Reviewerly/exHarmony huggingface dataset:
Test Intelligence: How Modern Analyses and Visualizations in Teamscale Support Software Testing

Software development, as well as software testing, of a system spawns lots of different artifacts of that many remain unused in further engineering of the system. In practice, we see a possible reason in the lack of knowledge how these artifacts can be processed to profit the most. This is a problem as additional information can improve software testing by making it more effective and efficient. If unused, as a consequence, the testing process is suboptimal, either slow or expensive and might, in a worse case, also result in a bad overall system quality. In this paper, we give an overview about how modern analyses and visualizations enable the access to processed artifacts that support software testing and make benefits clear to the engineering and management stakeholders of a system. We focus on analyses in the tool Teamscale which is an application for software quality evaluation. For each analysis presented, we state which problem it addresses and in which context it can be used. With this summarized presentation, we equip the reader with valuable analyses that are in the right scope for stakeholders in software testing. We present how test data can be used in a continuous feedback process by showcasing the analysis visualizations. Many of them are already successfully employed in the software engineering process of companies from a variety of industry sectors.
Hybrid Schemes for Cultural Algorithms

The distinctive framework of CAs offers the flexibility of their components and operators to be hybridised with other algorithms. The belief space of CAs provides an efficient information transmission mechanism that can potentially guide the search process. This has motivated researchers to develop a variety of hybrid versions of CAs with other meta-heuristic algorithms. This chapter discusses the different hybridisation schemes developed in the literature for CAs.
An open system architecture framework for interoperability

The interoperability of systems is a critical factor for firms to make informed operational and strategic decisions and achieve a competitive edge in the marketplace. As a result, open systems that have a higher level of interoperability with secured and stable operations have significant relevance in today's global economy. Interoperability is accomplished through appropriate system architecture and design. Thus, to achieve the open system interoperability, this paper proposes a framework that looks at the system architecture at various levels of abstraction/implementation and identifies the required attributes at each of these levels. This framework can be used as a reference to analyse and determine interoperability requirements at all levels and prioritise the required aspects of interoperability.
Interactive Fusion Network with Recurrent Attention for Multimodal Aspect-based Sentiment Analysis

The goal of multimodal aspect-based sentiment analysis is to comprehensively utilize data from different modalities (e.g.,, text and image) to identify aspect-specific sentiment polarity. Existing works have proposed many methods for fusing text and image information and achie-ved satisfactory results. However, they fail to filter noise in the image information and ignore the progressive learning process of sentiment features. To solve these problems, we propose an interactive fusion network with recurrent attention. Specifically, we first use two encoders to encode text and image data, respectively. Then we use the attention mechanism to obtain the semantic information of the image at the token level. Next, we employ GRU to filter out the noise in the image and fuse information from different modalities. Finally, we design a decoder with recurrent attention to progressively learn aspect-specific sentiment features for classification. The results on two Twitter datasets show that our method outperforms all baselines.
Reinforcement Learning for Exploring Pedagogical Strategies in Virtual Reality Training

Pedagogical learning among agents have the power to influence the learning of a new learner in several ways. While such pedagogical approaches of student-teacher interactions are applied on popular games with complex environments, we propose the use of pedagogical learning strategies among autonomous agents on a virtual reality (VR) training application in order to automate the existing tutoring system in the application. Creating four reinforcement learning (RL) agents with Q-Learning algorithm, we experiment the agents with different reward functions to choose the best performer as the tutor. We apply the pedagogical strategy with a budget between the tutor and the learner agent resulting in improvements in the learner’s performance.
Developing Testing Frameworks for AI Cameras

It is possible for inexpensive cameras to include AI based features such as face recognition. However, a test framework for such cameras is required that will allow comparison of accuracy under differing conditions. This will then lead to the improvement of training data and algorithms. A simple test framework has been developed and partially evaluated by testing multiple head/face accessories under different lighting conditions. Six participants took part and 300 pictures using a Huskylens were taken under a range of conditions. It was found that the camera could detect faces at a reasonable level of accuracy during ‘middle of the day’ lighting conditions, with or without head accessories. However, it delivers significantly lower detection rate with accessories that cover greater parts of the face and under green light. There is still a need to further investigate this area of study with a higher number of participants in a more controlled environment. It is anticipated that better testing frameworks will lead to better algorithms, training data and specifications for users.
MEANS OF PREPARATION OF A TEACHER FOR THE IMPLEMENTATION OF COMPETENCE-ORIENTED EDUCATION

