Reviewerly/exHarmony huggingface dataset:
Research on Influencing Factors of Students' Behavior in Online Teaching

This paper takes the online course of Instructional Technology as the basis to analyze, and the students participating in the online course as the research object. According to the data of learning platform, the relevant factors influencing students' performance are analyzed by Pearson correlation coefficient and information weight method. The results show that: students’ comprehensive scores are significantly positively correlated with 5 factors such as group work, but have no correlation with 6 factors such as the number of resource views. According to K-prototype cluster analysis, the students are divided into three parts: good, medium and weak. Through the questionnaire survey, the factors affecting students' learning behavior are analyzed from the aspects of student characteristics, curriculum characteristics, learning environment and teacher characteristics. The results show that factors such as student attitudes, the learning atmosphere of the class, the logic and systemic nature of the curriculum knowledge points have a greater impact on student behavior in online teaching. At the end of this paper, it puts forward some suggestive points on the improvement of students' learning behavior in online teaching.
X-SHOT: Learning to Rank Voice Applications Via Cross-Locale Shard-Based Co-Training

Virtual assistants such as Google Assistant and Amazon Alexa host thousands of voice applications (skills) that han-dle a very large and diverse array of customer utterances. However, the number of supported skills may be much lower in some locales, particularly in countries other than the United States. Accordingly, customer utterances handled in a popular locale may be going unclaimed in another locale. Moreover, locales with smaller skill ecosystems also suffer from lim-ited labeled data for training systems to route utterances to skills. To tackle these aforementioned challenges, we propose a Cross-locale SHard-based cO-Training model (X-SHOT) that uses an iterative label augmentation approach to retrieve relevant skills in a source locale for unclaimed utterances in a target locale. The obtained results could be further used by skill developers in the source locale to gauge the latent de-mand for their skills in other locales and therefore to prioritize the internationalization of their skills accordingly.
CNN-Based Automatic Modulation Classification over Rician Fading Channel

Automatic modulation classification (AMC) is a fundamental process in wireless communication. In our work, we proposed a Convolutional neural network based Automatic Modulation Classification (AMC) over Rician fading channel. We constructed a system to simulate the received modulated samples through the fading channel where 6 commonly used modulation methods are considered. The scheme of proposed convolutional neural network classifier is illustrated, and the classification accuracy is demonstrated. The classification accuracy of different mod methods are shown, respectively. And accuracies among different K-factor and maximum Doppler shift are investigated. Finally, we demonstrated the bit error rate of the system assume a successive modulation detection.
Feedback exponential stabilization of stochastic quantum systems based on state space division

In this paper, the global exponential stabilization of two-level stochastic quantum systems is achieved by using measurement-based feedback strategies, including state feedback and noise-assisted feedback, and the state convergence rate is improved by dividing the state space into multiple subspaces and using different feedback controls in different state subspaces. For both the combination of two continuous noise-assisted feedback and the combination of state feedback and noise-assisted feedback, the state space is divided based on the comparison of real-time state convergence rate under different measurement-based feedback strategies, respectively. The global exponential convergence and the effect of dividing state space in improving state convergence rate for two-level quantum systems are proved in theory and verified in numerical simulations.
A Comparative Analysis of a Mouse and Touchpad Based on Throughput and Locations for a Laptop Computer

In the present era, the use of laptop computer becomes an inseparable part of human life. This throws a challenge to the designers of the computer that the device may be designed keeping in mind the human capability. The human–computer interaction is generally done through the pointing devices such as a mouse, touchpad, joystick, etc. Hence, they should be designed to improve the performance by way of increasing the speed and accuracy during a certain task. In the present research, an analysis was carried out to study the two input pointing devices namely touchpad and mouse on the basis of throughput and location of the laptop computer. For this, an interface based on ISO 9241, [2000] was designed to study the performance of the subjects. Eighteen subjects were chosen to perform this task to test the accuracy and speed of these two pointing devices. The experiment was performed with five different locations of the laptop. Results are found in terms of throughput using modified Fitts law. From the analyses, it was found that throughput for mouse ranges from 2.86 to 3.27 bits/s, and for touchpad, it ranges from 1.89 to 2.16 bits/s. it was also evident from the results that the mouse gave better results at the farther position, while the touchpad was proved to be better at closer distances.
Stochastic Properties and Pricing of Bitcoin Using a GJR-GARCH Model with Conditional Skewness and Kurtosis Components

Using a flexible statistical framework that accounts for time-varying skewness and leptokurtosis, we examine the stochastic behavior of Bitcoin in comparison to five major currencies. The empirical findings reveal that the distribution of all series is leptokurtic. Once the effect of skewness-kurtosis is considered, the true price of risk is obtained, with implications on policymakers’ and investors’ strategies.
A Web Application Vulnerability Testing System

