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Synthetic-To-Real Video Person Re-ID

IEEE Transactions on Information Forensics and Security 2025年 20卷 5438-5450页

作者： Zhang, Xiangqun Han, Ruize Wang, Likai Song, Linqi Hou, Junhui Feng, Wei Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin China Shenzhen University of Advanced Technology City University of Hong Kong Shenzhen Institute of Advanced Technology China City University of Hong Kong Department of Computer Science Hong Kong

Person re-identification (Re-ID) is an important task and has significant applications for public security and information forensics, which has progressed rapidly with the development of deep learning. In this work, we investigate a novel and challenging setting of Re-ID, i.e., cross-domain video-based person Re-ID. Specifically, we utilize synthetic video datasets as the source domain for training and real-world videos for testing, notably reducing the reliance on expensive real data acquisition and annotation. To harness the potential of synthetic data, we first propose a self-supervised domain-invariant feature learning strategy for both static and dynamic (temporal) features. Additionally, to enhance person identification accuracy in the target domain, we propose a mean-teacher scheme incorporating a self-supervised ID consistency loss. Experimental results across five real datasets validate the rationale behind cross-synthetic-real domain adaptation and demonstrate the efficacy of our method. Notably, the discovery that synthetic data outperforms real data in the cross-domain scenario is a surprising outcome. 2005-2012 IEEE.

关键词： Self-supervised learning

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A Framework for Tile Processing on Edge Servers for Roadside Traffic Surveillance 23

A Framework for Tile Processing on Edge Servers for Roadside...

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Proceedings of the 7th International Conference on Algorithms, computing and Systems

作者： Saumya Jaipuria Arani Bhattacharya Ansuman Banerjee Advanced Computing and Microelectronics Unit Indian Statistical Institute India Department of Computer Science Indraprastha Institute of Information Technology India

ISBN: (纸本)9798400709098

Roadside traffic monitoring is increasingly performed by deploying roadside high-resolution video cameras and then running computer Vision (CV) models on the video data. Since computer vision models are compute-intensive as they utilize Deep Neural Networks (DNNs), the data is usually sent to one or more edge servers located adjacent to mobile base stations. Recent techniques propose running CV models on tiles of videos separately to detect and track small objects. Several CV models exist, each with different requirements of compute and memory. Since more compute and memory-intensive CV models provide higher accuracy, a key challenge of such techniques is to determine which vision model should be used on which tile. This becomes even more challenging if multiple videos are processed by the same edge server. In this paper, we first formulate this problem of model selection on edge devices as an Integer Linear Programming (ILP) problem, and then propose a heuristic to solve it. Our experiments show that it is quite effective in practice.

关键词： computer Vision Edge computing Integer Linear Programming

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The Performance Analysis and Comparison of Azure and AWS Hypervisor using Different Workloads in Virtualization

The Performance Analysis and Comparison of Azure and AWS Hyp...

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International Conference on ent computing and Control Systems (ICICCS)

作者： J Mary Ramya Poovizhi R Devi Department of Computer Science Schools of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Department of Computer Applications Schools of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai

The abstraction of IT infrastructure enables the integration and pooling of IT resources to be shared across several applications to compensate for declining resources. Growing business needs virtualization offers a conceptual encapsulation of current computing resources. It generates computer systems unrestricted by physical design. The implementation objective of virtualization in cloud computing is to simplify the provision of products by providing a platform for maximizing complex IT resources in a scalable way, hence decreasing the cost of cloud computing. Hypervisors are one of the essential components of the virtualization of Hardware. Many operating systems can run simultaneously on a single physical server in a virtualized environment. For cloud computing, the hypervisor is the best way to deliver a variety of operational environments. This work will compare the cloud centre hypervisor Azure and AWS with different environments and workloads. The result shows not all hypervisors will provide the same energy and power conception with workloads and environments.

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Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method 9th

Support Vector Machine for Satellite Images Classification U...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Mansor, Nur Suhaili Awang, Hapini Malami, Sarkin Tudu Shehu Zolkafli, Amirulikhsan Taiye, Mohammed Ahmed Maulana, Hanhan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Sokoto852101 Nigeria Department of Cultural Sciences Faculty of Arts and Humanities Linnaeus University Vaxjo351 95 Sweden Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Bandung40134 Indonesia

ISBN: (纸本)9789819995882

Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Si

关键词： Image classification

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Attentive Hawkes Process Application for Sequential Recommendation 28th

Attentive Hawkes Process Application for Sequential Recomme...

