Reviewerly/exHarmony huggingface dataset:
Augmented Reality based Support System using Three-Dimensional-Printed Model with Accelerometer at Medical Specimen Museums

Medical and nursing students often use medical specimens to enrich their knowledge of anatomy and physiology. Most medical specimens exhibited at the medical specimen museum are in a sliced form; therefore, visitors can only view the planar structure. However, it is important to understand the three-dimensional (3D) structure of the human body in medical learning. In this study, we proposed an augmented reality (AR)-based system that presents a virtual object and realized a virtual hands-on exhibit using a 3D-printed model of the human anatomy by displaying the positional relationship between the 3D-printed model and the specimen, using an accelerometer embedded in the 3D-printed model. The system successfully estimated the posture of the 3D-printed model of the human brain from the positional values measured by the sensor and matched it with the posture of the virtual object. The applicability of the proposed system was evaluated by conducting a questionnaire survey. As per the feedback from the visitors, they were able to operate the AR information by moving the 3D-printed model in different directions, making it easier to understand the actual 3D structure of the human body.
A Proposal for Testing Physical Random Numbers Using Stochastic Process

In this research, we propose a method for verifying a random sequence of a physical random numbers using a Markov process.
Phishing website detection method based on CNAIR framework

At present, phishing websites are growing, and there are fewer researches on detection of phishing websites. Most of the research is based on the traditional single algorithm model, and the detecti...
Virtual and Physical: Two Frames of Mind

Virtual and physical embodiments of interactive artificial agents utilize similar core technologies for perception, planning, and interaction and engage with people in similar ways. Thus, designers have typically considered these embodiments to be broadly interchangeable, and the choice of embodiment primarily depends on the practical demands of an application. This paper will make the case that virtual and physical representations elicit fundamentally different frames of mind in the users of the technology and follow different metaphors for interaction. These differences elicit different expectations, forms of engagement, and eventually interaction outcomes. It will discuss the design implications of these frames of mind, arguing for different domains of interaction serving as appropriate context for virtual and physical embodiments.
Research on breach prediction for big data through hybrid ensemble learning and logistic regression

Abstract The fast-growing online lending sector faces the risk of borrowers defaulting. Network lending data are often unbalanced. On the basis of EasyEnsemble algorithm, we proposed a hybrid ensemble algorithm, which combines the advantages of Bagging and Boosting. By comparing the performance of different learning models, the experimental results show that the hybrid ensemble algorithm has good classification accuracy and generalization ability. Further, we find that social capital has a negative effect on borrower default rate. This paper provides a relatively reliable method for the credit risk assessment of online borrowers, and also provides a new idea for risk monitoring in the field of online lending.
Classification of movements based on wearable device data in biometric authentication

Abstract The paper proposes a neural network algorithm for classifying human movements according to the accelerometer data, which is located in a mobile device. Intelligent algorithms for classifying movement types (single step, walking, walking on stairs, running) are considered on 9 types of different movements that a person performs in everyday life. The developed algorithm is proposed to be used in biometric authentication systems based on mobile phone data.
Motion Image Deblurring using AS-Cycle Generative Adversarial Network

To improve the problem of poor generalization ability of image deblurring model in real scenes, this paper proposes a model named AS-CycleGAN (Cycle Generative Adversarial Network based on Asymmetric Samples).The model trains on unpaired images by using two "dual form" Conditional Generation Adversarial Networks, adopting global residual connection and ResNetv2 residual module.To enhance the texture effect, the SFT layer is integrated.The experimental results on the data set of Gopro show that the SSIM and PSNR values of our algorithm are 15.97% and 0.75% higher than those of the benchmark model CycleGAN, respectively.By improving the residual structure and adding the SFT layer, the effect is even better.AS-CycleGAN provides a powerful help to solve the motion blur problem in the actual scene.
Design and Application of Intelligent Management System of Artificial Airway Airbag Pressure in Intensive Care

In order to solve the problem of continuous monitoring and automatic regulation of patient airbag pressure in intensive care unit, the study designed an intelligent management system of artificial airway airbag pressure. It can realize real-time monitoring and automatic control of airbag pressure. Its pressure data was sent to the PC in real time by the serial port. It can realize the display, store, review and analysis of pressure data. Its clinical application effect was discussed. Experiments showed that the system can monitor airbag pressure in real time and control the pressure to stabilize at 25~30 cmH2O. Compared with the control group, the experimental group had a statistically significant difference in the operation time of monitoring patients' airbag pressure, changes in airbag pressure, the instantaneous maximum value during nursing operation, and the number of aspiration and reflux cases. The clinical application of the system can reduce the workload of medical staff greatly, effectively reduce the number of patients with aspiration and reflux, reduce the incidence of ventilator pneumonia.
A Practical Fault Tolerance Approach in Cloud Computing Using Support Vector Machine

