Reviewerly/exHarmony huggingface dataset:
Scrum Team Ownership Maturity Analysis on Achieving Goal

Many companies have used the Scrum framework for system and product development. In the implementation of the scrum framework, we often encounter obstacles and challenges in the field, one of them is a sense of belonging to the team’s work itself. The scrum team should act on their own and already know what to do on each sprint. They are already committed to what they think can be accomplished and can be helped by feeling ownership of the work they do each sprint. If the Scrum team is committed to the work to be carried out, over time it will certainly foster a sense of ownership within the Scrum team itself, including satisfaction with the results they have achieved. The most important thing to a Scrum Team is to remember, that the team is to take ownership of themselves. They are responsible for their own accountability, If they are to be truly self-directed. A lot of research has been done to determine the maturity level of a scrum team with their respective methods such as the Scrum Maturity Model and Agile Maturity Model but not related to team ownership.
Secure MDE for Ethereum-based Decentralized Applications (ÐApps) Development

Ethereum Blockchain based Smart Contracts are computer programs that enable decentralized transactions among its participants. The development of smart contracts is a complex process because of the characteristics of Blockchain Technology like the immutability of blocks added to the blockchain. Also, the current evolving nature of this novice technology makes the development process insubstantial. Therefore, this doctoral dissertation highlights the vulnerabilities and complexities in smart contracts and proposes a model-driven engineering framework for designing secure smart contracts at the model level and generating vulnerability-free smart contract code.
Portable Small Items Stair-Lift (Detachable)

Loss of balance leads to the increasing numbers of stair falls experienced by elderly people. To help eliminate the hardships faced by elderly people from lack of balance, a portable small items stair-lift was developed. The prototype will have the ability to carry small items up and downstairs through the stair handrails. This reduces the risk faced by elderly people while going up and down. The key feature of this prototype is its portability, which differentiates it from other already available products in the market serving a somewhat similar purpose. The portable stair-lift is being controlled wirelessly through a developed user interface via Wi-Fi network. A live video streaming of the device view is also included which permits control for not only users nearby but even those away from home to help if in any case the people that are about to use it is incapable. Overall, this device will aid in increasing the safety aspects of stairs and promotes further advancement towards the quality of life in general.
Research and application of simulation system for reduction and stabilization process of chromium contaminated soil based on intelligent model

Reducing the impact of chromium-contaminated soil on humans and organisms is a worldwide problem, and it is urgent to treat and evaluate it. Stabilization efficiency is a key factor for screening and evaluating reduction stabilization process of chromium contaminated soil. In this study, various data-driven models were constructed to predict the reduction and stabilization efficiency of chromium contaminated soil, which has very important practical significance. The results show that both models can predict the stability efficiency, and the model constructed by multi-model combination modeling method has better prediction performance than the model established by single neural network. The prediction effect of multi-model nonlinear combination modeling method is better than that of linear combination modeling method, and the nonlinear combination model based on BPNN combiner combination SVM sub-model has the best performance. Our research provides a good idea for the modeling and prediction of stabilization efficiency, which greatly reduces the time and economic cost required for the reduction and stabilization process of chromium contaminated soil.
Knowledge Graph Construction using Information Extraction of Indonesia Cosmetic Product Text in Bahasa Indonesia

Knowledge graphs can be used for entity recognition in text, graph visualization, and to improve business processes, e.g. information retrieval in E-commerce. One of the information sources for building knowledge graphs is text data available in many digital system, such as E-commerce platform. In this paper, we proposed an approach to extract knowledge graph entities from product text available from E-commerce platforms. We utilize transfer learning technique with full-fine tuning from an existing trained model in order to recognize the entities due to the limitation of labeled data. Since some English terms are expressed in product texts, we used multilingual pretrained models with the Transformer Architecture, i.e. multilingual-BERT-base-cased (mBERT) and XLM-RoBERTa-base (XLMR) in our approach. The extracted entities were then mapped into a knowledge graph by adopting Text to Knowledge Graph (T2KG) framework components, i.e. using entity mapping and triple integration. The training data contains 1.500 labeled texts, while the test data contains 216 labeled texts conducted in three versions of data and four scenarios. Our evaluation result showed that the XLMR model performed better than mBERT for entity extraction task with an average F1-score of 0,895. Furthermore, we manually evaluate the knowledge graph mapping and construction using 1.445 product texts from two E-commerce platforms, which resulted in 338 entities formed in the knowledge graph with mapping precision 0,94.
Random verifiable multi-server searchable encryption scheme

