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GAM:A GPU-Accelerated Algorithm for MaxRS Queries in Road Networks

Journal of computer science & technology 2022年第5期37卷 1005-1025页

作者： Jian Chen Kai-Qi Zhang Tian Ren Zhen-Qing Wu Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

In smart phones,vehicles and wearable devices,GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real *** a set of weighted points and a rectangle r in the space,a maximizing range sum(MaxRS)query is to find the position of r,so as to maximize the total weight of the points covered by r(i.e.,the range sum).It has a wide spectrum of applications in spatial crowdsourcing,facility location and traffic *** of the existing research focuses on the Euclidean space;however,in real life,the user’s moving route is constrained by the road network,and the existing MaxRS query algorithms in the road network are *** this paper,we propose a novel GPU-accelerated algorithm,namely,GAM,to tackle MaxRS queries in road networks in two phases *** phase 1,we partition the entire road network into many small cells by a grid and theoretically prove the correctness of parallel query results by grid shifting,and then we propose an effective multi-grained pruning technique,by which the majority of cells can be pruned without further *** phase 2,we design a GPU-friendly storage structure,cell-based road network(CRN),and a two-level parallel framework to compute the final result in the remaining ***,we conduct extensive experiments on two real-world road networks,and the experimental results demonstrate that GAM is on average one order faster than state-of-the-art competitors,and the maximum speedup can achieve about 55 times.

关键词： road network maximizing range sum GPU acceleration pruning strategy

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Gauze:enabling communication-friendly block synchronization with cuckoo filter

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Frontiers of computer science 2023年第3期17卷 113-123页

作者： Xiaoqiang DING Liushun ZHAO Lailong LUO Junjie XIE Deke GUO Jinxi LI College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Institute of Systems Engineering AMSPLABeijing 100141China

Block synchronization is an essential component of blockchain ***,blockchain systems tend to send all the transactions from one node to another for ***,such a method may lead to an extremely high network bandwidth overhead and significant transmission *** is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of ***,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization *** this paper,we propose a novel protocol named Gauze for fast block *** utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)*** up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or *** on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth *** evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.

关键词： block synchronization cuckoo filter probabilistic data structure

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All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

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IEEE Transactions on Multimedia 2025年 27卷 3343-3355页

作者： Wen, Yuanbo Gao, Tao Li, Ziqi Zhang, Jing Zhang, Kaihao Chen, Ting Chang'an University School of Information Engineering Xi'an China Chang'an University School of Data Science and Artificial Intelligence Xi'an China Australian National University School of Computing CanberraACT Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications. © 1999-2012 IEEE.

关键词： Restoration

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Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

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Biological Sequence Classification:A Review on Data and General Methods

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Research 2023年第3期2022卷 243-261页

作者： Chunyan Ao Shihu Jiao Yansu Wang Liang Yu Quan Zou School of Computer Science and Technology Xidian UniversityXi’anChina Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhouChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina

With the rapid development of biotechnology,the number of biological sequences has grown *** continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence *** are many branches of biological sequence classification *** this review,we mainly focus on the function and modification classification of biological sequences based on machine ***-based prediction and analysis are the basic tasks to understand the biological functions of DNA,RNA,proteins,and ***,there are hundreds of classification models developed for biological sequences,and the quite varied specific methods seem dizzying at first ***,we aim to establish a long-term support website(http://***/~acy/BioseqData/***),which provides readers with detailed information on the classification method and download links to relevant *** briefly introduce the steps to build an effective model framework for biological sequence *** addition,a brief introduction to single-cell sequencing data analysis methods and applications in biology is also ***,we discuss the current challenges and future perspectives of biological sequence classification research.