Fault tolerance is an important issue in the field of cloud computing which is concerned with the techniques or mechanism needed to enable a system to tolerate the faults that may encounter during its functioning. Fault tolerance policy can be categorized into three categories viz. proactive, reactive and adaptive. Providing a systematic solution the loss can be minimized and guarantee the availability and reliability of the critical services. The purpose and scope of this study is to recommend Support Vector Machine, a supervised machine learning algorithm to proactively monitor the fault so as to increase the availability and reliability by combining the strength of machine learning algorithm with cloud computing.
Demand-Driven Acquisition for an Academic Architecture Library Collection:

Libraries employ various methods of collection development that incorporate in-case and in-time models for acquisitions to maintain a balance of collection growth and access. Acquisition of e-books, especially within design disciplines, remains an ongoing area of development. In addition to subscription packages and firm orders, demand-driven acquisition (DDA) programs for e-books offer a method for growing collections. Although they appear to limit the role of the librarian in the process, use of these programs can serve to enhance librarian-centered acquisition practices.
Video Based Human Head and Shoulders Detection Using Machine Learning

With the development of artificial intelligence, the application of computer technology for analyzing the objects in images and videos has been more and more widely used. This paper describes the human head and shoulders based on Haar-like features, and realizes the detection of the head and shoulders in the video using machine learning. Firstly, this paper selects the ViBe background model method to extract the moving area in the video. Then, Haar-like features are selected to describe the human head and shoulders. Finally, the head and shoulders in the video are detected by the soft cascade classifier. The algorithm is programmed using C++ language, and selects traffic intersection videos for experiment. Experimental results demonstrate that above 90% detection rate is achieved, which meets the design requirements.
Improving the steady-state coherence and entanglement of two coupled qubits via composite system-reservoir interactions

Abstract In this work, we study the improvement of steady-state coherence (SSC) and steady-state entanglement (SSE) of two coupled qubits by means of composite system-reservoir interaction constructed by a linear combination of orthogonal and parallel ones. We show that in the non-equilibrium case, the SSC and SSE can be significantly enhanced by increasing the parallel components of the interaction Hamiltonian between the system of interest and the heat reservoirs. In addition, we find that in the non-equilibrium case, increasing the parallel components can enlarge the temperature (temperature difference) region where the SSC can maintain nonzero values. In the equilibrium situation, however, the SSC and SSE are not affected by the parallel components of the composite system-reservoir interactions.
SORT with Depth Image Based Pedestrian Tracking Robots

Aiming at the problem of frequent position changes, crossing, and disappearance of pedestrians in the image coordinate caused by the low camera angle of view on the tracking robot, we have made a series of adaptive improvements to the SORT (Simple Online and Realtime Tracking), an online multiple object tracking algorithm, furthermore propose the SORT-DI (Simple Online and Realtimes Tracking with Depth Image). We use the RGB-D camera to obtain the position of the target in the robot coordinate system and use a simple and fast filter to calculate the difference between image features such as gradient features and color features, which solves the problem that the SORT is easy to identify swiches. This solution can also run faster and with low latency on the CPU and achieve the online tracking of designated pedestrians by the robot in large-scale pedestrians tracking scenarios.
Retraction: Research on Multimodal Emotion Analysis Algorithm Based on Deep Learning (J. Phys.: Conf. Ser. 1802 032054)

IOP Publishing Limited ("IOPP") is retracting this paper following an investigation in accordance with COPE guidelines. which has confirmed translated plagiarism. This work was originally published in Chinese [1] and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited request any citations to this article be redirected to the original work [1]. IOPP have made multiple attempts to contact the author but has not received a response. The author is encouraged to contact jpconf@ioppublishing.org . [1] LIN Min-hong, MENG Zu-qiang. Multimodal Sentiment Analysis Based on Attention Neural Network[J].Computer Science, 2020, 47(11A): 508-514. Retraction published: 23 April 2021
Binocular stereo vision technology based on human visual characteristics

Binocular stereo vision technology is an important research direction in the field of computer vision. It is based on the binocular disparity to accurately obtain three-dimensional information on the surface of space objects. Similar to the human eyes system, the binocular stereo vision is supposed to shoot the same scene with two cameras to capture the accurate distance. With the distance information, a three dimension picture can be rebuilt. The thesis includes the characteristics of the human eye's visual system, camera calibration and correction, and the stereo matching algorithm. After that, the image pair acquired by the binocular camera is used to perform the matching process. By combining with the human eye gaze model to simulate the effect of the human eye observation scene. Through simulation, the advantages and disadvantages of different matching algorithms are analyzed and combined with the foveation model to simulate the gaze effect of the human eye, which conforms to the intuitive experience of human vision.
Research on Intelligent Operation and Maintenance Model of Large Database for Electric Power Industry

