Reviewerly/exHarmony huggingface dataset:
Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation

Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robots to detect and avoid hazardous objects with a higher confidence level, even if the object is under occlusion. First, the image-level-contextual-encoding module was proposed and incorporated with the Faster RCNN ResNet 50 object detector model to improve the low-featured and occluded hazardous object detection in an indoor environment. Further, a safe-distance-estimation function was proposed to avoid hazardous objects. It computes the distance of the hazardous object from the robot’s position and steers the robot into a safer zone using detection results and object depth data. The proposed framework was trained with a custom image dataset using fine-tuning techniques and tested in real-time with an in-house-developed mobile cleaning robot, BELUGA. The experimental results show that the proposed algorithm detected the low-featured and occluded hazardous object with a higher confidence level than the conventional object detector and scored an average detection accuracy of 88.71%.
Subject-adaptive Loose-fitting Smart Garment Platform for Human Activity Recognition

The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.
Preface

The 2022 International Conference on Intelligent Equipment and Data Processing (IEDP 2022) was successfully held on October 21 st -23 rd , 2022 in Guangzhou, China (virtual conference). IEDP 2022 encouraged exchange of information on frontiers of research in different areas, connected the most advanced academic resources in China and the world, and turned research results into industrial solutions. We had the honor of having invited Prof. Guixiong Liu from South China University of Technology, China to serve as our Conference General Chair. 90 individuals participated in the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations, and online Q&amp;A discussion. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors one by one. During the conference, we were honored to invite four sophisticated keynote speakers to address their speeches. Among them, Prof. Wenbing Zhao from Cleveland State University, America delivered a speech on Video-Based Baseball Pitch Type Recognition. During the speech, he reported a preliminary study on video-based pitch type recognition using deep learning. A semi-automated way of building datasets based on publicly available video and pitch information for MLB games was proposed to facilitate the study and the two-stream inflated 3D convolutional neural network (I3D) was used for pitch type recognition. Next, Prof. Ming Jiang from Sun Yat-sen University, China addressed a presentation on Massive MIMO Beam Vector Assignment Based on Deep Learning and Location/Handover Information. In this paper, he proposed to apply the Gaussian anomaly detection (GAD) to improve the system performance, particularly in the handover zone, by exploiting the information of sea lane and locations of the user equipment (UE) and used convolutional deep belief networks (CDBN) and GAD in the further analysis. Their insightful speeches had triggered heated discussion in the third part of the conference. Besides, every participant praised this conference for disseminating useful and insightful knowledge. The proceedings of IEDP 2022 are a compilation of the accepted papers and represent an interesting outcome of the conference. These papers feature but are not limited to the following topics: Intelligent Inspection Technology, Hybrid Intelligent Systems, Integrated Data Terminals, Data Science and Big Data Technology, etc. All the papers have been checked through a rigorous review and process to meet the requirements of publication. We would like to acknowledge all of those who supported IEDP 2022 and made it a great success. Particularly, we would like to thank the Journal of Physics: Conference Series, for the efforts of all its colleagues in publishing this paper volume. All the help and contribution of each individual and institution was instrumental in the success of IEDP 2022. The Committee of IEDP 2022 List of Committee member is available in this pdf.
Face recognition for smart attendance system using deep learning

Attendance systems using traditional methods take much time and are less efficient. Face recognition is applied by comparing and recognizing the faces in the room with those in the database. However, we can address this issue by using a smart attendance system using face recognition to do attendance automatically and can save time in registering attendance. In this study, we propose a smart attendance system using the face detection model RetinaFace and MTCNN, then the face recognition model using FaceNet and ArcFace. This model will be evaluated using the WiderFace, Essex Faces 94, and Essex Faces95 datasets to evaluate its accuracy and speed. The final model results show that RetinaFace face detection has average precision (AP) results on the WiderFace dataset of 94.20% easy, 93.24% medium, and 83.55% hard, better than MTCNN with AP values of 83.31% easy, 80.32% medium, 83.55% hard. For the combination of face recognition models, FaceNet + RetinaFace obtained as the best combinations model in recognition and speed with a Rank-1 Recognition Rate of 99.114% and recognition speed per image of 118.90 ms.
Research on the Teaching Strategy of College Basketball Class