Online apps, as opposed to standard applications, are complex and collaborative. Standard testing methods and tools, however, are not appropriate for testing web applications. This study focuses on developing a web-based web application vulnerability scanner that hybridizes the black-box and white-box pen testing methodologies, thereby reducing scanning costs and improving efficiency while still proving a very low false-positive rate. The system can detect cross-site scripting (XSS), flash injection mixed content, cleartext passwords, and usage of insecure JavaScript libraries. The system had a 99% accuracy for the XSS, mixed content, and cleartext passwords. The system satisfies the vast majority of OWSAP top-ten criteria and provides a detailed actionable scan result that is easily understandable and easy to debug when compared to similar existing systems and other research works.
An Improved Online Multiple Pedestrian Tracking Based on Head and Body Detection

Multiple Object Tracking (MOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. Although many researchers have proposed effective method, they failed in crowd scene. The reason is that body detection and tracking is used in existing MOT methods. In crowd scene, many detections are missed detect and many overlap body bounding boxes decrease the quality of data association. To handle this issue, this paper propsed an online novel multiple pedestrians tracking, which is based on head detection. We first fuse the head and body detection to improve the detection result. Then, we use the head detection bounding box to replace the body detection bounding box for tracking. Finally, the experimental results demonstrate the effectiveness of our proposed method and achieve best performance with the state-of-the-art MOT trackers.
On the Need for a Generic Requirements Engineering Framework for Trustworthy IT Services

Security and general trustworthiness are crucial components of provided IT services. Nevertheless, in most models for requirements engineering in service engineering there are few approaches to determine or implement these requirements in a controlled manner, or to make them visible or usable. First, after presenting the current state of research in requirements engineering, service engineering, and the importance of trustworthiness, the necessity for the inclusion of, among other things, aspects of trustworthiness is demonstrated and elucidated. The previous, common approaches are then qualitatively examined in terms of defined criteria regarding the objective. From this, necessary further steps are derived for the creation of a generic and trust-building service requirements engineering framework.
Automatic generation of the ground truth for tumor budding using H&amp;E stained slides

Tumor budding (TB) is defined as a cluster of one to four tumor cells at the tumor invasive front. Though promising as a prognostic factor for colorectal cancer, its routine clinical use is hampered by high inter- and intra- observer disagreement on routine H&E staining. Pan-cytokeratin immunohistochemical staining increases agreement but is costly, nonroutine, and may yield tumor bud false positives. This makes the development of automatic algorithms to identify TB difficult. Therefore, we sought to develop an automated method to generate ground truth for tumor budding from routine H&E that benefits from pan-cytokeratin staining. Our database consisted of 1) 120 adjacent pan-cytokeratin and H&E tissue sections from which 648 manually-registered high-power fields were extracted and annotated for tumor regions and 2) ten adjacent sections from which 109 fields were extracted and annotated for tumor buds. Swin transformers were applied to segment tumor regions, resulting in 95% and 99% accuracy for tumor and non-tumor regions. This allowed for a narrow margin outside the tumor invasive front to identify TB. Next, we developed a novel method which registers pan-cytokeratin images to adjacent H&E images, transfers automatically-identified positive regions of pan-cytokeratin to H&E, then filters positive regions on H&E using size and number of nuclei. The average precision and recall were 0.3856 and 0.3254 on H&E when compared to pathologists on H&E, which exceeded 0.3470 and 0.1932 when comparing pathologists on H&E to pan-cytokeratin. Results provide preliminary evidence for the feasibility of our method to generate ground truth for TB using H&E.
Shape Trajectory Analysis Based on HOG Descriptor for Isolated Word Sign Language Recognition

Sign language is based principally on hands gestures. To have a robust recognition system, each hand modality must be correctly presented using both motion and shape descriptors. This paper proposes an enhanced isolated word sign language recognition system based on hands trajectories analysis able to solve the most challenges such as signer's interchangeability and speed variation. In this context, Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) are introduced. The performance of our proposed system is tested on public databases (RWTH-Boston-50 and RWTH-Boston-104) with signer-independent condition and outperformed the recent existing works.
Spatial structure stability evaluation of exhibition main beam with high support mode based on finite element method