Abstract Existing research on automated database operation and maintenance for the electrical industry mainly focuses on distributed and cloud platforms, and there is a lack of traditional large-scale database intelligent operation and maintenance research. This paper designs an overall operation and maintenance model framework of “intelligent perception-intelligent decision-intelligent execution”, and proposes feasible implementation plans, including: (1) Use the prophet time series forecasting model to perceive and predict important database performance indicators, and dynamically adjust the threshold of each performance indicator according to the predicted value; (2) Perform correlation analysis on abnormal indicators through the association rule model to construct “Indicators”-&gt;Operation” optimized combination operation strategy library for intelligent decision making; (3) According to the intelligent decision library, automatically restrict the associated operations under abnormal conditions to ensure the normal operation of the service and realize intelligent execution.
The History of the Data Economy: Part IV: The Future

Abstract Data is now the fuel that drives business – identifying potential markets, shaping new products and targeting consumers. This year, Significance has partnered with Impact, the magazine of the Market Research Society, to jointly publish a series exploring the past, present and future of the data economy. In this fourth and final part, Timandra Harkness considers what the coming years have in store for the data-driven industries
Detection Algorithm of Local Outlier Based on Variable Dense Cell

Aiming at the problems of low detection rate and poor time performance in the detection of abnormal points in the LOF algorithm, an improved algorithm for local outlier detection of variable dense cell is proposed. The algorithm has been improved in two aspects. One is the dynamic meshing of variable density cells, which can merge adjacent grids with similar data into a large grid, reducing the number of grids. The other is to quickly set some key parameters of dynamic mesh generation. The experimental results show that compared with the outlier detection algorithms in the literature, the improved algorithm has a certain degree of improvement in meshing effect, time performance and detection rate.
The Development of E-Instrument to Test Students Mental Models on Electrolyte and Non-Electrolyte Solutions

The purpose of this study is to design an instrument that can be used to determine students mental models in electrolyte and non-electrolyte solutions. Students do not have complete understanding of electrolyte and non-electrolyte solutions topic. It takes an instrument that can measure students mental models, which can then be used to find out why students do not have complete understanding of electrolyte and non-electrolyte solutions topic. The research method used is the RnD method with a 4D model. The research steps consist of define, design and development. The results of this study are e-instruments that can be used to measure students mental models on electrolyte and non-electrolyte solutions. The average results of the instrument feasibility from material experts and teacher experts were 89 percent and 83 percent, namely worthy with very good predicate and good predicate. These results indicate that the designed instrument is feasible to use. The instrument designed only reached the validation stage due to lack of time in research.
Building Common Sense Database By Using Social Media Content

In this study, it is aimed to create a dataset of Turkish concepts that contain various relations with each other by using social media contents. The problem is treated as an entity name recognition problem. Concepts and types of relationships that are related to each other are marked in the social media content used for training. The data were modeled using LSTM and BERT based methods. In this way, a large amount of unlabeled content was given to the models, and new concepts that were not in the training set and the relationships between them were found.
Working in the COVID-19 pandemic: an observational study

The restrictions of social isolation adopted to contain the spread of the COVID-19 pandemic have led many companies to adopt remote work in a mandatory and unplanned way. This sudden transition has caused profound changes in personal and professional relationships. In this article, we present the results of a qualitative observational study on the adaptations made in the activities of the software development process of two companies. These adaptations were intended to support the transition to teleworking during the pandemic. They were analyzed based on the theoretical framework of Olson and Olson for distributed collaboration. Software developers’ motivations and observed challenges are also presented. Based on these results, the article presents recommendations to facilitate the adaptation to remote work in software development teams.
Employing STEAM 6E Teaching Methods to Analyze the Academic Emotions of the Digital Video Practice Course

