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Cyber Resilience (ICCR), International Conference on

作者： Mohanad A. Dief Waleed Alomoush Hani Atta Osama A. Khashan Ahmed Solyman Rania ELGohary Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt School of Computing Skyline University College Sharjah United Arab Emirates Faculty of Engineering Zarqa University Zarqa Jordan Research and Innovation Centres Rabdan Academy Abu Dhabi United Arab Emirates School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In this research, a nonlinear suboptimal controller was provided for type 1 diabetes patients' blood glucose level regulation based on a Proportional-Integral-Derivative (PID) controller with Ant Colony Optimization (ACO). The ACO approach is used to determine the ideal PID controller gain settings within a stability area. This study simulates three cases of a physiological model of glucose regulation in people with type 1 diabetes (No insulin treatment, fixed insulin injection, and Controlled blood glucose concentration using a PID controller) by using the Bergman Model in the context of meal-induced disturbances. In terms of dynamic performance, such as a reduction in maximum overshoot, settling time, and rising time, simulation findings suggest that the proposed technique for PID tuning using the ACO algorithm is more adaptable and cost-effective.

关键词： Ant colony optimization Adaptation models Heuristic algorithms Regulation Stability analysis Glucose Diabetes

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Dialogue scenario classification based on social factors

Dialogue scenario classification based on social factors

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International Symposium on Chinese Spoken Language Processing

作者： Yuning Liu Di Zhou Masashi Unoki Jianwu Dang Aijun Li School of Information Science Japan Advanced Institute of Science and Technology College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Chinese Academy of Social Sciences Institute of Linguistics

ISBN: (纸本)9798350397970

The tendency of interlocutors to become more similar to each other in the way they speak, this behavior is known in the literature as entrainment, accommodation, or adaptation. Previous studies indicated that entrainment can be treated as a social factor in human-human conversations. However, previous research suggests that this phenomenon has many subtleties. One of these cues is that entrainment on an acoustic feature might be associated with disentrainment on another in conversation, which means we have to consider these features together. Therefore, we proposed a linear dimensionality-reduction method that combines acoustic features to calculate three entrainment metrics: proximity, convergence, and synchrony. The three entrainment metrics are referred to as social factors hereafter. Our results show these social factors play an important role in a classification task. We also found that these social factors perform a better classification accuracy than combining each individual acoustic feature’s entrainment. The proposed social factors can help the human-machine interface to have the ability to adapt to the different scenarios in dialogue.

关键词： Measurement Oral communication Social factors Acoustics Behavioral sciences Task analysis Man-machine systems

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Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate

arXiv

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arXiv 2023年

作者： Sun, Pengfei Wu, Jibin Zhang, Malu Devos, Paul Botteldooren, Dick Department of Information Technology WAVES Research Group Ghent University Technologiepark Zwijnaarde 126 Ghent9052 Belgium Department of Data Science and Artificial Intelligence The Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

Recurrent Neural Networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applications. Nevertheless, vanilla RNNs are confronted with the well-known issue of gradient vanishing and exploding, posing a significant challenge for learning and establishing long-range dependencies. Additionally, gated RNNs tend to be over-parameterized, resulting in poor computational efficiency and network generalization. To address these challenges, this paper proposes a novel Delayed Memory Unit (DMU). The DMU incorporates a delay line structure along with delay gates into vanilla RNN, thereby enhancing temporal interaction and facilitating temporal credit assignment. Specifically, the DMU is designed to directly distribute the input information to the optimal time instant in the future, rather than aggregating and redistributing it over time through intricate network dynamics. Our proposed DMU demonstrates superior temporal modeling capabilities across a broad range of sequential modeling tasks, utilizing considerably fewer parameters than other state-of-the-art gated RNN models in applications such as speech recognition, radar gesture recognition, ECG waveform segmentation, and permuted sequential image classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Gesture recognition

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A Chan–Vese Model Based on the Markov Chain for Unsupervised Medical Image Segmentation

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Tsinghua science and technology 2021年第6期26卷 833-844页

作者： Quanwei Huang Yuezhi Zhou Linmi Tao Weikang Yu Yaoxue Zhang Li Huo Zuoxiang He Key Laboratory of Pervasive Computing(Ministry of Education)and the Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Nuclear Medicine Peking Union Medical College HospitalBeijing 100730China School of Clinical Medicine Tsinghua Universityand also with Beijing Tsinghua Changgung HospitalBeijing 100084China

The accurate segmentation of medical images is crucial to medical care and research;however, many efficient supervised image segmentation methods require sufficient pixel level labels. Such requirement is difficult to meet in practice and even impossible in some cases, e.g., rare Pathoma images. Inspired by traditional unsupervised methods, we propose a novel Chan–Vese model based on the Markov chain for unsupervised medical image segmentation. It combines local information brought by superpixels with the global difference between the target tissue and the background. Based on the Chan–Vese model, we utilize weight maps generated by the Markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel *** method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity. In our method, users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation. By contrast, the result from deep learning based methods is *** performance of our method is assessed by using four Computerized Tomography(CT) datasets. Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques.

