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2020 International Conference on Computing and Information technology, ICCIT 2020

作者： Shaees, Shamoil Naeem, Hamad Arslan, Muhammad Naeem, Muhammad Rashid Ali, Syed Hamza Aldabbas, Hamza COMSATS University Department of Computer Science Lahore Pakistan Neijiang Normal University College of Computer Science Neijiang641100 China Sichuan University College of Computer Science Chengdu610065 China Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Salt-Jordan Jordan

ISBN: (纸本)9781728126807

Facial Emotion Recognition is considered very significant for Human computer Interaction (HCI) and they play a vital role in everyday human life. In recent years Convolutional Neural Network (CNNs) has become very popular among researcher for image analysis, because CNNs have generated remarkable results. However, CNNs needs a lot of data to train from scratch. This problem has been addressed by several researchers who have trained CNNs with millions of images, this training knowledge can also be used in a different task which is known as Transfer Learning. AlexNet is one of the best pre-trained CNNs. Our work reflects a brief comparison between modern pre-trained CNNs and using transfer learning with classification approach like Support Vector Machine (SVM), generally known as hybrid classifier. The testing has been done on two very popular expression database Cohn-Kanade+ (CK+) database and Natural Visible and Infrared Expression (NVIE) database. Results clearly depicts that pre-trained CNNs produces better result than handcrafted approaches. © 2020 IEEE.

关键词： Support vector machines

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Challenges and Opportunities of DevOps in Cyber-Physical Production Systems Engineering

Challenges and Opportunities of DevOps in Cyber-Physical Pro...

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Industrial Cyber-Physical Systems (ICPS)

作者： István Koren Felix Rinker Kristof Meixner Jasminka Matevska Jörg Walter Chair of Process and Data Science RWTH Aachen University Aachen Germany Christian Doppler Laboratory SQI Institute of Information Systems Engineering TU Wien Vienna Austria School of Electrical Engineering and Computer Science City University of Applied Sciences Bremen Germany Distributed Computing and Communication OFFIS - Institute for Information Technology Oldenburg Germany

DevOps is a set of practices that combines software development and operations to enable a continuous software product life cycle to improve the quality of software systems. Although DevOps is considered successful for typical software systems, it has not yet been widelyadopted in the area of - Physlcal Production Systems (CPPSs) that integrate physical components with computer-based control and communication systems. Related to industrial settings, many new challenges arise, like long-term investments, missing flexibility in asset-heavy production environments, and the inherent physicality of hardware. This paper examines the use of the DevOps methodology in the manufacturing domain. It identifies and discusses the unique challenges and describes first solution proposals to overcome those challenges, based on literature and experiences from the industry. The article provides useful guidance to researchers and practitioners on potential pitfalls and exciting opportunities.

关键词： Production systems Data collection Software systems Hardware Safety Stakeholders Security

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Shorting pin and SRR loaded multi-band Antenna for 4G/5G/Wi-Fi/WiMAX and IOT Applications

Shorting pin and SRR loaded multi-band Antenna for 4G/5G/Wi-...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Anubhav Kumar Divya Saxena Navneet Sharma Anil Gankotiya Department of Electronics and Communication Raj Kumar Goel Institute of Technology and Management Ghaziabad Uttar Pradesh India Applied Science & Humanities ABES Engineering College Ghaziabad Uttar Pradesh India Department of Electronics and Communication ABES Engineering College Ghaziabad Uttar Pradesh India Department of Computer Science & Engineering Poornima University Jaipur Rajasthan

In this paper, shorting pin and SRR-loaded multi-band antenna is presented for wireless communication application. The multiple radiation strips are used with the partial ground to design a multiband operating frequency antenna. The hook and L-shaped radiating strips are used to design a two-band antenna where SRR is loaded on the antenna to implement a triple band antenna which shifted the bandwidth of the antenna. The shorting-pin is used to ground the lower arm of the antenna and enhances the impedance bandwidth with four operating bands. The obtained antenna bandwidth is 2.2-2.4/3.1-3.6/4.9-5.05/5.35-5.6 GHz and covers multiple applications in the wireless communications.

