Reviewerly/exHarmony huggingface dataset:
ICRL: Using landmark ratios with cascade model for an accurate age estimation system using deep neural networks

Over the past decade, the computer vision community has given increased attention to the development of age estimation systems. Several approaches to more accurate and robust facial age estimation have been introduced. Apparent age datasets are typically collected from uncontrolled environments, leading to a number of challenges. In this paper, a cascade model system, which we called the ‘Integrated Classification and Regression with Landmark Ratios (ICRL), is introduced. Our system uses a classification model in order to learn the age label distribution, then uses this knowledge as an auxiliary input to a regression model. ICRL is based on context facial information and label distribution analysis. Facial context information is introduced through the extraction of precise facial landmark ratios. Extracted landmark ratios allow the system to distinguish each age label. The ICRL system uses a classification model to train the CNN network to learn the in-between relation of age labels. ICRL sufficiently models the aging process in the form of ordered and continuous imagery. The ICRL system minimizes the number of parameters needed as well as overall computational costs whilst maintaining robust and accurate results. Despite its simplicity, our system has outperformed other state-of-the-art approaches when applied onto the MORPH II, CLAP2015, AFAD and UTKFace datasets. ICRL achieved an overall superior predictive performance, reaching 99.67% with MORPH II, 99.51% with AFAD, 96.52 with CLAP2015, and 96.28% with UTKFace.
STM32-based Anti-fall Smart Vest System for the Elderly

With the increasing trend of population aging in our country, how to take care of the elderly has become the focus of attention from all walks of life. In order to reduce the occurrence of incidents that the elderly did not receive timely assistance due to falling, this article designed a smart vest system with STM32 single-chip microcomputer as the main control chip, which uses ultrasonic sensors to obtain the distance between the elderly and obstacles to realize the anti-collision reminder function. Connect the six-axis acceleration sensor for fall detection and control the ejection of the airbag to complete the fall protection function. At the same time, the alarm information is sent to the target associated mobile phone through the GSM network, and the HTTP communication protocol is used to communicate with the OneNET platform (China Mobile Internet of Things Open Platform) carry out data interaction and realize the function of positioning for help. The system also uses human heart rate sensors and body temperature sensors to collect real-time data on the physiological state of the elderly; this system adds a strong guarantee for the health and safety of the elderly.
Material bias

It is known that one specific concern in the development of indeterminacy and graphic scores among the so-called ‘New York school’ composers was to satiate the interest of one specific performer: the pianist David Tudor, famous for his genius at solving puzzles. It is also known that after a decade of playing the piano, Tudor started incorporating electronics into his acoustic instrument, subsequently composing music by connecting modular electronics into an instrumental network which also served as the ‘score’ for his performance. What is not known is the connection between these two seemingly disparate endeavours. This paper attempts to solve this puzzle at the heart of Tudor's activities through the notion of ‘material bias’. Tudor's realisations of graphic notation treated the composer's score as biased material, measuring its size, counting its components, and overlaying the parts together. His subsequent foray into electronics can be understood as a move from textual to physical bias, which reveals an underlying coherency in Tudor's approach too often eclipsed by the misleading narrative of transition from performer to composer. Applied more generally, the notion of material bias presents an unusual perspective on how things like musical scores and instruments work.
Deep Residual Network for Autonomous Vehicles Obstacle Avoidance

With the advancement of artificial intelligence and machine learning, autonomous automobiles are emerging as a lucrative subject of study and a source of interest for car companies. This framework contains our research. The fundamental purpose of this research is to propose an obstacle avoidance strategy for self-driving vehicles. We are looking at developing a model for high-quality obstacle avoidance prediction for autonomous cars that is based on images generated by our virtual simulation platform and then utilized with a ResNet50 deep learning technique. The primary challenge for an autonomous car is to move without collision. For autonomous vehicle simulation research, the suggested technique is feasible, efficient, and trustworthy. The performance of the proposed design is then compared to that of current architectures. The experimental results suggest that the ResNet50 design outperforms the other approaches tested.
Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

To improve the extraction accuracy of knee bending motion in ball motion image and reduce the extraction distance error and time consumption, a knee bending motion extraction algorithm using visual sensor is proposed. The visual sensor model is constructed based on the ball motion frame image, the trigger data is output through differential and logical judgment, and these data are normalized to generate the visual sensor sample set of the ball motion frame image. The sample set is used as the input of the convolution neural network (CNN) and the sample basis of the motion energy model. The CNN extracts the features of the sample set in the convolution layer, the motion energy model is combined with the local binary pattern to extract the features of the sample set, the weighted summation method is used to fuse the two features, and the Softmax classifier is used to classify and extract the knee bending motion. The results show that the proposed algorithm has good ball motion image collection effect, the knee bending motion extraction accuracy is always maintained at about 98%, the distance error is low, and the time consumption of ball motion feature extraction is only 2.65 s, which has high application value in the field of sports.
A design of object detection system in fog

