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2nd International Conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2022

作者： Chen, Wei Liu, Yiming Su, Bai shun Jiang, Tianjian School of Computer Science and Technology Henan Polytechnic University Jiaozuo China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China School of Electrical and Information Engineering Hunan University Hunan China

ISBN: (纸本)9798350334265

In recent years, pretraining models using supervised contrastìve loss have defeated the crossentropy loss widely adopted to solve classification problems using deep learning. However, this approach is limited by its inability to directly train neural network models. To overcome this difficulty, we propose a novel loss function based on supervised contrastìve loss, which can directly train deep models. Our extensive evaluations of the CifarlO and the Mnist datasets show that our method achieves good performance on CifarlO and the Mnist datasets. © 2022 IEEE.

关键词： Deep learning

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Bedi, Pradeep Goyal, S.B. Rajawat, Anand Singh Bhaladhare, Pawan Aggarwal, Alok Prasad, Ajay Computer Science and Engineering Galgotias University India Faculty of Information Technology City University Malaysia School of Computer Science and Engineering Sandip University Nashik India School of Computer Science University of Petroleum and Energy Studies Dehradun India

From the perspective of the Industry 4.0 paradigm, the machine learning (ML) discipline has had a significant influence on the manufacturing sector. The industry 4.0 concept promotes intelligent sensors, gadgets, and equipment to create technology infrastructure sectors that collect information constantly. By analyzing the obtained data, machine learning approaches allow actionable insight to boost industrial productivity without dramatically altering the necessary resources. Furthermore, the capacity of machine learning applications to provide actionable analytics has facilitated the detection of complex manufacturing trends and paved the path for an integrated intelligent process in various activities in the supply chain, including smart and constant inspection, preventative maintenance, quality enhancement, process optimization, supply chain advancement, and workflow scheduling. This paper aims to present recent advances in the field of quality inspection in Industry 4.0 and develop a framework for quality inspection that can be fully utilized in the Industry 4.0 context using adaptive bilateral filtering and Feature Correlated Auto encoder (FCA) machine learning technique. The suggested approach makes full use of information from all sources along the manufacturing chain. Therefore, it complies with quality management standards within the context of Industry 4.0. The suggested model makes use of corrective measures based on data patterns discovered through predictive analysis. Result analysis was shown on some pre-trained deep learning models such as ResNet18, Vgg19, Alexnet, Squeezenet, auto encoder, and FCA and observed that the proposed FCA(Feature Correlated Auto encoder) achieved a better result. © 2022 Elsevier B.V.. All rights reserved.

关键词： computer vision

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What You Write Represents Your Personality: A Dual Knowledge Stream Graph Attention Network for Personality Detection 11th

What You Write Represents Your Personality: A Dual Knowledge...

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11th Chinese National Conference on Social Media Processing, SMP 2023

作者： Yan, Zian Wang, Ruotong Sun, Xiao AHU-IAI AI Joint Laboratory Anhui University Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9789819975952

The goal of the personality detection task is to determine a person’s personality traits using their social media posts. Recently, researchers have turned away from a fully data-driven approach and begun employing prior knowledge about psycholinguistic to guide their research. People typically post on social media to express their opinions or share their emotions. Therefore, it is crucial to uncover the traits and disparities in how individuals with different personalities express themselves. However, current research based on psycholinguistic principles only examines these differences superficially, failing to conduct more granular analyses, such as exploring emotions. In this paper, we propose an innovative approach that blends psycholinguistic and prior emotional knowledge to acquire features at varying levels. Our model, named Dual Knowledge Stream Graph Attention Network (DKSGAT), comprises of two streams. One stream represents posts at the psycholinguistic level, while the other encodes words at a more finely-grained emotional level based on prior emotional knowledge. Both streams’ representations are then obtained to make joint inferences about personality traits. Our approach outperforms previous studies in predicting the Big Five personality and MBTI personality, as demonstrated through testing on two different public datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Social networking (online)

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Semantic Line Detection: A Survey 12th

Semantic Line Detection: A Survey

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Hou, Qinggang Li, Guangrong School of Computer Science and Technology Tiangong University Tianjin300387 China School of Information Science and Engineering Shandong Agriculture and Engineering University Shandong250100 China

ISBN: (纸本)9789811969003

Semantic line detection plays a specific guiding role in aesthetic composition, photography, and so on. This paper introduces the method of semantic line detection and summarizes its present situation. The existing semantic line detection techniques are summarized from the traditional and deep learning-based methods, and the corresponding advantages and disadvantages are pointed out. Based on some difficulties existing in the semantic line detection method, this paper puts forward its own prospects for the future development direction of semantic line detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Deformable Cross Attention for Learning Optical Flow 48

Deformable Cross Attention for Learning Optical Flow

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Abdein, Rokia Xiang, Xuezhi Lv, Ning Saddik, Abdulmotaleb El Harbin Engineering University School of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

ISBN: (纸本)9781728163277

Optical flow is the process of estimating motion in scenes. Each object in the scene has a homogeneous motion, i.e., moves in the same direction with the same velocity. Therefore, connecting the parts of an image globally provides an essential cue for learning accurate motion. Convolution-based methods estimate the motion features from the local regions, which miss this important cue. Recently, some methods used Transformer to model global dependencies to improve optical flow. However, Transformer suffers from excessive attention computations and still brings irrelevant parts into the region of interest. Therefore, we propose a deformable cross-attention for optical flow estimation, which provides two important advantages: connecting the parts of the image globally while deforming the attention to the objects' shapes in the image and reducing the memory consumption. Our proposed method achieved competitive performance on Sintel and KITTI 2015 datasets in terms of accuracy and efficiency. © 2023 IEEE.

