Reviewerly/exHarmony huggingface dataset:
Multi Camera Positioning Behaviour Based On A Director Style Using Fuzzy Logic for Machinima

The role of computer technology in the world of cinematography is getting widely. One of the supporting technologies is Machinima. Machinima is a system that uses 3D graphics rendering technology real time to produce a virtually cinematic product. In the process of making a cinematic product like a film, the camera positioning play an very important role. In the real world there is a director who will directing the cameramen to place the camera manually according to specific view point and behavior. This research proposes an intelligent multi behavior of cinematographic camera where the camera will be place automatically in accordance a behavior directorial style. By doing the appropriate camera positioning then a film will have different impact to audience. For positioning of the camera will be based on fuzzy logic. This research proves that an artificial intelligence engine for camera can be made for positioning the camera based on director's style. From the test result is obtained that the system can implement multi behavior cameras in machinima using fuzzy logic.
Methodological features of the preparation of primary school students for All-Russian test works in the main academic subjects

The article deals with the organization and implementation of the assessment of educational achievements of primary school students as a component of the overall system for assessing the quality of education. The issues of evaluation of metasubject results in the framework of VPR, including the level of formation of universal educational actions and mastery of intersubject concepts, are considered. It is pointed out that at the present stage of development of the education system, monitoring studies are considered as a mechanism for providing feedback, which allows to obtain data on the compliance of the actual results of the pedagogical system with its final goals. The analysis of the performed works of students of the fourth grades is given and ways of realization of the arisen difficulties are offered.
Time and Aging: Designing for Time in Retirement

Aging and time are mostly being focused separately in HCI. In this paper, we examine retired people's experiences around time through their daily routines with the lens of emerging time and design concepts from Human-Computer Interaction. In this Late-Breaking Work, we introduce a design concept based on the interviews with retired people about their time routines. Inspired by previous work in HCI on emerging non-clock time concepts such as plastic time and collective time, we detected several exciting spots for design explorations around organizing time in later life. Based on insights from the initial findings around collective rhythms and quality of time, we propose an artefact (with two versions) designed with the guidance of positive design theory to discuss creating meaningful experiences. Also, we pose questions on how to form positive time interactions for later life.
Systematic Development and Evaluation of a User-Oriented System for Public Transport Vehicles Identification

This paper describes how a user-oriented system for identification of public transport vehicles can be developed and evaluated. For this purpose, a vehicle design is first scaled with respect to the project requirements using a proven method and with decided consideration of customer and accessibility requirements. In a next step, it is demonstrated why the recognition of the bus by public transport users at bus stops has to be identified as critical. The following section systematically generates concepts for bus identification and presents them virtually. Finally, it is described how the concept evaluation can succeed with a pedestrian simulator and which results are obtained: For example, a number should do the primary bus identification, the route should be displayed at the boarding and the individual stop destination should be clearly highlighted on the route.
Research and Application of Scaffolding Theory in Computer Professional Courses-Take the data structure as an example

Data structure is one of the important courses of basic education for computer students. In this study, aiming at the disadvantages of traditional data structure teaching, we try to apply scaffolding theory to teaching to improve the whole teaching mode and effect to a great extent.
Research on Identification of Network Public Opinion Information based on Graph Convolutional Networks

Traditional public opinion information identification methods have poor performance, eitherlow accuracy, or rely on hand-designed features. This paper converts public opinion information identification to text classification problem, and proposes a public opinion information identification method based on Word2Vec and graph convolutional networks. First, Word2Vec is used to train word vector and word-article graphs are constructed; then, the graphs are trained and classified by graph convolutional neural network; finally, network public opinion information recognition is completed according to the classification results. The experimental results on the constructed Central Asian country data set show that the proposed method has achieved better performance,where the average identification accuracy of “Belt and Road” network public opinion information reached 85.58%.Furthermore, the performance on other data sets is also comparable to current mainstream text classification methods.
국악사양성소의 교육체제에 관한 고찰

The goal of this paper is to examine the contents of the overall educational system of the Gugaksa Yangseongso, which was responsible for the transmission of Korean music as the first Korean traditional music educational institution established after the war as the part of the National Center for Korean Traditional Performing Arts in 1955 until 1972 before being promoted to National Gugak High School.First of all, before examining the educational system, it examined how the basic policy for Korean traditional music education was established by reviewing related contents disclosed by the government at the time of establishment along with the establishment process of the Gugaksa Yangseongso.Regarding the education of the Gugaksa Yangseongso, it examined what kind of teachers were hired to cultivate Korean traditional musicians required by the new era as well as the changes in the academic system since establishment. In addition, it examined the curriculum and detailed plans. To this end, the entire curriculum for middle and high school students was reviewed, as well as academic subjects, class hours, and textbooks used for each subject to confirm the fundamental purpose and direction of education directed by the Gugaksa Yangseongso.
A fish eye recognition algorithm for switch on / off key in sequence control substation

