Reviewerly/exHarmony huggingface dataset:
STEM Professional Development of Teachers in Under-Resourced Contexts

Numerous studies have shown that the success of STEM education at the implementation stage largely depends on teacher preparedness.Within this conversation, positive results of STEM teacher preparation programmed appear to be primarily based on outcomes achieved in STEM-supported classrooms and entirely in resource-rich education settings, where access to resources is assumed mainly as given.STEM education can be costly to implement because it often necessitates a well-equipped infrastructure and access to numerous resources.Given the need to 1) foster higher-order thinking skills, educational literacy, and digital responsibility in 21st-century education, and 2) the scarcity of integrated STEM intervention-based research in these under-resourced settings, we argue that supporting teachers through well-designed professional development activities to prepare teachers to implement this approach within such settings is compelling.Therefore, this paper focuses on identifying STEM professional development strategies that may help teachers implement STEM despite their minimal access to resources.
Directive Speech Acts and Educational Values in the Dialogue of the Rentang Kisah Novel

This study aims to examine the directive speech acts and moral value education discovered in the dialogue of the Rentang Kisah novel by employing a descriptive qualitative method.The novel serves as the subject of study, while the objects are the directive speech acts and the moral value education contained in the dialogue.The sentences within the dialogue function as the data source for this study.The data collecting approach involved listening and note-taking.The extralinguistic equivalent approach was applied to analyze the data.Dialogue analysis of the Rentang Kisah book by Gita Savitri revealed five distinct types of directive speech acts in three fields.There were three forms of command speech acts, two in health and one in education.Three distinct forms of speech acts of invitation were discovered, one in health and two in the religious sector.There were ten request speech acts, six in health, two in education, and the rest were in religion.There were nine speech acts of advice, five in education, one in health, and three others in the field of religion.Regarding critical speech acts, 32 forms were discovered, 16 in education, three in health, and 13 in the religious field.These data indicate that the novel has 57 directive speech acts.Moreover, both good and bad moral value education was also acquired.The good values included caring for parents, surrendering to God, working hard, loving, and believing in God.Conversely, the negative values covered badmouthing parents and stubbornness.
Core Competitive Strategies in Sport Product Market

With the market share of various large sports brands almost stable, Lulu lemon squeezed into the market with its unique business strategy and grew rapidly.From a small shop next to the yoga studio to more than five hundred branches today, Lulu lemon has come a long way.This paper explains the background and success factors of Lulu lemon, and queries the strategy adjustment of it in recent years.Further analysis of this case provides a reference for other companies to develop strategies in a changing and evolving competitive environment.The paper is supported by theories of economics and marketing, through the horizontal comparison of the same type of sports brands, showing the significance of niche brand grasping segmentation and a conclusion of unique promotion methods and the dissemination of company ideas can have a positive impact on pricing power.However, this analysis is more suitable for niche brands with unique products, and has little reference value for mass products.And in terms of utility, the ideas and methods proposed in this paper are more specific, so the required application conditions are more stringent than the general strategy.For the corresponding enterprises, this paper can play a better reference role.
Research on Platform Economic Trust Crisis Management and Governance Mechanism under Blockchain Technology

The platform economy has injected infinite vitality and vigor into the economic development after COVID-19, which has developed rapidly and solved the problem of flexible employment at present.This paper discusses the platform economic operation mechanism under blockchain technology, especially the credit problems highlighted in its operation, combs the governance mechanism and framework, and puts forward constructive governance ideas.In this study, blockchain is positioned as a tool, not a single technology, which endows the "chain connection" of public chain, alliance chain and private chain of the platform, as well as the authority and source control of regulatory nodes to ensure the chain based on the authenticity and reliability of the data on the blockchain, the blockchain technology is used to empower the platform, so as to realize the cross-border integrated development of the platform economy under the blockchain in the intelligent era.
Brazilian Datasets for Teaching Portuguese

In Brazil -a plurilingual country with more than two hundred million Portuguese speakers -sociolinguistic diversity is a cultural heritage that, although documented in some linguistic data collections, lacks documentation via nationally defined parameters for the constitution of memory collections.Collections of this heterogeneous heritage can be explored in teaching (inter)actions in the context of mother language and non-mother language, in other discursive actions related to translation, interpretation, subtitling, dubbing, as well as in other areas of knowledge.Aware of the potential of this heritage, six researchers from ANPOLL's Sociolinguistics WG 178 designed a project for a national digital repository for cataloging and/or bringing together existing Brazilian collections and those to be built: the Platform for Brazilian Linguistic Diversity.This chapter aims to provide the reader with information about Portuguese speaking and writing data collections and this project, that is being developed by Brazilian Linguistics Association (ABRALIN), and then to contribute to a work with Portuguese that promotes attention and respect for linguistic and sociocultural diversity, for the ways of life of the population in Brazil.
Dynamic Analysis and Modeling of DNN-Based Visual Servoing Systems

