Reviewerly/exHarmony huggingface dataset:
Discussion of the Parameters of the Distance Learning System in Virtual Reality

The widespread use of virtual reality in recent years has offered researchers many new directions for research and development. One of the most important and obvious industries for the application of VR from the very beginning was education. Educational applications for VR are quite common today. The degree of immersion in the environment offered by VR, according to many authors, has a positive effect on the perception of information provided in applications. Another current trend in connection with the COVID-19 pandemic is the active transition of many institutions to distance or mixed learning. However, there are currently very few VR applications for distance learning. This article describes the possible format of such a system, as well as the requirements for it. Various aspects of the proposed system, such as financial, network, and technical, are considered. In particular, the necessary computer configuration and network parameters are described. The issues of the user interface and user security are touched upon. Possible approaches to solving these problems are proposed.
Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.
Determining the Optimal Number of Features for User Preference Elicitation in Recommender Systems

In this paper, we consider the user preference elicitation problem based on feature pairwise comparisons. In particular, we solve the problem of determining the optimal features number. This problem is also related to the «cold start» problem in recommender systems: insufficient information at the beginning of the system usage makes it impossible to use machine learning algorithms and/or evaluate the training quality directly. We propose a modification of the previously developed method for preference elicitation by pairwise comparison that allows solving the specified problem. We conduct an experimental study of the proposed method that confirms its efficiency and presents the experimental results.
A Development of an Acupoints Education Table using 3D Technology and Augmented Reality

Other SectionsAbstract서 론재료 및 방법결 과고 찰결 론AcknowledgementFundingData availabilityConflicts of interestFigureTableReference
AgileQube: An approach for specification and detection of agile smells

This paper presents the thesis entitled “AgileQube: an approach for specification and detection of agile smells” (Telemaco 2020 [24]) submitted to COPPE/UFRJ as part of the requirements for obtaining the degree of Doctor of Science (D.Sc.).
Research on MTI Teaching Mode in Universities with Business Characteristics in Post-epidemic Era — Based on Empirical Investigation of Shanghai University of International Business and Economics

Based on the problems existing in the MTI training process in China, this paper combs the current situation of teachers and students in the post-epidemic era, and provides new ideas for MTI teaching under the context of business. By means of questionnaires and interviews, this paper investigates MTI majors in Shanghai University of International Business and Economics (SUIBE) from four aspects: curriculum design, teaching methods, teachers' construction and practice platform. Based on the research results, it puts forward strategies such as the construction of teaching resource database, cloud platform construction and MTI teacher ' construction, which provides reference for building MTI talent training mode with business characteristics.
A Robust Stochastic Quasi-Newton Method with the Application in Machine Learning

We study a novel stochastic version of damped and regularized limited memory Broyden-Fletcher-Goldfarb-Shanno (Sd-REG-LBFGS) method for nonconvex optimization problems in this paper. Different from BFGS updating scheme for the strongly convex problems, it is a challenge to preserve the product of the correction pairs positive at each iteration for BFGS in nonconvex case. While utilizing regularization scheme to ensure the robustness of the second-order methods. The proposed method is able to keep the product of correction pairs being away from zero above a specified degree. To make the proposed method robust and computationally efficient, we propose to update curvature information at a spaced interval, in which the average of iterate points is utilized. The numerical experiments have shown that our proposed algorithm has better performance than SdLBFGS or they have almost the same performance. Especially in the problems which utilizes few samples, the proposed method has avoided the ill-conditioned problem and exhibited superior performance.
Optimizing the power consumption in WSN through target tracking

recently, wireless sensor networks (WSNs) have been employed for tracking maneuvering objects. However, considering diverse deployment nature of sensor device higher measurement error is induced during tracking operation. Enhancing lifetime of WSNs and improving tracking accuracy is utmost important. Here in optimizing the energy consumption a clustered based routing scheme is considered. Further, for reducing tracking error Kalman filter is applied in different models; However, Kalman Filter-based tracking model fails eliminate tracking error outlier; in eliminating outlier in measurement model in this paper an H-infinity filter is used. Experiment is carried out for evaluating outcome in terms of energy efficiency, root mean square position error and root mean square velocity error. The proposed H-infinity filter based tracking model outperforms Kalman Filter-based tracking model.
Hierarchical Attention-Based Video Captioning Using Key Frames