Images taken by intelligent monitoring equipment, unmanned driving, and intelligent transportation in foggy days have greatly affected the later object detection and information extraction. The main purpose of this research paper is to study and report how to implement an object detection system in foggy weather, and to implement a higher-precision Retinex algorithm on FPGA. The system uses Xilinx's ZYNQ 7 series development platform, the detection method is based on Tiny-YOLOv2. The Gaussian filter in Retinex algorithm uses a full variational design to enhance image quality and increase processing speed. The results prove that the accuracy of the YOLO object detection system is greatly improved after the retinex algorithm is processed, and it meets the requirements of fog object detection.
Raaz: A Transdisciplinary Exploration at the Intersection of Bioart, HCI, and Community Engagement

Living organisms and their biological properties, including the capacity for transformation and representation of information, offer exciting and inspiring opportunities for transdisciplinary art and design explorations. While an emerging body of work is increasingly investigating the possibilities at the intersection of interactive computing, biology, and art, more work is needed to investigate the potential of these approaches for supporting community and public engagement and participation in art, science, and technology. In this project, we describe a multimedia transdisciplinary bioart installation and hands-on agar art activity that we presented to members of the public in a community biology lab setting. Using short interviews, observations, and questionaries, we investigated attendees' reactions and impressions of the experience and found that the event generated transdisciplinary reflections, invited participants to bring their previous knowledge and experience to bear in engaging with different aspects of the work, and that the audience benefited from contextualization by artists.
Vegetation mantle change in the open pit mining area of Sin Quyen Copper Mine in Vietnam by satellite imagery data

The article presents the estimation results on the change in vegetation mantle in the region of Sin Quyen Mine (Bat Xat District, Lao Cai Province, Northern Vietnam) in the area of 19012 hectares. The normalized difference vegetation index (NDVI) and the vegetation mantle density change were calculated using satellite images Landsat 5 TM and Landsat 8 obtained between 1990 and 2020. In the period of copper mining for 30 years in the test area, the plant ecosystem has suffered the essential changes in the structure. From the remote sensing data and analytical results in 2020, the plant community is structured as follows. In the phase of high-rate mining (2000&ndash;2020), the sites with NDVI in the range of 0.4&ndash;0.6 prevailed (60.74%). The area of sites having NIDVI &gt; 0.6 reduced by 3330 times&mdash;to 0.2%. The area of sites with NDVI in the range of 0.2&ndash;0.4 grew to 33.78%. The areas of sites with NDVI less the 0 and from 0 to 0.2 made 1.42 and 4.04%, respectively. Such structural changes are explained by drilling-and-blasting and by operation of drill rigs, shovels, dump trucks and dozers. The dusting objects are pit walls, working and nonworking sites, and overburden dumps. At the same time, an increasing trend is revealed in the areas of sites with low NDVI (by 1.5&ndash;6 times). The analytical estimation produced an inverse dependence of decrease in the area of sites with dense vegetation cover with increasing areas of mining and dumping. This dependence is explained by the progressive degradation of ecological situation in the region of open pit mining of the deposit composed of sulfide-bearing rocks. The studies were carried out in the framework of international cooperation in the sphere of wider application of remote sensing.
A Text Code Recognition and Positioning System for Engineering Drawings of Nuclear Power Equipment

In this paper, a text code recognition and positioning system for engineering drawings of nuclear power equipment is designed to promote the informatization and automation of paper work created in nuclear power projects including construction procedure and maintenance procedure. Its core function is to analyze engineering drawings, and identify the user-searched code contents in them, and then extract the codes and related information, so that the staff can more quickly retrieve the required codes, obtain the drawing files containing the codes, and locate the codes on the engineering drawings precisely. The code recognition system proposed in this paper is based on the Teigha toolkit, ezdxf open source library, CRNN and PaddleOCR open source library. For engineering drawings in picture format, VGG16 is used for feature extraction of code regions. CRNN based on VGG16 is used to divide the entire coding area, and PaddleOCR is used to identify the coding information. The experiment results show that the system can be effectively used in engineering practice of nuclear power project with high usability, which can greatly improve the work efficiency of staff.
Calculation of the Optimum PV Panel Incline Angle for Mediterranean Climate

