Reviewerly/exHarmony huggingface dataset:
Application of metamorphic testing on UAV path planning software

Both the performance and reliability evaluation processes of the unmanned aerial vehicle path planning software rely on the determination of the correctness of the execution results of unmanned aerial vehicle path planning software. However, this task is hindered due to the testing oracle problem. In this paper, a framework is designed to overcome the oracle problem and verify the correctness of path planning software based on the grid searching algorithms. In this framework, a metamorphic testing-based method is proposed, and three operations-based metamorphic relations are proposed and proved towards the target software. While analysis of the software is conducted, the version with manually injected faults as well as the officially released version are both dealt with. It is shown that in the experimental results the injected faults can be effectively revealed by using the methods proposed in this paper. Besides, through the evaluation of different types of metamorphic relations, we find that the composed metamorphic relations have stronger fault detection capability compared to the individual ones.
INFER: Distilling knowledge from human-generated rules with uncertainty for STINs

As a long-time wish, researchers always want to find a way to fuse human knowledge directly into machine models that can fulfill intelligent tasks. Existing researches attempted to approach this goal by manually labeling data or writing simple rules without considering the human's inherent ability to estimate uncertainty. These approaches cannot take full advantage of human abilities and hence make the overall system inefficient. In this paper, we propose a novel approach INFER that can distill knowledge from humans in the form of rules with uncertainty. Firstly, we propose a new paradigm of providing human intelligence by expressing human knowledge as uncertain rules. After obtaining the set of rules, we design a rule aggregation model and a classification model, these two models are jointly trained in a knowledge distillation framework with the uncertainty knowledge. We further improve the distillation process with a curriculum learning-based training method. Through these, we can directly extract knowledge from inaccurate human knowledge. Empirical results on four different tasks demonstrate our proposed INFER approach can significantly improve model performance. Furthermore, the proposed uncertainty can provide more information and be effective in refining rules.
BASF starts supercomputer

BASF has started up a new supercomputer at its headquarters in Ludwigshafen, Germany. The system operates at 3.00 petaflops of computing power compared with the 1.75 petaflops of the firm’s existing supercomputer, which has been in operation since 2017. Built by Hewlett Packard Enterprise with AMD processors, the new system employs a warm water–cooling process that absorbs heat directly from the computer, significantly reducing energy costs compared with standard cooling methods. Like the computer it replaces, the new system is named Quriosity and is the largest supercomputer used in industrial chemical research.
FEATURES OF DE&amp;I: UNBIASED STRATEGIES IN THE NEW WORLD OF WORK

The article discuss the importance of implementing unbiased strategies in the workplace to promote diversity, equity, and inclusion. The article highlight the relevance of the topic in the context of the new world of work, where companies face increasing pressure to create fair and inclusive workplaces that attract and retain top talent. The article discuss the challenges associated with implementing unbiased strategies, including unconscious bias and resistance to change, and provide solutions to overcome these challenges, such as education, measurement, and cultural change. The article emphasize the benefits of implementing unbiased strategies in the workplace, including the creation of a positive and inclusive corporate culture, improved innovation and creativity, and a stronger employer brand. The article also emphasize the importance of leadership commitment in creating a culture of fairness and inclusivity. The research method used in the article is based on a literature review and expert opinion. The article draw on research studies and expert insights to support the importance of unbiased strategies in promoting diversity, equity, and inclusion in the workplace. The practical value of the article lies in its insights and recommendations for organizations seeking to create more inclusive and diverse workplaces. The article provide practical solutions for implementing unbiased strategies, such as creating diverse hiring panels, using objective evaluation criteria, and providing diversity and inclusion training. The article also highlight the benefits of implementing unbiased strategies, such as improved productivity, employee engagement, and employer brand reputation.
COLLABORATIVE LEARNING IN A PERSONALIZED MODEL OF PROFESSIONAL DEVELOPMENT OF FOREIGN LANGUAGE TEACHERS