In order to solve the problem of difficult verification of query results in searchable encryption, we used the idea of Shamir-secret sharing, combined with game theory, to construct a randomly verifiable multi-cloud server searchable encryption scheme to achieve the correctness of the query results in the cloud storage environment verify. Firstly, we using the Shamir-secret sharing technology, the encrypted data is stored on each independent server to construct a multi-cloud server model to realize the secure distributed storage and efficient query of data. Secondly, combined with game theory, a game tree of query server and verification server is constructed to ensure honesty while being efficient, and solve the problem of difficulty in returning search results to verify under the multi-cloud server model. Finally, security analysis and experimental analysis show that this solution effectively protects data privacy while significantly reducing retrieval time.
Impact Analysis of Intelligent Agents in Automatic Fault-Prone Components Prediction and Testing

Software quality is imperative for industrial strength software. This quality will be often determined by a few components present in the software which decides the entire functionality. If any of these components are not rigorously tested, the quality will be highly affected. Without knowing which of these components are really critical, it will not be possible to perform high level testing. Hence, to predict such fault-prone or critical components from the software prior to testing and prioritizing them during the testing process, an agent-based approach is proposed in this chapter. The framework developed as part of this work will certainly reduce the field failures and thus will improve the software quality. Further, this approach has also utilized important metrics to predict such components and also prioritized the components based on their critical value. Also, the work proposed in this research has also been compared with some of the existing approaches and the results reveal that, this work is a novel one and can both predict and test the components from the software.
Development of an algorithm for human face recognition

At present, a variety of software products have been developed and proposed for identifying a person by his model through a photo or video image in the format of the Friend-Foe systems. As a rule, these products are paid with closed codes. In the process of teaching students, analytical recognition programs are required to adapt them to specific problems that arise. The algorithm used (Color preprocessing, Sobel filter, golden ratio rules, selection of 25 objects in the photo, Fourier and search for a solution).
MODELING AND DEVELOPMENT OF THE INTERACTIVE GAME

The purpose of the article is development of the issue of increasing the interest in the issue of the interactive entertainment industry, in particular in such areas as the development of electronic games, as well as coverage of the issue of independent product development and content of the compound. During the research, such a program product was developed, with usage of author’s content, to avoid the similarities with the other products. Today, the information technology sphere remains one of the leading sectors of the economy. The field of computer games, in turn, is one of the most popular areas of information technology development. Over the past 50 years, this segment has only strengthened its position in the global entertainment market and continues to grow. Profits from the sale of games are growing, as is the demand for them, because nowadays more people play electronic games than ever before. Due to the inconvenience of the financial plan on the part of publishers involved in the distribution of computer games, the phenomenon of indie games has emerged - projects that are independent from a financial and creative point of view. This article is devoted to the issue of modeling and development of computer indie games. The above-mentioned computer game being developed is a representative of the "visual novel" genre. The main purpose of any game is to meet the needs of end users - players - in the organization of their own leisure, as well as making a profit from the game. The software product under development is an electronic (computer) game called "PRIESTESS", developed using the tools of the engine "Ren'Py" and high-level programming language "Python", the graphical shell of the project is created using the graphical editor "Photoshop CS5". All the necessary models, schemes, diagrams and algorithms were made before the development, and built by means of the Internet resource "draw.io".
Electric Current Profile Feature Extraction Algorithm with an Implementation for Point Machines

Failures, operational downtime and especially accidents are highly unwanted in the transportation sector. Every such incident may cause both enormous human and financial distress. This raises the demand for smart sensor solutions which are not only able to detect faults, but also to monitor the condition of critical components in the aforementioned sector. In order to monitor the condition of a device or machine, access to relevant parameters has to be granted. A commonly accessible value in the context of machines is the electrical supply current. In this paper, a feature extraction algorithm based on the electric current profile is presented. The algorithm is able to detect and extract relevant features out of the current profile and determine whether fault indicators are present, in order to inform the relevant authorities to act before any loss occurred. The presented solution is optimized by using data reduction methods and is able to run completely locally on a low-cost embedded microcontroller. This provides multiple benefits, such as cost minimization, machine data protection and minimum data traffic.
Skimming and Scanning for Efficient Action Recognition in Untrimmed Videos