The integration of deep learning and control techniques has created robotic systems capable of implementing visual servoing and navigating autonomously in unknown environments. However, analyzing the effect that timing interactions between controllers and deep neural networks have, has received little attention in the literature. In this paper we describe a novel model that includes the effects that detection loss and inference latency have on the controller of a visual servoing system. To test our model we created a target tracking system consisting of a video camera mounted on a moving platform that tracks objects using deep neural networks.
Study of Impact of Information Technology On the Organizational Performance of Moroccan Public Higher Education Institutions

The objective of this paper is to study the relationships between the different variables of the research model using simple regression analyses. To this end, the main research question is: To what extent do contingency factors impact organizational performance in Moroccan higher education institutions? Thus, in order to answer this question, we have mobilized both the literature related to the computerization factor and to organizational performance. As for the hypothesis test carried out on a sample of 82 observations, i.e. a return rate of 51.25, the results were quite different. In this regard, the results obtained through the questionnaire allowed us to note that the relationship between computerization is explained up to 71.6% knowing that the goodness of fit is accepted (R2 = 51.3%), and, therefore, the computerization factor positively impacts the organizational performance of Moroccan higher education institutions.
An Investigation into How Smartphones Can Be Secured Against MiTM Attacks: Financial Sector

MiTM attack aims to violate data in transmission through the air medium in a wireless network; MITM exploits compromise data confidentiality and integrity and are conceivably the most productive types of cyberattacks utilised today. The increasing use of personal devices like smartphones connecting to the internet via Wi-Fi has made wireless attacks on users more crucial. The cyber adversary becomes a “middleman” between two targets to intercept private communication, decrypt traffic, and exploit valuable information like bank details and credit cards. The new WPA3 protocol security features such as 256-bit encryption, OWE (Opportunistic Wireless Encryption), Simultaneous Authentication of Equals (SAE), and disallowing outdated legacy protocols provides risk mitigation against attacks. However, vulnerabilities in WPA3 have been reported whereby a device can be downgraded from WPA3 to WPA2, which opens the system up for DoS and MiTM attacks. This research investigates Wi-Fi-based exploits against the ecosystem of smartphones in the financial sector. Aircrack-ng and Ettercap are open-source tools accessible through the Kali Linux framework. These tools are utilised to demonstrate simulated DoS and MiTM attacks to explain the reported WPA3 vulnerabilities.
Estimation of Frictional Force Using the Thermal Images of Target Surface During Stroking

Abstract We propose a method for estimating the frictional force between a contacted surface and the human touch using thermal video images captured using an infrared thermographic camera. Because this method can estimate force remotely, its application to various situations, in which the measurement is difficult to obtain using conventional contact-based methods, is expected. Furthermore, thermal images have the advantage of measuring physical quantities directly related to frictional force. As a result of machine learning using the measured data from multiple subjects and materials, we succeeded in estimating the frictional force with a high accuracy from the information of the temperature change on the surface. In addition, we account for both the frictional and direct heat transferred between the finger and object affecting the temperature change; therefore, we attempted to improve the accuracy by extracting only frictional heat. Consequently, our method succeeded in improving the accuracy.
Q-Learning Scheduler for Multi Task Learning Through the use of Histogram of Task Uncertainty

Simultaneous training of a multi-task learning (MTL) network on different domains or tasks is not always straightforward.It could lead to inferior performance or generalization compared to the corresponding single-task networks.An effective training scheduling method is deemed necessary to maximize the benefits of multitask learning.Traditional schedulers follow a heuristic or prefixed strategy, ignoring the relation of the tasks, their sample complexities, and the state of the emergent shared features.We proposed a deep Q-Learning Scheduler (QLS) that monitors the state of the tasks and the shared features using a novel histogram of task uncertainty, and through trial-and-error, learns an optimal policy for task scheduling.Extensive experiments on multi-domain and multitask settings with various task difficulty profiles have been conducted, the proposed method is benchmarked against other schedulers, its superior performance has been demonstrated, and results are discussed.
Software security management in critical infrastructures: a systematic literature review

