Reviewerly/exHarmony huggingface dataset:
Using the Methods of Project Activities in Music Lessons to Develop Cognitive Interest

Modern conditions for society development require a reorientation of teaching from the assimilation of ready-made knowledge, skills and abilities to the development of the student's personality, his crea-tive abilities, independent thinking and a sense of personal responsibility, both a moral characteristic of a person and an intellectual and cognitive sphere of interests. In this pedagogical theory, the learner becomes a central figure, and his activity takes ac-tive cognitive character. The practice of applying the project method shows a positive trend in the disclo-sure of a person's creative potential. The atmos-phere of communication, intellectual search for so-lutions to the assigned tasks forms in each student independence and responsibility for the results of their work. The article describes in detail project activity as an active process of the formation of stu-dents’ creative skills and abilities and reveals the features of its implementation in music lessons.
Digital forensics appliance in corporate ecosystem considering limitations in the EU legal framework

This paper presents appliance of digital forensics in a corporate ecosystem i.e. appliance of digital forensics investigation in a corporate or internal investigation as a result of an incident caused by internal or external threat while considering necessity of coexisting corporate policies and legal regulations. Conducting digital forensic investigation refers to the process of acquiring and processing data for the purpose of investigation which are commonly performed by the corporate investigation team, i.e. by the third-party investigators. Today's organizations need to be forensically ready to minimize a response time necessary for timely reaction, both in the cases of internal or external digital forensics incidents. The goal of this paper is to provide oversights of various companies as well as deficiencies of EU legal system.
INTERACTIVE METHODS IN COMMUNICATIVE EDUCATION

The article deals with the problem of using of interactive methods and forms of teaching, which are based on mutual learning, and community education. The empirical material of the research was collected by the method of questioning of academic staff from Ukrainian universities. The respondents were asked questions, grouped in 2 blocks. In the first block, the respondents had to pick up the interactive methods, which they use in teaching. The second block was a question about of the effectiveness of interactive methods. The results of the study made it possible to conclude that lecturers of Ukraine’s higher education institutions use interactive methods and forms of teaching for the communicative skills and competencies development, developing communicative personality traits and accumulation of professional and communicative experience, but their choice is not diverse and there is a lack of teachers’ knowledge about interactive methods and forms of teaching. These results contradict modern educational trends, which imply the activation, stimulation and encouragement of the activity of each student in the educational process.
Gated Graph Pooling with Self-Loop for Graph Classification

Graph classification is a practical problem in many different domains including bioinformatics, chemoinformatics, social network analysis, and etc. For the graph classification task, the existing graph neural network approaches usually generate graph features using graph pooling at each step. However, this strategy of pooling only at the current step ignores the impact of self-loop. To eliminate this limitation, we propose a novel self-loop graph pooling strategy that can utilize the node information of the current step and the graph representation information of the previous step to generate an effective representation for the graph classification task. Further to measure the importance of self-loop, we also develop a gated approach, gated graph pooling with self-loop, that utilizes the simple fusion gate to enhance the representation capacity of the model. We evaluate our model on common benchmark datasets and experimental results have demonstrated the superior performance improvement on predictive accuracy.
How to reduce risk effectively in fixed price software development

In this paper we consider 20 years of software development at a medium-sized European software company (called adesso). We identify changes and trends in software project management and we name typical risks inherent to fixed price projects (beyond the common sense that unclear requirements, missing domain knowledge and breakdown of project communication are permanent risks). Fixed price projects and their related derivatives can have a huge economical upside for companies like adesso, which goes along with sometimes even larger risks. The goal of this research is to identify both risk drivers and risk reduction or even elimination strategies. On this basis, we introduce the notion of a Project Management Office (PMO) and supporting tools and mechanisms which help to identify project risks early. Experience reported is based on eight years of monitoring software development projects with these tools and mechanisms. We also give some insights how PMO at adesso has changed over time and why these changes were implemented. As key message of this paper we show the correlation between the systematic usage of the PMO tools and a decreasing overspend rate over eight years and more than 320 projects.
The Impact of Administrative Empowerment Dimensions on Organizational Performance: A Case Study of the National Authority for Electricity (Sudan)