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28th International Conference on Database Systems for advanced Applications, DASFAA 2023

作者： Yu, Shuodian Ma, Li Gao, Xiaofeng Guo, Jianxiong Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China

ISBN: (纸本)9783031306716

Sequential recommendation systems which aim to predict the users’ future clicking items are essential in both research and industry services. Based on the contextual characteristics of users, a large number of existing methods exploit abundant information from user behavior sequences and gain users’ interests through expressive sequential models like recurrent neural networks and self-attention mechanism to predict the next item. However, the temporal generative process of behavior sequences, which is crucial to the complex relationships and distributions of users’ dynamic preferences, is rarely considered. In this paper, we propose HawRec (Hawkes process based sequential recommendation), which is a new representation learning approach to model the interacted sequences of users from a temporal point process perspective. In particular, we leverage temporal self-attention mechanism to make an analogy to the self-exciting mechanism of Hawkes process. We bring expressive superiority of neural network into the Hawkes process with non-parametric conditional intensity kernel, resulting in more accurate recommendation and inheriting robustness from the temporal point process. Furthermore, extensive experiments conducted on four public benchmarks demonstrate that our HawRec outperforms various baselines of state-of-the-art sequential recommendation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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A Comprehensive Framework to Enhance the Location Privacy for Geospatial Query Resolution

A Comprehensive Framework to Enhance the Location Privacy fo...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： D.S.Eunice Little Dani K. Rohini Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The rapid expansion of geospatial data analysis has led to an increasing need for systems that can respond quickly. Storing and analyzing extensive spatiotemporal datasets is crucial for extracting valuable information for location-aware applications like route planning and weather forecasts. Efficiently coordinating data sources and internet services is crucial for extracting results within time constraints, given the current surge in processing geographic searches. Privacy models that incorporate fog computing provide a significant advantage in terms of cost reduction and low latency. However, in order to address consumers' concerns about data exposure, recent efforts have been made to integrate fog computing for enhanced privacy. Through the use of simulated outcomes, it has been shown that the privacy protection offered by the Geo-Fog framework for localization services is highly effective. The system demonstrated average latency values as low as 23 ms for query loads of 200. Furthermore, the CPU usage remained below $37 \%$ even under the maximum query load of $\mathbf{5 0 0}$. Fog computing plays a crucial role in the advancement of location-based applications that prioritize efficiency and privacy. By enabling fog devices or servers to handle geospatial queries within the geographic environment, it ensures optimal management.

关键词： Privacy Data privacy Web and internet services Random access memory Weather forecasting Geospatial analysis Central Processing Unit Spatial resolution Protection Edge computing

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IoT-Enabled Automated Smart Irrigation System Incorporating Sensor Parameters 5th

IoT-Enabled Automated Smart Irrigation System Incorporating ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Neeraj Srinivas Reddy, P. Hasan, Dler Salih Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mechanical Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This article presents the experimental findings of a smart irrigation system evaluated in two separate agricultural settings—small-scale farming and large-scale agriculture. The system combines sensors, microcontrollers, and actuators to precisely monitor and regulate the irrigation process in order to optimum water use and crop output. Water savings range from 20 to 45% when using smart irrigation technology in small-scale agriculture. By only turning on the irrigation pump when necessary, the technology optimizes water use and fosters sustainable farming methods. Furthermore, by sustaining perfect growing conditions, the approach boosts crop output, with yield benefits ranging from 10 to 22%. These improvements assist small-scale farmers in becoming more profitable and raising their level of living. In large-scale agriculture, smart irrigation systems allow excellent water management and resource optimization across huge agricultural fields. The technology consistently saves a large quantity of water, often between 25 and 50%, addressing water scarcity issues. Additionally, this method has led to a 12–25% increase in aggregate crop yields, thereby raising farm income and increasing agricultural firm profitability. Experiments shows that this smart irrigation system could bring cost-effective, sustainable techniques to extensive and intensive agriculture alike. Intelligence-guided decision-making technology is applied here, and cutting-edge technologies are brought to bear on the water use problem. The system conserves precious resources and increases agricultural output at the same time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart agriculture

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Product Reviews Based Sentimental Analysis by Feature Extraction and Classification Using Machine Learning Algorithm

