Reviewerly/exHarmony huggingface dataset:
Research on Effective Teaching in the Vision of Virtual Reality

Since human beings entered the industrial society, "the concept of efficiency is regarded as the noblest moral concept." This "highest moral concept" is also used to solve the contradiction between knowledge proliferation and inefficient teaching, effective teaching. Therefore, as an important concept throughout the teaching reform. Pursuing effectiveness has become an inherent requirement of teaching development and has guided teaching reform. In recent years, the rapid development of virtual reality technology has amazed the world, and this technology has been used in many industries to promote change and development, and the education field is also among them. Unlike other fields, virtual reality technology is rarely used in education. Even though a few domestic and foreign schools and educational institutions have tried to use virtual reality technology to assist teaching, this technology has not been able to study due to its shallow research and consideration. Play the expected ideal effect in education and teaching. Therefore, this paper starts from the frontier concept of virtual reality technology, combines the connotation and essence of effective teaching, analyzes the "virtual reality + augmented reality" teaching mode that can be used in current school classroom teaching, and further explains its operation outline and rationality. In order to provide a reasonable teaching mode under the vision of virtual reality for the school classroom.
A High Performance Text Vector Similarity Search Method Based on Overlapping Degree

Similarity search of massive high-dimensional data is an important issue in the field of text data computation and search, which seriously affects the efficiency and convenience of text search and use. This paper proposes a high-performance text vector similarity search method, and proposes overlapping degree to describe the statistical characteristics of query vectors recurring in inverted index. Based on overlapping degree, candidate data sets are generated and sorted, distance calculation is carried out in candidate data set sequence, unnecessary calculation is reduced, and computation scale is reduced. Similar vectors of query vectors can be quickly obtained with very short index construction time and small memory overhead. The experimental results show that compared with the representative four methods, the index construction time of our method is shortened to 22-223 times, the index size memory overhead is reduced by 2-18 times, and the query speed advantage can be obtained with high recall rate, which greatly improves the performance and efficiency of text data similarity search.
One-Year Mortality Prediction in ICU Patients with Diagnosis of Sepsis Driven by Population Similarities

Traditional Intensive Care Unit (ICU) outcome prediction models are based on the analysis of large populations, and often provide statistically rigorous results for an average patient but are also expensive, time-consuming, and prone to selection bias; moreover, these indicators usually lack the precision required for use at the individual level, since they present significant errors at patient data away from the average. In this work we present an evaluation of the impact and relevance of three different patient similarity metrics on the one-year mortality prediction when patients are related by the same diagnosis, sepsis. We use data of 16.000 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC-III) database.
Image processing technology for scanning electron microscopy

Quantification of the noise content in the scanning electron microscope image is an important parameter in the signal-to-noise ratio. The most common type of noise in SEM image is the Gaussian noise. We compared different noise reduction filters, like average, median, Gaussian Smoothing and Wiener filters, in order to see the image improvement. Based on the experiment results the most promising is the Wiener filter.
Some assessments on applications of fuzzy clustering techniques in multimedia compression systems

Data compression is the process of reducing the amount of necessary memory for the representation of a given piece of information. This process is of great utility especially in digital storage and transmission of the multimedia information and it typically involves various encoding/decoding schemes. In this work we will be primarily focused on some compression schemes which employ specific forms of clustering known as fuzzy clustering. In the data mining context, fuzzy clustering is a versatile tool which analyzes heterogeneous collections of data providing insights on the underlying structures involving the concept of partial membership. Several models employing the fuzzy clustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.
Assessing the Impact of a New Released Medicine Towards Medication Strategy Using Graph Based Visualization

Clinical research has led to the development of new medications and medication strategy may change to gain better treatment outcomes. With the increasing attention to the evidence based medical guideline, it is important for clinicians to study the medication strategy changes. This study is interested in evaluating long-term prescriptions of type 2 diabetes patients provided by Kyoto University Hospital following the release of a new medication in 2010. Frequent sequential pattern mining (FSPM) is a prominent tools for extracting frequent patterns. However, the number of the result set could be enormous and may inhibit clinicians in assessing the results. To help the clinicians, a medication progression graph (MPG) is constructed using adjacent (1-sequence) frequent patterns produced by the singleton pattern mining. Compared to conventional frequent patterns, singleton's patterns features full itemsets and 1-step distance. Hence, the pattern represents the true medication transition event. Using the graph, a clinical physician was able to observe a significant increase of physician's activities in changing the medication after the release (i.e., in MPG that start with Sulfonylurea after 2010, the number of edges is increase by 2.83 and the number of nodes is increase by 2.27 compared to before 2010). Our preliminary results show that the new visualization method enables a clinician to analyze a medication strategy changes after the new medication released.
Il panorama plurilinguistico in Macedonia, tra pianficazione linguistica e legislazione