The modern system of teacher professional development is increasingly focused on the personification and practical orientation of the educational process for the implementation of additional professional educational programs. The project of a personalized model of teachers’ advanced training implemented in St. Petersburg aims to provide the maximum conditions for the continuous professional growth of teachers. The results of monitoring the professional deficits of teachers are taken into account. The article discusses theoretical approaches and technological support to organize collaborative learning in the framework of the implementation of a personalized model for foreign language teacher professional development. The author studies the possibilities of collaborative learning while implementing the personalized model for foreign language teacher professional development and analyses the practical forms for its implementation. The research is based on the combination of system-activity, student-centered and participatory approaches. The combined use of these approaches enabled to identify the system and activity characteristics of collaborative learning, to design practical models for its implementation in the system of professional development for foreign language teachers. The author developed the educational trackers for the implementation of collaborative learning in a personalized model for foreign language teacher professional development, including the methods of pitching and facilitated discussion; a roadmap for the implementation of collaborative learning; a module-by-module technology to implement the designed educational trackers.
FUTURE SPECIALISTS’ VALUE ATTITUDE TO CONTINUING PROFESSIONAL SELF-DEVELOPMENT AS THE PEDAGOGICAL CONCEPT

In today's ever-changing world, special requirements are placed on university graduates. A graduate must be not only a highly educated person, able to easily integrate into the surrounding social environment, but also a person capable of continuing professional self-development. Therefore, the forming of the value attitude of future specialists to continuing professional self-development is one of the priorities of education. In pedagogy, the content of the concept for "value attitude" has been studied in sufficient the concept of "value attitude to continuing professional self-development" remains understudied. The purpose of the article is to define the concept of "value attitude of future specialists to continuing professional self-development" in pedagogy, where this quality is considered as one of the educational tasks. The author studied and analyzed the concepts of "attitude", "value", "value attitude", "continuity", "professional self-development", "continuing professional self-development", "value attitude to continuing professional self-development", "future specialist", determined the generic and specific features of these concepts. On the basis of the selected features, a definition of the concept "value attitude of future specialists to continuing professional self-development" is given.
Edge intelligence case study on Medical Internet of Things security

Medical Internet of Things (MIoT) systems produce many data sensing in the world. Consequently, there is a demand of scientific research in this field. Edge intelligence fit in this trend properly as one of the developing cutting-edge technology. A systematic approach had been applied on the medical/health informatics edge-intelligence devices survey. The observing and recording events that occur in the process of the research had been satisfactorily carried out. The analyzing of the case study data was carried out and eventually concluded with some results based on the investigation. Furthermore, a solution is proposed for the kind of medical edge intelligence device data cyber security problem solving. Edge intelligence is defined as the devices available at the edge layer that have some limited amount of computing resource which can be utilized and incorporated with machine learning or artificial intelligence (AI) algorithms to perform RT (real time) data analytics (IGI). Learning more in role of edge intelligence to leverage MIoT assisted system. This survey on edge intelligence had been carried out on MIoT, robotics, Raspberry Pi, autonomous vehicles (AVs), and so on as edge terminal–edge intelligence devices and the challenges they are facing. In particular, AI applications in edge intelligence devices have caused big data security threats. AI faces more challenges in edge intelligence computing. In this survey, through some case studies, some advantages and disadvantages had been analyzed. MIoT edge intelligence device challenges on big data security issues is discussed.
Effective Machine Learning Garbage Data Filtering Algorithm for SNS Big Data Processing

Social network services (SNS) are used more often today, which results in more SNS data being generated. Furthermore, greater emphasis is being placed on extracting various sorts of information through the collection, processing, and analysis of massive volumes of SNS data. Although big data processing can extract a lot of information from SNS data, it takes a long time and a lot of resources. As a result, gaining insights from SNS data necessitates a significant investment of time and money. In this section, we propose a data filtering approach for removing unnecessary SNS data from the data stream. To improve filtering accuracy, the suggested method employs Random Forest, Decision Tree, and XGBoost. Research shows that the suggested algorithm filters the experimental keywords by more than 70%.
Offline Signature Verification Using Image Processing

A person’s signature is merely a handwritten sign that closely resembles his/her name, frequently stylized and distinctive, and that expresses the person’s identity, intent, and consent. Two types of verifications are present. They are online signature verification and offline signature verification. Generally, Offline Signature verification is less efficient and slower process compare to online verification when come to the situation having larger number of documents and files to verify with in less time. Over the years, many researchers have developed so many methods for signature verifications to help the people or organizations to find whether the signature of a particular person is forged or genuine. To overcome this problems; In this paper we introduced a simple method to improve the verification of the signature in Image Processing using Convolution Neural Networks(CNN). Signature Verification it is used to authenticate various kinds of documents, including cheques, draughts, certificates, approvals, letters, and other legal ones, such verification is crucial for preventing document forgery and falsification. Previously, to verify a signature, it was manually checked against copies of real signatures. This straightforward approach might not be sufficient given that forgery and signature fraud techniques are becoming more sophisticated as a result of improving technology.
New Arabic Root Extraction Algorithm

