Reviewerly/exHarmony huggingface dataset:
Education as a Value in the Pre-Graduate Studies of Education in Ukraine

Abstract The paper focuses on the description of the value of education in pregraduate preparation of students of teaching disciplines in Ukraine. The aim of the text is to specify the course of preparation for the profession of teacher in the context of values from the perspective of students of selected pedagogical faculties in Ukraine. The issue of values in the pregraduate preparation of Ukrainian students of teaching is a partial part of the authors’ long-term interest in the issue of education and the values of Ukrainian pupils in schools in the Czech Republic. The research is carried out using a mixed QVAN - qval design. A Likert-type scale questionnaire supplemented with semi-closed and open items was chosen as the research tool. The data are analyzed with the predominance of a simple statistical analysis with additional analysis using grounded theory.
A Transformer Based Sales Prediction of Smart Container in New Retail Era

With the advent of the new retail era, the value of unmanned smart container is increasingly prominent. Fast and flexible self-service is favored by consumers. How to use accumulated historical sales data to predict sales in the future is an important part of smart container operation management. Reasonable sales prediction can not only reduce the inventory cost, but also reduce the shortage loss of the container. Based on the smart container sales data of Dalian Xiaode New Retail Co., Ltd., through detailed exploratory analysis in many aspects, this paper carries out the feature selection of sales prediction, and uses random forest, XGBoost, Transformer and other algorithms to predict sales. The experimental results show that the prediction accuracy of Transformer is better than traditional algorithms, whose MAPE is 14.67% lower than that of the worst one. Transformer can be well applied in the field of sales prediction of smart container. And in this experiment, compared with Transformer using sine and cosine functions for positional encoding, Transformer encoded by position index has better prediction performance and stronger stability.
Deep Self-Supervised Learning for Oracle Bone Inscriptions Features Representation

In this paper, we design a two-branch deep learning framework to tackle the problem of self-supervised representation learning for Oracle Bone Inscriptions (OBIs). This problem is very complicated in that, unlike natural-photos, OBI images present more abstract content and suffer from different drawing styles, resulting in the failure of many existing self-supervised learning methods to describe them accurately. The core idea of our framework is that we design two OBI-specific pretext tasks, i.e. rotation and deformation. These two kinds of pretext tasks can provide strong supervision signals for OBI features learning. And we perform OBI recognition downstream task to evaluate our self-supervised learned features. Experimental results show that, under the same dataset, our proposed method outperforms jigsaw and matting based self-supervised learning methods.
Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction

Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengths and shortcomings of each weighting scheme. In fact, in most cases researchers and practitioners resort to the well-known tf-idf as default, despite the existence of other suitable alternatives, including graph-based models. In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction. Our analysis reveals some interesting findings such as the advantages of the less-known lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods. Finally, based on our findings we discuss and devise some suggestions for practitioners. Source code to reproduce our experimental results, including a keyword extraction library, are available in the following repository: https://github.com/asahi417/kex
Interaction Design for Digital Saron Musical Instruments Using Call and Response System and Rhythmic Emphasis Weighting Methods

Gamelan digitization is one of the efforts to introduce and preserve traditional Indonesian musical instruments not to become extinct. The focus of digitization in research is only on one type of saron musical instrument because the saron instrument is the main melody in a gamelan instrument that can be played individually and is more harmonious when played in collaboration. The most important thing in playing gamelan instruments is playing several types of musical instruments at the same time, especially the saron instrument. In using the saron instrument itself must be played collaboratively according to the tempo. For this reason, to make the interaction design model, the Call and Response System method is used, which has a “your turn” indicator and the Rhythmic Emphasis Weighting method connected to the “Techno Rhythm Monitor”. The application of the two methods above, the interaction design modeling design uses the Design Thinking method. The research testing using the System Usability Scale (SUS) because the results have a high level of validity for a study. The test results from research using the Call and Response System and Rhythmic Emphasis Weighting methods resulted in an interaction design model playing the saron musical instrument that can collaborate according to the tempo. For the test results using SUS, which obtained a final result of 85%, it is at level B and means that the interaction design model that has been designed is acceptable because it has met the user’s goals (proper).
A new unsupervised pseudo-siamese network with two filling strategies for image denoising and quality enhancement

Abstract Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain.
JarBot: Automated Java Libraries Suggestion in JAR Archives Format for a given Software Architecture

Software reuse gives the meaning for rapid software development and the quality of the software. Most of the Java components/libraries open-source are available only in Java Archive (JAR) file format. When a software design enters the development process, the developer needs to manually select necessary JAR files via analyzing the given software architecture and related JAR files. This paper proposes an automated approach, JarBot, to suggest all the necessary JAR files for given software architecture in the development process. All related JAR files will be downloaded from the internet based on the extracted information from the given software architecture (class diagram). Class names, method names, and attribute names will be extracted from the downloaded JAR files and matched with the information extracted from the given software architecture to identify the most relevant JAR files. For the result and evaluation of the proposed system, 05 software design was developed for 05 well-completed software project from GitHub. The proposed system suggested more than 95% of the JAR files among expected JAR files for the given 05 software design. The result indicated that the proposed system is suggesting almost all the necessary JAR files.
Long range diffusion with control of the directional differences