In the last decades the model that associates a State with a language has spread in the Balkans, often merging the codification of a standard literary language with the ethnic identity of the nation. Macedonia is defining a new approach to the question of linguistic pluralism. Through some recent legislative changes such as the bill (2018) on the use of languages, now passed into law (2019), and the promotion of international agreements with neighbouring countries like the Agreement on Friendship, Neighbourhood and Cooperation with Bulgaria, and more recently the Prespa agreement with Greece, a new linguistic scenario both within borders and beyond them is taking shape.
Automatic Extraction and 3D Reconstruction of Bundle Conductor Based on Improved DPC Algorithm

This paper presents a method for automatic extraction and 3D reconstruction of bundle conductor based on improved DPC algorithm. Firstly, the least square method is used to fit the point clouds of bundle conductors with a parabola equation in three-dimensional space, and then the spatial coordinates of each bundle sub-conductor relative to the fitting curve are calculated. Next, an improved DPC algorithm is adopted to cluster the point clouds of the bundle sub-conductors in order to discriminate the number of the bundle sub-conductors and extract the point clouds of each bundle sub-conductor. The method is proved to be more robust than the K-MEANS algorithm. Finally, the least square method is developed to reconstruct the three-dimensional model of the bundle sub-conductors.
Research on Generation Method of Attention Situation Map Based on Context Awareness

When users perform tasks, they need to grasp the situation information closely related to their operation process in real time to help them understand the battlefield situation quickly and efficiently. The traditional static information subscription model can no longer meet the changing situational information needs of various users. With the rapid development of Internet technology, context-aware personalized recommendation services have attracted widespread attention. This paper introduces the context-aware recommendation technology into the automatic aggregation generation mechanism of user attention situation information, and proposes a situational information organization operation framework for shared understanding. On this basis, this paper expounds the knowledge construction method for user needs understanding, proposes the business-driven dynamic sensing method of situational elements, and designs the situation information focusing algorithm based on collaborative filtering.
Machine Learning Treasury Yields

We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer vision, bioinformatics/computational biology, document classification, etc.), but is often misconstrued and misused. We discuss how to properly apply NMF to Treasury yields. We analyze the factors based on NMF and clustering and their interpretation. We discuss their implications for forecasting Treasury yields in the context of out-of-sample ML stability issues.
Smart Healthcare Data Acquisition System

A general healthcare system monitors the parameters and simply display the readings. The records are maintained on paper. This paper aims in building an independent offline system for maintaining records in regions where network connectivity is an issue. After providing basic identification information it automatically logs vital parameters of patients acquired from the sensors. The records are maintained digitally and the system does a personalized data analysis for each patient, as “normality” is subjective to every patient. This system is trained to classify such minute details and create a cluster for each patient individually. As and when patient visit for checkup the data is added to his cluster and analysis is performed on it. Various suggestions are provided to the patient in order to avoid worsening the condition. It is a platform that provides the doctor with the patient’s entire history when they visit a non-digitized area.
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Towards a Bidirectional Machine Translator Generator for Multilingual Communication

Machine Translation (MT) systems are typically quite complex, especially those used in production environments where high-quality conversational text or speech translation from one language to another is important. As a result, the vast majority of MT systems support translation between a single language pair, often uni-directionally. This research study extends previous work to assess the efficacy of developing a bidirectional translator generator in Prolog programming language using Lexical Functional Grammars. The main research objective is building a machine translator generator for multilingual communication, i.e. developing a system whose inputs are linguistic descriptions of a desired source and target language and whose output is a program that translates between the two natural languages. The implementation of a bidirectional machine translator between English and Hungarian, developed as a proof-of-concept case study, is discussed and assessed in terms of four general classes of translation. The benefits and drawbacks of this approach as generalized to MT systems are also discussed, along with possible areas of future work.
On Identifying Similarities in Git Commit Trends—A Comparison Between Clustering and SimSAX