This research presents a new algorithm for Arabic root extraction, which aims to improve the accuracy of Arabic Natural Language Processing Algorithms by addressing the weaknesses and errors of existing algorithms.The proposed algorithm utilizes a database, that includes a collection of roots, patterns, and affixes, to generate potential derivation roots of a word without eliminating affixes initially.By matching the derived word with patterns to identify potential roots, the proposed algorithm avoids the inaccuracies caused by eliminating affixes based on expectation methods.The study conducted a comparison of the proposed algorithm with three commonly used Arabic root extraction algorithms.The evaluation process is performed on three corpora.Resultsshowed that the proposed algorithm achieved an average accuracy rate of 96%, which is significantly higher than the others.
Application of android voice assistant based on parallel storage in multimedia English teaching platform

In recent years, supercomputing technology has become more and more important in scientific research and economic construction, and its application fields have become more and more extensive. Supercomputers and data storage are entering the E-class era, the computing power of supercomputer systems is rapidly increasing, and the amount of data is also growing rapidly. The contradictory relationship between parallel storage and computing has become more and more obvious, which poses a major challenge to the processing capabilities of supercomputer systems. Android speech recognition technology has great theoretical value and broad application prospects. In recent years, it has received widespread attention and achieved great success. The research of artificial neural networks (especially deep neural networks) has greatly improved the accuracy and speed of speech recognition, and has shifted speech recognition technology from laboratories to commercial systems, gradually affecting and improving people's work and life. The network teaching platform based on computer technology is not only a tool that can help teachers share knowledge, but also a tool that encourages students to learn independently, emotional stimulation tools, and a rich teaching environment. These tools have been organically integrated into the classroom environment and become traditional teaching methods have brought fundamental changes. The multimedia English course combines a variety of functions, such as text, graphics, images, sound, animation, and video. It has multimedia presentations, personalized teaching methods, diversified teaching methods and autonomous learning environments.
Effectiveness of parallel computer data and video English course based on neural network

With the expansion of the scale of deep learning network and the rapid increase of the number of network training parameters, the network training time is getting longer and longer. In deep learning, convolutional neural network reduces the number of some parameters through weight distribution, but the problems such as large number of parameters and long network training time still exist. This paper implements convolutional neural network in distributed environment and proposes parallel computer data and time-based scheduling strategy to optimize distributed convolutional neural network. This paper also studies the effectiveness of video English curriculum. It is found that with the development of network technology, information technology is more and more widely used in the education industry, and students can realize distance autonomous learning. At present, the network teaching platform is diversified, with the functions of knowledge point learning, online examination and so on. Aiming at the problems existing in the video English course, the project response theory is introduced into the design and development of the platform system. Taking college users as the research object, this paper constructs a data model of College Students' learning behavior, provides all-round services for college students, and improves the effectiveness of video English teaching. At the same time, based on the project response theory, this paper analyzes the current situation of College Students’ autonomous learning, so as to improve the efficiency of College Students' autonomous learning. Based on neural network, this paper makes an in-depth study on parallel computer data and video English course, hoping to bring some help to the improvement of College Students' English learning.
The status quo of Indian data repositories indexed in re3data registry

Purpose The purpose of this study was to understand the landscape of Indian research data repositories (RDRs) indexed in the re3data.org. The study analysed the metadata elements of Indian RDRs to identify their disciplinary orientations, typology, standards adopted, foreign collaborations, etc. The study ascertained the current status of the Indian RDRs by visiting their respective websites and tried to identify and map the exact disciplinary orientation of each RDR. Design/methodology/approach The study used “content analysis” of the metadata elements extracted from re3data.org along with the information analysis of the respective websites of the registered RDRs. Findings The study identified that only 80% of the Indian RDRs listed by the re3data.org is currently active. Most of the Indian RDRs are hosted by the central and state governments and are almost equally distributed among Life Sciences, Natural Sciences and Social Sciences domains. The data provided by the re3data.org for the Indian RDRs are not complete and up-to-date. Practical implications The findings indicate the presence of a good number of inactive RDRs in the re3data.org. The study suggests using a revised version of the DFG subject classification scheme or considering a standard classification scheme for subject indexing. Originality/value To the best of the authors’ knowledge, this study is the first of its kind that critically analysed the metadata values extracted and moved further to identify the current status of Indian RDRs.
Factors of Blockchain Adoption for FinTech Sector: An Interpretive Structural Modelling Approach

