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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Dan, Meng Ye, Jin Jiang, Wenchao Shan, Yuanchao Guangxi Key Laboratory of Multi-media Communications and Network Technology Nanning530000 China School of Computer and Electronic Information Guangxi University Nanning530000 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9781665494571

Adaptive bitrate (ABR) technology is widely adopted in video streaming to improve quality of experience (QoE). However, existing methods ignore the inherent characteristic of Human Visual System (HVS), i.e., HVS has different sensitivity to the quality distortion of different video content. Therefore, video contents with high visual sensitivity have higher visual importance and need to be allocated more bitrate resources. In this paper, we propose a novel rate adaptive algorithm by incorporating newly-defined visual sensitivity model. More specifically, a set of relevant features are extracted from video content to establish a deep neural network model that accurately reflects the visual sensitivity. The network state, buffer occupancy and visual sensitivity are comprehensively considered under deep reinforcement learning framework to select appropriate bitrate for maximizing QoE. Experimental results show that the proposed algorithm can generate an ABR strategy consistent with visual sensitivity, and improves the perceptual video quality and user QoE by 14.2% and 16.3%, respectively. © 2021 IEEE.

关键词： Visualization Adaptation models Sensitivity Heuristic algorithms Bit rate Reinforcement learning Streaming media

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Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search

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Journal of computer Science & Technology 2021年第5期36卷 1167-1183页

作者： Dong-Hui Yang Zhen-Yu Li Xiao-Hui Wang Kavé Salamatian Gao-Gang Xie Network Technology Research Center Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Global Energy Interconnection Research Institute Co. Ltd.Beijing 102209China LISTIC Laboratory of Computer Science SystemsInformation and Knowledge ProcessingUniversitéSavoie Mont BlancChambéry 73011France Computer Network Information Center Chinese Academy of SciencesBeijing 100190China

Internet users heavily rely on web search engines for their intended *** major revenue of search engines is advertisements(or ads).However,the search advertising suffers from *** generate fake traffic which does not reach the intended audience,and increases the cost of the ***,it is critical to detect fraud in web *** studies solve this problem through fraudster detection(especially bots)by leveraging fraudsters'unique ***,they may fail to detect new means of fraud,such as crowdsourcing fraud,since crowd workers behave in part like normal *** this end,this paper proposes an approach to detecting fraud in web search from the perspective of fraudulent *** begin by using a unique dataset of 150 million web search logs to examine the discriminating features of fraudulent ***,we model the temporal correlation of fraudulent keywords as a graph,which reveals a very well-connected community ***,we design DFW(detection of fraudulent keywords)that mines the temporal correlations between candidate fraudulent keywords and a given list of *** particular,DFW leverages several refinements to filter out non-fraudulent keywords that co-occur with seeds *** evaluation using the search logs shows that DFW achieves high fraud detection precision(99%)and accuracy(93%).A further analysis reveals several typical temporal evolution patterns of fraudulent keywords and the co-existence of both bots and crowd workers as fraudsters for web search fraud.

关键词： community structure fraud analysis fraudulent keyword detection web search

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Wavelet Convolutional Neural network for Low-Resolution Brain MRI Images 22

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Oh, Soo Min Li, Yifan Chong, Jo Woon Massachusetts Institute of Technology Wireless Information and Network Sciences Laboratory Cambridge 02139 MA United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems Cambridge 02139 MA United States Texas A&m University Department of Statistics College Station 77843 TX United States Texas Tech University Department of Electrical and Computer Engineering Lubbock 79409 TX United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon 16419 South Korea

ISBN: (纸本)9798331520526

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training. © 2025 IEEE.

关键词： brain MRI images wavelet convolutional neural networks Wavelet transform

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Blockchain in Environmental Sustainability Measures: a Survey

arXiv

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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

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Predictive model for daily risk alerts in sepsis patients in the ICU:visualization and clinical analysis of risk indicators

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Precision Clinical Medicine 2025年第1期8卷 28-39页

作者： Hao Yang Jiaxi Li Chi Zhang Alejandro Pazos Sierra Bairong Shen Information Center West China Hospital of Sichuan UniversityChengdu 610041China Department of Computer Science and Information Technologies Research Center for Information and Communications TechnologiesUniversity of A CoruñaBiomedical Research Institute of a CoruñaA Coruña 15071Spain Department of Clinical Laboratory Medicine Jinniu Maternity and Child Health Hospital of ChengduChengdu 610031China Joint Laboratory of Artificial Intelligence for Critical Care Medicine Department of Critical Care Medicine and Institutes for Systems GeneticsFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengdu 610041China

This study introduces a novel Transformer-based time-series framework designed to revolutionize risk stratification in Intensive Care Units(ICUs)by predicting patient outcomes with high temporal *** sequential data from the eICU database,our two-stage architecture dynamically captures evolving health trajectories throughout a patient’s ICU stay,enabling real-time identification of high-risk individuals and actionable insights for personalized *** model demonstrated exceptional predictive power,achieving a progressive AUC increase from 0.87(±0.021)on admission day to 0.92(±0.009)by day 5,reflecting its capacity to assimilate longitudinal physiological *** external validation across geographically diverse cohorts—including an 81.8%accuracy on Chinese sepsis data(AUC=0.73)and 76.56%accuracy on MIMIC-IV-3.1(AUC=0.84)—confirmed robust ***,SHAP-derived temporal heatmaps unveiled mortality-associated feature dynamics over time,bridging the gap between model predictions and clinically interpretable *** findings establish a new paradigm for ICU prognostics,where data-driven temporal modeling synergizes with clinician expertise to optimize triage,reduce diagnostic latency,and ultimately improve survival outcomes in critical care.