One key sequence control of substation is the latest requirement of intelligent substation. The double confirmation in its operation specification requires that when the equipment is operated remotely, there should be at least two non homologous indicators to confirm that the equipment has been operated accurately. In this paper, a fish eye switch on / off status recognition algorithm based on image intelligent analysis is proposed. This algorithm realizes the judgment of on and off as well as abnormal and occlusion status. The on and off angle can also be quantified. The accuracy rate is 99.99% after field verification.
Unguided Depth Map Completion Using Weighted Schatten p-norm Minimization

As depth map completion is an ill-posed inverse problem, the regularization constraint plays an important role. Matrix completion with nuclear norm minimization and a data fidelity term with Frobenius norm leads to a convex optimization problem. However, its solution is approximate and different from that of original rank minimization problem. In this work a weighted regularization method for depth map completion without using its associated RGB image is proposed which involves a non-convex rank minimization process utilizing the weighted Schatten p-norm(0 <; <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> ≤ 1). A detailed experimental analysis shows that our approach, outperforms the state-of-the-art depth map completion approaches.
The Cayley–Hamilton Theorem

We recall that this famed theorem of linear algebra is stated.
A Table Recognition and Extraction Algorithm in Dongba Character Documents Based on Hough Transform

Dongba hieroglyphs is a text form that transitions from pictographs to pictographs and phonetic characters. In order to distinguish them from modern characters, many Dongba hieroglyphs use tables to distinguish between Dongba hieroglyphs and modern characters. Through the division function of the table, the structure of the document is clearly, and readers can also understand the reading order of Dongba characters more quickly. It can be seen that analyzing the structure of the document, identifying and separating the tables in the document, classifying and storing Dongba characters and annotations are very important tasks. Therefore, we used the Hough transform algorithm to realize the recognition and extraction of the table by analyzing the structural features of the document image. It lays the foundation for realizing the extraction, classification and storage of Dongba hieroglyphs and the establishment of Dongba hieroglyphs database.
Learning Representation of Multi-Scale Object for Fine-Grained Image Retrieval

Extracting discriminative local features has attracted many research focus in fine-grained image retrieval task. With attention mechanism and softmax-like loss functions, deep neural networks could locate and learn the representation of the most discriminative region of objects, however, which also makes other non-most discriminative regions be ignored to some extent. In our work, to extract more local features, we propose a method that could proposes multiple discriminative regions on different scales, which could provide more refined local and multi-sacle representation for fine-grained image retrieval. Experimental results show that our proposed method achieves excellent performance on two benchmark fine-grained datasets, which demonstrates its effectiveness.
Assessment of the quality of training of specialists in higher education institutions

Abstract The article presents the requirements and various ways to assess the quality of educational services and training of specialists in higher education institutions, and also offers a “comprehensive” indicator of the quality of management systems that takes into account the main results of educational activities of the University – the degree of development of educational programs by students and satisfaction of the main consumers of educational services.
Delivering Meaningful Representation for Monocular Depth Estimation

Monocular depth estimation plays a key role in 3D scene understanding, and a number of recent papers have achieved significant improvements using deep learning based algorithms. Most papers among them proposed methods that use a pre-trained network as a deep feature extractor and then decode the obtained features to create a depth map. In this study, we focus on how to use this encoder-decoder structure to deliver meaningful representation throughout the entire network. We propose a new network architecture with our suggested modules to create a more accurate depth map by bridging the context between the encoding and decoding phase. First, we place the pyramid block at the bottleneck of the network to enlarge the view and convey rich information about the global context to the decoder. Second, we suggest a skip connection with the fuse module to aggregate the encoder and decoder feature. Finally, we validate our approach on the NYU Depth V2 and KITTI datasets. The experimental results show the efficacy of the suggested model and show performance gains over the state-of-the-art model.
Using eye-tracking to evaluate the viewing behavior on tourist landscapes