关键词： medical image unsupervised segmentation Markov chain

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Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers

Autoregressive Queries for Adaptive Tracking with Spatio-Tem...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jinxia Xie Bineng Zhong Zhiyi Mo Shengping Zhang Liangtao Shi Shuxiang Song Rongrong Ji Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University School of Computer Science and Technology Harbin Institute of Technology Media Analytics and Computing Lab School of Informatics Xiamen University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently, the spatio-temporal information is far away from being fully explored. To alleviate this issue, we propose an adaptive tracker with spatio-temporal transformers (named AQA-Track), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then, we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally, based on the initial target template and learnt autoregressive queries, a spatio-temporal information fusion module (STM) is designed for spatiotemporal formation aggregation to locate a target object. Benefiting from the STM, we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker's performance on six popular tracking benchmarks: LaSOT, LaSOT ext , TrackingNet, GOT-10k, TNL2K, and UAV123. Code and models will be https://***/orgs/GXNU-ZhongLab.

关键词： Adaptation models Visualization Computer vision Target tracking Computational modeling Transformers Market research

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A Deep Learning Based Species Reciprocal System for Partridge 1st

A Deep Learning Based Species Reciprocal System for Partridg...

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1st International Conference on Deep sciences for computing and Communications, IconDeepCom 2022

作者： Aswini, J. Gayathri, A. Revathi, A. Vinoth Kumar, L. Affiliated to Anna University Chennai India Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Information Technology R.M.K Engineering College RSM Nagar Chennai India

ISBN: (纸本)9783031276217

Bird watching is a form of wildlife observation of birds which is a recreational activity. Bird watchers make use of bird books to know about the birds. To provide handy tool to birdwatchers and to admire the splendor of birds, to recognize bird species a web based Convolutional Neural Network is used to help out users. Avifauna species images were used to localize major features in the image and it is learnt by a convolutional neural network (CNN)s. To distill the shapes and colors of the object granularities then to balance the distribution of bird species, circumscribed region of interest is performed. Then, for feature extraction convolutional neural network (CNN) is used. Finally, activation function softmax is applied on the resulting class probabilities. To identify picture uploaded by users, the erudite parameters of bird features were used. Instead of training from scratch, the concept of transfer learning is used in which pre-trained models (VGG16, Xception, ResNet, etc.) are used. Here two of these models are used namely ResNet50 and VGG16. Among these two models ResNet50 have provided better accuracy. Transfer learning helps us to increase the classification accuracy. Hence, materialization of deep learning algorithms has resulted in highly intricate cognitive for computer vision & image recognition. The deep learning model uses CNN framework for bird image classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Birds

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ES-ATF: Early Smoke Detection based on Attention-aggregated Temporal Feature Extraction*

ES-ATF: Early Smoke Detection based on Attention-aggregated ...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Pengfei Li Muaz Al Radi Xueting Huang Said Boumaraf Yuhang Shen Fusen Guo Khalid Yousef Al Awadhi Jorge Dias Sajid Javed Hamad Karki Taimur Hassan Naoufel Werghi Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Abu Dhabi National Oil Company (ADNOC) Abu Dhabi UAE Department of Mechanical Engineering Khalifa University Abu Dhabi UAE Abu Dhabi University (ADU) UAE

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Smoke detection is an area where strong research interest was put since its practical meaning in extinguishing fire in the early stage. Detecting smoke not only reduces the life or property loss but also has environment-protection value as inefficient combustion can be restricted. In the literature, there are bunches of investigations on single-frame smoke detection via YOLO-based methods, however, these methods are always attenuated by the transparency and imperceptibility nature of smoke. Therefore, more useful information that can work as an extra prompt to the model is expected. To strengthen the information density of inputs, one possible solution is to explore inter-frame correlation within a smoke snippet. With this added temporal information, a boost in model performance can be expected. This paper adopts an attention-aggregated temporal feature extraction method, by which, the inter-image feature can be better exploited, and more frames can be detected at one time. Consequently, the smoke detection process is speeding up while simultaneously, the model's capability is enhanced in terms of alarming a potential smoke before it evolves to the late stage.