With the rapid development of the living standard of Chinese residents, physical education has undergone a transformation of expanded functions in the new era.Physical education in colleges and universities is not only to develop students' physical quality and enable them to have basic sports skills, but also to turn to the new function of "educating people" in physical education in colleges and universities.As one of the most popular sports in China, basketball is widely loved by teachers and students in colleges and universities.Taking it as an optional course will also be the first choice for many students.This paper explores the teaching methods and strategies of our teachers in the process of college basketball teaching to optimize the classroom and improve the teaching level.
Artificial Intelligence and Methods

AIArtificial Intelligence is a broad classification that includes all artificially implemented intelligence. While artificially implemented intelligence may or may not engage in learning, all types of AI that mimic human behavior are generally classified as AI. Research into AI has been actively conducted since the mid-1900s.
An End-to-End Knowledge Graph Based Question Answering Approach for COVID-19

Question Answering based on Knowledge Graph (KG) has emerged as a popular research area in general domain. However, few works focus on the COVID-19 kg-based question answering, which is very valuable for biomedical domain. In addition, existing question answering methods rely on knowledge embedding models to represent knowledge (i.e., entities and questions), but the relations between entities are neglected. In this paper, we construct a COVID-19 knowledge graph and propose an end-to-end knowledge graph question answering approach that can utilize relation information to improve the performance. Experimental result shows that the effectiveness of our approach on the COVID-19 knowledge graph question answering. Our code and data are available at https://github.com/CHNcreater/COVID-19-KGQA .
TFEEC: Turkish Financial Event Extraction Corpus

Event extraction from the news is essential for making financial decisions accurately. Therefore, it has been researched in many languages for a long time. However, to the best of our knowledge, no study has been conducted in the domain of Turkish financial and economic text mining. To fill this gap, we have created an ontology and presented a well-defined and high-quality company-specific event corpus of Turkish economic and financial news. Using our dataset, we conducted a preliminary evaluation of the event extraction model to serve as a baseline for further work. Most approaches in the event extraction domain rely on machine learning and require large amounts of labeled data. However, building a training corpus with manually annotated events is a very time-consuming and intensive process. To solve this problem, we tried active learning and weak supervision methods to reduce human effort and automatically produce more labeled data without degrading machine learning performance. Experiments on our dataset show that both methods are useful. Furthermore, when we combined the manually annotated dataset with the automatically labeled dataset and used it in model training, we demonstrated that the performance increased by %2,91 for event classification, %13,76 for argument classification.
PARSEME Meets Universal Dependencies: Getting on the Same Page in Representing Multiword Expressions

Multiword expressions (MWEs) are challenging and pervasive phenomena whose idiosyncratic properties show notably at the levels of lexicon, morphology, and syntax. Thus, they should best be annotated jointly with morphosyntax. We discuss two multilingual initiatives, Universal Dependencies and PARSEME, addressing these annotation layers in cross-lingually unified ways. We compare the annotation principles of these initiatives with respect to MWEs, and we put forward a roadmap towards their gradual unification. The expected outcomes are more consistent treebanking and higher universality in modeling idiosyncrasy.
Simple tool for three-dimensional reconstruction of coronal hippocampus slice using matlab

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Retno Supriyanti, Eko Wahyudi, Yogi Ramadhani; Simple tool for three-dimensional reconstruction of coronal hippocampus slice using matlab. AIP Conf. Proc. 21 February 2023; 2482 (1): 140002. https://doi.org/10.1063/5.0114412 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Real-Time Face Mask Detection and Alert System Using IoT and Machine Learning