The study aims at identifying the reality of applying the administrative empowerment on the National Authority for Electricity, assessing its approved reality and how to activate it. The study population consists of 42 “engineers” accountants and technicians working in the authority administration A questionnaire was distributed to them and 30 forms were returned, valid for analysis as a study sample the researcher used the Simple Linear Regression model. The study was conducted in the National Authority for Electricity in Sudan. The researcher adopted the descriptive-analytical method. The study concluded to the fact that there is the freedom to perform work and discuss ideas with the administration and there is the power to correct deviations when they occur, without referring to the immediate superior. The study recommended working on possessing the technical and administrative aspects necessary to obtain information and the need to exchange it, encouraging employees to promote the sense of strength and success by granting them more freedom to act in everything related to their jobs.
Zero-Shot Source Code Author Identification: A Lexicon and Layout Independent Approach

We tackle the challenge of Zero-Shot identification of authors of source code, which can be used with no prior samples of authors outside of the training data. In our approach, a feedforward neural network is first trained on a multi-class classification task. Then, a substantial part of this network is duplicated and reused to compare code samples. We refer to this design as Feedforward Duplicated Resolver (FDR) model. We propose new input features to train this model, called Variable-Independent Nested Bigrams, extracted from the Abstract Syntax Trees of code samples. These features provide robustness against lexical and layout obfuscation attacks frequently used in plagiarism attempts. This approach performs accurately even on code samples from unknown authors, on data obtained from Google Code Jam, an international coding competition platform. For example, for the task of predicting whether a pair of samples from 43 unknown authors have been written by the same person, we obtain an AUC of 0.96 and 0.91 for non-obfuscated and obfuscated code, respectively.
A Novel Method to Estimate Students’ Knowledge Assessment

Performance of learning can be enriched with proper and timely feedback. This paper proposes a solution based on a Bayesian network in machine learning that can examine and judge students’ written response to identify evidences that students fully comprehend concepts being considered in a certain knowledge domain. In particular, it can estimate probabilities that a student has known concepts in computer science at different cognitive ability levels in a sense. Thus, the method can offer learners personalized feedbacks on their strengths and shortcomings, as well as advising them and instructors of supplementary education actions that may help students to resolve any lacks to improve their knowledge and exam score.
Regularizing Pattern Recognition with Conditional Probability Estimates

Recent contributions in non-parametric statistical pattern recognition have investigated augmenting the task with information about the conditional probability distribution P(Y|X) away from the 0.5 level set, i.e. the decision boundary. Many hypothesis spaces satisfy generous smoothness criteria, so the behavior of a function away from the decision boundary can serve as a regularizer for its behavior at the decision boundary. This paper proposes a paradigm to capture observable information about the conditional distribution and describe a learning formulation that can take advantage of it. Finally, it investigates why conditional probability can be an effective regularizer for inseparable pattern recognition problems.
Externalizing Mental Images by Harnessing Size-Describing Gestures

People use a significant amount of gestures when engaging in creative brainstorming. This is especially typical for creative workers who frequently convey ideas, designs, and stories to team members. These gestures produced during natural conversation contain information that is not necessarily conveyed through speech. This paper investigates the design of a system that uses people's gestures in natural communication contexts to produce external visualizations of their mental imagery, focusing on gestures that describe dimension-related information. While much psycholinguistics research address how gestures relate to the representations of concepts, little HCI work has explored the possibilities of harnessing gestures to support thinking.
A Few-shot Dynamic Obstacle Avoidance Strategy in Unknown Environments

Obstacle avoidance is one of the basic capabilities of intelligent mobile robots. With the diversification of the application environment, mobile robots are required to avoid obstacles with higher generality. Benefit from the development of mobile platform and deep learning algorithm in recent years, we conceive a few-shot dynamic obstacle avoidance strategy to meet this higher generality demand. Under this metric-based meta-learning method, mobile robots can quickly adapt to unknown environments by learning from several samples. In order to verify its effectiveness, we use this strategy to train a model and deploy it to the mobile robot and run multiple obstacle avoidance recognition tests in the real-world environment. The results of experiments performed on the mobile robot platform illustrates a good performance and verifies our proposed strategy. In addition to analyzing the experimental results, the advantages, disadvantages as well as application potential of the proposed strategy as a decision aid are also discussed.
Forming Local Intersections of Projections for Classifying and Searching Histopathology Images

