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Companion of the 2025 International Conference on Management of Data

作者： Tsz Nam Chan Bojian Zhu Dingming Wu Yun Peng Leong Hou U Wei Tu Ruisheng Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong SAR China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Department of Computer and Information Science University of Macau Macao SAR China School of Architecture and Urban Planning Shenzhen University Shenzhen China

ISBN: (纸本)9798400715648

Line Density Visualization (LDV) has been widely used in different domains, e.g., transportation science, urban planning, and criminology. Therefore, various geographic information systems, e.g., ArcGIS and QGIS, can also support this tool. However, all these GIS platforms mainly adopt the naïve algorithm for generating LDV, which cannot be scalable to high resolution sizes and large-scale line-segment datasets. To tackle this issue, we have developed a new QGIS plugin, called Fast Line Density Analysis, for efficiently supporting two types of accurate approximation, namely (1) generating LDV with an ε-relative error guarantee (εLDV) and (2) generating LDV with multiple thresholds (τLDV). In this demonstration, we have prepared three large-scale datasets (with up to 15.7 million line segments) for participants to compare our plugin with QGIS and ArcGIS in terms of accuracy and efficiency. In particular, participants can also tune different parameters in our plugin for understanding how they can affect the visualization quality and efficiency. The demonstration video is available in the YouTube and Bilibili links, which are https://***/watch?v=EYl3Wieb-2I and https://***/video/BV1aJwaeMEHr, respectively.

关键词： GIS

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Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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DeepJDOT: Deep joint distribution optimal transport in facial recognition

DeepJDOT: Deep joint distribution optimal transport in facia...

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作者： Yuchen Cai Chenglong Wen Haoyuan Chen Yijia Liu Haofei Peng Computer Science and Software Engineer Department Rose-Hulman Institute of Technology Department of Physics University of Toronto Department of Physics University of Washington Department of Water Conservancy and Civil Engineering China Agricultural University Department of Information Engineering Xi'an Jiaotong University

This paper explores the application of deep learning in domain adaptation, with a particular focus on using the Deep Joint Distribution Optimal Transportation(DeepJDOT)method for face recognition. We test DeepJDOT on a series of visual domain adaptation tasks and find that it exhibits high accuracy and competitive advantage, especially under smaller image sizes, compared to recent state-of-the-art competitors. This research not only highlights the potential of deep learning in domain adaptation but also underscores DeepJDOT as an effective approach for improving the performance of applications like face ***, we introduce a domain adaptation-tailored loss function based on optimal transport to further enhance model performance. This study provides valuable insights and methods for research in visual domain adaptation and face recognition.

关键词： DeepJDOT Convolutional Neural Network computer vision

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Fast target-aware learning for few-shot video object segmentation

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science China(information sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

Transformer-based Text Classification on Unified Bangla Mult...

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information Technology (ACIT)

作者： Md Sakib Ullah Sourav Mohammad Sultan Mahmud Hua Zheng Mohammad Aljaidi Md. Simul Hasan Talukder Rejwan Bin Sulaiman Abdullah Hafez Nur Ahmad Al-Qerem School of Management Science and Engineering Shandong University of Finance and Economics Jinan China College of Computer Science and Software Engineering Shenzhen University Senzhen China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Dhaka School of Computer Science & Technology Northumbria University Newcastle Tayne United Kingdom Northumbria UK Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Due to its importance in studying people’s thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world’s most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.

关键词： Measurement Human computer interaction Sentiment analysis Analytical models Codes Accuracy Web 2.0 Text categorization Transformers information technology

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A Sequential-based Deep Learning Model for Dry Beans Classification

A Sequential-based Deep Learning Model for Dry Beans Classif...

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Smart Computing and Application (ICSCA), International Conference on

作者： R. Sujatha Jyotir Moy Chatterjee A. Rohith Rabie A. Ramadan Software and Systems Engineering Vellore Institute of Technology Vellore India Information Technology Lord Buddha Education Foundation Kathmandu Nepal Computer Engineering Cairo University Giza Egypt College of Computer Science and Engineering Hail University Hail Saudi Arabia

People take various types of cereals every day in their regular meals, but most people do not know their importance while consuming them. Each cereal has its benefit. One such cereal content is dry beans. Dry beans are a variety of beans that are produced in pods. These dry beans can be cooked and eaten, and it has numerous proteins and vitamins and are highly beneficial for our health. It also provides excellent immunity. Each bean has its characteristics, which can be identified with its unique features. These can be oval or kidney-shaped or even without a proper shape. According to their shape and features, they are classified separately. These nutrient-rich beans have to be classified based on their shapes. Human eyes sometimes may oversee or misclassify these tiny cereals for classification. This work involves the classification of 7 such dry bean varieties utilizing a deep learning context. The dataset utilized in this paper includes 13611 dry bean data for seven different UCI Machine learning repositories. In this work, we have taken seven different categorical labels: the dry bean varieties. The classification is done using deep learning techniques, and here we have utilized the Keras Sequential Algorithm for the classification. It is a supervised learning concept of machine learning used to predict two or more categorical labels. With the help of the deep learning approach, these dry beans are classified and obtained an accuracy of 94.88%.

关键词： Deep learning Proteins Industries Machine learning algorithms Shape Supervised learning Prediction algorithms

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SCALAR: A Part-of-speech Tagger for Identifiers

arXiv

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arXiv 2025年

作者： Newman, Christian D. Scholten, Brandon Testa, Sophia Behler, Joshua A.C. Banabilah, Syreen Collard, Michael L. Decker, Michael J. Mkaouer, Mohamed Wiem Zampieri, Marcos AlOmar, Eman Abdullah Alsuhaibani, Reem Peruma, Anthony Maletic, Jonathan I. Department of Software Engineering Rochester Institute of Technology RochesterNY United States Department of Computer Science Kent State University KentOH United States Department of Computer Science University of Akron AkronOH United States Department of Software Engineering Bowling Green State University Bowling GreenOH United States Department of Computer Science University of Michigan Flint FlintMI United States George Mason University School of Computing FairfaxVA United States Stevens Institute of Technology School of Engineering and Science HobokenNJ United States Prince Sultan University Department of Software Engineering Riyadh Saudi Arabia University of Hawaii at Manoa Department of Information and Computer Sciences HonoluluHI United States

The paper presents the Source Code Analysis and Lexical Annotation Runtime (SCALAR), a tool specialized for mapping (annotating) source code identifier names to their corresponding part-of-speech tag sequence (grammar pattern). SCALAR’s internal model is trained using scikit-learn’s GradientBoostingClassifier in conjunction with a manually-curated oracle of identifier names and their grammar patterns. This specializes the tagger to recognize the unique structure of the natural language used by developers to create all types of identifiers (e.g., function names, variable names etc.). SCALAR’s output is compared with a previous version of the tagger, as well as a modern off-the-shelf part-of-speech tagger to show how it improves upon other taggers’ output for annotating identifiers. The code is available on Github1 © 2025, CC BY.

关键词： computer software maintenance

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

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TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

arXiv

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arXiv 2024年

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University Guangdong China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA Few-Shot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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