Reviewerly/exHarmony huggingface dataset:
READINESS OF THE TEACHING STAFF FOR THE IMPLEMENTATION OF THE EDUCATIONAL INNOVATIVE PROJECT AS A STRATEGY FOR THE DEVELOPMENT OF THE EDUCATIONAL INSTITUTION

The article considers the issue of innovation in content and nature of the dynamics of global trends in educational processes, which requires Ukraine to choose further development, which significantly affects the education system and its general education component. The readiness of the teaching staff to implement educational innovative technologies in the development of the new Ukrainian school is determined, the peculiarities of effective management of innovative processes in general secondary education institutions are revealed.&#x0D; There are educational innovations that are a purposeful process of partial changes that lead to modifications of the purpose, content, methods, forms of upbringing and education, adaptation of the learning process to new requirements. The core of innovative educational processes is the implementation of the achievements of psychological and pedagogical science in practice, education, generalization, and spreading of pedagogical experience, as well as certain components of readiness of teaching staff in innovative activity, namely: individual characteristics of teachers-innovators, motivational orientation, creative, analytical, organizational opportunities.&#x0D; The article states that the teaching staff of educational institutions is actively testing domestic and foreign educational technologies, an alternative to traditional, author’s schools are created, author’s educational programs, methods, technologies, etc. are developed and introduced into the educational process. The main stages of the process of introducing innovations into the practice of the school education system are analyzed and it is determined that one of the conditions for implementing innovative pedagogical technologies is the readiness of teaching staff to innovate the essence of which is the interaction of motivational, content and evaluative components. These components provide a creative orientation of the teacher; his awareness of innovative technologies; determining the level of readiness for innovation; a reasoned system of tools that allows the teaching staff at the theoretical, philosophical levels of transformation of pedagogical reality to professionally carry out innovative activities, significantly increase the developmental and cultural-educational impact of teaching and upbringing on the individual.
Scrum for life, methodology applied in the scientific production of the teacher: "Writing a Paper"

This study shows the agile management of a project using the Scrum methodology. The main objective is to analyze the use of Scrum for life, specifically in scientific production when writing a Paper. The results obtained recognize that the common practices of the agile approach such as revision and retrospective allow that the writing of the article can be improved throughout its development and that the collaboration between the participants is efficient and effective. Likewise, a consistent and effective alternative is provided to the scientific community for the administration in the development of scientific products.
Graph-Based CPE Matching for Identification of Vulnerable Asset Configurations

In this manuscript, we propose a graph-based approach for identification of vulnerable asset configurations via Common Platform Enumeration matching. The approach consists of a graph model and insertion procedure that is able to represent and store information about CVE vulnerabilities and different configurations of CPE-classified asset components. These building blocks are accompanied with a search query in Gremlin graph traversal language that is able to find all vulnerable pairs of CVEs and asset configurations in a single traversal, as opposed to a conventional brute-force approach.
A Novel Approach of Intelligent Computing for Multiperson Pose Estimation with Deep High Spatial Resolution and Multiscale Features

Currently, human pose estimation (HPE) methods mainly rely on the design framework of Convolutional Neural Networks (CNNs). These CNNs typically consist of high-to-low-resolution subnetworks (encoder) to learn semantic information and low-to-high subnetworks (decoder) to raise the resolution for keypoint localization. Because too low-resolution feature maps in encoder will inevitably lose some spatial information, which cannot be recovered in the upsampling stages, keeping high spatial resolution features is critical for human pose estimation. On the other hand, due to scale variation of human body parts, multiscale features are also very important for human pose estimation. In this paper, a novel backbone network is proposed specifically for HPE, named High Spatial Resolution and Multiscale Networks (HSR-MSNet), which maintain high spatial resolution features in deeper layers of the encoder and meanwhile construct multiscale features within one single residual block via subgroup splitting and fusion of feature maps. Experiments show that our approach outperforms other state-of-the-art methods with more accurate keypoint locations on COCO dataset.
Defence Rights in the Context of Huge Data Sets and Big Data Forensic Tools in Criminal Proceedings