关键词： Bandwidth WiMAX Pins Internet of Things Impedance Artificial intelligence Loaded antennas

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Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection

arXiv

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arXiv 2022年

作者： Wu, Zhize Wang, Xiaofeng Xu, Tong Yang, Xuebin Zou, Le Xu, Lixiang Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

Object recognition from images means to automatically find object(s) of interest and to return their category and location information. Benefiting from research on deep learning, like convolutional neural networks (CNNs) and generative adversarial networks, the performance in this field has been improved significantly, especially when training and test data are drawn from similar distributions. However, mismatching distributions, i.e., domain shifts, lead to a significant performance drop. In this paper, we build domain-invariant detectors by learning domain classifiers via adversarial training. Based on the previous works that align image and instance level features, we mitigate the domain shift further by introducing a domain adaptation component at the region level within Faster R-CNN. We embed a domain classification network in the region proposal network (RPN) using adversarial learning. The RPN can now generate accurate region proposals in different domains by effectively aligning the features between them. To mitigate the unstable convergence during the adversarial learning, we introduce a balanced domain classifier as well as a network learning rate adjustment strategy. We conduct comprehensive experiments using four standard datasets. The results demonstrate the effectiveness and robustness of our object detection approach in domain shift scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Life Test Plan Optimization for Electrical Insulation During Temperature Load: A Semi-Optimal Approach

Life Test Plan Optimization for Electrical Insulation During...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Rahul Vishnoi Sanjeev Kumar Verma Vishal Sharma K. Yuvaraj S. Gopinath Deepika Dongre Department of ECE TMU Department of Applied Sciences Invertis University Bareilly Uttar Pradesh Film & Television Studies School of Media IIMT University Meerut Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore Vishwakarma Institute of Information Technology Pune

Considering that the Arrhenius equation between stress condition and lifetime is valid and that the proportional lifespan at an excessive pressure reflects some reliable statistical properties. It is essential to demonstrate the highest diagnostic strategy from the perspective of effectiveness under such living scenarios. Knowing the semi-optimal test plan, where effectiveness is similar to that of the maximum one and test conditions are straightforward, is additionally helpful. The goal of minimization is to determine the ideal amount of test subjects with each test stressors, and we assume that there are three sample stress levels. The root-average-squared error for the duration in use circumstance serves as the benchmark for optimization problems. We were using the hyperparameters in an actual trial situation to adjust for realism. If predictability of the Characteristic curve is necessary, we have ended up finding that there is just a subtle distinction between both the optimal results obtained and the mainstream test result when making comparisons it to the data achieved that use the mainstream tester, in which test samples are equitably represented to each experiment anxiety levels. The traditional testing phase is one of the partially ideal test plans, in our opinion. The agreement between the theory and computer findings has been examined.

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Clustering Method of Driving Scenes based on Road Shape by using Road Map Images

Clustering Method of Driving Scenes based on Road Shape by u...

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International Conference on Electrical, Communication, and computer Engineering (ICECCE)

作者： Kohjiro Hashimoto Daichi Yanagihara Hiroshi Kuniyuki Kae Doki Yuki Funabora Shinji Doki Dept. Applied Information Engineering Suwa University of Science Chino City Japan Dept. Mechanical and Electrical Engineering Suwa University of Science Chino City Japan Dept. Electrical Engineering Aichi Institute of Technology Toyota City Japan Dept. Electrical Engineering and Computer Science Nagoya University Chikusa-ku Nagoya Japan

Currently, vehicles are equipped with various sensors that can collect a series of driving behavior data while driving. The driving behavior data obtained in this way can be used to analyze the causes of traffic accidents and evaluate driver skills. However, when performing the above analysis, a specific driving scene is defined and driving behavior data for that driving scene is required. On the other hand, the series of driving behavior data obtained during one driving operation is enormous, and it is not easy to pick up desired driving scene data from among *** research aims to establish a driving scene clustering method for a series of driving behavior data. In this paper, a driving scene is defined as a scene in which road shape and operation are associated, and propose a driving scene clustering method considered road shape. Here, a road map image is acquired from position information obtained from a GPS sensor, and the relationship between the road map image and operation data is clustered based on Coupled-GP-HSMM.

