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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yu, Xinze Xie, Yunzhou Xu, Qiujie Xu, Zhuqing Xiong, Runqun Southeast University College of Software Engineering Suzhou China Imperial College London Department of Mathematics London United Kingdom Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350331684

Industrial Internet of Things (IIoT) is considered one of the most revolutionary technologies that can significantly improve manufacturing efficiency and realize intelligent production. With the increasing complexity of industrial manufacturing, the manufacturing process of a product often involves several different IoT domains (e.g., factories). To achieve a common production goal, devices from various domains share their data for cooperative work, which raises privacy and security concerns for cross-domain communication. Most existing data-sharing schemes rely on a trusted third party. Hence, the privacy and security issues in cross-domain data sharing are still challenging research directions. This paper proposes CBDS, a consortiumblockchain-based cross-domain IIoT data-sharing mechanism. Specifically, we introduce consortium blockchain to construct trust among different domains in IIoT. A group signature is presented to ensure each device's privacy to achieve anonymous authentication. In addition, the collected data should be stored in the ciphertext. The smart contract and proxy re-encryption are utilized in the CBDS to realize secure cross-domain data sharing. The experimental results demonstrate the effectiveness and efficiency of the proposed CBDS. © 2023 IEEE.

关键词： Smart contract

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One Gray Code Fits All: Optimizing Access Time with Bi-Directional Programming for QLC SSDs

One Gray Code Fits All: Optimizing Access Time with Bi-Direc...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Li, Shaoqi Wang, Tianyu Zhu, Yongbiao Ma, Chenlin Wang, Yi Shen, Zhaoyan Shao, Zili College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9783982674100

Gray code, a voltage-level-to-data-bit translation scheme, is widely used in QLC SSDs. However, it causes the four data bits in QLC to exhibit significantly different read and write performance with up to 8 × latency variation, severely impacting the worst-case performance of QLC SSDs. This paper presents BDP, a novel Bi-Directional Programming scheme. Based on a fixed Gray code, BDP combines both the normal (forward) and reverse programming directions to enable runtime programming direction arbitration. Experimental results show that BDP can effectively improve the read and write performance of SSD compared to representative schemes. © 2025 EDAA.

关键词： Gray Code QLC SSD Two-Step Programming

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Blockchain-Based Forensic Investigation Metamodel (BBFIM) for Investigating IoT Cybercrimes

Blockchain-Based Forensic Investigation Metamodel (BBFIM) fo...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Daifallah Zaid Alotaibe Department of Software Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin Kingdom of Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

As smart devices are rapidly being developed, and people trust data transfer mechanisms such as Wi-Fi and the way they use them, as well as the fact that they rely heavily on data transfer mechanics, there has been an increase in cyber-attacks. Therefore, examining IoT safety threats and potential countermeasures can support investigators in determining how to deal with a range of stressful scenarios encountered when conducting cyber-crime studies to develop acceptable methods for dealing with them. The goal of this paper is to develop Blockchain-Based Forensic Investigation Metamodel for Investigating IoT Cybercrimes called (BBFIM). The purpose of the developed BBFIM is to improve the productivity and accuracy of examinations by providing a safe and decentralized environment where digital evidence, related to IoT-related crimes, can be stored, and analyzed to improve the productivity and accuracy of examinations. Through the integration of blockchain technologies the developed BBFIM ensures that the reliability of the evidence is retained throughout the investigation. It also provides transparency throughout the process. This study utilizes design science methods for development and validation purposes. Therefore, 889 IoT forensic models and frameworks have been reviewed in this study, and 15 models have been selected to develop the BBFIM. The developed BBFIM consists of three main IoT forensic practices: Forensic investigation phases, Forensic Tools and Techniques, and Blockchain Infrastructure. Compared to the existing IoT forensics models the developed BBFIM is fully decentralized which can guarantee the authority, consistency, and privacy of the data.

关键词： Productivity Accuracy Forensics Design methodology Data transfer Blockchains Safety Smart devices Cyberattack Wireless fidelity

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ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks 24

ReForm-Eval: Evaluating Large Vision Language Models via Uni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Zejun Wang, Ye Du, Mengfei Liu, Qingwen Wu, Binhao Zhang, Jiwen Zhou, Chengxing Fan, Zhihao Fu, Jie Chen, Jingjing Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Software College Northeastern University Liaoning Shenyang China Alibaba Group Zhejiang Hangzhou China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities to perceive visual signals and perform visually grounded reasoning. However, the capabilities of LVLMs have not been comprehensively and quantitatively evaluated. Most existing multi-modal benchmarks require task-oriented input-output formats, posing great challenges to automatically assess the free-form text output of LVLMs. To effectively leverage the annotations available and reduce the manual efforts required for constructing new benchmarks, we propose to re-formulate existing benchmarks into unified LVLM-compatible formats. Through systematic data collection and reformulation, we present ReForm-Eval benchmark, offering substantial data for evaluating various capabilities of LVLMs. Through extensive experiments and analysis in ReForm-Eval, we demonstrate the comprehensiveness and reliability of ReForm-Eval in assessing various LVLMs. Our benchmark and evaluation framework is now available at https://***/FudanDISC/ReForm-Eval © 2024 ACM.