Critical infrastructure (CI) is an integrated set of systems and assets that are essential to ensure the functioning of a nation, including its economy, the public's health and/or safety.Hence, protecting critical infrastructures (CI) is vital because of the potential severe consequences that may emerge at the national level.Many CIs are now controlled by software, and likewise, software is often the major source of many security problems in critical infrastructures.Software security management in CIs has been addressed in the literature and several useful approaches have been provided.Yet, these approaches are fragmented over multiple different studies, often do not explicitly relate to CIs, and a synthesized overview of the state-of-the-art on software security in CIs is lacking.To this end, this article presents the results of a systematic literature review (SLR) that identifies and synthesizes how software security has been addressed in CIs.This study identifies and synthesizes the current approaches applied for security management in critical systems in terms of identified security threats, adopted solutions, CI domains, and evaluation approaches.Hereby 32 primary studies were retrieved from electronic databases to respond to the research questions defined in this study.Based on the outcome of the SLR the reported approaches are discussed, and a roadmap is described for security management in CIs.The results of the SLR identify the current open challenges and pave the way for further research.In addition, practitioners can benefit from the best practices in the security management of CIs.
University-High School Collaborative Education of Manufacturing Engineering Based on Reciprocal Relationships Under New Normal Society

Universities are required to educate in collaboration with high school education. At Hachinohe Institute of Technology, we conducted “problem research” with Hachinohe Institute of Technology Daiichi High School and university students, avoiding the crowds for preventing the COVID-19. Although remote devices are used under New Normal, it was shown that the satisfaction level of high school students can be improved by taking multiple methods such as activating communication between teachers-university students-high school students and utilizing audiovisual media. It is expected that university students will be able to establish their knowledge, and high school students will be motivated by making their image more concrete in the next few years.
A Graph-Based Holistic Recognition of Handwritten Devanagari Words: An Approach Based on Spectral Graph Embedding

In this paper, we make an attempt to give graph representation to handwritten Devanagari words. Each edge in resulted word graph is weighted with its corresponding stretching (or length). For transforming/embedding word graphs into feature vectors their Eigen decomposition aka spectral decomposition is carried out from their weighted associated matrices. Since, these associated matrices contain complementary information we fused their sorted Eigen values at decision level by exploiting multi-class support vector machines (SVM). In order to corroborate the experimental results, we employed Legal amount dataset. From the experimentation, we observe some interesting insights that might be useful for future investigations.
Smart Pedagogy-Focused Design and Development of Innovative Curriculum for Data Visualization

Data visualization enables the users to uncover actionable insights by presenting information and data in various graphical forms, and often as interactive graphs, charts, diagrams and maps. The advanced data visualization systems and well-educated experienced users transform messy, massive data sets into better understanding of existing issues, well-structured discussions of those problems, well-justified data-driven proposals, predictions and strategic decisions. As any applied area, data visualization has its own set of concepts, methods, approaches, algorithms, best practices, technologies and systems that must be learned by the users. However, these days it is important not just to teach a new subject or topic, but also to teach it in a smart way - using smart pedagogy - with the goal to maximize student learning outcomes. This paper presents the up-to-date findings and outcomes of a multi-aspect project at the Department of Computer Science and Information Systems, the Department of Education, Counseling and Leadership, and the InterLabs Research Institute at Bradley University (IL, USA) that is aimed to design, develop and test the innovative data visualization curriculum based on corresponding features of smart pedagogy.
A Cheating Detection System in Online Examinations Based on the Analysis of Eye-Gaze and Head-Pose

The devastating effect of COVID-19 ensued the closing of educational institutes throughout the world and led to a shift in the education mode from offline to online.Online exams became the new way of testing the academic knowledge of a student.The major concern which arises in an online exam is the apparent risk of malpractices emerging due to the remote invigilation.Therefore, cheating in online exams is widespread regardless of the levels of development.In this paper, our main goal is to propose a smart system which can automatically detect cheating in online exam.For this, the students' webcam is used to perceive his/her eye-gaze and head-pose.Based on the analysis of eye-gaze and head-pose, we have followed the students' intension to indulge in cheating.
Evolutionary algorithm for designing artificial neural networks with resource redistribution

The article describes the approach of resource redistribution for training artificial neural networks in the process of evolution of the structure of these networks. The results of solving classification problems with and without the proposed approach are presented. It is shown that in a number of tasks, the proposed method allows achieving better classification accuracy relative to the standard approach when using an equal amount of resources.
Prediction of Gold Price Movement Considering the Number of Infected With the Covid 19

