Reviewerly/exHarmony huggingface dataset:
Analyzing the Current Status of Video Media Content for Chinese Education and Learning Based on the Web 2.0 Platform and Exploring Its Development Direction - Focusing on ‘YouTube’ -

This paper discussed the current status analysis and future development direction of video media contents for Chinese education and learning provided on YouTube, the web2.0 platform. In the near future, video-based educational and learning contents are expected to become more popular than text-based educational and learning contents. The development from paper textbooks to digital media textbooks has become a trend that can no longer go against the times. The YouTube video content, which serves as such a textbook, is expected to be produced and provided in more advanced forms in the future. The following are the main discussions discussed in this paper for this reason.<BR> First, YouTube is a “Web 2.0” platform that enables interactive communication between producers and users with the keywords “opening,” sharing and participation.<BR> Second, YouTube is an optimized platform for the use of video content, and its utilization as an information search engine is also high.<BR>Also, the video content currently available on YouTube plays the role of an online teacher called ‘Mr. You.’<BR> Third, most of the Chinese-related video media on YouTube are entertainment-style content that causes interest and fun, and only a small number of educational and learning video content.<BR> Fourth, among the Chinese education and learning video media provided on YouTube, contents such as “Chinese pronunciation, vocabulary, grammar, and conversation” can be largely divided into lecture-style videos and entertainment videos, most of which are lecture-style videos.<BR> Fifth, video content on YouTube is highly likely to develop into interactive media content that is interactive between producers and consumers. Sixth, video media content related to Chinese language, including Chinese language education and learning purposes on YouTube, is likely to be created in the form of interactive ‘interactive web documentaries’, and it seems certain that such video content will become mainstream in the near future.<BR> The above content discussed in this paper is the result of analyzing and exploring the representative platform of ‘Web 2.0’ with YouTube. It is highly likely that video media content from other ‘Web 2.0’ platforms will evolve into this direction. One unfortunate thing about this debate was that we only presented the direction that Chinese educational and learning video content would develop into an interactive web documentary format in the future, but failed to discuss how to produce such content and what content to contain. Based on this discussion, we will continue to discuss how to produce and use video media contents in the form of interactive web documentaries for Chinese education and learning.
Automatic Generation of Electromyogram Diagnosis Report

Electrophysiological tests, especially, electromyogram (EMG) and nerve conduction velocity (NCV) test are commonly used in clinical practice for diagnosis of muscle and nerve diseases. Report-writing of these tests can be problematic for under-experienced physicians and time-consuming for experienced physicians. In this paper, we apply several neural based natural language generation (NLG) methods to automatically generate diagnosis reports, a first attempt in this domain. Specifically, we use tabular diagnostic records of electrophysiological tests to generate Findings & Impression, which together constitute the diagnostic report. We further use gram-based metrics to evaluate our models and conduct a case study for the result.
Learner-Active Technology-Infused Classroom

With the continued rise of importance in developing digital learners, this article seeks to review a study centered on an example of innovative, technologically-advanced curriculum through the learner-active technology-infused classroom (LATIC) format as well as to discuss the possibilities of engaging learners in this format through virtual education settings. The LATIC approach pursues student achievement through three primary principles: academic rigor, student engagement, and student responsibility. In order to accomplish these goals, students are encouraged to work both independently and collaboratively, to use technology seamlessly as a tool for learning, to set goals, manage projects, assess progress, and identify their own resources for learning. In the midst of the COVID-19 global crisis and its tremendous impact on learning, this article first reviews the original case study of a 3rd-grade LATIC and then further discusses the potential for incorporating these concepts into online and homeschool education.
Development of Slum District Application in The City of Bekasi Based on Web

The Government of Bekasi City through The Department of Public Housing, Settlement Areas and Lands (DISPERKIMTAN) has a program to reduce slum areas with the City Without Slum Program (KOTAKU). District data collection is currently being carried out manually by the team and then the data is entered into the information system which can only be accessed internally. Existing data are not updated quickly so it is sometimes difficult to determine policies related to development assistance. Rukun Tetangga (RT) and Rukun Warga (RW) are part of the government structure closest to the community so that if data collection is carried out by them, the data will be fast and updated. This slum application development was developed using the SDLC model of waterfall method, while the stages include analysis, system design, system implementation, and system testing. Testing the application using a black box and the results are in accordance with the scenario and expectations.
Comparative Analysis and Enhancement of Sentiment Intensity Based Tools