关键词： Visual languages

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A Practical Learning-Based FTL for Memory Constrained Mobile Flash Storage

A Practical Learning-Based FTL for Memory Constrained Mobile...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Zelin Du Kecheng Huang Tianyu Wang Xin Yao Renhai Chen Zili Shao Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR College of Computer Science and Software Engineering Shenzhen University Shenzhen China Huawei Technologies China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapidly growing mobile market is pushing flash storage manufacturers to expand capacity into the terabyte range. However, this presents a significant challenge for mobile storage management: more logical-to-physical page mappings are desired to be efficiently managed and cached while the available caching space is extremely limited. This motivates us to shift toward a new learning-based paradigm: rather than maintaining mappings for individual pages, the learning-based approach can represent mapping relationships for a set of continuous pages. However, to construct linear models, existing methods that either consume the already-limited memory space or reuse flash garbage collection demonstrate poor model construction capabilities or significantly degrade flash performance, making them impractical for real-world use. In this paper, we propose LFTL, a practical, learning-based on-demand flash translation layer design for flash management in mobile devices. In contrast to prior work that centered around gathering sufficient mappings for linear model construction, our key insight is that linear patterns can be extracted and refined by leveraging the orderly, LPA-aligned write stream typical of mobile devices. By doing this, highly accurate linear models can be constructed regardless of the constraints of mobile device's cache limitation. We have implemented a fully functional prototype of LFTL based on FEMU. Our evaluation results show that LFTL is more adaptable to memory-constrained storage devices than state-of-the-art learning-based approaches.

关键词： Performance evaluation Adaptation models Maximum likelihood detection Translation Accuracy Storage management Prototypes Nonlinear filters Mobile handsets Load modeling

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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computer Systems science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Class-attention video transformer for engagement prediction

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Multimedia Tools and Applications 2024年 1-20页

作者： Ai, Xusheng Sheng, Victor Li, Chunhua Yang, Han Cui, Zhiming Software and Service Outsourcing College Suzhou Vocational Institute of Industrial Technology 1 Zhineng Avenue Jiangsu Suzhou215104 China Department of Computer Science Texas Tech University 2500 Broadway LubbockTX79409 United States School of Electronics and Information Engineering Suzhou University of Science and Technology No.1 Kerui Road Jiangsu Suzhou215009 China

In this paper, we propose the Class Attention in Video Transformer (CavT), an end-to-end method designed to process both long and short variant-length videos for student engagement prediction. CavT introduces a single vector for class embedding and incorporates the Binary-Order Representatives Sampling (BorS) technique to augment the dataset by adding multiple video sequences. Our method outperforms the state-of-the-art with MSE values of 0.0495 on the EmotiW-EP and 0.0377 on the DAiSEE datasets, providing a robust and scalable solution for engagement prediction. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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A Comprehensive Survey on Communication-Efficient Federated Learning in Mobile Edge Environments

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IEEE Communications Surveys and Tutorials 2025年

作者： Jia, Ninghui Qu, Zhihao Ye, Baoliu Wang, Yanyan Hu, Shihong Guo, Song Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Nanjing211100 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Kowloon Hong Kong

In traditional centralized machine learning, transmitting raw data to a cloud center incurs high communication costs and raises privacy concerns. This is particularly challenging in mobile edge environments, where devices are dynamic and resource-constrained. Federated Learning (FL) addresses these issues by allowing devices to train models locally and upload parameters to a central server without sharing local data. However, limited wireless channel resources and dynamic transmission performance make communication overhead a major bottleneck of FL in mobile edge environments. Concerning this challenge, this survey provides a comprehensive summary of methods to improve communication efficiency in FL, focusing on: 1) minimizing communication complexity to reduce total transmission volume, 2) scheduling resources appropriately to improve training efficiency, 3) utilizing over-the-air computation (OTA) to integrate computation into communication for accommodating the computation/communication characteristics of FL in mobile edge environments. Thus, this work analyzes research from the perspective of convergence and data heterogeneity to reduce communication rounds by optimizing algorithm performance. We hope that this survey could offer insights into communication-efficient FL for future research. © 2025 IEEE.

关键词： Federated learning

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An Ensemble Model for software Defect Prediction 2

An Ensemble Model for Software Defect Prediction

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Ali, Amad Rizwan Ur Rehman, Attique Nawaz, Ali Ali, Tahir Muhammad Abbas, Muhammad UserMaven Inc United States University of Sialkot Department of Software Engineering Sialkot Pakistan College of Electrical and Mechanical Engineering National University of Science and Technology Rawapindi Pakistan Gulf University for Science and Technology Department of Computer Science College of Science and Arts Kuwait

ISBN: (纸本)9781665498197

software testing is one of the important ways to ensure the quality of software. It is found that testing cost more than 50% of overall project cost. Effective and efficient software testing utilizes the minimum resources of software. Therefore, it is important to construct the procedure which is not only able to perform the efficient testing but also minimizes the utilization of project resources. The goal of software testing is to find maximum defects in the software system. As world is continuously moving toward data driven approach for making important decision. Therefore, in this research paper we performed the machine learning analysis on the publicly available datasets and tried to achieve the maximum accuracy. The major focus of the paper is to apply different machine learning techniques on the datasets and find out which technique produce efficient result. Particularly, we proposed an ensemble learning models and perform comparative analysis among KNN, Decision tree, SVM and Naïve Bayes on different datasets and it is demonstrated that performance of Ensemble method is more than other methods in term of accuracy, precision, recall and F1-score. The classification accuracy of ensemble model trained on CM1 is 98.56%, classification accuracy of ensemble model trained on KM2 is 98.18% similarly, the classification accuracy of ensemble learning model trained on PC1 is 99.27%. This reveals that ensemble learning is more efficient method for making the defect prediction as compared other techniques. © 2022 IEEE.

关键词： software testing

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