The change in worldviews of society, the integration of Ukraine into the European educational space determine the latest requirements for the level of teacher training. The country’s education system should prepare students for professional activity in the world, which requires competencies to construct an innovative reality. In the studies of Ukrainian scientists, it was repeatedly pointed out the existence of a contradiction between the requirements of the competence approach to the organization of the educational process and the modern practice of higher pedagogical educational institutions. The purpose of the article is to analyze modern scientific research devoted to the study of ways to prepare a modern teacher for the implementation of competence-oriented education, to systematize and summarize information on the didactic aspects of the effective use of teaching aids, in particular those based on information and computer technologies, in the conditions of the transition to competence-oriented education. One of the main trends in the development of informatization of education is the desire to use educational Internet resources, which provides users with access to domestic and foreign sources of information, provides the opportunity to choose the form and place of study, the level of education. One of the key points of the transition to competence-oriented education is a significant increase in the importance of independent work of students and the use of active and personally-oriented forms of education. Effective organization of this type of student activity involves ensuring the solution of such educational tasks as the development of critical thinking; mastering the techniques of independent mental activity; training technicians to work with large amounts of information and to construct new knowledge; effective interaction with others. Active and competent use of information and communication technologies and digital learning tools is a necessary condition for the further implementation of the competence-oriented education model. How effectively this process will take place depends on the level of ICT competence of both teachers and students of education, high-quality electronic content, the activity of forming network educational communities as an innovative environment, the exchange of the best educational methods and practices.
Neural network parametric identification of an eddy current sensor model using a reservoir computing system

The development of modern automated systems for monitoring, control and management of technological processes is associated with the improvement of methods for identifying and studying the primary means of collecting and processing information. An algorithm for neural network parametric identification using reservoir calculations is proposed. An algorithm for parametric identification orient to conducting a computational experiment on a given nonlinear model, forming training samples based on the results of the experiment, training dynamic and static neural networks and calculating estimates of parameters of a nonlinear model using trained networks according to experimental data. Keywords eddy current sensor, identification, dynamic model, equivalent circuit, static neural network, dynamic neural network, reservoir computing, mathematical model
Determining the Sampling Size with Maintaining the Probability Distribution

Sampling is a fundamental method in data science, which can reduce the dataset size and decrease the computational complexity. A basic sampling requirement is identically distributed sampling, which requires maintaining the probability distribution. Numerous sampling methods are proposed. However, how to estimate the sampling boundary under the constraint of the probability distribution is still unclear. In this paper, we formulate a Probably Approximate Correct (PAC) problem for sampling, which limits the distribution difference in the given error boundary with the given confidence level. We further apply Hoeffding’s inequality to estimate the sampling size by decomposing the joint probability distribution into conditional distributions based on Bayesian networks. In the experiments, we simulate 5 Bayesian datasets with size 1, 000, 000 and give out the sampling size with different error boundaries and confidence levels. When the error boundary is 0.05, and the confidence level is 0.99, at least $$80\%$$ samples could be excluded according to the estimated sampling size.
Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Social Media in many fields and, more specifically, for financial markets and economics. In this paper, we leverage the predictive power of Twitter and Reddit sentiment together with Google Trends indexes and volume to forecast the log returns of ten cryptocurrencies. Specifically, we consider Bitcoin, Ethereum, Tether, BinanceCoin, Litecoin, EnjinCoin, Horizen, Namecoin, Peercoin, and Feathercoin. We evaluate the performance of LASSO-VAR using daily data from January 2018 to January 2022. In a 30 days recursive forecast, we can retrieve the correct direction of the actual series more than 50% of the time. We compare this result with the main benchmarks, and we see a 10% improvement in Mean Directional Accuracy (MDA). The use of sentiment and attention variables as predictors increase significantly the forecast accuracy in terms of MDA but not in terms of Root Mean Squared Errors. We perform a Granger causality test using a post-double LASSO selection for high-dimensional VARs. Results show no “causality” from Social Media sentiment to cryptocurrencies returns
Conditions for the development of cultural and cultural-creative competencies of students in the process of artistic creation (from historical experience to the present day)