Every tourist website employs images to attract potential tourists. In particular, destination tourism websites use environmental images, such as landscapes, to attract the attention of tourists and to address their purchase choice. Nowadays the effectiveness of these tools has been enhanced by the use of eye-tracking technology. That allows measuring the exact eye position during the visualization of images, texts, or other visual stimuli. Consequently, eye-tracking data can be processed to obtain quantitative measures of viewing behavior that can be analyzed for several purposes in many fields such as to cluster consumers, to improve the effectiveness of a website and for neuroscience studies. This work is aimed to use eye-tracking technology to investigate user behavior according to different types of images (e.g. natural landscapes, city landscapes). Specifically, we compare different statistical descriptive tools with supervised and unsupervised models. Furthermore, we discuss the effectiveness of their results and their capacity to provide satisfactory and interpretable solutions that can be used by decision-makers.
Analysis of incentive mechanism in Repchain

In recent years, the blockchain that is the basis of Bitcoin has received much attention. However, the blockchain also faces many challenges, such as security and scalability, which have been the subject of recent researches. Much work has been done to solve the scalability problem in blockchain; one of these methods is sharding. This method is based on dividing the network into different groups and validating transactions in parallel. These methods use traditional consensus algorithms. One of the problems in this regard is the incentive that should be provided for nodes to participate in these consensus algorithms. In this paper, Repchain, one of the existing methods in this field, is examined, and the problems that this method has is analyzed. Next, it is proved that the proposed method causes the network nodes not to follow the protocol and also causes collusion between network nodes.
Research on Adaptive Algorithms for Video Service with Network-assisted

The video service occupies a large proportion of the entire multimedia business. How to transfer the video files stored on the server to the client better and faster, and bring the user the best viewing experience, is a problem that we need to solve and optimize. In this paper, firstly, the SAND (Server and Network-assisted DASH) technology was simulated and successfully transmitted the network bandwidth information to the client as a reference. Finally, the extra another network-assisted method is proposed: all parameters of the physical layer of the network are extracted at intervals, and then these parameters are classified by means of machine learning, and the quality of the channel is represented sequentially from low to high. The reference value thus obtained is closer to the actual network condition. Compared with the traditional DASH algorithm, the QoE improvement can be seen very clearly.
Towards Adversarial Robustness Via Compact Feature Representations

Deep Neural Networks (DNNs), while providing state-of-the-art performance in a wide variety of tasks, have been shown to be vulnerable to adversarial attacks. Recent studies have posited that this vulnerability arises because DNNs operate over a grossly overspecified input space with very sparse human supervision due to which they tend to learn spurious features that humans would ignore. These spurious features provide an attack vector for the adversary because perturbing these features would not alter the human’s decision but may alter the model’s prediction. In this paper we explore hypothesis that reducing the size of the model’s feature representation while maintaining its generalizability would discard spurious features while retaining perceptually relevant ones. We find that after the size of the feature representation has been reduced the models exhibit increased adversarial robustness, while suffering only a minimal loss in accuracy. In addition to being more robust, models with compact feature representations have the benefit of being more resource efficient.
SiamMT: Real-Time Arbitrary Multi-Object Tracking

Visual object tracking is of great interest in many applications, as it preserves the identity of an object throughout a video. However, while real applications demand systems capable of real-time-tracking multiple objects, multi-object tracking solutions usually follow the tracking-by-detection paradigm, thus they depend on running a costly detector in each frame, and they do not allow the tracking of arbitrary objects, i.e., they require training for specific classes. In response to this need, this work presents the architecture of SiamMT, a system capable of efficiently applying individual visual tracking techniques to multiple objects in real-time. This makes it the first deep-learning-based arbitrary multi-object tracker. To achieve this, we propose global frame features extraction by using a fully-convolutional neural network, followed by the cropping and resizing of the different object search areas. The final similarity operation between these search areas and the target exemplars is carried out with an optimized pairwise cross-correlation. These novelties allow the system to track multiple targets in a scalable manner, achieving 25 fps with 60 simultaneous objects for VGA videos and 40 objects for HD720 videos, all with a tracking quality similar to SiamFC.
Artifact: Distribution-Aware Testing of Neural Networks Using Generative Models

The artifact used for the experimental evaluation of Distribution-Aware Testing of Neural Networks Using Generative Models is publicly available on GitHub and it is reusable. The artifact consists of python scripts, trained deep neural network model files and data required for running the experiments. It is also provided as a VirtualBox VM image for reproducing the paper results. Users should be familiar with using VirtualBox software and Linux platform to reproduce or reuse the artifact.
Building Professional Competence Of Future Humanities Specialists At University