Criminal cases increasingly revolve around extensive data sets consisting of digital evidence and advanced technological search tools such as Hansken, a big data forensic tool developed by the Netherlands Forensic Institute. In this context, lawyers and scientists have already argued that advanced search tools significantly strengthen the position of the prosecution at the expense of the defence, which generally does not have access to these tools. As a result, the defence has little influence to help determine what counts as relevant information in a criminal case and has hardly any possibilities to find exculpatory evidence and to test the reliability of digital evidence. This in turn leads to significant power and knowledge asymmetry. The principle of equality of arms as a core part of the right to a fair trial in Article 6 of the European Convention on Human Rights provides a good basis for the development of new or more extensively interpreted defence rights that are needed in the digital age. Based on an analysis of recent ECtHR case law, I discuss the level of established rights of the defence with regard to access to the dataset(s) and the advanced search tool in criminal proceedings. Thinking further as to the possible and desirable procedural guarantees in this novel context, I then discuss how the reliability of digital evidence and the process though which it was produced can be ensured and tested by the defence through a logging obligation and a right to counter-expertise.
Interpretability Comparison of Popular Decision Tree Algorithms

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.
Phishing Detection using Deep Learning

The rapid advancements in technology come with complex security challenges. One such challenge is phishing attack. Often a fake website is deployed to trick users into believing the website is legitimate and is safe to give away sensitive information such as their passwords. Anti-phishing frameworks have been developed in various forms. The most recent implementation involves datasets used to train machines in detecting phishing sites. This chapter focuses on implementing a Deep Feedforward Artificial Neural Network using supervised learning to detect phishing URLs. Several models were created that used a single feature to train. We compared how effective each feature was in detecting phishing URLs. Groups of features were also used to train models. Most models using only one feature yielded low accuracies, while models using more features showed better accuracies.
Study on mechanical properties testing of highway concrete pavement construction machinery materials

In order to solve the problems of large error and long time-consuming in the traditional mechanical property test methods, a new mechanical property test method for highway concrete pavement construction machinery materials is proposed. The relationship between the deformation component and the displacement component of the microstructure of the concrete pavement in the equilibrium coordinate system is obtained. The limit value of the critical thickness of the construction material of the highway concrete pavement is obtained by the second derivative function. Based on the analysis principle of AFM force curve, the relationship between mechanical materials and elastic modulus of highway concrete pavement construction is determined, and the performance analysis of highway concrete pavement construction mechanical materials is realised. The test results show that the error of mechanical properties analysis of highway concrete pavement construction machinery materials by the proposed method is only 0.3%, and the test time is short.
Gaussian Filter Based Digital Detail Enhancement in Thermal Images

Thermal images have low contrast due to thermal radiation dispersion in the scene. It is generally difficult to separate objects from the background in these images. Thus, ,in IR image processing, it is a critical task to increase the details in the scene (Digital Detail Enhancement-DDE) and to reduce the noise, sharpening the edges and increasing the contrast in order to distinguish the targets in the scene properly. In this study, a suitable approach is proposed for improving thermal images by increasing the contrast and revealing details, low processing load and suitable for the hardware. In the proposed approach, thermal image is divided into low frequency and high frequency components using Gauss filter. Firstly, the adaptive double plateau histogram equalization method is applied to the low frequency component, followed by the Gamma Transform. In order to better reveal the details of the high frequency component, details were revealed by applying stepped gain. Then the images that hold the improved low frequencies and high frequencies are combined. In the final stage, noise removal is performed using Wiener Filter. When the experimental results are examined, it is shown that the proposed method performs well compared to other methods.
FPGA Implementation of Digital Watermarking System for Robust Detection Using Discrete Wavelet Transform

Watermarking is the process of providing authentication rights by ingraining ownership information in the form of text, audio, video or images called watermarks on the original image. The paper aims to implement an efficient watermarking algorithm using second-level discrete wavelet transform (DWT) for increased robustness. A hardware description language (HDL) has been developed for Haar wavelet due to less computation complexity. The proposed watermarking embedding algorithm has been implemented in two FPGA boards. First one is a low-cost and low-power ARTIX FPGA-based Zynq 7000 SoC-based ZedBoard. Next one is Kintex FPGA-based FPGA Genesys 2 board which is suitable for DSP applications. An input image of size 16 × 16 and a watermark image of size 8 × 8 are used, and results of device utilization, power and time delay are compared. The results obtained suggest that the correlation and PSNR between the input image and the watermarked image are high, and the minimum device utilization indicates an effective watermarking algorithm.
GG-Net: Gaze Guided Network for Self-driving Cars

