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Answering reachability queries with ordered label constraints over labeled graphs

Frontiers of computer Science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of computer Science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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Advancements in Image Caption Bots: A Deep Learning Approach 6

Advancements in Image Caption Bots: A Deep Learning Approach

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Dabral, Adarsh Gusain, Raj Shekhar, Sushant Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Computer Science Engineering Dehradun India Propagation Research Lab Department of Electronics and Communication Engineering Dehradun India Kanpur India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350306613

In recent years, artificial intelligence (AI) has changed the area of computer vision, and image captioning is one of the many applications that have benefited from these advancements. An image caption bot is an intelligent system that generates a textual description of an image. This research paper will explore the development of image caption bots, the underlying technologies, and the various techniques used to build these systems. Image caption bots are developed with the help of deep learning models (DLM) such as convolutional neural networks (CNNs) the most popular nowadays and recurrent neural networks (RNN s) which is also popular in researchers. Featureextraction has been done from the images with CNN sand RNN s are used to generate the captions. These deep-learning models have significantly improved the accuracy and performance of image captioning systems. During training, the CNN extracts the relevant features from the images, and the RNN generates the corresponding captions. In order to decrease the discrepancy between generated and ground truth captions, the model is trained. The accuracy and efficiency of image caption procedures could be greatly increased by using the image caption bots developed in this research. These systems have several applications and can greatly aid visually impaired individuals in understanding images, assist search engines to better index and search images, and improve the accessibility of social media platforms for users with disabilities. Further research in this field could lead to more accurate and comprehensive Image captioning systems. © 2024 IEEE.

关键词： Caption-Bots CNN Deep Learning Image caption bot RNN

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An Ensemble-Based Machine Learning Approach to Identify SARS-CoV-2 Virus Infection by Analyzing S Protein Sequences 2nd

An Ensemble-Based Machine Learning Approach to Identify SARS...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Moni, Raka Zahid Hasan, Md. Shahriar Shakil, Md. Ferdous, Most. Jannatul Arefin, Mohammad Shamsul Bhuiyan, Touhid Health Informatics Research Lab Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9789819989362

Coronavirus disease (COVID-19), an effect of the SARS-CoV-2 virus, is an emerging infectious disease that infects humans due to interspecies transmission. Typically, Spike (S) protein plays a crucial role in entering host cells and beginning infection in contrast to the Membrane (M) and Envelope (E) proteins which are primarily involved in virus assembly. The purpose of this research is to examine the S protein sequences of the human virus to forecast SARS-CoV-2 infection potential. Using computational modeling and bioinformatics approaches, we analyzed the dataset taken from the Virus Pathogen Database except for 47 SARS2-CoV sequences from the SARS2-CoV Database containing aligned sequences of lengths 2396. This study suggests using several machine learning methods to determine infectivity based on the sequence of the coronavirus spike protein. The classical machine learning approach such as k-nearest neighbors (KNNs), Gaussian Naive Bayes (GaussianNB), support vector machine (SVM), decision tree (DT) is used. Bagging Classifier, Gradient Boosting algorithm, and Random Forest are used as the ensemble machine learning algorithm. Artificial Neural Network (ANN) is also applied as a deep learning model. The Gradient Boosting algorithm demonstrates the best accuracy in determining infectivity status. According to our research, the outcome of this study has a chance to accurately forecast coronavirus infectivity from genetic information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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In-band full-duplex MIMO PLC systems for relaying networks

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Digital Communications and Networks 2025年第1期11卷 145-159页

作者： Francisco J.Canete Gautham Prasad Lutz Lampe Communications and Signal Processing Lab Telecommunication Research Institute(TELMA)ETS Ingenieria TelecomunicacionUniversidad de MalagaSpain Ofinno RestonVAUSA Department of Electrical and Computer Engineering The University of British ColumbiaCanada

In Power Line Communications(PLC),there are regulatory masks that restrict the transmit power spectral density for electromagnetic compatibility reasons,which creates coverage issues despite the not too long ***,PLC networks often employ repeaters/relays,especially in smart grid neighborhood area *** in broadband indoor PLC systems that offer a notable data rate,relaying may pave the way to new applications like being the backbone for wireless technologies in a cost-effective manner to support the Internet-of-things *** this paper,we study Multiple-Input Multiple-Output(MIMO)PLC systems that incorporate inband full-duplex functionality in relaying *** present several MIMO configurations that allow end-to-end half-duplex or full-duplex operations and analyze the achievable performance with state-of-the-art PLC *** reach this analysis,we get channel realizations from random network layouts for indoor and outdoor *** adopt realistic MIMO channel and noise models and consider transmission techniques according to PLC *** concepts discussed in this work can be useful in the design of future PLC relay-aided networks for different applications that look for a coverage extension and/or throughput:smart grids with enhanced communications in outdoor scenarios,and“last meter”systems for high-speed connections everywhere in indoor ones.

