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Distinguishing Orthodontic Experts From Laypersons Through Gaze Analysis

ieee ACCESS 2023年 11卷 44444-44453页

作者： Kamahara, Junzo Nagamatsu, Takashi Ito, Kyoko Hiroe, Mamoru Sao, Haruki Aoyagi, Saizo Nagata, Junko Takada, Kenji Kobe Univ Grad Sch Maritime Sci Kobe Hyogo 6580022 Japan Kyoto Tachibana Univ Fac Engn Kyoto 6078175 Japan Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan Komazawa Univ Fac Global Media Studies Tokyo 1548525 Japan Univ Miyazaki Fac Med Miyazaki 8891692 Japan Ask Prof Oral Hlth Consulting Osaka 5300001 Japan Osaka Seikei Univ Fac Data Sci Osaka 5330007 Japan Kobe Univ Grad Sch Med Kobe Hyogo 6500017 Japan Nomura Res Inst Ltd Tokyo 1000004 Japan

Visual inspection is an important process conducted as an initial diagnostic step in medical examinations. It is assumed that the gaze movements of an orthodontist (expert) differ from those of a layperson. In this study, to examine whether the degree of proficiency in conducting a visual examination can be estimated from gaze movement, we conducted a gaze measurement experiment in which facial images (frontal and lateral images of three patients) were viewed by ten experts and ten laypersons. The performance in discriminating whether a subject was an expert or layperson exhibited a certain improvement when applying an aggregation method for the gaze data, that is, the grid gaze frequency and AOI gaze frequency. We examined whether proficiency levels could be determined using machine learning techniques. The results demonstrated that our method distinguished experts and laypersons relatively effectively using gaze frequency based on the grid and area of interest set by an expert for each face part.

关键词： Frequency measurement visualization Medical diagnostic imaging Position measurement Gaze tracking Frequency conversion Machine learning Expertise eye tracking gaze machine learning orthodontist

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Classification and Grading for Power Sensitive data Based on Hybrid data Distribution Learning 11

Classification and Grading for Power Sensitive Data Based on...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Huang, Xiuli Zhai, Yujia Shi, Congcong Jiang, Yiwen Zhang, Shijun State Grid Smart Grid Research Institute Co. LTD. State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China Big Data Center of State Grid Corporation of China Beijing China State Grid Jibei Electric Power Co. LTD. Beijing China

ISBN: (纸本)9798350333664

Power industry data contain various sensitive information, and the leakage of such data will cause critical damage to both the company and the country. However, the unbalanced and various data types make the traditional data classification and grading method fail to achieve enough performance. To better classify and grade the power sensitive data, we propose a Hybrid data Distribution Learning Network, which simultaneously learns the distribution of multiple data types, including Character-based, Text-based, and Numerical data. Furthermore, we use an optimized nonlinear Support Vector Machine model to detect the grade of power data based on the learned data distribution. The experimental results show that our method outperforms other baselines and achieves a precision of 0.994, a recall of 0.985, and an F1 of 0.989. The visualization results also show that our method effectively learns the distribution of multiple types of power data. © 2023 ieee.

关键词： classification and grading hybrid data distribution learning network nonlinear SVM model sensitive power data

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Design of Deep Learning Models for the Identification of Harmful Attack Activities in the Industrial Internet of Things (IIOT) 10

Design of Deep Learning Models for the Identification of Har...

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10th ieee Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Rajesh, M. Vincent, Rajiv Kathuria, Sakshi Jamalpur, Bhavana Durgam, Thirupathi Jaiswal, Tarun School of Computer Science and Engineering Vellore Institute of Technology Chennai India K. R. Mangalam University University Computer Science and Engineering Haryana Gurugram India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Ece India National Institute of Technology Department. of Computer Application C.G. Raipur India

ISBN: (纸本)9798350382471

Traditional industrial processes benefit greatly from the Industrial Internet of Things (IIoT), which increases efficiency as well as productivity. However, as IIoT becomes more connected, critical infrastructure is exposed to a variety of cyber risks. In order to accurately identify dangerous attack behaviors within IIoT systems, the present research focuses on the development and execution of deep learning models, combining recurrent neural networks (RNNs) including artificial neural networks (ANNs). The approach uses Python-based visualization techniques, model creation, and data pretreatment. By proactively identifying and mitigating cyber risks, our research seeks to strengthen IIoT security and ensure the resilience and honesty of industrial operations in a world that is becoming more linked. © 2023 ieee.