Software products evolve increasingly fast as markets continuously demand new features and agility to customer’s need. This evolution of products triggers an evolution of software development practices in a different way. Compared to classical methods, where products were developed in projects, contemporary methods for continuous integration, delivery, and deployment develop products as part of continuous programs. In this context, software architects, designers, and quality engineers need to understand how the processes evolve over time since there is no natural start and stop of projects. For example, they need to know how similar two iterations of the same program or how similar two development programs are. In this paper, we compare three methods for calculating the degree of similarity between projects by comparing their Git commit series. We test three approaches—the DNA-motifs-inspired SimSAX measure and clustering of subsequences (k-Means and Hierarchical clustering). Our results show that the clustering algorithms are much more sensitive to parameters and often find similarities that are not correct. SimSAX, on the other hand, can be calibrated to find fewer similarities between the projects; the similarities are also more consistent for SimSAX than they are for the clustering. We conclude that it is better to use DNA-inspired motifs as they provide more accurate results.
Multi-interactive Teaching Model of German in Colleges and Universities Under the Information Technology Environment

The integration and application of the network multimedia technology and the German curriculum in the information age represents the development trend of the German teaching reform nowadays. The teaching mode can promote the teaching effect twice with half the effort. The multi-interactive teaching mode based on the constructivism theory is a new teaching mode supported by the information technology. The multi-interactive German teaching mode in universities under the information technology environment has changed the relationship between teachers and students in the course of the classroom teaching and between teachers and students and the teaching contents. It can improve the quality of the German teaching, effectively improve the teaching effect and improve students’ abilities to use the German language. A new teaching mode supported by the information technology fully embodies the principle of learning as the main body. It focuses on the connotation and theoretical basis of the multi-interactive teaching mode to improve the quality of the German teaching in universities.
Multi-function Bus Station System Based on Solar Energy

At present, solar power is used in many bus stations in China. But most solar panels are installed in a fixed mode, which cannot make the sunlight stay perpendicular to the solar panel in real time. It result in insufficient utilization of solar energy resources. And because most bus stations are not connected to power, there is no real-time display of vehicle movement information and voice broadcast function. Aiming at the above problems, a multi-function bus system based on solar energy is designed in this paper. In the system, the direction of sunlight exposure is collected through the photosensitive sensor, which is transmitted to the STM32 main control system, so that the solar panel is always perpendicular to the sun’s rays. The day and night are judged by the system clock, and the working mode is automatically switched accordingly. And the collected solar energy is stored by batteries for use by the bus station system. With the electronic bus stop sign in the system, the GPS positioning information is received by the STM32 main control system, then latitude and longitude information is sent by GSM to the station receiving system. Display bus arrival and voice broadcast are controlled by CPU in the station. Through GPRS and server interaction, the bus coordinate information is transmitted to the mobile phone platform to change the previous phenomenon of passengers blindly waiting for the bus. Through the test, the system can run stably and has high use value.
Adversaries Meet Partial Differential Equations - A First Look at Fluidic Adversarial Robustification

Machine Learning models are vulnerable to adversarial examples, viz. these models misclassify data points that are slightly deviated from the correctly classified examples drawn from the same data distribution. These adversarial examples are a result of and reveal inherent blind spots in our training algorithms. In this article, we proposed to build a blind robustification system which can be applied on all input samples such that it targets to retrieve the original labels of adversarial examples without causing changes to the prediction of non-adversarial samples. We first concretely propose an energy functional designed to target adversarial perturbation. We then derive its gradient descent flow equation using the Euler-Lagrange method. Following this, we derive an efficient discretization scheme for the proposed partial differential equations. Finally, we perform a thorough analysis of the performance of the proposed system by testing it for a state-of-art network and a universal bench-marking dataset.
A Compound Technique for Multiple Objects Detection Based on Markov Clustering Networks and Viola-Jones Algorithm

In this paper, we present a computer vision technique for detecting multiple objects simultaneously. In particular, our proposal specifically aims to detect the presence of faces and text within an image. To achieve this goal, our technique relies on Markov Clustering Networks (MCN) and Viola-Jones algorithm. These algorithms perform object detection without prior consideration of the external factors, such as the image size, gradient, orientation, and characters. While we identify the presence of such objects within a single image, our technique is also able to detect multiple lookalike faces (e.g. doll, mask) and multiple textual objects with different orientation. As a result of the experimental evaluation, our proposed technique able to detect image with frontal face and text with 90% accuracy and around 80% accuracy for images with non-frontal face and text. Finally, our technique achieves a new state of the art performance with an average precision of 88%, performs real-time inference with a frame rate of above 50 Frame Per Second (FPS) and has a faster computation time when compared with other techniques.
Spatial Adaptive Regularized Correlation Filter for Robust Visual Tracking