关键词： PLC MIMO Relaying networks OFDM Spatial correlation Performance analysis

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HoloStream: a GPU-powered high-speed user interface for holographic microscopy imaging

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Optics Express 2025年第7期33卷 15648-15660页

作者： Morales, Johan Obando-Vásquez, Sofia Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering EAFIT University Medellin Colombia Optical Imaging Research Lab Electrical and Computer Engineering Department University of Massachusetts Dartmouth DartmouthMA United States

This paper presents HoloStream, a GPU-powered high-speed user interface designed for holographic microscopy imaging. The platform reconstructs quantitative phase images rapidly for off-axis digital holographic microscopy (DHM) systems operating in the telecentric regime. By optimizing critical computational procedures, the HoloStream app reconstructs phase maps of 1280 × 960 pixels up to 11 frames per second (FPS) in live visualization and 90 fps for pre-recorded holographic videos. This real-time phase compensation is performed by upgrading the semi-heuristic phase compensation (SHPC) algorithm through GPU acceleration via PyCUDA. The interface also offers a user-friendly experience with flexible control over acquisition modes, visualization formats, and 3D tracking functionalities. Experimental validation demonstrates the platform’s capability to achieve fast and accurate phase imaging for live microscopy applications. The HoloStream app advances DHM technology by combining high-speed processing with ease of use, making it suitable for a wide range of DHM imaging applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： computer graphics equipment

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Development of LiFi System for Communication and Control in Hilly Regions 6

Development of LiFi System for Communication and Control in ...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Dabral, Adarsh Gusain, Raj Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Computer Science Engineering Dehradun India Propagation Research Lab Department of Electronics and Communication Engineering Dehradun India Department of Electronics and Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350306613

In hilly regions where cable distribution is challenging and costly traditional communication systems often face difficulties. LiFi technology emerges as a promising solution in such contexts, leveraging communication through visible light. It uses light as a medium to provide data speeds that surpass those of Wi-Fi, This research paper presents an approach for a communication system specifically designed for hilly terrains using Light Fidelity (LiFi) technology. The proposed paper start with an introduction of LiFi technology and advantages of it over conventional communication methods in these regions. However, it also acknowledges the inherent limitations of Li-Fi, such as its low range, which is especially relevant in hilly topographies. The main body of the paper details the system's architecture, comprising a transmitter for sending data, a receiver to receive data, and dual operation: text for sending massage and operate devices, and audio for communication. To underscore its practicality, the system is showcased through two applications: an SOS emergency feature for hilly terrains and a device activation/deactivation application. In conclusion, the paper provides an in-depth examination of how this tailored system functions and its potential benefits for communities and operations in hilly landscapes. © 2024 IEEE.

关键词： Arduino Hilly region communication methods Laser LiFi Microcontroller Remote Station Communication

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer Science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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SmartCitrus: An Efficient Deep Learning Approach for Real-Time Detection and Classification of Citrus Leaf Diseases

SmartCitrus: An Efficient Deep Learning Approach for Real-Ti...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin Orchi, Nabiha Tasnim Alam, Md Ashraful Dipto, Shakib Mahmud Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dept. of Computer Science and Engineering Dhaka Bangladesh University of Liberal Arts Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dhaka Bangladesh

ISBN: (纸本)9798350350289

Bangladesh is a prominent citrus exporter. Annually, the country has been exporting citrus fruits to over 60 countries. Distinguishing various diseases affecting citrus leaves requires a significant investment of time, effort, and specialized knowledge. Consequently, it is essential to create an innovative method for detecting citrus diseases. In this study, we have devised a valuable methodology by employing CNN models to identify diseases in citrus leaves. By employing a distinctive ensemble strategy, we successfully trained the model using varying numbers of classes in each stage. In reality, it allowed us to utilize suitable varieties of leaves for various ailments. Furthermore, it has enhanced the rate at which models learn during the later stages. In addition, it has reduced the level of model intricacy in comparison to frequently employed ensemble models. The identification of plant diseases in the present study involved the utilization of leaf photographs and algorithms for segmentation and feature extraction. Ultimately, we have successfully attained a 96 % accuracy rate for each class, signifying a substantial potential for mitigating production losses. © 2024 IEEE.

关键词： Citrus fruits

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