The relevance of the studied topic owes to the problem of the humanization and humanitarization of modern education. The goal of the study is to determine the conditions for the development of students’ cultural and cultural-creative competencies in the process of artistic creation proven by the historical experience of Russian art education and upbringing, as well as to assess their practical importance for contemporary schooling from the standpoint of modern art pedagogy. The problem is examined based on the example of students’ creative competitions and educational and exhibition projects (cases) of 1997-2020 focused on the development of students’ cultural and cultural-creative competencies in the process of artistic creation and the perception of exhibitions presenting students’ creative works alongside the works the same theme by authors of different generations. In conclusion, the conditions for the development of students’ cultural and cultural-creative competencies in the process of artistic creation are identified.
Long Short-Term Memory Recurrent Neural Network for Automatic Recognition of Spoken English Digits

Automatic recognition of isolated spoken digits is one of the utmost demanding tasks in Automatic Speech Recognition (ASR) because of its complexity. Recently, deep learning approaches have been deployed for this task, and this has outperformed the traditional machine learning approaches such as ANN. Particularly; deep learning methods such as Long Short-Term Memory (LSTM) has achieved improved performance in ASR. LSTM method is however restricted to processing continuous input streams that are not segmented into sub-sequences. In this work, an enhanced deep learning LSTM RNN model is proposed to resolve this shortcoming. In the proposed model, a Recurrent Neural Network (RNN) is incorporated as a “forget gate” to the memory block to allow resetting of the cell states at the beginning of sub-sequences. The architecture of the proposed model will in turn modify the standard architecture of the LSTM networks, to be able to make effective use of the model parameters while addressing the computational efficiency problems of large networks for large vocabulary speech recognition. The result of the experiment shows an overall accuracy of 99.36% on the well-established public benchmark spoken English digits dataset through a deep supervised learning approach, and the Back Propagation-Through Time (BPTT) method of Adam optimization algorithm for updating error weight parameter.
BIMI specification as another technical approach in the fight against e-mail phishing

Phishing attacks in various forms are currently considered to be the most common and at the same time the most successful form of computer attack, when the attacker tries to obtain sensitive information or convince employees of organizations to perform certain actions, such as installation of malicious software. Areas with a large amount of processed personal data are particularly attractive to attackers. As part of our research supported by the Slovak Research and Development Agency, grant number APVV-20-0457, we focused on phishing attacks carried out in the field of healthcare and thus supplemented the previous experience in the field of education. The following article presents the characteristics of the most common forms of phishing attacks and an indication of the emerging BIMI specification intended for the effective fight against phishing attacks carried out through the exchange of e-mail messages.
Loose to Compact Feature Alignment for Domain Adaptive Object Detection

Recently, great achievements have been made for deep learning based object detection methods. But their performance drops significantly when domain shifts occur.To address this problem, in this work we propose a loose to compact feature alignment method under an unsupervised domain adaptation framework.The entire feature alignment is performed in a divide and conquer manner, so as to distribute the alignment difficulties at two steps.At the first step, we loosen the goal at both image and instance levels. At the image level, a new Mask Guided Foreground Alignment (MGFA) module is proposed to make the alignment focus more on easier foreground regions, leaving the more diverse and more difficult background regions to the second step; at the instance level, we propose a Class-Wise Instance Alignment (CWIA) module with separated domain classifiers for different categories so as to ease the alignment.At the second step, the alignment is performed per pixel and per instance, achieving a more compact and better aligned feature space.We conduct experiments on three different adaptation scenarios, where we achieve comparable results, demonstrating the effectiveness of our proposed method.
Abortion drug products classification using text mining: A case study of PT XYZ

There are several online media closed by the Ministry of Communication and Informatics for selling abortion drug. To keep PT XYZ from being shut down by the Ministry of Communicatison and Information, PT XYZ pays attention for the circulation of this abortion drug by developing the pending system. However, the pending system only waited for the title of the product using specific keywords related to the drug input by the team so that there were still abortion drug products that passed from the system because there were products that used general keywords and sometime seller play with the keywords. Therefore, this study is conducted to build text classification model derived from the existing abortion drug products in PT XYZ which will be used for the detection of future abortion drug. This study uses the CRISP-DM model for the data mining life cycle. This study compares the model with price features and without price features. The best result is Naive Bayes model with price features generates 99,34% of accuracy and 99,34% of f1-score.
Perancangan Sistem Inventaris Barang di Bank Sumsel Babel Cabang Pembantu Lemabang

