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International Conference on Image Information Processing

作者： N. P. G. Bhavani R V Sudha K.Shanmuga Priya S R Arun Raj Vinay Kumar Yadav Allam Balaram Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Tamilnadu India Department of Computer Science and Business Systems Knowledge Institute of Technology Kakapalayam Selliampalayam Tamil Nadu India Department of Information Technology KSR Institute for Engineering and Technology Tiruchengode TamilNadu India Department of Electronics & Communication Engineering U.B.D.T College of Engineering Davanagere Karnataka India Glocal School of Science and Technology Glocal University Uttar Pradesh India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Pedestrian detection is a critical task in computer vision with the aim of accurately identifying pedestrians in images or video frames. In this work, we tackle the issue of enhancing pedestrian detection precision while reducing processing time. To achieve this, we propose a novel technique called Softsign Gaussian Deep Belief Neural Network (SGDBNN) for pedestrian detection. The proposed SGDBNN technique leverages the power of recurrent deep neural networks to handle the complexities of pedestrian detection. The input layer receives images as input, which are then processed in the hidden layer. Here, a Gaussian activation function is employed to extract relevant features, effectively detecting pedestrian objects in the images. The incorporation of deep learning and recurrent behavior in the neural networks results in reduced incorrect classifications, leading to a lower false positive rate and improved pedestrian detection accuracy. The softsign activation function at the hidden layer allows the proposed technique to efficiently compare extracted features with pre-stored templates, enabling effective pedestrian identification in the images. By effectively utilizing the recurrent behavior and advanced activation functions, the SGDBNN demonstrates superior performance compared to existing methods, such as Deep Convolutional Neural Networks and Extended Deep Model. Experimental evaluations illustrate the effectiveness of the proposed SGDBNN technique. It achieves a significant increase in Positive Detection Accuracy (PDA) of up to 20% and 16%, while simultaneously reducing the False Positive Rate (FPR) by up to 18% and 3% compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Additionally, the proposed SGDBNN reduces training time by up to 30% and 12% and decreases memory requirements by up to 47% and 30% when compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Likewise, the SGDBNN tec

关键词： Training Deep learning computer vision Pedestrians Computational modeling Memory management Information processing

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Learning generalizable agents via saliency-guided features decorrelation 23

Learning generalizable agents via saliency-guided features d...

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

作者： Sili Huang Yanchao Sun Jifeng Hu Siyuan Guo Hechang Chen Yi Chang Lichao Sun Bo Yang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and School of Artificial Intelligence Jilin University China Department of Computer Science University of Maryland College Park School of Artificial Intelligence Jilin University China Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. To achieve generalization in both situations, agents are required to accurately understand the impact of changed features on the decisions, i.e., establishing the true associations between changed features and decisions in the policy model. However, due to the inherent correlations among features in the state space, the associations between features and decisions become entangled, making it difficult for the policy to distinguish them. To this end, we propose Saliency-Guided Features Decorrelation (SGFD) to eliminate these correlations through sample reweighting. Concretely, SGFD consists of two core techniques: Random Fourier Functions (RFF) and the saliency map. RFF is utilized to estimate the complex non-linear correlations in high-dimensional images, while the saliency map is designed to identify the changed features. Under the guidance of the saliency map, SGFD employs sample reweighting to minimize the estimated correlations related to changed features, thereby achieving decorrelation in visual RL tasks. Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.

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Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

Research Square

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Research Square 2021年

作者： Kumar, V. Jayapandian, N. Balasubramanie, P. Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Christ University Bangalore India Department of Computer Science and Engineering Kongu Engineering College Perundurai India

Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide the best method of attachments are included. The Problem coverage find changeability network route due to which traffic and delays keep the performance to be very high. A newer version of Gravity Analysis Algorithm (GAA) is used to solve this problem. This proposed new approach GAA is introduced to improve network lifetime, increase system energy efficiency and end delay performance. Simulation results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA protocols. The proposed method provides a set of data collection and increased throughput in wireless sensor networks. © 2021, CC BY.

关键词： Clustering algorithms

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Avant-Grade framework Routing Optimization Based on Established Network Lifetime and Energy Management in WSN

Research Square

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Research Square 2021年

Wireless Sensor Network (WSN) is known to be a highly resource constrained class of network where energy consumption is one of the prime concerns. The existing system of LTDC has more drawbacks in network routing optimization lifetime and energy level improvement needed. In this research, a cross layer design methodology was adopted to design an energy efficient routing protocol entitled Avant-Grade framework Routing Optimization [AGFRO]. AGFRO is designed to minimize energy consumed in each node by reducing the amount of time in which a sensor node is in an idle listening state and reducing the average communication distance over the network. The performance of the proposed system has been critically evaluated in the context of network lifetime, throughput, and energy consumption of the network per individual basis and per data packet basis. The research results were analyzed and benchmarked against the well-known AGFRO protocols. The outcomes show a significant improvement in the WSN in terms of energy efficiency and the overall performance of WSN. © 2021, CC BY.