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Revisiting the antiferromagnetic structure of Tb14Ag51. The importance of distinguishing alternative symmetries for a multidimensional order parameter

arXiv

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arXiv 2023年

作者： Pomjakushin, V. Perez-Mato, J.M. Fischer, P. Keller, L. Sikora, W. Paul Scherrer Institut PSI VilligenCH-5232 Switzerland Facultad de Ciencia y Tecnología Universidad del País Vasco UPV/EHU Apartado 644 BilbaoE-48080 Spain Faculty of Physics and Applied Computer Science AGH University of Science and Technology KrakowPL-30-059 Poland

We revisit the antiferromagnetic structure of Tb14Ag51 (Fischer et al., 2005) with the propagation vector [ 1313, 0] and parent space group P6/m using both magnetic symmetry and irreducible representation arguments. We have found a new magnetic structure under the hexagonal Shubnikov magnetic space group P6¯0, which fits much better the experimental data. This new solution was obtained by constraining the spin arrangement to one of the three possible magnetic space groups of maximal symmetry that can be realised by a magnetic ordering transforming according to the 4-dimensional physically irreducible representation that is known to be relevant in this magnetic phase. The refined model, parameterised under P6¯0, implicitly includes the presence of a third harmonic with the propagation vector at the gamma point [0, 0, 0], which has an important weight in the final result. The structure consists of 13 symmetry-independent Tb magnetic moments with the same size of 8.48(2)µB, propagating cycloidally in the ab-plane. The modulation has a substantial deviation from being purely sinusoidal due to the contribution of the mentioned third harmonic. Copyright © 2023, The Authors. All rights reserved.

关键词： Vector spaces

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FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack Detection

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China Communications 2020年第5期17卷 151-167页

作者： Liangchen Chen Shu Gao Baoxu Liu Zhigang Lu Zhengwei Jiang School of Computer Science and Technology Wuhan University of TechnologyWuhan 430063China Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Applied Technology China University of Labor RelationsBeijing 100048China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method

关键词： fuzzy entropy weighted KNN network attack detection fuzzy membership natural nearest neighbor network security intrusion detection system

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Advantages of Using Runtime Procedural Generation of Virtual Environments Based on Real World Data for Conducting Empirical Automotive Research 22nd

Advantages of Using Runtime Procedural Generation of Virtual...

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22nd International Conference on Human-computer Interaction, HCII 2020

作者： Barz, Arthur Conrad, Jan Wallach, Dieter HCI2B Group Faculty of Computer Science and Micro Systems Technology Hochschule Kaiserslautern – University of Applied Sciences Zweibruecken Germany

ISBN: (纸本)9783030599867

Driving simulations have become important research tools for improving the in-car user experience. Simulations are especially indispensable when conducting empirical studies in a human-centered development process, where iterative testing in real-world environments is very time-consuming, expensive and potentially dangerous. Constantly advancing possibilities offered by new generations of driving simulations increase the demands and expectations of simulation systems. While the implementation of many components such as simulation hardware or driving physics is very mature by now, the realization of virtual driving environments and scenarios is still a challenging endeavor. A partially automated, algorithmic generation of virtual environments based on real world data provides a possible solution to simplify set-up and execution of research scenarios. The data formats used for this purpose are digital elevation models and map data that can be iteratively processed, improved and expanded for the use in a concrete study. This approach usually requires additional manual steps after the procedural generation is completed. This paper outlines an automated, algorithmic generation of virtual environments based on real-world data, realized at runtime and without subsequent manual post-processing. Significant benefits in automotive research enabled by this approach such as rapid prototyping of study scenarios, support for large-scale virtual environments and the analysis of driving behavior in familiar environments are described. © 2020, Springer Nature Switzerland AG.

关键词： Virtual reality

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare Engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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