关键词： Artificial Neural Networks (ANNs) Attack Identification Cyber Threat Detection Deep Learning Models IIoT Security Industrial Cyber security Recurrent Neural Networks (RNNs)

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An Audio-Visual Separation Model Integrating Dual-Channel Attention Mechanism

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ieee ACCESS 2023年 11卷 63069-63080页

作者： Zhang, Yutao Wu, Kaixing Zhao, Mengfan Hebei Univ Engn Sch Informat & Elect Engn Handan Hebei Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Sound source separation is the separation of targeted sounds from a noisy environment, which plays an important role in signal processing and has been studied extensively. However, most of these researches only extract audio information for processing, ignoring visual information, resulting in the waste of feature information. In addition, some researchers have fused the extracted features but have not noticed the weights of different features, resulting in a poor model effect. This paper uses multi-modal information to separate sound sources to solve these problems. We constructed a multi-modal Audio-Visual separation model integrating the Dual-channel Attention mechanism named AVDA. The model realizes sound source separation through the dynamic fusion of visual and audio features. Specifically, firstly, we take audio video as input for data preprocessing and segment it to obtain video frames and audio. Then they are introduced into the visual and audio feature extractors, which integrate the attention mechanism to extract features. Finally, the extracted visual and audio features are introduced into the fusion prediction component to obtain the predicted spectrogram, which is compared with the ground truth spectrogram for subjective evaluation. In addition, three indexes of signal distortion ratio (SDR), signal interference ratio (SIR) and signal artifact ratio (SAR) are introduced for quantitative comparison. The experimental results on MUSIC-21 data set show that the model achieves 10.96, 17.91 and 12.77 respectively in three indexes, and its performance is significantly better than other models in audio-visual separation tasks.

关键词： Feature extraction Source separation visualization Spectrogram data mining Predictive models Deep learning Audio-visual systems Audio-visual separation multi-modal information dual-channel attention mechanism feature information spectrogram

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Inherit With Distillation and Evolve With Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第10期45卷 11932-11947页

作者： Zhao, Danpei Yuan, Bo Shi, Zhenwei Beihang Univ Image Proc Ctr Sch Astronaut Beijing 100191 Peoples R China

As a front-burner problem in incremental learning, class incremental semantic segmentation (CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods have utilized knowledge distillation to transfer knowledge from the old model, they are still unable to avoid pixel confusion, which results in severe misclassification after incremental steps due to the lack of annotations for past and future classes. Meanwhile data-replay-based approaches suffer from storage burdens and privacy concerns. In this paper, we propose to address CISS without exemplar memory and resolve catastrophic forgetting as well as semantic drift synchronously. We present Inherit with Distillation and Evolve with Contrast (IDEC), which consists of a Dense Knowledge Distillation on all Aspects (DADA) manner and an Asymmetric Region-wise Contrastive Learning (ARCL) module. Driven by the devised dynamic class-specific pseudo-labelling strategy, DADA distils intermediate-layer features and output-logits collaboratively with more emphasis on semantic-invariant knowledge inheritance. ARCL implements region-wise contrastive learning in the latent space to resolve semantic drift among known classes, current classes, and unknown classes. We demonstrate the effectiveness of our method on multiple CISS tasks by state-of-the-art performance, including Pascal VOC 2012, ADE20K and ISPRS datasets. Our method also shows superior anti-forgetting ability, particularly in multi-step CISS tasks.

关键词： Task analysis Semantics Training Semantic segmentation data models visualization Computational modeling Class incremental learning contrastive learning knowledge distillation semantic segmentation

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Target Detection Algorithm Incorporating Visual Expansion Mechanism and Path Syndication

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ieee ACCESS 2023年 11卷 56973-56982页

作者： Lin, Qingyao Wang, Rugang Wang, Yuanyuan Zhou, Feng Guo, Naihong Yancheng Inst Technol Sch Informat Technol Yancheng 224051 Peoples R China Yancheng Xiongying Precis Machinery Co Ltd Yancheng 224006 Peoples R China

Because the lack of semantic information exchange between characteristic layers, the SSD (Single Shot multibox Detector) algorithm has insufficient detection performance. To address this problem, a detection algorithm called VPE-SSD (Visual Path Enhancement SSD) by incorporating a visual expansion mechanism and path syndication proposed in this paper. Firstly, a visual expansion mechanism is added to the shallow characteristic layer to increase the perceptual field. This enables the semantic information in the shallow layer to be more fully utilized by the network. It can also achieve the purpose of enhancing the expressiveness of the shallow feature layer. Then, the processed deep and shallow characteristic layers are fed into the path syndication module for bi-directional fusion. This improves the global information of the feature layers and generates multi-scale global feature maps. Next, to enhance the detailed information of deep characteristics and improve their expression, the deep characteristic enhancement module is applied to the last three characteristic maps. Finally, using the blended attention module to reduce the negative interference and improve the expression of characteristic maps during target detection. The experimental analysis of the VPE-SSD algorithm is conducted on VOC and COCO, and the mAP is 83.4% and 48.4%. From the result, VPE-SSD algorithm can make better use of the different size characteristic information which helps to improve the performance of the algorithm.