In this paper, we propose a novel image descriptor called "Forming Local Intersections of Projections" (FLIP) and its multi-resolutional version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply parallel projections in small local neighborhoods of gray-level images. Using equidistant projection directions in each window, we extract unique and invariant characteristics of the neighborhood by taking the intersection of adjacent projections. Thereafter, we construct a histogram for each image, which we call the FLIP histogram. Various resolutions provide different FLIP histograms which are then concatenated to form the mFLIP descriptor. Our experiments included training common networks from scratch and fine-tuning pre-trained networks to benchmark our proposed descriptor. Experiments are conducted on the publicly available dataset KIMIA Path24 and KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show promising results in all experiments. Using KIMIA Path24 data, FLIP outperformed non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed fine-tuned Inception-v3 in feature extracting.
HLNet: Modeling High and Low Frequencies for Scene Parsing

In this paper we propose to model high and low frequencies of segmentation map, based on the observation that the map can be seen as a mixture of different frequencies. Based on the sparsity of high frequencies and local similarity of low frequencies, we design special building blocks and further a novel High and Low frequency Network (HLNet) with two branches based on FCN to predict high and low frequencies of the segmentation map, respectively. Specifically, we design a high frequency branch with a small kernel size and high-resolution features to predict a sparse high frequency component. Mean-while, a low frequency branch with similarity computing and low-resolution features is employed to predict a low frequency component. On top of two branches, we combine two different frequency components to generate final result for scene parsing. We empirically demonstrate that the designed model achieves superior performance 44.07% on ADE20K, and 80.14% mIoU on Cityscapes datasets.
Research on microwave heating structure optimization of auxiliary material box of road maintenance vehicle

Abstract The arrangement and spacing of microwave comprehensive maintenance vehicle antenna array directly affect the heating efficiency and heating uniformity of asphalt mixture. In order to improve the heating uniformity of asphalt mixture, this paper proposes a microwave heating structure based on auxiliary materials box, and optimizes the microwave heating structure of auxiliary materials box of road maintenance vehicle through CST simulation platform. Firstly, in the way of side-by-side arrangement of the antenna array, the spacing of the antenna array is optimized, and the heating uniformity of the asphalt mixture in the auxiliary material box is the best when the spacing is 132mm×155mm under side-by-side arrangement. Then, under the optimal arrangement spacing, the orthogonal arrangement of antenna array was put forward, and it was concluded that under this arrangement, the heating uniformity of asphalt mixture increased by 12.1%, and the proportion of asphalt mixture increased by 8.7% at 80°C -160°C. This conclusion has certain reference value to the improvement of the heating effect of the auxiliary material box heating system.
Developing a Quality Model to Study the Impact of Variability Over the Reusability and Maintainability of Software Product Line Variability Models

Software Product Line (SPL) is focused on representing the variability and commonality of a software family associated with a common set of assets. OVM model and state chart diagram are important artifacts of SPL, which are used to diagrammatically represent the commonality and variability of SPL and are developed in the initial stages. The variability of SPL needs to be systematically managed as it withstands all the functional and non-functional needs of the product line and impacts the quality of products. During initial literature review, it was observed that during the early phase of development, assessment of few quality attributes was considered but assessment of variability and its impact over quality attributes remains untouched. In the current research, a quality model is proposed to assess variability and its impact over reusability and maintainability of SPL. The advantage of this study would be that variability would be managed together with reusability and maintainability leading to improved SPL quality.
THIRD EYE—Shopfloor Data Processing and Visualization Using Image Recognition

This paper describes how superimposed data using an Augmented Reality based system is appropriate in manufacturing and warehousing locations to improve operations. Access to Data is penultimate in the manufacturing industry, and giving the required data in real-time based on the component or a machine can help in increasing productivity with an added insight of using, a handheld mobile phone, thus avoiding any interaction with computers. Production and machine details give the shop floor managers the flexibility to analyze machines at the shop floor, which aids on time defect resolution and machine status. This paper focuses on object detection based data analytical application, which can differentiate between two or more similar machines using feature extraction methods and texture mapping to enhance object detection in the used game engine and also to develop the application and map those to the database of that machine, hence provide an analytical information in a hands-free manner. The effectiveness of the algorithm was tested on machines that look similar or near to identical was differentiate using our methodical image processing algorithm and the data pertaining to the machine is processed on the cloud. Improvements to process the data on the device to improve overlay efficiency and speed was experimented using various studies. Compared to various image processing software, THIRD EYE was rated 93.6% efficient in image recognition and data overlay to the user was one new feature we provide as an application to the user.
Image Retrieval With Lingual And Visual Paraphrasing Via Generative Models