The COVID-19 pandemic is causing a worldwide prosperity crisis so the affirmation procedure is to wear a facial covering in open regions, as demonstrated through the field wellness endeavor (WHO). The COVID-19 pandemic restricted country-run organizations universally to constrain lockdowns and limit tainting transmissions. Audits show that effective facial covers clearly reduce transmission. A useful and monetary method of utilizing AI lays out a safeguarded climate game plan. Facial protection has hindered the region of photograph handling and computer imaginatives. Facial discovery has one-of-a-kind use cases going from facial acknowledgment to catching facial movements, where the last option requires the face to be uncovered with extraordinarily excessive accuracy. Because of the quick progression in the area of AI calculations, the dangers of facial covering identification innovation have not been tended to yet. This innovation is more pertinent today since it is utilized to distinguish faces, continuously reviewing and providing oversight. It fosters a model for locating unique individuals who are not wearing face covers. This model will recognize whether or not the individual is wearing a covering. We foster a model to send a warning to persons who are not wearing masks utilizing his/her subtleties. The procedure of distinguishing individuals' countenances and isolating them into two classes, specifically individuals with covers and individuals without covers, is finished with the assistance of picture handling and profound learning. With the assistance of this undertaking, an individual who is planned to screen individuals can be situated in a far-off region but can nevertheless screen proficiently and give guidelines.
Utopian Thinking or Business as Usual: Pondering Next Steps for SIGCAS

With the advent of a new year and the impending end of my term as SIGCAS Chair it seems just about the right time to share some thoughts on SIGCAS, its mission and where we all stand in relation to it. I'd love to hear your thoughts and reactions to this, either directly via email, or on the SIGCAS-Talk distribution list or our Discord server.
About the Creation and Experimentation of ‘Sound Visualization’ Film

It is a film based on the combination of sound and image, where the entire film has been predetermined and the picture is determined by the sound.This is a new attempt at sound recording.In order to break the tradition of picture-driven films, the project plans to complete the recording work first, relying on sound to represent the pictures.The picture presentation of the film will reflect the different sensations brought by the sound by inviting actors.The project will use an independent production approach to complete the entire study.Combining theories of audience research, semiotics, and psychology, the director and creative team will create an immersive experience during the filming process, allowing the actors to perform physically, combined with some scenes and props, to replace the audience's feeling of sound changes, which will then be presented in the form of a short film.The project creates an immersive atmosphere to complete the exchange of information and interaction.
The future of cyber risk management: AI and DLT for automated cyber risk modelling, decision making, and risk transfer

Blockchain-based smart contract technology has the potential to facilitate and accelerate the contract management process of cyber risk transfer (derivatives) contracts. Each validated derivative contract on an openly shared infrastructure would contain data and self-executable codes inherent to that contract. When an underlying event occurs, which meets the prespecified conditions, a blockchain-based smart derivative would pick up the predefined data sources of all participants, and then automatically activate and determine pay-outs to or from the contract parties. We propose an AI-driven, DLT-based smart contract system for secured sharing of threat intelligence, risk modelling, structuring of risk transfer instruments, and automated settlement process. To this end, an architecture for automation of decision-making process for cyber risk mitigation and transfer of residual risks with AI-driven DLT-based smart contracts is proposed, and a qualitative assessment of the same is presented.
Pembuatan Media Pembelajaran Instalasi Penerangan Listrik Berbasis Android

Purpose of Making Android-based Learning Media using ispring suite on electrical lighting installation materials to produce valid Android-Based Learning Media. The application was made using the 4-D development model research method), respondents to media expert lecturers from the department of electrical engineering, Faculty of Engineering, Padang State University, and teachers of class XI TITL at SMK Negeri 5 Padang. The research instrument is a learning media validation questionnaire. Results response analysis show that the validation of each validator is declared valid, thus the Android-Based Electric Lighting Installation Learning Media using the Ispring Suite application is declared valid as a learning media that is worth using.
The Effectiveness of Learning English Song to Improve Employees’ Listening Skill at Pt. Astanita Sukses Apindo (Firedome)

This study was conducted in order to improve employees' listening skill at PT Astanita Sukses Apindo (Firedome).This study aims to invention the experimental suggestion whether English song effective to improve employee's listening skills at PT Astanita Sukses Apindo (firedome).The methods that used was listening English song.The employees were given the treatment to listened English song for 4 times then after that, their fill the questions with fill in the blanks and matching lyrics.This research consisted with observation, interview, and test.The data were gathered through quantitative data were obtained from the employees' listening score of pre-test and posttest.The results of the study showed that there was a development on employees' listening mastery, it can be seen from the average of pre-test score was 74 and the average of posttest score was 86.So, it can be seen that there is an effectiveness of learning English song to improve employees' listening skill.
ANALYSIS OF THE POSSIBILITIES OF APPLICATION OF QUEST TECHNOLOGIES IN PROFESSIONAL ORIENTATION WORK