Blockchain Technology (BT) is rapidly becoming one of the most promising emerging economy innovations. Financial Technology (FinTech) has been disrupted by blockchain, and its market size is growing by the day. Payments are closely related to banking, and blockchain has become very famous in the banking industry. This study aims to analyse the factors influencing behavioural intention to adopt blockchain in FinTech. Total 13 factors were extracted from the literature, and later relations among these variables were analysed using Interpretive Structural Modelling (ISM). The study's conceptual model was built and validated by academic experts working in blockchain. Later, MICMAC analysis was performed to study these variables' driving and dependence power. Blockchain has various challenges as well as opportunities but due to its advantages its implementation is recommended for FinTech. As per our results, the implementation of blockchain in FinTech is required as it promotes data privacy and traceability and involves more trust than traditional means.
A Novel Framework for Ancient Text Translation Using Artificial Intelligence

Ancient script has been a repository of knowledge, culture and civilization history. In order to have a greater access to the valuable information present in the ancient scripts, an appropriate translation system needs to be developed keeping complexity and very less knowledge of the script available in consideration. In this study, a translation and prediction system has been implemented using Artificial Intelligence. The training has been developed using Sunda-Dataset and self-generated dataset, whereas the translation from ancient script viz. Sundanese script to English text is done using two layers Recurrent Neural Network. The technique used is compared with an existing translator called IM Translator. The results shows that the BLEU score is increased by 8% in comparison to IM Translator further WER is decreased by 10% in contrast to IM Translator. Furthermore, the N-Gram analysis results indicate 3% to 4% increase in 100% contrast value.
Speakers are more cooperative and less individual when interacting in larger group sizes

Cooperation, acoustically signaled through vocal convergence, is facilitated when group members are more similar. Excessive vocal convergence may, however, weaken individual recognizability. This study aimed to explore whether constraints to convergence can arise in circumstances where interlocutors need to enhance their vocal individuality. Therefore, we tested the effects of group size (3 and 5 interactants) on vocal convergence and individualization in a social communication scenario in which individual recognition by voice is at stake.In an interactive game, players had to recognize each other through their voices while solving a cooperative task online. The vocal similarity was quantified through similarities in speaker i-vectors obtained through probabilistic linear discriminant analysis (PLDA). Speaker recognition performance was measured through the system Equal Error Rate (EER).Vocal similarity between-speakers increased with a larger group size which indicates a higher cooperative vocal behavior. At the same time, there was an increase in EER for the same speakers between the smaller and the larger group size, meaning a decrease in overall recognition performance.The decrease in vocal individualization in the larger group size suggests that ingroup cooperation and social cohesion conveyed through acoustic convergence have priority over individualization in larger groups of unacquainted speakers.
A Novel Sentimental Analysis for Response to Natural Disaster on Twitter Data

The response to a natural disaster ultimately depends on credible and real-time information regarding impacted people and areas. Nowadays, social media platforms such as Twitter have emerged as the primary and fastest means of disseminating information. Due to the massive, imprecise, and redundant information on Twitter, efficient automatic sentiment analysis (SA) plays a crucial role in enhancing disaster response. This paper proposes a novel methodology to efficiently perform SA of Twitter data during a natural disaster. The tweets during a natural calamity are biased toward the negative polarity, producing imbalanced data. The proposed methodology has reduced the misclassification of minority class samples through the adaptive synthetic sampling technique. A binary modified equilibrium optimizer has been used to remove irrelevant and redundant features. The k-nearest neighbor has been used for sentiment classification with the optimized value of k. The nine datasets on natural disasters have been used for evaluation. The performance of the proposed methodology has been validated using the Friedman mean rank test against nine state-of-the-art techniques, including two optimized, one transfer learning, one deep learning, two ensemble learning, and three baseline classifiers. The results show the significance of the proposed methodology through the average improvement of 6.9%, 13.3%, 20.2%, and 18% for accuracy, precision, recall, and F1-score, respectively, as compared to nine state-of-the-art techniques.