In order to improve the stability of the spatial structure of the main beams in the exhibition, a stability evaluation method based on finite element method was designed. The redundancy of the main beam evaluation is calculated and the Shopuver comprehensive evaluation is analyzed. The stability evaluation model of space structure with high formwork support is established, and the spatial structure with high formwork support is evaluated by GNR distribution under FEM. The method was compared with traditional evaluation under different bending strength. The experimental results show that the evaluation value of punching bearing capacity obtained by this method is high, indicating that the evaluation effect is good.
Improved distance measures for ‘fixed-content miscellanies’: an adaptation for the collections of sayings of the desert fathers and mothers

Abstract Collections of sayings of the desert fathers and mothers are extant in manuscripts in many languages and are organized differently. They are ‘fixed-content miscellanies’ (FCM): they include material that belongs to the same genre, but is variable both when it comes to appearance and order. Distance measurement methods are particularly suitable for large text traditions including variable content in the so-called mixed-content miscellanies, such as recipes, anthological compilations of shorter text passages, or catalogues, but can also be suitable for text genres like collections of sayings, that are equally variable in appearance and order of sayings, even though the genre is fixed; hence ‘fixed-content miscellanies’. In the article, collections of sayings in seven languages were compared using four distance measures methods. Each segment of the sayings was given a unique id to be comparable. The first method used, the Jaccard distance measure, disregards the linear order of items and instead considers each collection compared only as a ‘bag of stories’. In two other methods used (Birnbaum and Levenshtein methods), the order in which the narratives of each saying appear is compared. All three methods yielded interesting results, but the collections that were apparently closely related were clustered together so tightly that it was not possible to make more nuanced analyses. In order to remove false negatives, particulars concerning lacunes in the material were taken into account in the proposed modified Levenshtein method, the fixed-content miscellanies (FCM)-Levenshtein method. By applying the FCM-Levenshtein method, previously unknown relations between collections witnessed in different languages could be detected.
Recent Advancements in Design and Implementation of Automated Sign Language Recognition Systems

Sign language has been used for a long time by deaf and mute people to communicate their thoughts and feelings. Since there is no universal sign language, the needy people use country-specific sign languages. For example, American Sign Language (ASL) is popularly used by Americans and Indian Sign Language (ISL) is commonly practised in India. Communication between two people who know the specific sign language is quite easy. But, if a mute person wants to communicate with another person who is not familiar with sign language, it is a difficult task, and a sign language interpreter is required to translate the signs. This issue motivated the computer scientist to work on automated sign language recognition systems that are capable of recognizing the signs from specific sign languages and converting them into text information or audio so that the common people can understand it easily. This chapter will be a useful reference for the researchers who are planning to start their research study in the domain of sign language recognition.
Discussion on Operational Reliability Test Scheme and Evaluation Method of Underwater Complex System

The operational mission profile of the underwater complex system is analyzed as a foundation. Combined with a variety of factors such as small sample, time and funds, this paper discusses three operational reliability verification test schemes and evaluation methods of the underwater complex system. The application principles of various schemes are given. It provides technical reference for the reliability test verification and evaluation of the underwater complex system.
Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification

Explainable artificial intelligence (XAI) becomes a hot research topic in the domain of biomedical and healthcare applications. Owing to the benefits of handling massive and complicated data, XAI concept finds useful in several applications, particularly health care. With the developments of machine learning (ML) and XAI, healthcare service quality can be considerably improved. This article designs an optimal boosting label weighting extreme learning machine for mental disorder prediction and classification (OBWELM-MDC) technique. The goal of the OBWELM-MDC technique is to determine the different levels of DAS. In addition, the OBWELM-MDC technique involves the design of boosting label weighted extreme learning machine (BWELM) model for prediction process. Besides, the BWELM model can be derived by the incorporation of the label weighted extreme learning machine (LW-ELM) with boosted ensemble learning model. Moreover, the parameter tuning of the BWELM model takes place by the use of chaotic starling particle swarm optimization (CSPSO), where the inertia weight and acceleration coefficient of the PSO algorithm are modified via logistic chaotic map. The application of CSPSO algorithm has improved the predictive performance of the BWELM model. The experimental result analysis of the OBWELM-MDC technique takes place using benchmark dataset, and the results are examined under several measures. The experimental results showcased that OBWELM-MDC technique has accomplished maximum predictive outcomes over the other methods.
Using Artificial Neural Networks and Wavelet Transform for Image Denoising

The paper presents an approach for image denoising that reduces the noise components level such as Gaussian and salt-and-pepper when performing a discrete wavelet transform (DWT) of images. Denoising is achieved by using artificial neural networks (ANN) as an element that allows restoring information distorted due to noise exposure. The structure of the ANN and data for its training are given in the paper. It is shown that using ANN with DWT increases the image restoration quality for Gaussian and salt-and-pepper noise types by an average of 10 dB in the peak signal-to-noise ratio (PSNR). The obtained results were confirmed through the simulation of the proposed approach for the test images like “Lena” and “Forest”. The comparison with the non-local means (NLM) algorithm and CNN shows the advantage of our approach in PSNR.
Preregister Your Ethical Redlines: Vulnerable Populations, Policy Engagement, and the Perils of E-Hacking