关键词： steps exponentially hundreds

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Empowering agricultural security with IoT and deep learning driven real-time alert systems

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International Journal of Information technology (Singapore) 2024年第7期16卷 4627-4635页

作者： Singh, Priya Krishnamurthi, Rajalakshmi Department of Computer Science Jaypee Institute of Information Technology

In the realm of smart agriculture, our primary objective is to enhance security in the agriculture field by combining IoT and computer vision technologies. By leveraging these advanced tools, we strive to protect field crops from unauthorized access, aligning with the core principles of smart agriculture that prioritize the safeguarding of agricultural assets. Various governmental initiatives have been innovated to tackle the issue of ensuring food security. However, the majority of farmers face limitation due to insufficient knowledge and financial means, to effectively manage issues such as field monitoring and object detection. Hence, we aim to create an energy-efficient real time system that intelligently detects objects and sends alerts to farmers specifically designed for agricultural purposes. By integrating ultrasonic sensors, Raspberry Pi edge, and real-time trained object detectors like Embed-YOLOv3 and EmbedT-YOLOv3, we achieve superior accuracy, faster processing, and enhanced speed. This system efficiently identifies and categorizes objects, instantly notifying farmers through a real time android application. Identification of objects followed by immediate action can preserve yields, significantly reducing crop damage and strengthening the country’s economy. This proactive approach can alleviate distress among numerous farmers. Achieving an exceptional precision rate of 97%, recall rate of 96%, and accuracy of 95.86%, our system harnesses low-power IoT devices coupled with deep learning to deliver robust crop protection in the agricultural field. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep learning Internet of things (IoT) Object detection Raspberry pi Ultrasonic sensors YOLOv3

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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Securing Cloud-based Data Sharing with Accountable Proxy Re-Encryption 4

Securing Cloud-based Data Sharing with Accountable Proxy Re-...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Mahalakshmi, E. Vaidhegi, A. Jayakumar, D. Ramkumar, M.O. Rajmohan, R. Department of Computer Science and Engineering Villupuram India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350349900

Cloud computing has been a considerable way to store and share data among different communities in the past few decades. However, it has always been a challenge to ensure that the data is secured during cloud data transfer. The proposed paper resolves the major challenges being faced in cloud data sharing with the new approach of Accountable Proxy-ReEncryption (APRE). The proposed paper deals with protocols and anticipates cryptography measures, namely homomorphic encryption, by ensuring two things: confidentiality and integrity. Both work on the shared data of the cloud. The major aspect of homomorphic encryption is that it has an inbuilt feature of function execution over the encrypted data without decryption. So, it communicates the data confidentially by maintenance. Thus, re-encryption of the proxy used in this research would also allow for safe delegation to the proxy servers. The APRE protocol brings about different accountability mechanisms. In this case, it deals with the aspect of transparency and data supervision in communication. The homomorphic signatures in the paper enable the data owner to monitor and audit the proxy functions. It strictly enforces the decryption policies and minimizes the rate of mishandling of shared information. In so doing, this layer boosts the trust of cloud data sharing, especially in terms of provision of confidentiality, preservation of privacy, and the allowance of the robustness of behaviour in verifying and tracking data access. Finally, this proposed system uses a comprehensive assessment framework to check the effectiveness and efficiency of the APRE protocol. It can, therefore, be inferred that from the outcome obtained from the results, this proposed system outperforms the other existing systems in security, performance, and effectiveness. © 2024 IEEE.

关键词： Cloud platforms

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Semisupervised learning-based depth estimation with semantic inference guidance

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science China(Technological sciences) 2022年第5期65卷 1098-1106页

作者： ZHANG Yan FAN XiaoPeng ZHAO DeBin Department of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given *** it is an ill-posed problem,to fit the projection function from the given scene to the 3D structure,traditional methods generally require mass amounts of annotated *** pixel-level annotation is quite labor consuming,especially when addressing reflective surfaces such as mirrors or *** widespread application of deep learning further intensifies the demand for large amounts of annotated ***,it is urgent and necessary to propose a framework that is able to reduce the requirement on the amount of *** this paper,we propose a novel semisupervised learning framework to infer the 3D structure from the given ***,semantic information is employed to make the depth inference more ***,we make both the depth estimation and semantic segmentation coarse-to-fine frameworks;thus,the depth estimation can be gradually guided by semantic *** compare our model with state-of-the-art *** experimental results demonstrate that our method is better than many supervised learning-based methods,which proves the effectiveness of the proposed method.

关键词： depth estimation semisupervised learning semantic information neural networks

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