The performance of an assignment to each job requires much of the role of technology to support the activities to be carried out. The role of technology makes every activity more effective and efficient and treated information more precise and accurate. Bank Sumsel Babel Capem Lemabang is an organization that is financially oriented and this the one of organization that has developed considerable information. But at the asset inventory processing in Bank Sumsel Babel Capem Lemabang still USES Microsoft excel which has no database system. This would require an inventory system to record assets Bank Sumsel Babel Capem lemabang had to tidy up and make the annual report easier. It is hoped that with this inventory system Bank Sumsel Babel Capem Lemabang asset recorders could help asset recorders be more organized and structured. The design of the system will use DFD and erd, and for the database will use mysql, the system will be developed using the waterfall method.
Multi-attention YOLOv5 for Recognition of Abnormal Helmets Wearing in Power Stations

Safety helmets must be worn by people who work in the power plant construction area. In order to automatically monitor whether workers are wearing safety helmets correctly and ensure safe operation in substations, a safety helmet wearing detection algorithm based on improved YOLOv5 named, multi-attention YOLOv5, is proposed in this paper. First, based on the YOLOv5 algorithm, a multi-attention mechanism module is proposed including the spatial attention mechanism and the channel attention mechanism. By redistributing the weights to different features, the semantic features are enhanced by two attention mechanisms, and the recognition ability of the YOLOv5 for small objects is improved. Then a novel loss function is designed, which aims to alleviate the gradient problem and slow convergence problem of the traditional loss function used in YOLOv5. Finally, we propose a dataset collected from various electrical work areas. The results of experiments on the dataset demonstrate the superior potential of our model and it can be used to accurately recognize abnormal behavior of helmets wearing.
Towards Tabular Data Extraction From Richly-Structured Documents Using Supervised and Weakly-Supervised Learning

Tabular information extraction from richly structured documents is a challenging task, due to rich table and document structures. Supervised document table detection approaches include image classification and object localization methods, typically relying on manually annotated data which is often costly to acquire specially on domain specific dataset. Self-supervised learning is quickly closing the gap with supervised methods in computer vision research [1]. This paper investigates the impact of a self-supervised image classifier as the primary backbone in supervised object detection for document table detection. Furthermore, we study an approach for table structure recognition based on the pix2pix Generative Adversarial Networks (GAN) approach [2]. We propose these approaches as the basis of a machine learning pipeline for table detection and structure recognition. Our evaluation results on different publicly available datasets, as well as a domain specific dataset demonstrate the efficacy of the presented approaches towards tabular information extraction pipelines from richly structured documents.
Manipulation for Food Bolus Formation

This paper proposes a robotic manipulation method to reproduce the process of human’s food bolus formation. Decomposing and simplifying complicated mastication behavior of human, we define three primitives for robot manipulation: crushing, mixing, and gathering. We design the structure and motion of robot to execute the primitives, and develop a prototype. Executing these primitives sequentially, we try to reproduce the process of human food bolus formation. In experiment, human and robot masticate food samples and their food bolus images are acquired. By analyzing the images with convolutional neural network, we evaluate whether the robot can reproduce the human’s food bolus formation.
Viewpoint. Rebuilding trust with blockchain technology

Alastair Low-Macrae argues that blockchain technology may hold the key to some of the issues around trust and transparency that the industry is currently grappling with, particularly in the context of BIM.
KLM-Style Defeasibility for Restricted First-Order Logic

In this paper, we extend the KLM approach to defeasible reasoning beyond the propositional setting. We do so by making it applicable to a restricted version of first-order logic. We describe defeasibility for this logic using a set of rationality postulates, provide a suitable and intuitive semantics for it, and present a representation result characterising the semantic description of defeasibility in terms of our postulates. An advantage of our semantics is that it is sufficiently general to be applicable to other restricted versions of first-order logic as well. Based on this theoretical core, we then propose a version of defeasible entailment that is inspired by the well-known notion of Rational Closure as it is defined for defeasible propositional logic and defeasible description logics. We show that this form of defeasible entailment is rational in the sense that it adheres to the full set of rationality postulates.
Immediate Discrimination of Hand Movements Using Recurrent Neural Networks and Quantification of Cognitive Tasks Using Rock-Paper-Scissors Games

Hand movement is an essential motor function in daily life, and maintaining the motor function of the hand is important to prevent a decline in quality of life. Hand exercises are effective in preventing dementia, promoting health, and improving motor functions, and are actively incorporated into rehabilitation. However, the accelerating aging of the population has led to an increase in the number of patients and a shortage of therapists, so we need a system that can a patient evaluates the motor function of his own by oneself. In this paper, we aim to realize a system that enables quantitative evaluation of hand movement functions by games using a motion sensor, Leapmotion, and a recu rrent neural network.