A fast, spatially adaptive filter for smoothing colour images while preserving edges is proposed. To preserve the edges, we use a constraint that prohibits the increasing of the gradients in the process of diffusion. This constraint is shown to be very effective in preserving details and flexible in cases where more smoothing is desired. In addition, a filter of exponentially increasing diameter is used to allow averaging non-adjacent pixels, including those separated by strong edges.
Learning grounded word meaning representations on similarity graphs

This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy.The lower level of the hierarchy models modality-specific word representations through dedicated but communicating graphs, while the higher level puts these representations together on a single graph to learn a representation jointly from both modalities.The topology of each graph models similarity relations among words, and is estimated jointly with the graph embedding.The assumption underlying this model is that words sharing similar meaning correspond to communities in an underlying similarity graph in a lowdimensional space.We named this model Hierarchical Multi-Modal Similarity Graph Embedding (HM-SGE).Experimental results validate the ability of HM-SGE to simulate human similarity judgements and concept categorization, outperforming the state of the art.
Deep Learning-Based Image Captioning Used to Recognize Malicious or Emergency Activity in an Image

Image captioning is one of the fundamental problems of computer vision technology. Our objective in this work is to use existing image captioning architectures and extend it to develop an architecture to recognize activity happening in an image. We will be focusing on classifying the output of the image captioning model to either positive activity or negative activity. This architecture can be used to detect malicious or emergencies from a CCTV camera automatically. Videos are nothing but a series of frames. In other words, videos are images with temporal dimensions added to it. We will solve this problem in two steps. First, we will have an image captioning model where we will use deep neural networks to produce caption, and then we will use classification models to produce our final output. There are existing deep learning architectures in computer vision that can detect fire, smoke, accident on highways, or any other malicious or emergencies, but these systems exist individually. The proposed system has the potential of automatically detecting any emergency situations from an image.
An Efficient and Accurate Method for Detecting Objects in Aerial Images

Aerial image object detection in aerial images is a hot and challenging task in computer vision, due to the bird-view perspective, complex backgrounds, variant scales and appearance of objects and extremely dense objects distribution. It has previously been observed that existing methods cannot meet the application requirements of accuracy and speed at the same time. In this paper, we propose an efficient and accurate method for aerial image object detection. The pipeline of our method has only two stages. The oriented bounding boxes of objects are predicted by utilizing a simple network in the first stage, and the rotated bounding box predictions are then sent to non-maximum suppression (NMS) to produce final detection results. Besides, atrous spatial pyramid pooling (ASPP) network is added to the pipeline to extract multi-scale features, and Bi-directional long short term memory network (BiLSTM) is adopted to improve detection performance of long and slender instances. Experiments on the challenging DOTA dataset have shown the propose method outperforms existing methods in terms of detection rate and speed.
Pedestrian detection algorithm based on improved muti-scale feature fusion

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.
Face recognition algorithm and edge computing technology based on deep learning

With the rapid development of deep learning, more and more attention has been paid to the research and application of face recognition and expression recognition in this era of big data. In security monitoring, human-computer interaction and other aspects, the related applications are more and more. At present, the common face recognition system mainly has two ways of work, offline work and network work. Offline face recognition system algorithm calculation speed is fast, but the recognition accuracy is low; The algorithm recognition accuracy of the face recognition system working in the network is high, but the calculation and transmission time is high. Aiming at the above problems, this paper focuses on the design of two intelligent human face recognition systems. Firstly, a face verification system with fast computation speed and high recognition accuracy is designed and developed on a small embedded board by using deep face recognition neural network and model compression technology. Secondly, using face recognition algorithm, edge computing technology and enhancement learning Leavening algorithm, design and develop an intelligent video surveillance system that works in the network. The system can process the monitoring data intelligently through the synergistic effect of the front and rear end face recognition algorithm and the decision algorithm of the decision-maker module, so as to solve the problem that the traditional monitoring system occupies too much storage and computing resources and can not respond in time. Finally, this paper introduces some work on facial expression recognition. An expression recognition method based on the fusion of multiple local features is proposed. The algorithm improves the accuracy of facial expression recognition and solves the problem of poor performance of traditional single expression feature recognition.
Revisiting Hybrid Private Information Retrieval

Private Information Retrieval (PIR) allows a client to request entries from a public database held by k servers without revealing any information about the requested data to the servers. PIR is classified into two classes: (i) Multi-server PIR protocols where the request is split among k≥2 non-colluding servers, and Single-server PIR protocols where exactly k=1 server holds the database while the query is protected via certain computational hardness assumptions.
Analysis of Textbook use in 4th and 5th grades of primary school

