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Emerging Technology (INCET), International Conference for

作者： Pappala Kumar Aditya Naga Sushanth Kumar Vuppala Kalla Likhit Sai Eswar Ramesh Guntha Sruthy Anand Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Rural empowerment is possible only by empowering the whole community; in this context, women's empowerment is pivotal to achieving rural upliftment. There are several studies on women's empowerment and the role of digital tools in empowering rural communities. In this work, our focus is on designing and developing the software to empower the rural SHG community. Our work also presents details of some of the existing work and introduces a user-friendly software solution to streamline the management and administration of one of the rural Self-Help Groups (SHGs) in India, which works in several hierarchies such as clusters, groups, and users. We adopted a user-centric design of the software solution as it has to accommodate users with diverse digital literacy levels. We used Google Flutter and *** framework to develop this software.

关键词： Economics Software design Navigation Scalability User centered design Programming Software

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Energy-Efficient Clustering Algorithm Analysis for Intelligent Marine Ecosystem Monitoring

Energy-Efficient Clustering Algorithm Analysis for Intellige...

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Renewable Energies and Smart Technologies (REST), International Conference on

作者： Qamil Kabashi Isak Shabani Almas Abbasi Aaqif Afzaal Abbasi Department of Electronics Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosova Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosova Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Department of Earth and Marine Sciences National Biodiversity Future Center University of Palermo Palermo Italy

ISBN: (数字)9798350358902

ISBN: (纸本)9798350358919

Marine ecosystems are complex and dynamic environments. Monitoring these ecosystems is essential for understanding and identifying potential threats and conservation needs. Different sensory devices such as wireless sensor networks (WSNs) are used to monitor these environments. WSN have been in industrial use for the last two decades. However, energy efficiency in WSNs remains a topic that requires attention. With the rise of artificial intelligence and machine learning, numerous techniques have been proposed for developing energy-efficient clustering algorithms. These algorithms aim to improve the efficiency of battery- driven sensors to prolong the network's lifetime by balancing the energy consumption among all nodes in the network. In this paper, we present a comprehensive evaluation and literature review of state-of-the-art energy-efficient clustering algorithms that can be used for intelligent marine ecosystem monitoring. The review aims to dive into the theoretical aspects of clustering in sensor networks and its application to energy optimization, offering insights into the various clustering schemes proposed for wireless sensors, specifically focusing on energy efficiency.

关键词： Wireless communication Wireless sensor networks Renewable energy sources Machine learning algorithms Reviews Clustering algorithms Marine ecosystems

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Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Bi-directional Feature Fusion Generative Adversarial Network...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kexin Sun Zhineng Chen Gongwei Wang Jun Liu Xiongjun Ye Yu-Gang Jiang Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science & Shanghai Fudan University Shanghai Qi Zhi Institute Peking University People's Hospital Department of Urology National Cancer Center & National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College

The cost of pathological examination makes virtual restaining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual restaining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple postprocessing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bidirectional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the inter-patch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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CWT IoT Device for Detecting Rare Events of Orchid Disease

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IEEE Internet of Things Journal 2024年第12期11卷 22830-22842页

作者： Chen, Claire Y. T. Lin, Yi-Bing Chen, Wen-Liang Wu, Kuan-Chieh Lin, Yun-Wei Sun, Edward W. Liu, Chun-You Montpellier Business School Montpellier34090 France Department Of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan China Medical University Taichung 404 China Miin Wu School Of Computing National Cheng Kung University Tainan70101 Taiwan The Research Center For Information Technology Innovation Academia Sinica Taipei115 Taiwan College Of Biological Science And Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan College Of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Talence33405 France Department Of Biomedical Informatics China Medical University Taichung404 Taiwan

Existing non-image-based solutions for detecting Phalaenopsis orchid diseases rely on time-domain analysis of environmental conditions. These solutions are suitable for loosely controlled open farm fields. Conversely, the diseases are infrequent events in Internet of Things (IoT)-based smart orchid greenhouses, where environmental conditions are well-controlled. In tightly controlled settings, standard time domain methods fall short in detecting subtle changes in environmental conditions during rare orchid disease events. We observed that the influence of the time series of temperature and humidity on orchid fungus within the well-controlled greenhouse is not readily apparent, and the time-frequency coefficients of temperature and humidity must be used to detect orchid disease in this rare event case. In order to address this challenge, we propose OrchidTalk, an IoT-based deep learning platform, which employs a time-frequency domain analysis using the continuous wavelet transform (CWT) filter, for the purpose of detecting rare events of orchid diseases. Through a nontrivial process, we select the Gaussian wavelet with four derivatives to serve as the mother wavelet of the CWT in the data extraction phase, and invent the CWT IoT device to translate the time-domain IoT raw data to the time-frequency domain IoT data. The resulting CWT features are used as inputs to the Orchid-3D model (3-D ConvLSTM), which significantly outperforms the previously proposed Orchid-1D (CNN and LSTM) and Orchid-2D (2-D ConvLSTM) models. For the rare event detection of 0.2% of sick orchid plants, OrchidTalk achieves a recall of 0.902, precision of 0.92, accuracy of 0.99, and an F1-score of 0.911. To the best of our knowledge, this represents the highest prediction performance achieved for a well-controlled orchid greenhouse. © 2014 IEEE.