Sentiment intensity of a text indicates the strength of its association with positive or negative sentiment, which is measured in terms of continuous numerical value. Predicting sentiment intensities can achieve more fine-grained sentiment analysis as compared to polarity classification. This research paper is focused on sentiment intensity detection and comprises of two main parts; to find and address loopholes through rule-based approach by making a comparison of existing sentiment intensity measuring tools and a technique to enhance sentiment intensity-based classification by a lexicon-based approach to incorporate Word Sense Disambiguation (WSD). There is a strong need to conduct a thorough comparison of sentiment intensity-based methods to understand the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of five popular sentiment intensity-based analysis methods. The second part is focused on constructing a human-annotated lexicon of adjectives paired with nouns triggering ambiguity, mapped with intensity as well as polarity. This Lexicon is further integrated with Enhanced Vader to measure performance; the result has outperformed with classification f-score 72%, much better than sentiment detection without lexicon.
ISKO 16’s Bookshelf: Knowledge Organization on the Verge of the Pandemic—An Editorial

The Sixteenth International Conference on Knowledge Organization was to have been held in Aalborg, Denmark in July 2020. Cancelled due to COVID-19, the proceedings were published online on 4 December 2020 containing 48 full papers, 17 short papers and 14 posters. Informetric analysis of the proceedings reveals the shifting intension and extension of the knowledge organization domain. International participation was extensive as usual. There is a much larger share of empirical and applied technical research, and therefore much less historical or analytical work than before. The shape of the research front continues to revolve around concept theory and domain analysis, but cultural and ethical issues are more prevalent than before, having attained nearly core status. There is new emphasis on concepts around interdisciplinarity and phenomenon-based knowledge organization systems, and facet analytical theory has been extended into new approaches in the linked data environment. There are more journal articles than before. No monographs are highly cited but interdisciplinarity, Wittgenstein, domain analysis and music classification are prominent alongside archival science and ontology construction on the bookshelf for this conference. Among conferences ISKO and its chapters predominate. The discourse represented by the works on ISKO 16’s bookshelf seems to be rooted in classical concept theory. There is some concretization of discourse concerning interoperability and the continued distancing from ideas of single or “universal” knowledge organization systems. There is a recurrence of concepts of warrant buttressing the extension of research into issues of culture and identity.
The Efficiency Of Higher Education Institutions: Evaluation And Management Of Managers

The article discusses the potential opportunities for leaders of higher education to monitor and implement development management functions using a system of key performance indicators, which is often used by various business entities. The authors adapted it to the needs of higher education institutions, integrating them with their characteristics.The formation of a system of key performance indicators in the article is disclosed from the point of view of improving the management system and motivation of the management and teaching staff of higher education. Approbation of the proposed methodology was implemented in the Institution of Higher Education, where it showed its effectiveness. The new system allowed the university professors to influence directly on the bonus part of income through their own work and efficiency.
TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types

We present TEyeD, the world's largest unified public data set of eye images taken with head-mounted devices. TEyeD was acquired with seven different head-mounted eye trackers. Among them, two eye trackers were integrated into virtual reality (VR) or augmented reality (AR) devices. The images in TEyeD were obtained from various tasks, including car rides, simulator rides, outdoor sports activities, and daily indoor activities. The data set includes 2D\&3D landmarks, semantic segmentation, 3D eyeball annotation and the gaze vector and eye movement types for all images. Landmarks and semantic segmentation are provided for the pupil, iris and eyelids. Video lengths vary from a few minutes to several hours. With more than 20 million carefully annotated images, TEyeD provides a unique, coherent resource and a valuable foundation for advancing research in the field of computer vision, eye tracking and gaze estimation in modern VR and AR applications. Data and code at this https URL
RoI Tanh-polar Transformer Network for Face Parsing in the Wild