The aim of the study is to analyze the effectiveness of implementing the ideas of the competency-based approach in education and to describe the problems and contradictions observed in the building process of the professional competence of humanities specialists.The authors developed and described pedagogical technology.The idea of implementing the technology is to combine theoretical knowledge, practical skills, as well as pragmatism of the required qualifications with creating the value of future specialists.The leading idea of the technology for the professional training of humanities specialists is orientation towards the creation of spiritual and moral values, including the values of future professional activity.To check the effectiveness of the technology, authors have developed pedagogical methods, techniques, forms of education and training of students, as well as methods of experimental work and analysis of the data obtained.The received data were used in practical pedagogical activities as part of the educational process of the university.The experiment involved the use of empirical methods, which provided a significant amount of information.To assess the changes received, researchers surveyed students at the stages of establishing, forming and generalizing the experiment.The use of the proposed technology demonstrated positive changes in the general cultural level of students, the level of development of their professional culture, professional competence, expansion of professional interests.The effectiveness of the implementation of pedagogical technology was experimentally confirmed.
The application of MATLAB teaching in the cultivation of non-professional quality of undergraduates

The development speed of education informationization is faster and faster. How to make better and more efficient use of this tool to improve the comprehensive quality of talents is an important research topic for every educator. The cultivation of non-professional quality of college undergraduates is directly related to the employment of students and the good or bad of their future life. A lot of good qualities are not only built on a few of campus activities and a little time to participate in several kinds of comptitive events. This paper discusses the attempts of colleges and universities teachers to infiltrate the cultivation and training of non-professional quality by rationally and skillfully using the teaching opportunity of professional courses of MATLAB.
Data Mining Techniques for Fraud Detection—Credit Card Frauds

As the world is evolving, the technological advancements radically transform our society and continue to reform the world. Credit card frauds are snowballing as payment requests and transactions happen mostly through the Internet. Recent fraudsters have a wide range of mediums and techniques they can use which makes each attack unique even if the same person is behind the attack. They leverage these techniques to portray standard user conduct (Amanze and Onukwugha in Int J Trend Res Dev 5:2394–9333, 2018 []). This type of fraud usually takes place when a purchase is made without authorization by either misusing the card that is stolen or lost or by making illegal transactions online or in person using your stolen PIN, CVV, or account number. In this paper, we use data mining techniques to predict fraudulent activities by analyzing unusual patterns and gather insights from transaction details. The aim is to accurately detect fraudulent activity by categorizing them as legal and illegal transactions. Such techniques and advanced security measures for credit card fraud detection are getting more elegant by the day and are making it more difficult for the fraudsters to accomplish their illegal activities.
A simple technique for measuring the spatial correlation of photon pairs for complete interference in the Michelson interferometer

Abstract In this research, a simple technique for measuring the spatial correlation or angular correlation of photon pairs was developed. The system consisted of two aluminium rails which had the same pivot at the position of a nonlinear crystal. The rails were manually moved to find the position of the correlated photon pairs. At this position, the photon pairs were collected by the collimators attached at the end of the rails. The pairs were produced by type I spontaneous parametric down-conversion process using a 405 nm diode laser and a BBO crystal. The crystal was specifically cut for emitting the photon pairs at an angle of 3° with respect to the direction of the pump laser. These pairing photons were correlated and interfered in the Michelson interferometer, then, the correlated photons were demonstrated. It was found that the visibilities in the case of perfect alignment, near perfect alignment and misalignment were V = 1.0000 ± 0.0004, 0.869 ± 0.002, 0.591 ± 0.002 and 0.168 ± 0.002, respectively.
Quantification: The View From Natural Language Generation

Quantification is one of the central topics in language and computation, and the interplay of collectivity, distributivity, cumulativity, and plurality is at the heart of the semantics of quantification expressions. However, its aspects are usually discussed piecemeal, distributed, and only from an interpretative perspective with selected linguistic examples, often blurring the overall picture. In this article, quantification phenomena are investigated from the perspective of natural language generation. Starting with a small-scale, but realistic scenario, the necessary steps toward generating quantifier expressions for a perceived situation are explained. Together with the automatically generated descriptions of the scenario, the observations made are shown to present new insights into the interplay, and the semantics of quantification expressions and plurals, in general. The results highlight the importance of taking different points of view in the field of language and computation.