The development of modern Ukrainian education requires the use of fundamentally new tools of activity in the educational process. Focusing on the formation of a personality capable of independently solving situational tasks and making the most effective decisions is the task of a modern, competent teacher. A correctly chosen profession and the development of the necessary competencies and practical skills in the selected activity of each person form a developed and educated society. Such a society strives for growth in all social processes of the country, which in modern conditions needs highly qualified and modern specialists. Quest (web-quest) technology is one of the effective tools for the comprehensive development of students. The paper describes the approaches of scientists to defining the essence and features of the quest (web-quest) technology. The forms of quests that solve various tasks with the help of the Internet, help students' personality development and help in career guidance activities are considered. Ways of implementing this tool in educational activities, which are aimed at creating conditions for high-quality training and career guidance activities that contribute to the correct choice of the future specialty of each member of society, are analyzed. The importance of using communication technologies when using the described technology is emphasized. The article discusses the main tasks of the technology quest, the implementation of which ensures the development of the student's intellectual abilities and forms the necessary professional competencies for a particular activity. The role of the quest in the career guidance activities of education representatives is also considered. The main competencies that develop during the use and implementation of quest technology in students and students have been studied. It was also analyzed how the combination of play and learning contributes to the effective interaction of the student with all elements of the educational process and the achievement of high results in education. The pedagogical goals of implementing educational quests are described, contributing to the correct choice of a person's future specialty and practical training in the chosen activity.
A Dehazing Benchmark with Real Hazy Outdoor Images

A single image dehazing is one of the problemin image processing. The main aim of using this method is to obtain certain transmission map to abolish hazes from a single input image. An optical model is evaluated and the basic transmission map under an additional filter is modified. For better conservation of haze image, the globally guided image filtering can be applied to produce sharper images and preserves details in regions of ﬁne structure visibly.
Transfer learning with different modified convolutional neural network models for classifying digital mammograms utilizing Local Dataset.

Breast cancer is the leading cause of cancer-related mortality among women worldwide. The incidence and mortality increased globally since starting registration in 1990. Artificial intelligence is being widely experimented in aiding in breast cancer detection, radiologically or cytologically. It has a beneficial role in classification when used alone or combined with radiologist evaluation. The objectives of this study are to evaluate the performance and accuracy of different machine learning algorithms in diagnostic mammograms using a local four-field digital mammogram dataset.The dataset of the mammograms was fullfield digital mammography collected from the oncology teaching hospital in Baghdad. All the mammograms of the patients were studied and labeled by an experienced radiologist. Dataset was composed of two views CranioCaudal (CC) and Mediolateral-oblique (MLO) of one or two breasts. The dataset included 383 cases that were classified based on their BIRADS grade. Image processing included filtering, contrast enhancement using contrast limited adaptive histogram equalization (CLAHE), then removal of labels and pectoral muscle for improving performance. Data augmentation was also applied including horizontal and vertical flipping and rotation within 90 degrees. The data set was divided into a training set and a testing set with a ratio 9:1. Transfer learning of many models trained on the Imagenet dataset was used with fine-tuning. The performance of various models was evaluated using metrics including Loss, Accuracy, and Area under the curve (AUC). Python v3.2 was used for analysis with the Keras library. Ethical approval was obtained by the ethical committee from the College of Medicine University of Baghdad Results: NASNetLarge model achieved the highest accuracy and area under curve 0.8475 and 0.8956 respectively. The least performance was achieved using DenseNet169 and InceptionResNetV2. With accuracy 0.72. The longest time spent for analyzing one hundred image was seven seconds.This study presents a newly emerging strategy in diagnostic and screening mammography by using AI with the help of transferred learning and fine-tuning. Using these models can achieve acceptable performance in a very fast way which may reduce the workload burden among diagnostic and screening units.