Face parsing aims to predict pixel-wise labels for facial components of a target face in an image. Existing approaches usually crop the target face from the input image with respect to a bounding box calculated during pre-processing, and thus can only parse inner facial Regions of Interest~(RoIs). Peripheral regions like hair are ignored and nearby faces that are partially included in the bounding box can cause distractions. Moreover, these methods are only trained and evaluated on near-frontal portrait images and thus their performance for in-the-wild cases has been unexplored. To address these issues, this paper makes three contributions. First, we introduce iBugMask dataset for face parsing in the wild, which consists of 21,866 training images and 1,000 testing images. The training images are obtained by augmenting an existing dataset with large face poses. The testing images are manually annotated with $11$ facial regions and there are large variations in sizes, poses, expressions and background. Second, we propose RoI Tanh-polar transform that warps the whole image to a Tanh-polar representation with a fixed ratio between the face area and the context, guided by the target bounding box. The new representation contains all information in the original image, and allows for rotation equivariance in the convolutional neural networks~(CNNs). Third, we propose a hybrid residual representation learning block, coined HybridBlock, that contains convolutional layers in both the Tanh-polar space and the Tanh-Cartesian space, allowing for receptive fields of different shapes in CNNs. Through extensive experiments, we show that the proposed method improves the state-of-the-art for face parsing in the wild and does not require facial landmarks for alignment.
Learning with Plasticity Rules: Generalization and Robustness

Brains learn robustly, and generalize effortlessly between different learning tasks; in contrast, robustness and generalization across tasks are well known weaknesses of artificial neural nets (ANNs). How can we use our accelerating understanding of the brain to improve these and other aspects of ANNs? Here we hypothesize that (a) Brains employ synaptic plasticity rules that serve as proxies for GD; (b) These rules themselves can be learned by GD on the rule parameters; and (c) This process may be a missing ingredient for the development of ANNs that generalize well and are robust to adversarial perturbations. We provide both empirical and theoretical evidence for this hypothesis. In our experiments, plasticity rules for the synaptic weights of recurrent neural nets (RNNs) are learned through GD and are found to perform reasonably well (with no backpropagation). We find that plasticity rules learned by this process generalize from one type of data/classifier to others (e.g., rules learned on synthetic data work well on MNIST/Fashion MNIST) and converge with fewer updates. Moreover, the classifiers learned using plasticity rules exhibit surprising levels of tolerance to adversarial perturbations. In the special case of the last layer of a classification network, we show analytically that GD on the plasticity rule recovers (and improves upon) the perceptron algorithm and the multiplicative weights method. Finally, we argue that applying GD to learning rules is biologically plausible, in the sense that it can be learned over evolutionary time: we describe a genetic setting where natural selection of a numerical parameter over a sequence of generations provably simulates a simple variant of GD.
Algorithms for finding the maximum clique based on continuous time quantum walks

In this work, the application of continuous time quantum walks (CTQW) to the Maximum Clique (MC) problem was studied. Performing CTQW on graphs can generate distinct periodic probability amplitudes for different vertices. We found that the intensities of the probability amplitudes at some frequencies imply the clique structure of special kinds of graphs. Recursive algorithms with time complexity O(N^6) in classical computers were proposed to determine the maximum clique. We have experimented on random graphs where each edge exists with different probabilities. Although counter examples were not found for random graphs, whether these algorithms are universal is beyond the scope of this work.
Linguodidactic functions of the peer review method in teaching students written speech utterance

The peer review method is one of the problem learning methods. Its essence lies in the mutual study of each other’s written works by students in order to comment and evaluate them for further revision. Like any teaching method, the peer review method has linguodidactic properties – characteristics that underlie it, essential for the methods of teaching foreign languages and distin-guishing this method from others. Also, the method of peer review has specific linguodidactic functions – an external manifestation of the linguodidactic properties of the teaching method, which show what educational and cognitive tasks are solved by using this method in practice in foreign language teaching. Based on the analysis of the literature, the following linguodidactic functions of this method are identified: a) the development of student mentoring in teaching; b) sharing of responsibility by students for mastering the educational material; c) the use of blended learning in the implementation of the peer review method; d) changing the role of a teacher from a “bearer of knowledge” to a moderator of educational and cognitive activities of students; e) changing the ratio between classroom and extracurricular educational and cognitive activities of students; f) reduction of the terms of checking students’ training written works. The work describes in detail each of the selected functions.
Summary of Pathfinding in Off-Road Environment

Pathfinding or path planning refers to the search in navigation, focusing on the planning process from one start location to another target place. Navigation in the off-road environment portrays entirely different challenges to the on-road environment due to its uneven terrain. In this paper, a comparison of global and local pathfinding is provided, and its practical application in the real world. The existing off-road pathfinding algorithm is discussed in detail, including its path optimality and time complexity. Remaining issue with future direction is highlighted. The concluding remarks indicate that off-road pathfinding has been effectively applied in various fields despite its complexity on uneven terrain.
Implementation of Unscented Kalman Filter to Autonomous Aerial Vehicle for Target Tracking