The beginning of the health and economic crisis caused by the appearance of the new Covid-19 virus showed us that, in the perception of investors, gold is still highly valued as a guardian of value.This paper aims to test several models and select the best one for predicting the price of gold on the world market for the next day, in five and ten days, taking into account the number of cases and deaths from the Covid-19 virus.We believe that predictions with Covid-19 parameters give more accurate results than predictions that take only historical gold prices as information.These predictions can help decision-makers whether, at what point, and in what amount, it is best to invest in gold and gold-related financial instruments, relative to the projected price of gold from the model.The paper tests models called Decision tree, K-nearest neighbours, Linear regression model, and Support vector machine based on the information on gold prices and the number of cases and deaths from the Covid-19 virus.It will be seen in the paper that even models with only information on the price of gold give quite reliable predictions, but in unstable times like this, models that take into account the instability factor give more accurate predictions.The research is aiming to determine the optimal amount of information on which the models will "learn" to give the most accurate possible result.This work's data processing and models are done in the Python programming language.
Group Signatures with Designated Traceability over Openers’ Attributes

We propose a group signature scheme with a function of designated traceability; each opener has attributes, and a signer of a group signature can be traced by only the openers whose attributes satisfy the boolean formula designated by the signer. We describe syntax and security definitions of the scheme. Then we give a generic construction of the scheme by employing a ciphertext-policy attribute-based encryption scheme.
Stimulating Elementary Student Higher Order Thinking Skills Through Educational Games in Chromebook

This study focuses on efforts to stimulate Higher Order Thinking Skills (HOT Skills) in elementary students by using Educational Games in Chromebook.The subject is Mathematics, because Mathematics contains many elements of HOT Skills.The students used in the research were 3 rd grade elementary school students in Boyolali Regency with a total of 21 students.This exploration utilizes a qualitative approach, the sort is field research.Information examination procedure utilized is descriptive qualitative analysis.The outcomes showed that the utilization of chromebook included educational games could increase the HOTS of elementary school students, especially in the learning outcomes of 3 rd elementary school students in Mathematics.
Interactive Deep Annotation as DARos: Object Detection Supervision for Efficient Instance Segmentation

We propose DARos (Deep Annotation Region-based by object supervision), a high-quality and generic annotation solution minimizing the cost of human-computer interaction. The two-stage instance segmentation approach is designed combining full-image-based object detection, RoI-based Inside Outside Guidance approach and salient object detection. It automatically identifies the object category. Only two clicks are required for easy cases and three clicks for hard cases to obtain a first segmentation prediction. Furthermore, interactive correction is supported via additional clicks. Extensive experiments demonstrate the accuracy and robustness of DARos on diverse popular benchmarks: PASCAL VOC, GrabCut, Berkeley, MSCOCO and Cityscapes. It outperforms state-of-the-art with a reduced annotation effort by 12%.
ArmSpeech: Armenian Spoken Language Corpus

The Armenian language is an independent branch of the Indo-European language family and the official language of the Republic of Armenia and the Republic of Artsakh. According to various reliable sources, an average of 3 million people in Armenia and 10-12 million people in the Armenian Diaspora use the Armenian language as their native language. The largest communities outside of Armenia are in the United States of America, Canada, the Russian Federation, the Islamic Republic of Iran, the French Republic, the Syrian Arab Republic and the Lebanese Republic. This paper presents the ArmSpeech speech corpus. ArmSpeech is a collection of annotated Armenian speech intended for natural language processing (NLP) technologies research and development. ArmSpeech is designed for speech-to-text and text-to-speech purposes but can be used in other domains also (e.g. language identification). Corpus contains 6206 high-quality audio samples: 11 hours 46 minutes and 26 seconds (11.77 hours) of annotated native Armenian speech of multiple speakers of any age, gender and accent. According to the research results, this is the most extensive Armenian speech corpus in the public domain for speech recognition, speech synthesis and spoken language identification systems.
Information Security Resource Allocation Using Evolutionary Game

Abstract Based on the discussion of related concepts and technical theories, the information security resource allocation influencing factors index system is constructed from four aspects: resources, threat sources, vulnerabilities and security measures. With the further analysis of information security factors and their affecting mechanisms, the basic theoretical framework of information security resource allocation is established based on the evolutionary game. Under this framework, the subject relationship in various situations is analyzed. This research work can conduct a reasonable allocation of resources related to information security.