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Zero-Shot Synth-to-Real Depth Estimation: From Synthetic Street Scenes to Real-World Data

Zero-Shot Synth-to-Real Depth Estimation: From Synthetic Str...

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Brazilian symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Luís Gustavo Lorgus Decker Jose Luis Flores Campana Marcos Roberto e Souza Helena de Almeida Maia Helio Pedrini Institute of Computing University of Campinas Campinas SP Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

this paper introduces a novel method for estimating depth maps from single images using a convolutional neural network (CNN) architecture. Our approach leverages synthetically generated data that simulates front views from autonomous vehicles. the model employs a ConvNeXt encoder and a U-Net-based decoder, achieving effective depth estimation performance. A scale- and shift-invariant loss function is utilized during training to enhance generalization capabilities. the proposed model demonstrates strong results on real-world datasets without requiring fine-tuning, highlighting the effective transfer of information from synthetic to real-world data. Additionally, we show that high-quality data significantly improves performance compared to larger, low-quality datasets.

关键词： Training Graphics Sensitivity Filtering Data integrity Estimation Feature extraction Data models Convolutional neural networks Synthetic data

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the AllScale API

The AllScale API

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IEEE International Conference on e-Science and Grid computing

作者： Philipp Gschwandtner Herbert Jordan Peter thoman thomas Fahringer Department of Computer Science University of Innsbruck Innsbruck Austria

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

Effectively implementing scientific algorithms in distributed memory parallel applications is a difficult task for domain scientists, as evident by the large number of domain-specific languages and libraries available today attempting to facilitate the process. However, they usually provide a closed set of parallel patterns and are not open for extension without vast modifications to the underlying system. In this work, we present the AllScale API, a programming interface for developing distributed memory parallel applications with the ease of shared memory programming models. the AllScale API is closed for modification but open for extension, allowing new, user-defined parallel patterns and data structures to be implemented based on existing core primitives and therefore fully supported in the AllScale framework. Focusing on high-level functionality directly offered to application developers, we present the design advantages of such an API design, detail some of its specifications and evaluate it using three real-world use cases. Our results show that AllScale decreases the complexity of implementing scientific applications for distributed memory while attaining comparable or higher performance compared to MPI reference implementations.

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Designing Efficient Irregular Networks for Heterogeneous Systems-on-Chip

Designing Efficient Irregular Networks for Heterogeneous Sys...

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Euromicro symposium on Digital System Design

作者： C. Neeb N. Wehn Erwin-Schrödinger-Strasse University of Kaiserslautern Kaiserslautern Germany

Networks-on-Chip will serve as the central integration platform in future complex SoC designs, composed of a large number of heterogeneous processing resources. Most researchers advocate the use of traditional regular networks like meshes, tori or trees as architectural templates which gained a high popularity in general-purpose parallel computing. However, most SoC platforms are special-purpose tailored to the domain-specific requirements of their application. they are usually built from a large diversity of heterogeneous components which communicate in a very specific, mostly irregular way. In this work, we propose a methodology for the design of customized irregular networks-on-chip, called INoC. We take advantage of a priori knowledge of the applications communication characteristic to generate an optimized network topology and routing algorithm. We show that customized irregular networks are clearly superior to traditional regular architectures in terms of performance at comparable implementation costs for irregular workloads. Even more, they inherently offer true scalability and expansibility which can normally not be accomplished by traditional approaches

关键词： Network topology Network-on-a-chip Routing Costs Parallel processing Design methodology Silicon computer architecture Bandwidth Electronic mail

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Parallel Multi-hop Routing Protocol for 5G Backhauling Network Using HPC Platform

Parallel Multi-hop Routing Protocol for 5G Backhauling Netwo...