Video captioning used in social media like YouTube and Facebook plays a major role for better understanding of the video even when the audio is not clear. In this work, we propose a key frame-based model for video captioning. Here, instead of using all frames in a video only the key frame from the videos are used for video representation. The key frames are extracted from video by comparing images using structural similarity index to identify the difference between the frames and extract only informative frames for the video captioning. We extract the features of the key frames using pre-trained convolutional neural network. We also extract the semantic features from the frames. The key frames are applied to an object detection algorithm to identify the objects and extract the features of the objects. Hierarchical attention is applied on the key frames feature, semantic feature, and the features of the objects identified from the key frames of videos and given as input to LSTM in order to generate the caption for the video.
Track Prediction Algorithm Based on Multidimensional Local Hausdorff Distance

Track prediction is of great significance in situation assessment. By analyzing predicted tracks and real-time tracks of targets, the system can identify targets' intentions, and can detect abnormal behaviors of targets, and then conduct situation assessment and cognition. In order to improve the accuracy and the real-time performance of track prediction, a track prediction algorithm based on multi-dimensional local Hausdorff distance was proposed. First, the current point trace of the track to be predicted was calculated. Then the correlation threshold for rough screening was set, and the multi-dimensional Hausdorff distance between the candidate characteristic track set obtained by rough screening and the track set to be predicted was calculated, so as to obtain the multi-dimensional local Hausdorff distance set between the track to be predicted and the characteristic track set. Then, the multi-dimensional local Hausdorff distance set was filtered to obtain the corresponding characteristic track and prediction starting point of the track to be predicted. Finally, the tracklet after the characteristic track starting point was translated to the track to be predicted, and obtain the predicted track. Through the simulation data test, the effectiveness of the algorithm was verified, and the accuracy and real-time performance are improved compared with Kalman filter algorithm and K-means algorithm.
A Security Access Control Scheme of Smart Community under Epidemic Situation

Identity authentication and access control are important technologies to realize the security control of the smart park. In this paper, attribute-based access control technology is used to judge whether users can pass the intelligent access control, and new conflict detection and resolution method is proposed for the possible conflict of joint authorization of different park managers. Through theoretical analysis and experimental verification, the attribute-based access control mechanism proposed in this paper can carry out more flexible and fine-grained permission control for users in smart parks.
Feminist struggles against criminalization of digital violence: Lessons for Internet governance from the global south

Abstract Why have feminists in Mexico been arguing with women's groups and against the state over the criminalization of digital violence, and what do these struggles mean for its governance? This article analyzes the social struggles surrounding passage of the Olimpia Law of 2019, which criminalizes digital violence in Mexico. Although criminalization of digital violence as a means of governing online behavior has recently attracted much attention globally, this study proposes that such measures can, at the same time, put at risk the human rights of women actively participating in the political realm (human rights defenders, activists, and journalists). I further contend that governing digital violence is not so much a regulatory question but should, rather, be understood as a field of struggle among diverse collective projects. Thus, I argue that there is a need to further reconceptualize digital violence against women in politics as a way to address the multiplicity of actors and perspectives involved in internet governance. Following textual analysis of documents from feminist organizations arguing against the criminalization of digital violence, I conclude by proposing public policies to fight this phenomenon beyond criminalization.
Satisfaction, Perspective, and Attitude Toward E-learning among Emergency Medicine Physicians

Objective: E-learning gained popularity since the sudden pandemic entry, which revealed the need to adapt to our practice after becoming mandatory.Thus, this study aimed to investigate the perceptions toward e-learning among emergency medicine physicians.Methods: In this questionnaire-based cross-sectional study, an online questionnaire was given about demographics, self-efficacy, satisfaction, self-control, anxiety, perceived usefulness, and attitudes toward e-learning was conducted on emergency medicine residents and specialists who are remotely involved in emergency medicine training in their departments due to the pandemic.The self-efficacy, satisfaction, interactivity, perceived ease of use, perceived benefit, self-control, and attitude scores were calculated based on the total Likert points of statements, and correlations were analyzed.Results: This study included 74 participants who remotely performed emergency medicine training during the study.Experience in years showed statistically higher satisfaction (p=0.018),self-control (p=0.003),ease of use (p=0.042),perception of benefit (p=0.022), and attitude (p=0.030)rates in residents.Experienced participants has a statistically lower self-efficacy (p=0.044) and higher perception of ease of use (p=0.034).Perception of benefit (p=0.009),self-control (p=0.006),perception of ease of use (p=0.01), and attitude (p=0.018) was statistically higher in high computer skill levels.Perception of benefit (r=0.543) is associated with higher computer skill levels, satisfaction (r=0.407), and perception of interactivity (r=0.498).Satisfaction had a positive correlation with self-control (r=0.543,p=0.000) and benefit perception (r=0.543,p=0.000). Conclusion:Increased interaction, improved users' computer skills and maintained higher self-efficacy and benefit perception are important to increase the satisfaction and adoption with e-learning.
Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science

Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques. The methodological questions addressed in this book include the problems of validity,
"Zero-Shot" Super-Resolution Based on SinGAN

SinGAN is a single image generation model, which can be applied to image super-resolution. When SinGAN is directly applied to super-resolution reconstruction, the image quality is poor. In this paper, a new network structure based on SinGAN is proposed, which can learn effective information from a single image more fully. SinGAN adds random noise to the generator of each scale, which is easy to bring bad influence in the super-resolution task of the image. Therefore, the generator of this algorithm removes the input of noise. In order to ensure a depth convolutional neural network supervision, effective gradient flow characteristics and the ability to reuse, the remaining structure and the introduction of dense connections SinGAN generator to accelerate the convergence speed. Furthermore, introduce attention mechanism into the network. The network performance is further improved by learning the weights of feature channels. Experimental results show that this method has better performance and higher efficiency in image super-resolution task.
EXPLORING THE EFFECTS OF IMPLEMENTING GUIDED READING APPROACH TO ENHANCE THE READING PROFICIENCY OF ENGLISH SECOND LANGUAGE LEARNERS IN SOUTH AFRICA

Guided reading (GR) approach has been shown to have positive effect on reading fluency and comprehension. However, most studies on GR have focused on primary schools, specifically the Foundation Phase. Therefore, this study employed the GR approach to examine its possible effects on the reading proficiencies of Grade 9 English Second Language (ESL) learners in Gauteng province. Social and cognitive constructivism underpinned the study, which employed action research to determine the possible effects of implementing GR on the reading proficiencies of a sample of eight Grade 9 ESL learners who were purposively selected to participate in this study. Data were collected through interviews, observations, and reflection cards, oral and written reading tests. Results indicated that GR approach had caused a significant improvement in these learners’ reading fluency and comprehension because of collaborative learning and scaffolding. In addition, GR also developed a sense of ownership in their learning, a sense of community and innovative and inquisitive minds. The study recommends that learners who struggle with reading be identified early, and GR intervention strategies be implemented and monitored. Every school needs to have a reading policy and the Education Department should ensure that it is implemented and monitored. Keywords: guided reading, reading proficiencies, reading fluency, reading comprehension, second language
Evaluation of Search Strategy for Autonomous Rescue Mobile Robot

In this study, six different search algorithm have been examined for use in decision-making of autonomous rescue robot. These algorithms are Left Wall Follow, Right Wall Follow, Breadth First Search, Depth First Search, A* and Greedy Best Search. Real-time data is collected by mobile robot for three different environments with these algorithms. Electronic circuits have been developed for the communication of the computer with the mobile robot. Also, the interface is designed for communication in the computer environment. The data collected includes; reach the target, the distance to reach the target, and the number of nodes traversed. Analysis conducted with real mobile robot, wall follow algorithm was found to be successful in unidirectional environments without switching between each other. However, in complex environments it was found to be unsuccessful. All other algorithms have been successfully reach the goal. However different distances traveled to reach the goal. This study was performed with a single mobile robot, heuristic greedy best search algorithm search and depth first search algorithm yielded successful results for different environments.
Study and Review of Various Techniques on Searching Optimal Route for Online Ordering System

The package delivery time is the most important and crucial factor which is directly proportional to customer satisfaction in any e-commerce-based application. In this paper, we have studied various methods used by e-commerce companies to deliver the package to customers in an optimal way and the various parameters involved in it. This technique provides the delivery boy with the best route to deliver in a short time and with less effort. After studying all the methods, we have proposed a method for our application that offers the best solution for tiffin delivery considering the parameters of our app. We have chosen the bicycle as transportation means so that the petrol expense, traffic, and wideness of road do not matter much for calculating route cost. We gave much weight to the road condition and the shortest route which covers all the delivery points. The main objective is to reduce delivery time and minimize the need for vehicles.
Analyzing BTC's Trend During COVID-19 Using A Sentiment Consensus Clustering (SCC)