关键词： visualization Convolutional neural networks Object detection Semantics data mining Target tracking Detectors Target detection visual expansion mechanism path syndication deep characteristic enhancement attention mechanism thesaurus

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Analyzing the Research Hotspots and Trends in Crisis Leadership in the Big data Era - - A Bibliometric visualization Analysis Based on WoSCC 9

Analyzing the Research Hotspots and Trends in Crisis Leaders...

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9th International Conference on Big data and Information Analytics, BigDIA 2023

作者： Luo, Jiali Yang, Qingqing Liu, Guangda National University of Defense Technology College of Systems and Engineering Changsha China

ISBN: (纸本)9798350330076

In order to comprehensively understand the development context and evolution trends of international crisis leadership research in the big data era, this study uses the journal papers retrieved from the science Citation Index Expanded (SCI-EXPANDED) and Social sciences Citation Index (SSCI) databases of the Web of science Core Collection (WoSCC) as the main data source, and with the help of visual analysis tool CiteSpace software, carries out a scientific literature metric analysis on the core authors, research countries, research institutions, publishing journals, theme keywords, and hotspot trends of international crisis leadership research. The series of knowledge maps are drawn. The results show that: The annual number of publications on crisis leadership research has experienced three stages: a slow growth period (2002-2008), a significant growth period (2009-2019), and a rapid growth period (2020-2023). The cumulative publication volume shows a continuous increase trend. The crisis leadership field research literature mainly comes from a few core author groups, and the research of core authors has laid the foundation for crisis leadership. The United States ranks first among the countries conducting research on crisis leadership, followed by England, and China is the most productive country in Asia. © 2023 ieee.

关键词： Big data

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Deep Learning Assisted Cyber Criminal Profiling 6

Deep Learning Assisted Cyber Criminal Profiling

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6th International Conference on Big data and Artificial Intelligence, BDAI 2023

作者： Bokolo, Biodoumoye George Chen, Lei Liu, Qingzhong Sam Houston State University Dept. of Computer Science HuntsvilleTX United States Georgia Southern University Dept. of Info. Tech. Statesboro Georgia

ISBN: (纸本)9798350303667

This paper presents an approach for cybercriminal profiling using pre-trained DistilBert, LSTM, and BERT models. By analyzing criminal behaviors and linking them to offender characteristics, the proposed method utilizes structural and parameter learning techniques. Digital forensics, as a means to locate criminal and cybercriminal activity, is highlighted as increasingly important. The suggested strategy incorporates tools such as technical competency tests, a dynamic criminal knowledge base, and visualization to provide investigators with a comprehensive understanding of the case. The paper also discusses the potential benefits of integrating this approach into a cloud-based infrastructure, offering a faster and more cost-effective solution. © 2023 ieee.

关键词： Cybersecurity

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Interactive Dashboard on Online Assessment Tool to Test Competency Levels of Trainees 7

Interactive Dashboard on Online Assessment Tool to Test Comp...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Daivakeshwar, P. Akshita, A. Anwen, S.N. Grace Ananya, V. Gampala, Veerraju Ande, Pavan Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350397284

Analytical dashboard is a place, where all the analysis of the data is displayed using various visualizing techniques. Visualizing is a process of presenting all the hidden patterns and trends in dataset. This analytical dashboard is used by data analysts to check all the variations of a particular variable that occurred over a period of time. The visualizations include different methodologies, where likely and unlikely characters of the dataset are compared with each other in order to find the result and the future impacts of these characters. The prediction theory intends to obtain useful insights from graphical representations is important when it comes to stock market analysis, weather forecasting etc. The proposed dashboard acts as a monitor to analyze the performance effectively. The main purpose of this study is to successfully develop a dashboard to analyze the student data. © 2023 ieee.

关键词： visualization

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3D visualization of Femoral Shaft Fractures 4

3D Visualization of Femoral Shaft Fractures

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Mi, Lam Xin Hoon, Saw Seow Wang, Khor Siak Hui, Saw Seow Utar Faculty of Information and Communication Technology Dept. of Information Systems Perak Malaysia Utar Dr. WLT-CRCD Utar Sg. Long Faculty of Science Dept. of Allied Health Sciences Perak Malaysia Utar Faculty of Information and Communication Technology Department of Computer Science Perak Malaysia

ISBN: (纸本)9798350318432

The absence of an independent learning platform to address femoral shaft fractures poses challenges in understanding and classifying these fractures. The existing anatomy platforms, such as 3D interactive anatomical atlases, are insufficient in providing comprehensive information on the fractures and the high cost of these interactive anatomical atlases can be a barrier for learning to proceed due to the needs of the actual bones and human models. This study aims to develop an accessible, cost-effective educational 3D mobile application that caters to the learning needs associated with human femoral shaft fractures. Although femoral fractures are relatively uncommon, the increased cases of fractures necessitate awareness and preparedness among individuals as well as medical professionals. The proposed mobile application will enhance users' understanding of anatomical structures in femoral shaft fractures and contribute to improved patient care as well as enhance medical education. © 2023 ieee.

关键词： Fracture

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