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International Conference on computer Engineering and Systems

作者： Somaya A. Aboulrous Ali A. El-Moursy Mohamed Saad Amany Abdelsamea Salwa M. Nassar Hazem Abbas Computer and Systems Engineering Ain Shams University Cairo Egypt Computer Engineering University of Sharjah Sharjah UAE Electronics Research Institute Giza Egypt

ISBN: (数字)9780738105598

ISBN: (纸本)9781665405232

Fifth generation (5G) systems are considered as the future of telecommunications and data processing. 5G systems are envisaged to increase current network capacity by 1000-fold. In addition to increasing spectral efficiency and utilizing wider bandwidths, ultra network densification with wireless small cells is considered a significant capacity-enhancing method for meeting the ever-increasing demand of 5G data traffic requirements. the enormous amounts of data traffic generated in inter-small-cell 5G backhaul networks require efficient routing protocols to speed up the routing decisions, while ensuring high data rates, low latency, and low power consumption requirements. therefore, a parallel routing protocol is proposed to speed up routing decisions in 5G backhaul networks, using high performance computing (HPC). We explore the efficiency of utilizing HPC to manage and speed up the parallel routing protocol using different communication network sizes. Our numerical results indicate that our HPC implementation achieves a routing speed-up of 37× for large network size (2048).

关键词： Wireless communication 5G mobile communication Spectral efficiency Spread spectrum communication Routing Routing protocols Telecommunications

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms

Machine Learning Techniques for Identifying Child Abusive Te...

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International Conference on computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Mohammad Kamal Uddin Mohammad Tarek Aziz Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. the primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. the dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. the performance of the proposed model was assessed using precision, recall, and F1 score metrics. the findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention.

关键词： Deep learning Support vector machines Radio frequency Measurement Machine learning algorithms Social networking (online) Prevention and mitigation Computational modeling Natural language processing Long short term memory

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Assessment of Water Conservation Function Based on Deep Autoencoder

Assessment of Water Conservation Function Based on Deep Auto...

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Xiaoqing Fan Xianghong Lin Hao Liu College of Computer Science and Engineering Northwest Normal University Lanzhou China College of Agriculture Ningxia University Yinchuan China

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

the accuracy of water conservation assessments is crucial for formulating water resource management and ecological protection policies. However, existing methods overly rely on expert judgment and struggle with precision when handling high-dimensional data. To address this, we propose a deep autoencoder-based method for evaluating water conservation functions. For the Gannan water conservation area, we developed a comprehensive evaluation index system and integrated multisource data to create an ecological dataset. By employing a deep autoencoder model combined with convolutional neural networks and a joint training strategy, we achieved feature extraction and dimensionality reduction, mapping high-dimensional data to a low-dimensional latent space. Subsequently, we used the K-means clustering algorithm to validate the model’s classification performance. the clustering accuracy and FMI based on AE-CNN extracted latent variables were significantly higher than those obtained by clustering the raw data directly. this demonstrates that the model effectively extracts data features and significantly enhances classification accuracy, providing robust support for ecological protection and water management.

关键词： Training Accuracy Biological system modeling Ecosystems Water conservation Feature extraction Indexes Data mining Convolutional neural networks Protection

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Energy-efficient dynamic scheduling on parallel machines 08

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Proceedings of the 15th international conference on high performance computing

作者： Jaeyeon Kang Sanjay Ranka Department of Computer and Information Science and Engineering University of Florida

ISBN: (纸本)9783540898931

Energy consumption is a critical issue in parallel and distributedsystems. Workflows consist of a number of tasks that need to be executed tocomplete an application. these tasks typically have precedence relationshipsthat have to be observed during execution for correctness. DAGs (DirectedAcyclic Graphs) can be used to represent many such workflows. the staticalgorithms to schedule for energy minimization under the deadline constraintsare based on estimating worst case execution time for each task to guaranteethat the application completes by a given deadline. During execution, manytasks may complete earlier than expected during the actual execution. thisallows for adjusting the schedule for the tasks that have not yet begun executionto incorporate the extra slack. this has to be done with the dual goal ofreducing the energy requirements while still meeting the deadline constraints. Inthis paper, we present a novel dynamic algorithm for remapping tasks forenergy efficient scheduling of DAG based applications for DVS enabledsystems. Our experimental results show that the combination of our dynamicassignment and dynamic slack allocation leads to significantly better energyminimization compared to not changing the static schedule and/or onlyperforming dynamic slack allocation. Furthermore, its execution timerequirements are small enough to be useful for a large number of applications.

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Internet of things Enabled Precision Aquaculture Monitoring and Measurements

Internet of Things Enabled Precision Aquaculture Monitoring ...