In order to solve the problems faced by students when employing traditional teaching. The study uses the STEAM 6E teaching methods for curriculum design, allowing students to create videos, and then analyzes the emotional changes of students at each stage. In this study, students are divided into 2 groups: the experimental groups and the control groups. The teaching strategy of the experimental group is to integrate the course content into the STEAM 6E teaching methods, while the teaching strategy of the control group is to use the general traditional teaching methods. The teaching experiment tool is video editing software. In the courses, students can create their own works through hands-on practice based on the Maker practical teaching philosophy and can continue to learn by sharing their works and creative ideas with other students afterwards. The emotional performance of each stage through AEQ is analyzed to explore the relevant research on the relationship between academic emotions when STEAM and Maker is combined with the STEAM 6E teaching methods. The results have shown that through STEAM 6E teaching methods, students, even with negative emotions in learning, can still feel hopeful and proud toward learning.
Word Segmentation of Offline Handwritten Bangla Text Lines

Agarwal, Komal Mantry, Akshat Halder, ChayanSegmentation of words from handwritten text lines is one of the most important and challenging prerequisites of the handwritten optical character recognition (HOCR) system. In this manuscript, a simple yet effective handwritten word segmentation technique for Bangla is proposed. The method is developed on the basis of determining the inter- and intra-word gaps of components before the final identification of segmentation positions. It has been tested on the Bangla handwritten WBSUBNdb_text database which has produced 93.25% word segmentation accuracy.
Mining BilingualWord Pairs from Comparable Corpus using Apache Spark Framework

Bilingual dictionaries are essential resources in many areas of natural language processing tasks, but resource-scarce and less popular language pairs rarely have such.Efficient automatic methods for inducting bilingual dictionaries are needed as manual resources and efforts are scarce for low-resourced languages.In this paper, we induce word translations using bilingual embedding.We use the Apache Spark ® framework for parallel computation.Further, to validate the quality of the generated bilingual dictionary, we use it in a phrase-table aided Neural Machine Translation (NMT) system.The system can perform moderately well with a manual bilingual dictionary; we change this into our inducted dictionary.The corresponding translated outputs are compared using the Bilingual Evaluation Understudy (BLEU) and Rank-based Intuitive Bilingual Evaluation Score (RIBES) metrics.
CNN Face Live Detection Algorithm Based on Binocular Camera

Abstract In this paper, a convolutional neural network (CNN) detection analysis is performed for live face detection by binocular cameras, and a binocular stereo matching network with fused edge detection is designed and implemented to target the quality of image details and parallax prediction at edges. Experiments show that the random sample pair confusion loss function can effectively improve the accuracy and generalization of the face live detection algorithm; the multi-task training approach can improve the performance of both faces live detection and face recognition; the algorithm shows excellent performance in both faces live detection and face recognition, especially the generalization of face live detection is greatly improved. A pre-trained convolutional neural network is used to extract features, and a content loss function and a domain loss function are designed to measure the feature distance between two images, and a feedforward neural network is trained as an image transformation network to migrate samples to the same domain. Experiments show that the algorithm can reduce the feature differences between the face live detection data of the two domains and can be used to improve the generalization of the face live detection algorithm.
On genesis of massive sulfide polymetallic ore deposits of Rudny Altai

Many geologists assign most of large- and medium-sized massive sulfide polymetallic ore deposits of Eastern Kazakhstan to the VMS type. These ore deposits formed in the Devonian, under conditions of rifting and active basalt-andesite-rhyolite volcanism. Ore bodies of these deposits are noted to be clearly confined to formations of several geochronologic levels (D1e to D3fm). Hydrothermal-sedimentary syngenetic and hydrothermal-metasomatic ores are distinguished. High concentrations of base metals in the ores (above 10 % sum metals) and their rather simple mineral composition (chalcopyrite, pyrite, galena, and sphalerite) are a characteristic feature of all the massive sulfide polymetallic ore deposits of Rudny Altai. The ores are noted to be multicomponental, with elevated contents of the admixtures of precious metals and rare elements (Cd, Se, Bi, Te, Ta, W, etc.). Mineralogical investigations of the ores have demonstrated an intricate relationships of the major ore minerals (chalcopyrite, pyrite, sphalerite, galena) that exhibit several generations and different geochemical specialization. Minerals of Au, Ag, Te, Bi, and other elements are encountered as individual grains or microscopic inclusions and stringers in minerals of Cu, Pb, and Zn. A significant vertical range of the ore mineralization (more than 100 m), the complexity and long duration of the ore-forming processes, the clearly defined confinement of the ore mineralization to certain geochronologic levels, – all these allow us to suppose a possibility of discovery of new ore lodes or individual ore deposits within the already known ore fields of the Kazakhstan segment of Rudny Altai
SHARED AND VIRTUAL CHEMISTRY LABORATORIES FOR DISTANCE EDUCATION

V. Stoffova1, M. Zboran2 1Trnava University, Faculty of Education (SLOVAKIA) 2Comenius University in Bratislava, Faculty of Mathematics, Physics and Informatics (SLOVAKIA)