关键词： Metalearning Correlation Event detection Feature extraction Combustion Few shot learning Videos

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A decision-making module for fertilization and irrigation control system in rice farming using markov chain process and SARSA algorithms 11

A decision-making module for fertilization and irrigation co...

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2021 11th International Workshop on Computer science and Engineering, WCSE 2021

作者： Bamurigire, Peace Vodacek, Anthony Jayavel, Kayalvizhi Ngoga, Said Rutabayiro African Center of Excellence in Internet of Things College of Science and Technology University of Rwanda Kigali Rwanda Chester F. Carlson Center for Imaging Science Rochester Institute of Technology RochesterNY United States School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Rwanda Information Society Authority Kigali Rwanda

ISBN: (纸本)9789811817915

The merging of modern sensor technologies, the Internet, and advanced control of irrigation and fertilization with an Internet of Things (IoT) approach allow a relatively precise control of agriculture. This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water and fertilizer use, even in countries such as Rwanda with low levels of economic development as long as appropriate and low cost technologies are used. In this work, we add to our previous IoT design for an irrigation system by adding a fertilization system. The proposed low cost system will provide individual farmers fertilization and irrigation options informed by reservoir capacity, water level, predicted rainfall, and temperature along with soil condition and pH. IoT data are assessed in the context of rice growth stages as a Markov Chain process, or in the case of IoT system fault, assessed using the SARSA temporal difference technique. Simulations for the Muvumba Valley rice project in Northeast Rwanda demonstrate the potential of this IoT system to increase rice yield while decreasing fertilizer and water use. © 2021 11th International Workshop on Computer science and Engineering, WCSE 2021. All Rights Reserved.

关键词： Internet of things

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Sparse and Non-Regular Point Cloud Refinement Based on Local Geometry Estimation

SSRN

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SSRN 2022年

作者： Li, Yushi Baciu, George Li, Chenhui Chen, Rong School of Advanced Technology Xi’an Jiaotong-liverpool University China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology East China Normal University China Information Science and Technology College Dalian Maritime University China

Different from 3D CAD models and laser-scanning captured structures, the point clouds reconstructed using multi-view algorithms, especially image-based, are generally with sparse and non-uniform distributions. Meanwhile, the non-regular outliers and noising elements generated in practical image-based reconstruction are difficult to avoid, resulting in strong negative impact on the follow-up processing. In this paper, we present a novel approach that integrates a minimization framework with density estimation to get the nonlinear optimum filter of complicated point clouds. To distinguishes the diverse points in 3D space, we take both density estimation and local neighborhood into account. In addition, a local geometry optimization is consolidated in our method to efficiently refine the surface geometry of point clouds with varying scales. Apart from removing spatial outliers and noise, our method well maintains and refines the structural details. The effectiveness and performance of our approach are demonstrated by experiments conducted on synthetic shapes and various 3D models reconstructed from real-taken images. © 2022, The Authors. All rights reserved.

关键词： Noise abatement

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Salt Deposits Identification Using Enhanced U-net-based Mixed Attention Loss Function for Semantic Segmentation

Salt Deposits Identification Using Enhanced U-net-based Mixe...

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Data science and information System (ICDSIS), IEEE International Conference on

作者： D Anitha Kassem Al-Attabi M S Priyanka Thota Soujanya I. Bhuvaneshwarri Department of Computational Intelligence School of computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI-ML Nitte Meenakshi Institute of Technology Bengaluru India School of Agricultural Sciences SR University Warangal Telangana India Department of Information Technology Government College of Engineering Erode Tamil Nadu India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Numerous areas of Earth’s surface with huge accumulations of oil and gas have high salt deposits under surface. Determining such deposits assists several countries in enhancing the storage capacity of petroleum reserves and exploring new ones. However, determining such a deposit is challenging because expert seismic images need human interpretation of salt bodies, which causes biased and greatly variable translations. This research proposes the Enhanced U-net with Mixed Attention Loss Function (EU-net-based MALF) to automatically and accurately identify whether or not the seismic images have salt deposit regions. Initially, the images are gathered from Tomlinson Geophysical Services (TGS) dataset to evaluate the model performance. Then, the nearest neighbor interpolation method and min-max normalization are employed for image resizing and normalizing. At last, the EU-net-MALF is performed for semantic segmentation. The EU-net-MALF achieves a better accuracy of 98.26% compared to existing methods like fully convolutional DenseNet, U-Net and deep supervision, and Interactive Salt Segmentation based on Convolutional Neural Network and GraphCut (SaltISCG).

关键词： Interpolation Accuracy Semantic segmentation Oils Data science Explosions Decoding

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