A modern trend in quality textbooks is to stimulate different areas of student development social , cognitive, moral, and emotional, considering students' learning specifics. Textbooks are adapted to students if they consider their developmental level, experience and level of understanding ; use appropriate language; encourage reading through comprehension, and include illustrations in the text (Marentič Požarnik, 2016). The quality of learning resources that students can also use to learn independently is important. The main purpose of the Slovenian project The Quality of Slovenian Textbooks (KaUč) is to develop textbook quality indicators for practical use in validation and evaluation processes. As part of the KaUč project, an empirical research was conducted on the research sample of 505 students, with its research aim to analyse the use of textbooks and workbooks in 4th and 5th grades of primary school. Based on the performed analyses, we can determine that the use of printed textbooks prevails in all educational subjects. Textbooks are most often used for the first foreign language subject, science and technology, and for social studies.
PROTOTYPE DESIGN OF TIMBULREJO VILLAGE DEVICE PERCENTAGE SYSTEM BASED ON QR CODE

QR code is a two-dimensional matrix code as a development of the evolution of barcode codes, the present system is an important thing in every office and other institution to see the results of attendance. Modern era like today by creating a QR Code persistence system with the concept of BYOD (Bring Your Own Devices), so that the percentage system becomes more modern. The village apparatus percentage system aims to form the discipline of the Timbulrejo Village Apparatus, and is useful for accelerating and facilitating absentee reporting to be more effective and efficient. Using the Prototype method to describe the system, the user or system owner has a picture of the system development that will be carried out, using Fishbone and context diagrams, to describe the analysis and design of the system. The process of collecting data is by observing, interviewing, and studying literature. In making this or code-based persistence system using the PHP &amp; MySQL programming language so that the system can be made according to the design, such as the home page, the admin menu which consists of the dashboard menu, the data menu which contains data for village apparatus and its offices, the menu for the Village hall and the menu. weekdays, then there is a menu to take a QR Code and scan a QR Code, a menu of attendance data which consists of a menu of history and presentation recap, finally, there is a set menu consisting of user management and menu management the resulting system is expected to greatly facilitate all users
Probability and Statistical Inference: From Basic Principles to Advanced Models

The discipline of data science is comprised of many STEM fields but the two most prominent influences come from statistics and computer science. At a surface level, a data scientist should be a bet...
Optimal Prediction of Markov Chains With and Without Spectral Gap

We study the following learning problem with dependent data: Observing a trajectory of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> from a stationary Markov chain with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> states, the goal is to predict the next state. For <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \leq k \leq O(\sqrt {n})$ </tex-math></inline-formula> , using techniques from universal compression, the optimal prediction risk in Kullback-Leibler divergence is shown to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta \left({\frac {k^{2}}{n}\log \frac {n}{k^{2}}}\right)$ </tex-math></inline-formula> , in contrast to the optimal rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta \left({\frac {\log \log n}{n}}\right)$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=2$ </tex-math></inline-formula> previously shown in Falahatgar et al. (2016). These rates, slower than the parametric rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O\left({\frac {k^{2}}{n}}\right)$ </tex-math></inline-formula> , can be attributed to the memory in the data, as the spectral gap of the Markov chain can be arbitrarily small. To quantify the memory effect, we study irreducible reversible chains with a prescribed spectral gap. In addition to characterizing the optimal prediction risk for two states, we show that, as long as the spectral gap is not excessively small, the prediction risk in the Markov model is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O\left({\frac {k^{2}}{n}}\right)$ </tex-math></inline-formula> , which coincides with that of an iid model with the same number of parameters. Extensions to higher-order Markov chains are also obtained.
Time-optimal cornering trajectory planning for car-like mobile robots containing actuator dynamics

Abstract We establish a highly feasible algorithm for time-optimal cornering trajectory planning (TP) for car-like mobile robots (CLMRs) based on a dynamic model that contains actuator dynamics. First, we formulate an accurate dynamic model of a robot that contains DC motor actuators; this includes steering braking (caused by the lateral force of the front steering wheel) and two types of friction (viscous and Coulomb) under a nonslip condition. Our TP algorithm can utilize the full power of the DC motor actuators within proper pulse width modulation bounds and generated torque limits. Then, we establish an algorithm for a time-optimal cornering trajectory planning for CLMRs (TOCTP-CLMR). Our algorithm divides the trajectory into five sections comprising three turnings and two translations to minimize the travel distance. Then, we utilize the quickest rotation when turning to construct the time-optimal trajectory that satisfies the bang-bang principle. In addition, simulations are performed to demonstrate the validity of this method. Finally, we conduct open-loop experiments to validate our dynamic model and a trajectory tracking experiment to demonstrate the feasibility of the TOCTP-CLMR trajectory.