Imitation learning is used massively in autonomous driving for training networks to predict steering commands from frames using annotated data collected by an expert driver. Believing that the frames taken from a front-facing camera are completely mimicking the driver’s eyes raises the question of how eyes and the complex human vision system attention mechanisms perceive the scene. This paper proposes the idea of incorporating eye gaze information with the frames into an end-to-end deep neural network in the lane-following task. The proposed novel architecture, GG-Net, is composed of a spatial transformer network (STN), and a multitask network to predict steering angle as well as the gaze map for the input frame. The experimental results of this architecture show a great improvement in steering angle prediction accuracy of 36% over the baseline with inference time of 0.015 seconds per frame (66 fps) using NVIDIA K80 GPU enabling the proposed model to operate in real-time. We argue that incorporating gaze maps enhances the model generalization capability to the unseen environments. Additionally, a novel course-steering angle conversion algorithm with a complementing mathematical proof is proposed.
Smart museums: enjoying culture virtually Case of Virtual Museum of the National Museums Foundation. Rabat - Morocco

Before, the city was a space dedicated to offering a range of services to all the inhabitants who lived there, such as public transportation, education, housing.... However, things have undergone a certain change with the introduction of the Internet and ICTs. To this end, we have today began to hear about -Smart City-this new emerging concept leaves no major metropolis untouched.The digital and modern age has affected almost every sector, even those who imagined they were frozen in time, thanks to the cities have experienced the technologies were throughout the early 21st century. Here, we cite for example: the world of banks, telecommunications, service companies even the public sector such as: education, culture, administration.Future museums may also contribute to human culture in a society witnessing rapid changes via smart technology that encourages constructive participation with the public.Digital technologies did not emerge overnight in museums and started by replacing conventional analog broadcasting media without altering the experience of visitors. What is really new is the appearance, which includes screens near the works giving more information.The term museum has been defined as follows: ... a collection of digitized objects logically articulated and composed of various supports which, due to its connectivity and character multi-access, allow you to transcend traditional modes of communication and interaction with the visitor... .Our study case will also reach the Virtual Museum of the National Museums Foundation Rabat Morocco. Here, we will discover the exhibits the Virtual Museum in partnership with Virtuelli, a virtual imaging agency which is a platform, open to the general public and art lovers that hosts virtual tours of art exhibitions organized by the National Foundation of Museums under its supervision.
The detection method for uneven settlement of foundation in the area of industrial waste miscellaneous filling

In order to overcome the problems of large error and long time in the detection process of traditional foundation subsidence detection methods, this paper proposes a multi-feature fusion based detection method for uneven foundation subsidence in the area of mixed industrial waste. The method USES wavelet transform technology to filter the ground image of the industrial waste filling area and improves the accuracy and timeliness of settlement detection fundamentally. After obtaining the optimal threshold values of different sub-blocks of the foundation image, combined with the multi-scale ridge edge fusion algorithm, the uneven settlement of the foundation area was detected by the feature fusion process. The experimental results show that the detection error of this method is basically kept below 4%, the detection process takes less than 50ms all the time, and the detection cost is less than 60,000 yuan, which fully demonstrates the effectiveness of this method.
Redesigning blended learning to E-mooting in moving towards a paperless assessment method with a total usage of technologies

Currently, mooting is a compulsory subject in most universities or law schools in most countries. Moot is the argument of the legal issues raised by a hypothetical case that takes place in the imaginary setting of the court of law. It is a course that challenges the students on their understanding and application oflaws through writing and oral presentation skills. Electronic mooting is beneficial since students and the judges can interact without the constraint of time and geographical location, it is cost-saving and flexible. This paper discusses traditional mooting courses, blended learning, and online mooting. The discussionis limited to Multimedia University (MMU). In MMU, 70% of the process involving compulsory mooting is done through blended learning. In conducting this research, some of the students have submitted their bundle of documents online. This paper aims to look at respond of the students whether this subject should be conducted online. Quantitative and qualitative research methods are adopted in preparing this paper. The result showed that the majority of the respondents supported the idea for e-mooting.
Research Guides: Library 101: Maps and Virtual Tour

This guide will take you through the ins and outs of your library! Learn about services, policies, and all of the opportunities that await you!
Empirical Investigation of Resampling Techniques in an Intruder Detection System

