extremely challenging task. Unlike existing text detection approaches that only perceive texts based on limited feature representations, we propose a novel framework, namely TextFuseNet, to exploit the use of richer features fused for text detection. More specifically, we propose to perceive texts from three levels of feature representations, i.e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection. The multi-level feature representation can adequately describe texts by dissecting them into individual characters while still maintaining their general semantics. TextFuseNet then collects and merges the texts' features from different levels using a multi-path fusion architecture which can effectively align and fuse different representations. In practice, our proposed TextFuseNet can learn a more adequate description of arbitrary shapes texts, suppressing false positives and producing more accurate detection results. Our proposed framework can also be trained with weak supervision for those datasets that lack character-level annotations. Experiments on several datasets show that the proposed TextFuseNet achieves state-of-the-art performance. Specifically, we achieve an F-measure of 94.3% on ICDAR2013, 92.1% on ICDAR2015, 87.1% on Total-Text and 86.6% on CTW-1500, respectively. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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Towards AI-Empowered Crowdsourcing

arXiv

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arXiv 2022年

作者： Wang, Shipeng Li, Qingzhong Cui, Lizhen Yan, Zhongmin Xu, Yonghui Shi, Zhuan Min, Xinping Shen, Zhiqi Yu, Han School of Software Shandong University Jinan250100 China Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University Jinan250100 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing to improve its efficiency - which we refer to as AI-Empowered Crowdsourcing (AIEC). We propose a taxonomy which divides AIEC into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Crowdsourcing

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Parallel spiking neurons with high efficiency and ability to learn long-term dependencies 23

Parallel spiking neurons with high efficiency and ability to...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Wei Fang Zhaofei Yu Zhaokun Zhou Ding Chen Yanqi Chen Zhengyu Ma Timothée Masquelier Yonghong Tian School of Computer Science Peking University China and Peng Cheng Laboratory China School of Artificial Intelligence Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China and Peng Cheng Laboratory China Department of Computer Science and Engineering Shanghai Jiao Tong University China and Peng Cheng Laboratory China Peng Cheng Laboratory China Centre de Recherche Cerveau et Cognition (CERCO) UMR5549 CNRS - Univ. Toulouse 3 France School of Computer Science Peking University China and Peng Cheng Laboratory China and School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies. We find that when removing reset, the neuronal dynamics can be reformulated in a non-iterative form and parallelized. By rewriting neuronal dynamics without reset to a general formulation, we propose the Parallel Spiking Neuron (PSN), which generates hidden states that are independent of their predecessors, resulting in parallelizable neuronal dynamics and extremely high simulation speed. The weights of inputs in the PSN are fully connected, which maximizes the utilization of temporal information. To avoid the use of future inputs for step-by-step inference, the weights of the PSN can be masked, resulting in the masked PSN. By sharing weights across time-steps based on the masked PSN, the sliding PSN is proposed to handle sequences of varying lengths. We evaluate the PSN family on simulation speed and temporal/static data classification, and the results show the overwhelming advantage of the PSN family in efficiency and accuracy. To the best of our knowledge, this is the first study about parallelizing spiking neurons and can be a cornerstone for the spiking deep learning research. Our codes are available at https://***/fangwei123456/Parallel-Spiking-Neuron.

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