Video action recognition (VAR) aims to classify videos into a predefined set of classes, which is a primary task of video understanding. We mainly focus on the VAR of untrimmed videos because they are most common videos in real-life scenes. Untrimmed videos have redundant and diverse clips containing contextual information, so sampling the clips is essential. Recently, some works attempt to train a generic model to select the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> most representative clips. However, it is difficult to model the complex relations from intra-class clips and inter-class videos within a single model and fixed selected number, and the entanglement of multiple relations is also hard to explain. Thus, instead of “only look once”, we argue “divide and conquer” strategy will be more suitable in untrimmed VAR. Inspired by the speed reading mechanism, we propose a simple yet effective clip-level solution based on skim-scan techniques. Specifically, the proposed Skim-Scan framework first skims the entire video and drops those uninformative and misleading clips. For the remaining clips, it scans clips with diverse features gradually to drop redundant clips but cover essential content. The above strategies can adaptively select the necessary clips according to the difficulty of the different videos. In order to further cut computational overhead, we observe the similar statistical expression between lightweight and heavy networks. Thus, we explore the combination of them to trade off the computational complexity and performance. Comprehensive experiments are performed on ActivityNet and mini-FCVID datasets, and results demonstrate that our solution surpasses the state-of-the-art performance in terms of accuracy and efficiency.
Enhanced Serpent algorithm using Lorenz 96 Chaos-based block key generation and parallel computing for RGB image encryption

This paper presents a new approach to enhance the security and performance of the Serpent algorithm. The main concepts of this approach is to generate a sub key for each block using Lorenz 96 chaos and then run the process of encryption and decryption in ECB parallel mode. The proposed method has been implemented in Java, openjdk version "11.0.11"; and for the analysis of the tested RGB images, Python 3.6 was used. Comprehensive experiments on widely used metrics demonstrate the effectiveness of the proposed method against differential attacks, brute force attacks and statistical attacks, while achieving superb results compared to related schemes. Moreover, the encryption quality, Shannon entropy, correlation coefficients, histogram analysis and differential analysis all accomplished affirmative results. Furthermore, the reduction in encryption/decryption time was over 61%. Moreover, the proposed method cipher was tested using the Statistical Test Suite (STS) recommended by the NIST and passed them all ensuring the randomness of the cipher output. Thus, the approach demonstrated the potential of the improved Serpent-ECB algorithm with Lorenz 96 chaos-based block key generation (BKG) and gave favorable results. Specifically, compared to existing encryption schemes, it proclaimed its effectiveness.
DMI image information recognition based on improved YOLOv4 with focal loss

In the ATP function test project of train control system, the image information recognition of driver machine interface (DMI) is an important part of realizing test automation, because of the reason the real-time detection of DMI image information is very important. In the process of realizing DMI image information recognition based on machine vision, aiming at the problem of low generalization of the model caused by small amount of training data and single background of training image in machine vision system, combined with a variety of data enhancement strategies, a data set construction scheme of the system is proposed. In order to solve the problem of low attention to hard samples when using YOLOv4 model for small target detection, based on the idea of focus loss, a weighted fusion loss function of focus loss + CIoU loss is proposed, and the optimal parameters are obtained through experiments. The results show that the average accuracy of this method for DMI image information recognition is 98.76%, which is 2.6% higher than the traditional YOLOv4 detection model, and the single detection time is 76.9ms, which has a certain engineering application value.
Online Learning Methods for Effective Communication Between Teachers and Students

The issues of organization and effectiveness assessment of the educational process in the distance learning system are discussed. The action research methodology is used for analysis of teaching and to assess the quality of communications between teachers and students in the process of online learning. The technical aspects of using distance technologies based on the Moodle platform are considered. The advantages and disadvantage of distance learning are analyzed in the opinion of students and teachers. Recommendations for improving the quality of education in the context of blended and distance learning are presented.
Analysis of Reading Time and the Number of Characters within One Gazing Point