Tweets from social media can help in providing an early sign of market mood in the business sector. Opinion mining and machine learning can be used to discover the underlying sentiment. There's a link between Twitter sentiment and Bitcoin price changes in the future. Using the concept of Consensus clustering, this paper leverages Tweets collected during the COVID-19 timeframe to forecast early Bitcoin movements following the outbreak. Results from text datasets such as Twitter with various attributes, settings, and degrees show the superiority of the proposed consensus approach in predicting the BTC trend during and after the COVID-19 pandemic.
Dual-Encoding Style Transfer for Korean Font Generation

Font generation is time-consuming and labor-intensive, in particular for Korean fonts with 11792 letters in total. Herein, we propose a dual-encoding style transfer network that is specifically designed for Korean font generation. Provided with the letters in a source target, the objective of the network is to learn the style of a target font and to generate letters in the target font. The network consists of a dual-encoder and a decoder. The dual encoder takes a source letter and a combination of one or two consonants and a vowel of a target letter, extracting letter embeddings. The decoder receives the embeddings and generates a letter in the target font. In the learning phase, the network receives 2350 letters and learns the style of the font of interest. Then, the network generates the rest of 8822 letters that the network has not seen during the learning phase. We employ 27 Korean fonts to evaluate the proposed network. The experimental results demonstrate that the proposed network is able to learn the style of various fonts and to generate the full letter set, leading to an efficient and effective design and creation of Korean fonts.
You Can See All that from Right Here? A Content Analysis of in Situ Augmented Reality Tweets

As social media continues to evolve, some applications are experimenting with augmented reality (AR) visualizations that can call up posts generated around the user’s precise location and overlaid on top of the physical place. Despite being publicly available, little academic research has looked at AR displays of social media in space. While the location has been studied with regard to social media posts, existing work has primarily examined the location as a collective topic or how to map those geographically at a macrolevel. While research studies into location-based games and neighborhood forums have more explicitly looked at the role of location as the mediator of information, they have not considered the overlay of social media posts or spatial AR displays. This study reports on data from a Layar channel called Tweeps Around, which augmented tweets posted around your physical location. The researchers manually cataloged tweets (N = 277) sent near one fixed location over several months and coded them for key themes. This study helps understand what content is generated from one place as well as some of the content implications of in situ AR tweets. Unlike Twitter feeds that are sorted by who you follow, Tweeps Around can call up strangers’ tweets by proximal location alone, which changes the potential audience of viewers, the risks of that content, and the user experience. Understanding these types of technologies can help build on our understanding of not only communication about places but also communication through places, and what social media may become in the near future.
Improved GRASP-MHT for Possibly Unresolved Measurements

The multiple hypothesis tracker (MHT) generally assume that each measurement originates from one target independently, but sensors with finite resolution can only generate one unresolved measurement for multiple targets when they are in close proximity. The possible presence of unresolved measurements complicates the data association problem and significantly degrades the performance of the tracking algorithm. An improved greedy randomized adaptive search procedure MHT (GRASP-MHT) algorithm is proposed to handle the situation of unresolved measurements. In this algorithm, the scores of track hypothesis associated with unresolved measurement are calculated, and the data association problem of unresolved measurements is modeled as a maximum weight independent set problem (MWISP). Simulation results show that the improved GRASP-MHT can achieve a better tracking performance in the presence of unresolved measurement and retains most of the advantages of GRASP-MHT.
A Trusted Networking Scheme for Unmanned Equipment Based on Blockchain

Unmanned equipment has been increasingly used in many important fields. Especially in some fields with harsh operating environments, how to ensure the normal communication and communication security between unmanned equipment has attracted people's attention. This paper proposes blockchain-based scheme for unmanned equipment building a trusted networking. This solution combines the advantages of blockchain, containing distributed storage, P2P network, and data modification prevented, with the requirements of unmanned equipment networking, so that it can build a device trust domain, and use smart contracts to enable unmanned equipment building a networking without trusted third-party. This paper also analyzes the security and robustness of our proposed scheme from the aspects of protecting the authenticity of the device node's identity, two-way authentication and resistance to single point of failure attacks and communication attacks.