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International Conference on computing and Networking Technology (ICCNT)

作者： G R Kanagachidambaresan P Rohini M Muralidhar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India ICAR - Central Institute of Brakishwater Aquaculture Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the shrimp export industry in India, valued at over 23 thousand crores, faces challenges due to the high-risk, high-reward nature of shrimp farming and the demanding monitoring requirements it entails. Existing automation and IoT solutions often struggle to effectively monitor the diverse marine, brackish, and freshwater environments crucial for shrimp cultivation. this paper introduces the aqua buoy, a specialized monitoring system designed specifically for shrimp farming environments. Alongside detailing the aqua buoy’s functionality, the paper discusses its utility in monitoring various diseases that afflict shrimp cultivation. Furthermore, it explores how farmers are tackling these challenges through enhanced biosecurity measures, selective breeding programs, and sustainable practices. Offering insights into Litopenaeus vannamei shrimp farming, the article sheds light on the industry’s evolution, current hurdles, and promising prospects for aquaculture’s future. With its swift response times, continuous monitoring capabilities, and over $\mathbf{9 8 \%}$ accuracy rate in real-time environments, the aqua buoy leverages a machine learning-based approach for sensor calibration, ensuring optimal performance in shrimp farming operations.

关键词： Water quality Sensor systems Real-time systems Velocity measurement Time factors Sustainable development Monitoring Farming Aquaculture Diseases

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Data Compression Techniques for high-Speed NetworkCommunications

Data Compression Techniques for High-Speed NetworkCommunicat...

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International Conference on computing and Networking Technology (ICCNT)

作者： Manish Nagpal G Rajalakshmi Nitish Vashisht N. Beemkumar Jayashree V. Bagade K. Yuvaraj Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Mechanical Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Data compression is an important factor of excessive-pace network communications, as it may lessen the number of facts that need to be transmitted over a given network. Facts compression techniques search to lessen the scale of data via encoding it in a more green format. Common compression techniques utilized in networks consist of static Huffman encoding, variable period encoding, arithmetic encoding, differential coding, run-period encoding, and compression with a chance distribution. Every approach has its personal deserves and boundaries, so the selection of a compression technique is dependent on the type of information and the network situations. Additionally, some of these techniques carry out better while implemented in aggregate with one another, offering a usual development in network throughput.

关键词： performance evaluation high-speed networks Aggregates Data compression throughput Encoding Safety Arithmetic

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Enhancing Cloud Virtualization Security with a Z-Score Based Conjugate Self-Organizing Migration ML Model

Enhancing Cloud Virtualization Security with a Z-Score Based...

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International Conference on computing and Networking Technology (ICCNT)

作者： Sagar Sidana Sujata Negi thakur Anurag Sinha N Indhumathi Petheru Raj Ahmed Al-Khayyat Vyom Modi Aditya Mishra Anita Kumari Department of computer science and engineering Maharshi Dayanand University Rohtak Haryana India Department of computer science and engineering Graphic Era Hill University India School of Computing and information Science IGNOU New Delhi India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode India Reliance Jio Platforms Ltd Bangalore India Department of computer science and engineering Islamic University Iraq Marwari Education Foundations Group Of Institutions Rajkot India Department of computer science and engineering Applications GLA University Mathura India Department f English Literature RKDF University India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this study presents a new machine learning model for detecting malware in cloud computing settings called Conjugate Self-Organizing Migration (CSOM), which is based on Z-score. Feature vectors are standardized by the model through the use of Z-score normalization, which improves its flexibility with changing data distributions. By merging self-organizing migratory processes with statistical analysis, the CSOM model effectively reduces the threat of growing malware. Using Z-score normalization to guarantee correct representation of changing data, the imaging approach depicts the cloud environment as a dynamic, self-organizing landscape. through conjugate migration techniques, the CSOM model dynamically modifies synaptic weights to minimize false positives and enable the identification of tiny fluctuations in harmful actions. the Z-score-based CSOM model is effective in detecting malware at high accuracy rates in a variety of dynamic cloud environments, as demonstrated by the experimental results. the model demonstrates its flexibility in responding to changing cyberthreats with its strong performance in real-world settings. Finally, with its better accuracy and adaptability, the suggested Z-score-based CSOM model offers a potential approach for cloud computing virus detection. In order to handle the constantly shifting environment of cloud-based cyber threats, further research may examine scalability and more refinement.

关键词： Cloud computing Adaptation models Accuracy Statistical analysis Computational modeling Scalability Vectors Security Virtualization Viruses (medical)

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