This essay makes a modest proposal: scholars who conduct fieldwork among vulnerable populations in fragile settings and who seek to influence policy should preregister their ethical redlines. Public preregistration can help avoid shifting ethical standards (``e-hacking'') during fieldwork while improving our baseline risk and harm assessments among participants and their communities. The essay identifies the causes and varieties of ``e-hacking'' before providing a conceptual framework that helps researchers locate their ethical redlines at five different levels of analysis. Eight concrete suggestions are then provided for turning ethics into action to improve our efforts at both ethical fieldwork and policy engagement.
A Bottom-Up Method for Pose Detection of Multiple People on Real-Time Video

This article describes a realtime algorithm to determine a person’s posture at a certain point in time.
Human-Centred Technology for Sustainable Development Goals - Workshop Results

This paper presents the results of the workshop on Human-centred Technologies for Sustainable Development Goals (HCT4SDG) - Challenges and Opportunities. The workshop was part of the 18th International Conference promoted by the IFIP Technical Committee 13 on Human-Computer Interaction (Interact 2021). Nine papers were presented by authors from several different countries and discussed in the workshop. Six of these papers were extended in this proceedings. After discussion, seven challenges and eight research opportunities were listed as expression of the participants’ views on HCT4SDG.
A Load Balancing Strategy Of "Container Virtual Machine" Cloud Microservice Based On Deadline Limit

Aiming at the problem of workflow scheduling under the "container virtual machine" two-tier structure in cloud microservice workflow application, a "container virtual machine" load balancing strategy based on deadline limit (container virtual machine load) is proposed. Balance strategy), CV LBMs strategy first obtains the optimal economic configuration of the corresponding instance of the micro service according to historical data, and calculated the list of remaining resources in the leased virtual machine; Determined whether the virtual machine is on the virtual machine according to the container image list in the virtual machine. Whether a corresponding container image exists, and create a new container on the virtual machine that already has a container image to reduce the time to extract the container image. The workflow simulation results showed that compared with the existing algorithms, the C-V LBMs strategy had the ability to meet the deadline success rate and reduced the cost.
Deep Learning Applications in Industrial Grading System

Automation of the classification processes for diamond crystals is an actual challenge since the classification is a direct component of the sorting process. The collection of data necessary for the crystal’s class determination is carried out using a camera. Within the framework of this paper, we developed a software prototype which initial stages are the transformations of the previously obtained images. These transformations are aimed at reducing image noise and highlighting object boundaries in the image. Also, we have carried out an analysis of the popular pretrained artificial neural network (ANN). As a result of this analysis, the most suitable pretrained model for the task was selected. One of the main selection criteria was the ability to add new quality classes and retrain the pretrained network model. This software is based on a convolutional neural network that allows you to determine the characteristics of an object in an image to determine the pattern of the certain class of diamond crystals. A special neural network is used to make the final decision on a crystal quality class. It uses the metrics from each of the seven projections and summarizes the result. This solution allows you to reduce the size of the neural network.
Enterprise Financial Management based on Random Forest Algorithm

The valuable information in the company's financial data is very important to evaluate the business situation of enterprises. Based on the five factors of cash flow, growth ability, operation ability, solvency and profitability, this paper determines the financial operation status of 27 enterprises, and proposes to use particle swarm optimization random forest algorithm to complete the classification and prediction of enterprise financial operation status. At the same time, the performance evaluation indexes of recall, accuracy, precision and F1 score algorithm are determined. Compared with other classification prediction methods. The accuracy, F1 value, precision, recall and AUC of PSO-RF algorithm are the best, which are 99.36%, 99.36%, 99.35%, 99.32% and 98.98% respectively. This study will help to realize the classification and prediction of enterprise financial operation.
Adapting Agile Practices During the Evolution of a Healthcare Software Product

Agile Software Development (ASD) can be considered the mainstream development method of choice worldwide. ASD are used due to features such as easy management and embrace of changes, where change in requirements should be taken as a positive feature. However, some domain verticals, such as medical-healthcare, are classified as critical-safety system, which usually requires traditional methods. This chapter presents a practical use case describing the evolution of a software product that was conceived as a wellness software for end-users in mobile platforms to a medical-healthcare product restricted to regulatory standard recommendations. It presents the challenges and how the ASD is compatible to standards such as ISO/IEC 82304-1.