A new approach that improves text-based image retrieval (hereinafter referred to as TBIR) performance is proposed in this paper. TBIR methods aim to retrieve a desired image related to a query text. Especially, recent TBIR methods allow us to retrieve images considering word relationships by using a sentence as a query. In these TBIR methods, it is necessary to uniquely identify a desired image from similar images using a single query sentence. However, the diverse expressive styles for a query sentence make it difficult to uniquely identify a desired image. In this paper, we propose a novel TBIR method with paraphrasing on multiple representation spaces. Specifically, by paraphrasing a query sentence on lingual and visual representation spaces, the proposed method can retrieve a desired image from various perspectives and then it can uniquely identify a desired image from similar images. Comprehensive experimental results show the effectiveness of the proposed method.
Retrieve Information From Articles By Using Quick Response Code

Due to the growing number of articles and the ever-increasing number of information provided by them, the process of extracting important information needed by a researcher requires a lot of time and effort. In this research, we propose a system that uses QR codes to retrieve key data from articles, so that researchers can obtain information with less time and effort compared to traditional ways of searching and reading. This smart and fast searching approach can be done by placing QR codes on articles with high storage ratio. The QR code contains key information about the article such as paper title, keywords, place and date of publication, evaluation and other basic information needed by researchers. The system implemented and designed as Android smart phone application with easy-to-use interface.
Cluster Developing 1-Bit Matrix Completion

1-bit matrix completion plays a significant role in recommender systems. However, conventional 1-bit methods do not consider the interactive clustering nature of customers and products. To tackle the gap, we introduce Group-Specific 1-bit Matrix Completion (GS1MC) by first-time consolidating cluster effects in 1-bit matrix completion tasks. To empower GS1MC even when grouping information is unobtainable, GS1MC is extended as Cluster Developing Matrix Completion (CDMC) by integrating subspace clustering techniques. CDMC can group entities and utilize their cluster effects simultaneously. Experiments show that GS1MC outperforms current 1-bit matrix completion methods and CDMC successfully captures items' genre features only with sparse binary rating data. Notably, GS1MC provides a new insight to incorporate and evaluate the efficacy of different clustering methods while CDMC can be served as a new tool to explore unrevealed social behavior or market phenomena.
Robust Depth Estimation From Multi-Focus Plenoptic Images

This paper describes a robust depth estimation algorithm for multi-focus plenoptic images. The main feature of the proposed method consists of a hybrid template matching scheme built-upon intensity and local phase information, which adapts to the blurriness of neighbouring lenslet microimages. By reducing the impact of defocusblur on the template matching accuracy, the proposed method efficiently handles the varying triangulation baseline over the depth-of-field, thus discarding the need for scene-related information such as the expected range of disparities. Experimental results demonstrate the robustness of the proposed method over the most used commercially available depth estimation algorithm, achieving a reduction of 73% on the depth estimation error.
Mathematical Model of Dynamics of Generated Electric Power by Photovoltaic Installation Taking into Account a Seasonality Factor

A task of building of a mathematical model of dynamics of generated electric power by photovoltaic installation is considered in the paper. Built interval discrete model of a real photovoltaic installation takes into account a seasonality factor. To build the model, application of a structural identification method of interval discrete models based on artificial bee colony algorithm has been proposed and justified. Numerical experiments that confirmed an efficiency of the proposed approach to building of non-autonomous models based on an interval data analysis has been conducted.
Research on Key Technologies of Network Traffic Identification in Complex Network Environment Based on Deep Learning

Network security defense is one of the important tasks in the use of complex network environments. It has attracted many network security experts and enterprises to study and proposed defense technologies such as deep learning software, antivirus software, firewalls, or self-organizing networks. Based on this research background, the paper proposes a vector machine-based stack self-coding deep learning prediction method. Experimental simulation results show that the algorithm has good prediction accuracy and applicability.
Applying Model‐Based Systems Architecture Processes (MBSAP) Methodology for Diversified MBSE Projects with Efficient Systems of Systems Accomplishments

Abstract This is a paper I will like to share with my Systems Engineering Development Life Cycles (SDLC) tactics by adapting to the MBSAP methodology from the text applicable to the real‐world MBSE tasks. These involved diversified MBSE developments and research according to my working projects scope aspects. The text introduces MBSE as the state of the practice in the dynamic Systems Engineering discipline that manages complexity and integrates technologies and design approaches to achieve effective, affordable, and balanced system solutions to the needs of all stakeholders within the systems development resources. All my successful accomplishments will be demonstrated that MBSAP is an effective MBSE and Systems Architectures research methodology for any future digital systems engineering development life cycles.