Due to the increasing energy needs and the limited resources that are depleted, more importance is given to renewable energy sources and their use with the highest efficiency in many areas.In order to benefit from solar energy, which is one of the most important renewable energy sources, with the highest efficiency, various parameters must be optimized.In this study, the optimization of the photovoltaic (PV) panel angle is emphasized.By using the values of solar radiation incident on the horizontal plane, the optimum PV panel angle for Adana province related to the months was determined according to the south direction.In total, 12 different panelangle estimation models specific to the region were developed and brought to the literature.When the results are reviewed, it is thought that the usability of these data for designers and academics is appropriate.
Real-Time Tracking Method for Sports Targets in Smart Cities under the Internet+ Background

Real-time tracking of video sports targets has been widely used in the sports industry. In order to further improve the accuracy of real-time tracking of sports targets, this paper proposes a real-time tracking method for sports targets in smart cities in the context of Internet+. This method collects data and information on the targets in the sports process, fully considers the diversity of the sports process, and performs real-time tracking and detection of the upgraded sports targets. Finally, the experimental research shows that the method in this paper can track the target accurately and quickly in real time during the movement process, and the accuracy and real-time tracking are relatively better than other tracking methods, which can be used as a powerful tool for subsequent moving target tracking.
Surrogate-Assisted Artificial Bee Colony Algorithm

Search strategies play an essential role in the artificial bee colony (ABC) algorithm. Different optimization problems and search stages may need different search strategies. However, it is not easy to choose an appropriate search strategy efficiently. In order to select an appropriate search strategy with few evaluations, this paper proposes a surrogate-assisted ABC (called SAABC). Based on our previous work, we construct a strategy pool that contains three search strategies. Then, the radial basis function (RBF) network is applied to evaluate the offspring generated by the search strategies. The search strategy with the best evaluation value will be used to guide the population. A set of 22 classical benchmark problems with 30 dimensions are utilized to verify the performance of SAABC. Experimental results show that SAABC achieves better performance than five other ABC algorithms.
Using the Model of Benchmarking of Educational Services in a Socially Responsible Education-Innovation Cluster during the Covid-19 Pandemic

Purpose of the study is to substantiate the feasibility of using the model of benchmarking of educational services in the socially responsible educationalinnovative cluster in the context of Covid-19 pandemic.Specifically, the authors focus on justify a "nuclear" approach to the cluster formation.The processoriented benchmarking model was used to apply best practices of providing higher educational service in the context of Covid-19 pandemic.The method of factor analysis was used to determine the impact of each of the 4P subsystems of benchmarking.Using the method of benchmarking makes it possible to develop a final competitive product in the context of the Covid-19 pandemic -an educational service for all industry and territorial stakeholders.In the light of Covid-19 pandemic, the formation of socially responsible educational-innovative clusters in Ukraine is one of the most promising and effective trends to modernize the provision of educational service.The uniqueness of the educational service is created by strengthening and synergizing the competitive advantages and competencies of all participants in the educational cluster: teachers, students, employers, research staff, the local community.
Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models

Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers’ subjective fit is often based on shortest distances. Our results suggest that adding vertical lines (“lollipops”) supports more accurate fit estimation in the steep area of model curves ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://osf.io/fybx5/</uri> ).
Research on Museum Educational Display Based on Image Recognition Tracking

Based on the VR display project of the museum, this paper elaborates the temporal restoration, spatial restoration, and immersive experience of virtual reality technology and discusses the basic research of virtual reality technology in the museum, the display of the platform, and the prospect of cultural communication. By studying the working principle of somatosensory interaction technology, the development status of the somatosensory interaction device Kinect, and the programming algorithm, this paper explores its development trend and application prospect in the field of interactive exhibit design and production in science and technology museums. A prototype system of human-machine interaction is realized in the exhibit design, using Unity3D as the engine to build the interaction platform, and using webcam and other interactive devices to realize somatosensory interaction, improve the design scheme of interactive science exhibits, and practice diversified and multisensory innovative exploration of science forms.
ShareCert: Sharing and Authenticating Certificates and Credentials on Blockchain

The process of certificate and credential verification of every candidate is an essential step in the recruitment phase of every organization. Today, most of this work is usually outsourced to external agencies. Owing to the presence of third parties and lack of transparency in the verification process, there is a possibility of fraud or undetected error creeping in at every stage. The recruiter is at the risk of hiring a fraudulent candidate and the candidate at the risk of facing a rejection due to inefficient verification. Moreover, the existing procedure for this verification takes around 30 days per candidate. Today, blockchain technology assisted with smart contracts have applications ranging from Supply Chain Management to Digital identity, Banking, Insurance and many (any) other scenarios where multiple entities form a network to trade or query any kind of assets. Using smart contracts, a decentralized data store, content-hash based verification and blockchain, we’ve developed a design for decentralized applications to address the existing issue of certificate and credential verification. These decentralized applications bring about transparency, privacy, provenance tracking and trust to the procedure of sharing and authenticating credentials and certificates. We demonstrate the efficacy of the proposed design based on the results from our implementation on Ethereum and Hyperledger Fabric. In terms of efficiency, our implementations support near real-time verification of certificates and credentials, thereby providing a faster, secure and transparent verification mechanism.
Nursing Student’s Nervous System Assessment Virtual Simulation Practice Experience