关键词： Energy utilization

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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A New Embedded Surveillance System for Reducing COVID-19 Outbreak in Elderly Based on Deep Learning and IoT

A New Embedded Surveillance System for Reducing COVID-19 Out...

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Othman, Nashwan Adnan Al-Dabagh, Mustafa Zuhaer Nayef Aydin, Ilhan Knowledge University College of Science Department of Computer Science Erbil Kurdistan Region446015 Iraq Firat University Computer Engineering Department Elazig Turkey

ISBN: (纸本)9781728196756

As a result of the fast spread of Coronavirus disease (COVID-19) throughout the world, it became urgent to evolve an aided-intelligent system to help healthcare organizations to control and early detect COVID-19 outbreak, especially after massive development in the computing. Artificial Intelligence and Deep Learning methods could introduce real assistance to many healthcare organizations in this global problem by monitoring, detecting, and reporting on infected persons in early-stage. As is well known, the elderly are most vulnerable to the effects of COVID-19, therefore, we aim through this paper is to present the embedded system has the ability to detect and report on the elderly in the endemic areas. An age estimation from a facial image based on deep learning methods and Internet of Things is also applied to send notification to mobile or any other device systems that the application is embedded on it based on IoT. Depending on the experimental results on the proposed system through the Mean Average Error rate, the proposed system gave better or equivalent to the results of the state-of-the-art techniques, and the prediction results of an average accuracy achieved 89.45%. Finally, the proposed system has the capacity to help reduce the intensity of spread COVID-19 by identified older people and prevent them from prohibited in dangerous areas. © 2020 IEEE.

关键词： COVID-19

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Algorithmic Intelligence Instantaneously for Complex Real-Time Supply Chain Optimization

Algorithmic Intelligence Instantaneously for Complex Real-Ti...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： G Lalitha Himanshu Kumar Rajora Asha V Ginni Nijhawan Praveen Karrar.R.Al- Shami Institute of Aeronautical Engineering Dundigal Hyderabad Docse GyanGanga Institute of Technology and Sciences Jabalpur M.P India Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Department of Forensic Sciences College of Science National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The modern world of supply chain management is complex and ever-evolving, thus the ability to make informed decisions and react quickly are of the utmost importance. In response to these issues, we have created a ground-breaking method that we call “Dynamic Real-Time Supply Chain Optimization with Algorithmic Intelligence (DRT-SCAI).” Better real-time visibility, autonomous decision-making, predictive accuracy, efficiency, customer happiness, risk mitigation, and sustainability are all outcomes of this suggested methodology’s use of state-of-the-art algorithmic intelligence, which simultaneously optimizes the supply chain in an instant. By using this approach, several enhancements are realized. To achieve these improvements, it is necessary to use this strategy.

关键词： Technological innovation Supply chain management Heuristic algorithms Supply chains Decision making Prediction algorithms Real-time systems

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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

arXiv

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arXiv 2023年

作者： Liu, Yixuan Zhao, Suyun Xiong, Li Liu, Yuhan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Engineering Research Center Ministry of Education on Database and BI China Information School Renmin University of China China Department of Computer Science Emory University United States

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users’ attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users’ varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating "echos" from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model. © 2023, CC BY.

关键词： Learning systems

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S3map: Semi-Supervised Aspect-Based Sentiment Analysis with Masked Aspect Prediction

SSRN

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SSRN 2022年

作者： Yang, Zhiyao Wang, Bing Li, Ximing Wang, Wenting Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science University of Texas Dallas United States

Aspect-Based Sentiment Analysis (ABSA) refers to a fine-grained task of detecting the sentiment polarities of sentences at the aspect level. To resolve this task, training samples of ABSA must be annotated with aspect words and the corresponding sentiment polarities. However, collecting such fine-grained training samples is expensive and time consuming. Therefore the available ABSA training samples are often scarce. To break the data scarcity challenge of ABSA, we investigate Semi-supervised Aspect-Based Sentiment Analysis (SemiABSA), which trains ABSA models by using a limited amount of expensive labeled sentences and more unlabeled-yet-cheaper ones. We propose a novel SemiABSA framework, namely Semi-supervised aspect-based Sentiment analysis with Masked aspect prediction (S3MAP), built on the self-training paradigm. For unlabeled sentences, we form pseudo-aspect words and pseudo-sentiment polarities and improve model training. Specifically, a BERT-Encoder-based Masked Aspect Prediction (MAP) task achieves the pseudo-aspect words generation. Based on S3MAP, we thoroughly investigate the potential of SemiABSA from various perspectives. Empirical results show that S3MAP can consistently improve the performance by leveraging unlabeled sentences even with those from different domains. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Sparse Training Data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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