关键词： Time domain analysis

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Extremal Problems on Forest Cuts and Acyclic Neighborhoods in Sparse Graphs

arXiv

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arXiv 2024年

作者： Botler, F. Couto, Y.S. Fernandes, C.G. de Figueiredo, E.F. Gómez, R. dos Santos, V.F. Sato, C.M. Institute of Mathematics and Statistics University of São Paulo Brazil Department of Computer Science Federal University of Minas Gerais Brazil Center for Mathematics Computing and Cognition Federal University of ABC Brazil

Chernyshev, Rauch, and Rautenbach proved that every connected graph on n vertices with less than 11 5 n − 185 edges has a vertex cut that induces a forest, and conjectured that the same remains true if the graph has less than 3n − 6 edges. We improve their result by proving that every connected graph on n vertices with less than 94 n edges has a vertex cut that induces a forest. We also study weaker versions of the problem that might lead to an improvement on the bound obtained. Copyright © 2024, The Authors. All rights reserved.

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A Deep Reinforcement Learning Deep Q Network-Based Approach for Systematic Detection of Distributed Denial of Service Attacks in Software-Defined Networking

SSRN

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SSRN 2024年

作者： Eke, Roberts Ndukwe Onyejegbu, Laeticia Nneka Information and Communication Engineering Center of Information & Communication Technology Faculty of Engineering University of Port Harcourt Rivers State Nigeria Computer Science Department Faculty of Computing University of Port Harcourt Nigeria

Network security is now seriously threatened by cyberattacks, particularly in contexts where software-defined networking (SDN) is used. Deep Reinforcement Learning (DRL) has become a potent method for handling challenging decision-making situations in recent years. In this work, a machine learning model was developed using Deep Q-Network (DQN), which is a kind of DRL algorithm known as DRL-DQN, to forecast cyberattacks in SDN. In comparison to the conventional DQN, which had 0.9900 accuracy, assessment time of 5.40 s, and training time of 290.5 s, the proposed DQN model produced an accuracy of 0.99935 with evaluation time of 1.126 seconds (s) and training time of 100 s at 20 epochs. In order to validate the improved DQN model, the result was compared with the traditional machine learning (ML) algorithms. The proposed DQN has a higher accuracy and lower training time. Additionally, it is clear that the proposed DQN performed better than the other ML algorithms in terms of recall measures and training times, which is the performance metric used in this work. This methodology is proactive for detecting and mitigating the impact of Distributed Denial of Service (DDoS) attacks in SDN. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Real-time Holistic Robot Pose Estimation with Unknown States

arXiv

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arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

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ML-based Modeling to Predict I/O Performance on Different Storage Sub-systems

ML-based Modeling to Predict I/O Performance on Different St...

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International Conference on High Performance computing

作者： Yiheng Xu Pranav Sivaraman Hariharan Devarajan Kathryn Mohror Abhinav Bhatele Palantir Technologies Inc. Denver USA Department of Computer Science University of Maryland College Park USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore USA

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward even for storage experts. The relationship between an application's I/O characteristics (such as I/O volume, processes involved, etc.) and the best storage sub-system for it can be complicated. As a result, adapting parallel applications to use burst buffers efficiently is a trial-and-error process. In this work, we present a Python-based tool called PrismIO that enables programmatic analysis of I/O traces. Using PrismIO, we identify performance bottlenecks when using burst buffers and parallel file systems, and explain why certain I/O patterns perform poorly. Further, we use machine learning to model the relationship between I/O characteristics and file system selections. We use IOR, an I/O benchmark to gather performance data for training the machine learning model. Our model can predict the better performing storage system for unseen IOR scenarios with an accuracy of 94.47% and for four real applications with an accuracy of 95.86%.

关键词： Training Accuracy File systems Computational modeling High performance computing Machine learning Predictive models Benchmark testing Data models Supercomputers

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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