Concomitant with the ubiquity of lightweight electronic terminals, such as smartphones and tablets, e-books have increased in popularity.However, studies have shown that their display method and information-presentation efficiency need improvement.We investigated visual information processing during reading to develop a more efficient display method that presents information in an easier to see fashion than conventional methods.Specifically, we focused on the information-processing ability of the brain within the gazing point during reading to clarify various factors that make a book easier to read such as the minimum display time and line length that can be read accurately and the difference in reading time depending on the font.An experimental method using a "window" was utilized that facilitates changing of the display information in the window by moving the window in conjunction with the eye movement.Using this method, it was found that if the number of characters displayed in the window is more than six and the window display time is 56 ms or more, reading can be performed in the same manner as when the window is not displayed.Thus, here we focused on letter size to investigate the relationship between reading time and font size.Experimental results indicate that among sentences in three font sizes 12, 18, and 24 pt normal reading without any restriction of view is possible when the font size is 18 pt or more.These results suggest that normal reading can be performed even with a very short display time of 56 ms by using characters with a font size of 18 pt or more in Japanese, which is useful for proposing a new display method for e-books.In addition, this experimental method can be applied to languages other than Japanese, and can contribute to the development of an optimum display method for each language.
LiDAR-Interacted and Self-Guided Further Scene Parsing for Road Detection

Road detection is a crucial task for unmanned systems to assess the drivable area and avoid the obstacles and potholes to ensure vehicle safety. Despite the sparse data, LiDAR could provide accurate height and distance clues in all road conditions, which is an incremental complement to enhance the detection limited by camera sensors with rich visual features. In this paper, transformed 2D LiDAR data is used as a confident mask and interacts with visual images to cut down useless visual semantic information captured by cameras. After acquiring detection result from scene parsing network with interacted visual data and LiDAR data, a second parsing stage is adopted which further pushes the proposed model to preserve better information and thus gives more robust road identification. We report the experimental results on KITTI dataset to show its effectiveness and efficiency.
RB01 Technique Used To Applying The Generalized Data

In this digital era, data plays an indispensible role in human lives. In particular, data is more available in different platforms such has social media, healthcare, education etc. Most of the time, the digital data are prone to the cyber attacks. To overcome this challenge, this research work applies the novel technique called RB01. This technique has four main stages. The first stage applies the T-test technique; the second stage applies the odd operations; the third stage applies the even operations; and the last stage applies the quadratic equations. The proposed RB01 technique leverages high security while compared to ChaCha method.
OSNN: An Online Semisupervised Neural Network for Nonstationary Data Streams

Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of target functions and data distributions over time (concept drifts). Most existing work relies solely on labeled data to adapt to concept drifts in classification problems. However, labeling all instances in a potentially life-long data stream is frequently prohibitively expensive, hindering such approaches. Therefore, we propose a novel algorithm to exploit unlabeled instances, which are typically plentiful and easily obtained. The algorithm is an online semisupervised radial basis function neural network (OSNN) with manifold-based training to exploit unlabeled data while tackling concept drifts in classification problems. OSNN employs a novel semisupervised learning vector quantization (SLVQ) to train network centers and learn meaningful data representations that change over time. It uses manifold learning on dynamic graphs to adjust the network weights. Our experiments confirm that OSNN can effectively use unlabeled data to elucidate underlying structures of data streams while its dynamic topology learning provides robustness to concept drifts.
Rethinking Interpretative Arguments

Problems in the interpretation of legal provisions often lead to disagreements in the establishment of statutory meaning in law. When facing real or potential conflicts of opinion, legal practitioners generally provide reasons to support one interpretation over another, resorting to different types of arguments to support their claim. Such arguments were tentatively categorized by MacCormick and Summers (Interpreting statutes. Routledge, New York, 1991) in the form of an interpretative model for statutes. In this paper, I explain how this model overlaps with general principles of text analysis as proposed by Textual Discourse Analysis, a theory of text linguistics, and how statutory interpretation and text analysis are intertwined. I reconsider the notion of interpretative arguments as suggested by the model, arguing how linguistic arguments in fact function as a gateway to all other argument types, and discuss certain analytical possibilities when applied to the interpretation of commitments in hypothetical contexts adopting Multilateral Environmental Agreements as a central point of reflection. It is suggested that a framework could be developed with the purpose of providing a tool for establishing statutory meaning based on textual data from a linguistic perspective, while also considering the role played by interpretative argumentation in this process. It is further proposed how pragma-dialectics could contribute to this endeavor, taking into account, for example, its theoretical affinities with Textual Discourse Analysis and distinct heuristics for the analysis and evaluation of argumentation, after which I reflect on how argumentation structures could be used as descriptive tools of statutory meaning.