关键词： sepsis Transformer time-series visualization predicting mortality

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Graph Representation Learning for Microarchitecture Design Space Exploration 23

Graph Representation Learning for Microarchitecture Design S...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Xiaoling Yi Jialin Lu Xiankui Xiong Dong Xu Li Shang Fan Yang State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and School of Microelectronics Fudan University Shanghai China ZTE Corporation China and State Key Laboratory of Mobile Network and Mobile Multimedia Technology China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350323481

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRL-DSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.

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Class Incremental Learning for Knowledge Acquisition Based on Prototype network Gating Mechanism

Class Incremental Learning for Knowledge Acquisition Based o...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Xiaolong Jin Haozhe Liang Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

With the development of information technology, the medical field has accumulated a large amount of medical data, which continues to grow. Efficient extraction and effective utilization of knowledge from this vast amount of medical data are crucial for realizing its immense value. Traditional knowledge acquisition algorithms face challenges in effectively identifying and acquiring new types of knowledge in practical applications. To address this, this paper proposes a class-incremental learning method based on a prototype network gating mechanism, called PGMoE. This method integrates multiple experts through a prototype network-based gating mechanism, dynamically adjusting the model's response to new and old knowledge, thereby effectively mitigating the issue of catastrophic forgetting in class-incremental learning. PGMoE not only improves the stability and accuracy of the model in continuous learning processes but also ensures the model's ability to remember historical knowledge. Comparative experiments on multiple datasets with existing class-incremental learning methods demonstrate that PGMoE has significant performance advantages in handling continuous learning tasks.

关键词： Learning systems Accuracy Incremental learning Knowledge acquisition Scalability Heuristic algorithms Prototypes Logic gates Stability analysis Information technology

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ANNS: An Intelligent Advanced Non-Convex Non-Smooth Scheme for IRS-Aided Next Generation Mobile Communication networks

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Miaojiang Liu, Anfeng Xiong, Neal N. Ren, Yingying Song, Houbing Herbert Guangxi University School of Computer Electronics and Information China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Central South University School of Computer Science and Engineering Changsha410083 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States

Enhancing the communication rate and quality has become the primary goal for the development of next-generation mobile communication networks, and traditional techniques such as MIMO and increasing the transmit power of the base station (BS) have not achieved a leapfrog effect. The emergence of Intelligent Reflective Surfaces (IRS) provides more reliable technical support for providing high-energy and high-rate communications. However, IRS-aided joint optimization with communication rate and quality of service constraints is a non-convex non-smooth optimization problem, and the optimal global solution cannot be obtained due to its computational complexity. In this paper, we propose an intelligent Advanced Non-convex Non-smooth Scheme (ANNS) in IRS-aided Next Generation Mobile Communication networks for making the transmission rate of mission communication and communication quality effective. To ensure that the inequality constraints in the joint optimization problem are not violated and the equation constraints are satisfied, a hybrid deep reinforcement learning and data-experience-driven constraint security layer network is proposed, which maps the constraint violations into the safe feasible domain by mapping the constraint variables into the constraint variables mapping method, and the convergence of the algorithm is theoretically demonstrated. Experimental results show that the proposed ANNS performs superior optimization compared to SAC, DDPG, and A2C for solving non-convex non-smooth problems. The proposed ANNS has the potential to be generalized to other mobile computing applications with non-convex non-smooth characteristics. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Transforming computed tomography scans into a full-immersive virtual museum for the Antikythera Mechanism

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Digital Applications in Archaeology and Cultural Heritage 2023年 28卷

作者： Anastasovitis, Eleftherios Roumeliotis, Manos Computer and Network Systems Technologies Laboratory (CNST Lab) Department of Applied Informatics University of Macedonia 156 Egnatia Street Thessaloniki GR 54636 Greece

The extended research of the Computed Tomography (CT) scans of the fragments of the Antikythera Mechanism was the cornerstone for revealing the structure and functions of the first mechanical computer. In this article we present a unique methodology for the transformation of CT-scans files into photorealistic three-dimensional (3D) objects where other techniques, such as photogrammetry or 3D scanning, cannot be applied due to specific obstacles. These digital assets were reconstructed in the Lab, by using the CT-scans dataset for mesh generation, and only two high-resolution images for applying the textured materials. A quantitative evaluation from 62 volunteers’ testers of the Virtual Museum that hosts the digital representations of the fragments of the Antikythera Mechanism, confirms the high level of the achieved photorealism, in addition to usability, usefulness, and level of satisfaction. © 2023 Elsevier Ltd

关键词： Antikythera mechanism Computed tomography Cultural heritage Evaluation Immersion Virtual museum Virtual reality

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