Unscented kalman filter is applied for tracking of a 3-dimensional autonomous aerial target. The noise corrupted measurements are smoothed and at the same time the vehicle's velocity components are found out. Detailed study is carried out in Monte-Carlo simulation. The outputs of the algorithm are compare with that of extended Kalman filter and are useful for releasing weapon on to the target.
Radar Word Set Construction Method for Non-Cooperative Multi-functional Radar

Aiming at the problem that the previous method of solving the optimal radar word based on matching classification technology is not suitable for the non-cooperative Multi-functional radar(MFR), this paper proposes a radar word set construction method based on PRI sequence clustering. The radar word set of MFR is extracted from the data set through PRI type identification, similarity measurement selection and clustering of waveform units. This method does not need to use the prior knowledge of the number and content of radar words, and has good engineering applicability. At the same time, the practicability and effectiveness of the proposed method are verified by the comparison simulation experiment with the traditional clustering algorithms.
Automatic Portraiture Drawing System Based on Robotic Arm

This designed system consists of a principle computer within a camera and an ABB robotic arm 120 with its controller IRC5 compact. The image acquisition is realized by the Ni-vision-acquisition block in labVIEW for software. The design involves the median filtering for image smoothing and Canny edge check in the edge extraction area. In the path planning part, a coherent path can be created by searching adjacent 3*3 blocks. Also, five-point mean smoothing is used for path optimization. RAPID Message Queue is chosen for communication between computer and robot controller. According to the experiment, It only takes 4 minutes and 30 seconds to finish drawing a 300mm*400mm size portraiture in a A3 paper.
Aspect-based Opinion Mining on Beauty Product Reviews

Product reviews play an important role in consumer decision making. Nowadays, they can be found on most of the marketplaces and online forums. Among Indonesian women, beauty product is the most discussed topic, which leads to an increasing number of reviews. Considering the number, extracting aspect-based information from unstructured review text is a challenging task for consumers. Therefore, providing automatic aspect-based opinion mining will be a very valuable service for the consumers. In this study, we performed aspect-based opinion extraction and polarity classification by using Naïve Bayes. We applied Synthetic Minority Oversampling Technique (SMOTE) and obtained 50.55% for overall aspect F1-Score. We also used 10 different preprocessing settings that combine filtering and stemming for Indonesian and English language. The result shows that setting with filtering and stemming for the Indonesian language achieved the highest score of 53.04% for F1-Score.
Research on Enterprise Computer Network Security Protection Technology Based on Information Technology

As information technology slowly penetrates into people’s daily lives, the application range of computer networks has become wider and wider, becoming one of the indispensable communication methods for people. The popularization of computer networks in the information age has brought great convenience to people on the one hand, but also has major hidden dangers that damage public privacy and safety. Enterprises must not only rely on computer networks and databases to share data, but also protect sensitive and valuable data from being stolen or tampered with. By trying to analyze the current status of information security protection strategies, this paper proposes an information security evaluation method in a complex network environment to guide information security protection work and provide a reference for evaluating the effectiveness of network protection.
Multi-view Multi-label Learning with Dual-Attention Networks for Stroke Screen

With the acceleration of urbanization, there is an explosive growth trend of the stroke burden in China. Consequently, it is necessary to adopt a regular screen to reduce brain damage and other complications. For one thing, the risk factors screened can be divided into 6 subsets: medical history, family history, fasting blood, lifestyle, physical characteristics, and demographic information. For another, the screen results can be categorized into 5 groups: normal carotid plaque, thickening, unstable plaque, and arterial stenosis. To address the above problems, Multi-View Multi-Label (MVML) learning provides a fundamental framework for handling such tasks, which treats the risk factors as multi-view data and the screen results as multi-label data. Besides, it is a challenging issue of how to effectively utilize view correlations and label correlations in MVML learning. In this paper, we propose a novel Multi-view Multi-label Learning with Dual-Attention Networks (MML-DAN) for stroke screen, which can strike a balance between label-specific views and label correlations. Meanwhile, the attention mechanism aims to focus on the most pertinent information of the corresponding labels rather than using all available risk factors equally. Experiments on one real-world dataset of stroke screen demonstrate the superiority of the proposed MML-DAN for stroke screen prediction. To further validate the effectiveness and generalization capability of MML-DAN, extensive experiments on 5 benchmark MVML datasets show that MML-DAN significantly outperforms other state-of-the-art MVML algorithms.