본 연구는 간호대학생의 신경계 사정을 위해 개발된 게임 기반인 가상 시뮬레이션 실습 경험에 대해 알아보고자 하였다. 참여 대상자들은 G광역시의 일개 대학에서 통합실습 교과목에 참여하는 4학년 학생들 중 참여의사를 밝힌 49명을 대상으로 진행되었다. 자료수집은 2021년 5월 28일부터 6월 10일까지 면접과 설문에 의해 시행되었으며 Luborsky"s 의 주제 분석기법을 이용하여 분석하였다. 연구결과, 4개 영역, 7개 표제와 17개 주제가 도출되었다. 신경계 사정은 이론학습 이후에도 숙지가 어려운 점과 실습기회 부족을 감안하여 게임기반으로 흥미유발과 반복학습이 가능한 점이 유익하였으며 추후 인터넷 지연 등의 기술적 문제와 간호사례의 다양성이 고려된다면 보완적 실습 교육 방안으로써 활용성이 높을 것으로 기대한다. The purpose of this study is to investigate the game-based virtual simulation practice experience for the nervous system assessment of nursing students. The participants were conducted among 49 fourth-year students who participated in the integrated practice course at a university in G Metropolitan City and expressed their intention to participate. Data collection were conducted through interviews and questionnaires from May 28 to June 10, 2021. That data were analyzed using Luborsky"s thematic analysis technique. As a result of the study, students" experiences were derived from 4 domain, 7 topics and 17 themes. They were difficult to master the nervous system assessment even after theoretical learning and the lack of practical opportunities. We will use possible to arouse interesting and repeated self-directed learning through the game-based virtual simulation. If various cases and technical development are made up, the game-based virtual simulation will be highly useful as a supplementary practical education method in nursing education.
Generation and Optimization of Teaching Decision Generation Under a Smart Teaching Environment

Teaching decisions need to be optimized to guide learners to stick to the right learning method, and maintain learning interests. However, the existing evaluation systems for college teachers’ teaching decision ability cannot adapt to online teaching decision-making. What is worse, the previous studies on college teachers’ teaching decisions rarely consider online teaching decision-making. Therefore, this paper attempts to optimize college teachers’ teaching decision under the smart teaching environment. Specifically, the roadmap of teaching decision generation and optimization was presented, the teaching decision bases were specified for different teaching decision scenarios, and a teaching decision model was established under the smart teaching environment. In addition, teaching decisions were generated based on deep reinforcement learning algorithm, and optimized by certain rules under the experience replay mechanism. The proposed algorithm was proved effective and feasible through experiments.
Research on Comprehensive Evaluation of Economic Management Performance Based on Improved Fuzzy Clustering Algorithm

In order to solve the problems of low recall rate and precision rate, high error rate, and long evaluation time in traditional evaluation methods, a comprehensive evaluation of economic management performance based on an improved fuzzy clustering algorithm is designed. The improved magnetic optimization algorithm was used to optimize the fuzzy C-mean algorithm, the improved fuzzy clustering algorithm was completed, and the improved fuzzy clustering algorithm was used to mine the economic management performance data. Using data mining findings and AHP’s weighting formula, a complete method for evaluating economic management effectiveness was developed. The BP neural network was improved using a genetic algorithm based on the index weight calculation findings, and the full-assessment model of economic management performance was constructed. Using this approach, it is possible to accurately and quickly assess the economic management performance of a company with a high rate of recall and accuracy; the error rate of a thorough assessment ranges between −3 percent and 4 percent; the average duration for an assessment is 0.81 seconds.
Motion Planning to Retrieve an Object from Random Pile

It is challenging to retrieve a target object from a randomly stacked pile by using a robot due to the occlusion of the target object.In this study, we propose a novel retrieval method in which a robot selects the viewpose to observe the occlusion part of the target object using the RGB-D images, and then selects the motion of grasping/dragging to retrieve the object depending on the configuration of the pile.We experimentally confirm that a robot effectively observes a pile with a complex configuration and successfully retrieves a target object.