Product Reviews Based Sentimental Analysis by Feature Extrac...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： A.S. Sridhar S. Nagasundaram Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Sentiment analysis and opinion mining are two of the most popular domains for evaluating and extracting insights from text data from various sources, like Facebook, Twitter, Amazon, and so on. It is essential for giving companies the ability to actively work on refining their company plans and thoroughly understand what customers have to say about their offerings. This research proposes novel technique in product review based sentimental analysis by online reviews data feature extraction with classification using machine learning model. here the input has been collected as online review data and processed for noise removal and normalization. Then, this data features are extracted and classified using graph recurrent component analysis with support Bayesian fuzzy neural network (GRCA-SBFNN) for detection of the user sentiment for the reviewed product. the experimental analysis has been carried out for various online product review dataset in terms of training accuracy, average precision, area under the curve (AUC), F-1 score and recall. The proposed technique Training accuracy 98%, Average precision 97%, AUC 91%,Recall 96%, F-1 SCORE 92%.

关键词： Training Sentiment analysis Analytical models Reviews Social networking (online) Machine learning Companies Feature extraction Fuzzy neural networks Data mining

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Translation-Invariant Quantum Algorithms for Ordered Search are Optimal

arXiv

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arXiv 2025年

作者： Carolan, Joseph Childs, Andrew M. Kovacs-Deak, Matt Schaeffer, Luke Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland United States David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo Canada

Ordered search is the task of finding an item in an ordered list using comparison queries. The best exact classical algorithm for this fundamental problem uses [log2 n] queries for a list of length n. Quantum computers can achieve a constant-factor speedup, but the best possible coefficient of log2 n for exact quantum algorithms is only known to lie between (ln 2)/π ≈ 0.221 and 4/log2 605 ≈ 0.433. We consider a special class of translation-invariant algorithms with no workspace, introduced by Farhi, Goldstone, Gutmann, and Sipser, that has been used to find the best known upper bounds. First, we show that any exact, k-query quantum algorithm for ordered search can be implemented by a k-query algorithm in this special class. Second, we use linear programming to show that the best exact 5-query quantum algorithm can search a list of length 7265, giving an ordered search algorithm that asymptotically uses 5 log7265 n ≈ 0.390 log2 n quantum queries. © 2025, CC BY.

关键词： Consensus algorithm

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IoT based Monitoring of Mushroom 2

IoT based Monitoring of Mushroom

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2nd International Conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Eben, J. Lysa Kaur, Chamandeep Thelly, Mathews T Parimita Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Computer Applications Chennai India Jazan University Department of Computer Science and Information Technology Jazan Saudi Arabia Kuriakose Elias College Department of Botany Mannanam India Sam Higginbottom University of Agriculture Technology & Sciences Agricultural Engineering Prayagraj India

ISBN: (纸本)9781665491990

Most of India's population relies on farming for their livelihood. Every year brings new advances in agricultural technology. The use of wireless sensors in contemporary agriculture is crucial. The employment of wireless sensors across a wide range of agricultural applications has a profoundly favourable effect on crop productivity and economic efficiency. The mushroom business is still relatively young and modest compared to other sectors of India's agricultural market. The white button mushroom is India's most commercially significant fungus and is a global favourite. It may be grown anywhere with a suitable climate and soil, but most of the world's supply comes from North India in the winter. For mycelia to flourish, the ideal temperature range is between 22 and 25 degrees Celsius. In contrast, the sweet spot for developing fruit bodies is between 14 and 18 degrees Celsius with high relative humidity. Equipment for maintaining a steady temperature and pasteurising the mushrooms, should be present in the rooms used for growing. The primary objective of this study is to automate the mushroom production plant and keep track of the crop room's environmental state to reduce the amount of human care required for the mushroom plant. The Internet of Things has also modernized agriculture industries, creating an environmental monitoring and regulating system that is used in the mushroom farm in this work. Using the Speak internet platform, users may monitor ecological conditions in a mushroom farm on their Android mobile, including the ambient temperature, relative humidity, carbon dioxide level, and light intensity. The Arduino prototype platform is employed as the sensors station's controller, ensuring that the plant parameters are within the user-specified range. Using a pair of low-power ESP8266 as Wi-Fi modems, the current parameter status is relayed to a distant monitoring station. The integrated Arduino uses the programming environment to write the controller's cod

关键词： Controllers

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