The intruder detection system plays a fundamental function in recognizing assaults in networks. To design an intelligent intruder detection system invites researchers from the machine learning domain to work in this area. With the availability of KDD99 datasets, some researchers encounter a class imbalance problem in it. This article performs a detailed empirical investigation of various resampling techniques to mitigate the effect of class imbalance. The study is performed on NSL-KDD multi-class datasets using fivefold cross-validation with G-Mean and AUC as evaluation metrics considering the decision tree as a classifier. The study inferred that the SMOTE technique performs well compared with the rest of the art.
INTERNAL TESTING RESULT MANAGEMENT SYSTEM: (Studi Kasus : PT. Solmit Bangun Indonesia)

One of the most important stages in the software development process is the testing phase. Tests on the software are performed with the aim of ensuring the functions of the software are working properly. PT. Solmit Bangun Indonesia as a company engaged in information technology services has obligations to it is clients to provide the best service by producing a software product that suits the needs. One effort made to ensure the software product has been running properly and as needed is the implementation of a system that can manage and monitor bugs in the software. Internal testing result management system is a system that helps the management and monitoring of data resolving issues (bugs) on software both in development stage and that have been implemented.The methodology used in this study is the method of by using Unified Modeling Language (UML) as a modeling language. For the development of the system using the prototype method. The programming language used in the design of this system is PHP Hypertext Preprocessor which is built using the framework code igniter (CI) with MySQL database. The thing that has been achieved in this research is has made a system that assist in management and monitoring of solution of issues (bugs) in software at PT. Solmit Bangun Indonesia. Keyword: Bugs, Management System, Software Testing
INFORMATION AND EDUCATIONAL ENVIRONMENT FOR TRAINING SPECIALISTS OF SOCIONOMIC PROFESSIONS IN HIGHER SCHOOL

INFORMATION AND EDUCATIONAL ENVIRONMENT FOR TRAINING SPECIALISTS OF SOCIONOMIC PROFESSIONS IN HIGHER SCHOOL
Retrieval Enhancements for Task-Based Web Search

The task-based view of web search implies that retrieval should take the user perspective into account. Going beyond merely retrieving the most relevant result set for the current query, the retrieval system should aim to surface results that are actually useful to the task that motivated the query.This dissertation explores how retrieval systems can better understand and support their users’ tasks from three main angles: First, we study and quantify search engine user behavior during complex writing tasks, and how task success and behavior are associated in such settings. Second, we investigate search engine queries formulated as questions, and explore patterns in a large query log that may help search engines to better support this increasingly prevalent interaction pattern. Third, we propose a novel approach to reranking the search result lists produced by web search engines, taking into account retrieval axioms that formally specify properties of a good ranking.
Activity Comparison of the Participants Using Japanese as L2 and Their L1 in Group Discussion

In order to supplement the decreasing working population, there are more and more foreigner workers working in Japanese society. In such a situation where small numbers of foreigner workers need to work with Japanese people as the majority, inefficiency of the group occurs due to language or cultural barriers. This work-in-progress project collects a data corpus for understanding the problems may occur in this “unbalanced” group work and aims to provide computer aided support in the future. We collected 8.5 h of the task-oriented conversation of small groups in the following settings: one Chinese speaker/three Japanese talking in Japanese (C-J), four Chinese speakers talking in Chinese (C-C), and four Japanese talking in Japanese (J-J). In this specific paper, we analyzed a subset (9 15-min discussion sessions) of the data corpus to see the activity changes between C-J and C-C settings.
Trumpets: Injective Flows for Inference and Inverse Problems

We propose injective generative models called Trumpets that generalize invertible normalizing flows. The proposed generators progressively increase dimension from a low-dimensional latent space. We demonstrate that Trumpets can be trained orders of magnitudes faster than standard flows while yielding samples of comparable or better quality. They retain many of the advantages of the standard flows such as training based on maximum likelihood and a fast, exact inverse of the generator. Since Trumpets are injective and have fast inverses, they can be effectively used for downstream Bayesian inference. To wit, we use Trumpet priors for maximum a posteriori estimation in the context of image reconstruction from compressive measurements, outperforming competitive baselines in terms of reconstruction quality and speed. We then propose an efficient method for posterior characterization and uncertainty quantification with Trumpets by taking advantage of the low-dimensional latent space. Our code is publicly available at https://github.com/swing-research/trumpets.