关键词： Deep learning Deformable cross-attention Optical flow Transformer

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Hotel information System Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

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computers, Materials & Continua 2022年第5期71卷 3145-3160页

作者： Vatsala Anand Sheifali Gupta Deepika Koundal Shubham Mahajan Amit Kant Pandit Atef Zaguia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum&Energy StudiesDehradunUttrakhandIndia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost *** paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy *** proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense *** the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗*** dermoscopy images are taken from the HAM10000 *** proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and *** models are simulated with 32 batch size and Adadelta *** proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as ***,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease *** model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

关键词： Dermoscopy images CNN deep learning classification optimizer ResNet diagnosis skin disease

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Diffusion-activated high performance ZnSnO/Yb_(2)O_(3) thin film transistors and application in low-voltage-operated logic circuits

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Journal of Materials science & technology 2021年第11期70卷 49-58页

作者： Bing Yang Gang He Wenhao Wang Yongchun Zhang Chong Zhang Yufeng Xia Xiaofen Xu School of Physics and Materials Science Radiation Detection Materials&Devices LabAnhui UniversityHefei 230601China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Mechanical Engineering Anhui Vocational and Technical CollegeHefei 230011China

The flourishing metal-oxide high-k dielectric materials have been regarded as the vital components of low voltage operated flexible transparent electronic *** herein report that ytterbium oxide(Yb_(2)O_(3))and ZnSnO(ZTO)thin films were firstly integrated into ZTO-based thin film transistors(TFTs)with superior *** have indicated that the 500℃-annealed ZTO/Yb_(2)O_(3) TFTs possess the large saturation mobility of 9.1 cm^(2) V-1S^(-1) and the high on/off current ratio of 2.15×10^(7),which even surpass those of reported In-based *** deteriorative electrical properties in the aging process can be attributed to the carrier capture ***,the 460℃-processed TFTs demonstrate a tenfold increase in saturated mobility and an increase in on/off current ratio after 10 days *** inspiring electrical properties are attributed to the diffusion-activated carrier enhancement mechanism and electrons donor role of water molecular,which introduces a facile method to boost the device performance at lower processing *** neglected threshold voltage variations of 0.06 V and-0.2V have been detected after bias stability *** superior bias stability can be attributed to the charge delay effect induced by the continuous electric ***,the ultrahigh on/off current ratio of 1.1×10^(7) and the recoverable transferring performance have verified the aging-activated *** confirm its potential application in digital circuits,a resistor-loaded inverter with gain of 5.6 has been constructed and good dynamic response behavior have been detected at a low voltage of *** a result,it can be concluded that the high temperature annealing TFTs need immediate encapsulation,while the performance of the lower temperature processing samples can be optimized after aging treatment,indicating the potential prospect in low power consumption large-scale flexible transparent devices.

关键词： Ytterbium oxide(Yb_(2)O_(3)) Thin-film transistors(TFTs) Diffusion-activated Bias stability Inverter

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Advanced Wound Care Training with IoT and LSTM for Dynamic Learning Environments 5

Advanced Wound Care Training with IoT and LSTM for Dynamic L...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Viswanathan, C. Lalitha, K. Selvi, J. Jeslin, J. Gnana Vignesh, C. Chandru Murugan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tami Nadu Chennai India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Shree Sathyam College of Engineering and Technology Department of Civil Engineering Sankari Tamil Nadu Salem India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tami Nadu Vellore India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

Wound treatment is an essential part of patient care that requires continuous training and education for medical personnel. Using Long Short-Term Memory (LSTM) networks for time-series feedback and Internet of Things (IoT), technologies provide a paradigm shift in wound education in this research. The platform combines LSTM-based learning algorithms with real-time data collected from IoT wound monitoring to provide healthcare providers with ongoing training and feedback. A battery of trials illustrates that strategy outperforms the status quo regarding wound assessment accuracy and treatment decision-making. This approach promotes ongoing professional growth in wound care management by allowing for individualized learning experiences. Its work shows that continuous training using LSTM powered by the IoT has great promise for better wound education and patient outcomes in healthcare. © 2024 IEEE.

关键词： Decision making

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Boundary Discretization and Reliable Classification Network for Temporal Action Detection

arXiv

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arXiv 2023年

作者： Fang, Zhenying Yu, Jun Hong, Richang The School of Computer and Information Engineering Hefei University of Technology 188065 China The School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Temporal action detection aims to recognize the action category and determine each action instance’s starting and ending time in untrimmed videos. The mixed methods have achieved remarkable performance by seamlessly merging anchor-based and anchor-free approaches. Nonetheless, there are still two crucial issues within the mixed framework: (1) Brute-force merging and handcrafted anchor design hinder the substantial potential and practicality of the mixed methods. (2) Within-category predictions show a significant abundance of false positives. In this paper, we propose a novel Boundary Discretization and Reliable Classification Network (BDRC-Net) that addresses the issues above by introducing boundary discretization and reliable classification modules. Specifically, the boundary discretization module (BDM) elegantly merges anchor-based and anchor-free approaches in the form of boundary discretization, eliminating the need for the traditional handcrafted anchor design. Furthermore, the reliable classification module (RCM) predicts reliable global action categories to reduce false positives. Extensive experiments conducted on different benchmarks demonstrate that our proposed method achieves competitive detection performance. Our source code is available at https://***/zhenyingfang/BDRC-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Merging

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