Correlation filter is a simple yet efficient method to deal with the visual tracking task.However, the unwanted boundary effects hinder further performance improvement.Spatially Regularized DCF (SRDCF) has been proposed to address this problem with a pre-computed spatial penalty matrix, which improves the tracking performance greatly.In this paper, aiming to achieve more accurate spatial regularization, we present our spatial adaptive regularized correlation filter (SARCF).A coarse-to-fine scale estimation approach is proposed to change the spatial penalty area, which can efficiently deal with large scale variation.Moreover, temporal regularization is introduced for long-term tracking.Experimental results show that the proposed algorithm outperforms most advanced algorithms in tracking accuracy and success rate.
Face Alignment by Supervised Descent Method with Head Pose Estimation

Abstract Face alignment, which aims at locating facial key points automatically, is an important topic in computer vision community. And many works have been done to solve this problem. The most well-known solution is Supervised Decent Method(SDM). However, SDM has been designed to use mean shape as initial shape, which is vulnerable to large pose variation. In this paper, we present a novel approach for detection of the facial key points, getting initial shape from a special shape according to the head pose of the data. Experiments show that our approach achieves significant improvement. In both 21 points and 68 points detection cases, our method achieves nearly 50% improvement on challenging dataset IBUG, and about 1% improvement in HELEN and LFPW test set.
URBAN PLANNING EDUCATION FOR SUSTAINABLE DEVELOPMENT OF SMART CITIES

Modern urban planning educational trend defines the sustainable development of smart cities as one of the most dynamic areas of city policies around the world. A smart city is a high-tech urban living environment in which innovative solutions are embedded in its constituent elements and processes to improve its quality and living standards. The concept of a smart city is aimed at sustainable development of urban areas and offers technology as a tool for solving problems in the field of planning, design, integrated engineering landscaping, as well as in the provision of urban services to the population. The article discusses the contents of the smart city concept, which reflects the main directions of the curriculum of the master's program «Sustainable Smart City». Its main purpose is to prepare urban planners who have professional competencies in the development of smart cities in the implementation of urban processes.
Evaluation of Oil Workers' Performance Based on Surveillance Video

We present our research on the applicability of computer vision techniques for extracting various oil workers' performance metrics. This paper focuses on learning two metrics associated with the workers' location. The first metric e1 is the percent of frames in which only some part of the crew is present. If its value is bigger than some threshold value, the crew's performance is declared inefficient. We propose to perform human detection in each video frame and count people present in order to calculate e1. The Faster R-CNN and single-shot detectors with several types of feature extractors were tested on a specially collected dataset. By finetuning the most accurate of them we've achieved 0.99 precision and 0.91 recall. The second metric e2 considers workers' distance from an automated gas control system, which is the main subject of maintenance. We propose using some markers on the uniform for worker recognition and estimation of his/her position relative to an automated gas control system. We've tested the ArUco and the RUNETag markers on synthetic data and proved that they cannot be applied to our problem. We've also carried out some preliminary research on uniform numbers detection, as they can be also considered as markers. The Connectionist Text Proposal Network (CTPN) used for text detection achieved an accuracy of 0.76. Text recognition performed by Tesseract OCR failed with 0.05 recall. However, we plan to collect a dataset for number detection and recognition in the future and test more approaches.
Metaphors, Signifiers, Affordances, and Modalities for Designing Mobile and Embodied Interactive Systems

Metaphor in human-computer interaction (HCI) can support users in understanding an unfamiliar system with references to familiar objects, and also can support designers in forming and exploring analogical concepts during the process of designing. We posit that new metaphors can assist in forming a common mental model for users and designers, and can support designers in creating novel interaction experiences. We present 3 metaphorical concepts as a basis for characterizing interactive designs: device, robot, and friend. We focus on identifying the signifiers, and affordances associated with these metaphors. We performed a study in which participants identify common design elements for the 3 metaphors and the factors that influence adopting the metaphors for new designs. The results show that people associate distinct signifiers and affordances for each of the 3 metaphors and associate different interaction modalities with each metaphor.