Reviewerly/exHarmony huggingface dataset:
Using Mobile-based Learning to Promote Cognitive Engagement among Secondary School Learners in Kenya

Typically, disruptive technology has dominated all aspects of our lives; economy, communication, workplace environment and governance. However, the Covid-19 pandemic escalated disruptive technology to new heights, affecting areas that were previously limited to its adverse impact. Particularly, Covid-19 restrictions caused closure of schools and institutions in over 190 countries. As a result, a significant number of education institutions have adopted technology-enabled learning in in-person teaching, blended online and online learning. // Despite developing countries adopting technology-enabled learning, there are still some glaring gaps hindering equal access to education. Mainly, there is a significant digital gender divide, with girls facing the risk of losing out in benefiting from technology-enabled learning due to difficulties in using digital technology. Secondary school learners in Kenya lack regular access to digital technology which affects their ability to familiarize themselves with digital devices, platforms and services that propel technology-enabled learning. // This proposal suggests the adoption of mobile-based learning and with content that promotes cognitive engagement to bridge the digital divide in Kenya. Particularly, the proposal identifies the utilization of the Split Attention Principle that requires presentation of information in more than one form to suit the needs of the learners.
No keys to the kingdom required

Nowadays, applications expose administrative endpoints to the Web that can be used for a plethora of security sensitive actions. Typical use cases range from running small snippets of user-provided code for rapid prototyping, administering databases, and running CI/CD pipelines, to managing job scheduling on whole clusters of computing devices. While accessing these applications over the Web make the lives of their users easier, they can be leveraged by attackers to compromise the underlying infrastructure if not properly configured.
An efficient of estimation the load profile analysis of photo voltaic system with different shading of local city

In this paper, we analysis the last technology of photovoltaic (PV) system and the main effective factors of operation in unique efficiency and optimize performance. the first of all we take the general view of physical aspects of sun light and motion of earth with respect of sun position, and analysis of light spectrum and its effect for each regen from spectrum on PV. We illustrated the results of variation of irradiance from deferent sites and show size system for same energy demand. We start by analysis of load-by-load profile working and go through to specify the economic azimuth degree and altitude degree depend on load profile and final put some technical suggestions to minimize the effect of shading and benefit from using a new technology by face shield (half cut cell). These calculations are proposed to make available well originated and equivalent important information so as to allow PV strategies to change quicker obsessed by arenas of application with different shading of local cityin AL-Mausaib Babylon, Iraq. The&lt;em&gt; &lt;/em&gt;ratios of shading conditions (10%, 25%, 50%, 75%) and without shading were used as effectiveness restrictions. The outcomes confirmation that the performance of PV system is extensively reduced and the system can’t control the batteries, if the shading close 75% or additional for single panel.
Selective Ensembles for Consistent Predictions

Recent work has shown that models trained to the same objective, and which achieve similar measures of accuracy on consistent test data, may nonetheless behave very differently on individual predictions. This inconsistency is undesirable in high-stakes contexts, such as medical diagnosis and finance. We show that this inconsistent behavior extends beyond predictions to feature attributions, which may likewise have negative implications for the intelligibility of a model, and one's ability to find recourse for subjects. We then introduce selective ensembles to mitigate such inconsistencies by applying hypothesis testing to the predictions of a set of models trained using randomly-selected starting conditions; importantly, selective ensembles can abstain in cases where a consistent outcome cannot be achieved up to a specified confidence level. We prove that that prediction disagreement between selective ensembles is bounded, and empirically demonstrate that selective ensembles achieve consistent predictions and feature attributions while maintaining low abstention rates. On several benchmark datasets, selective ensembles reach zero inconsistently predicted points, with abstention rates as low 1.5%.
Automatic Negative Content Monitoring and Blocking using NSFWJS for Kids Browser Apps

&lt;p&gt;Technological developments make everything easier to access through their respective devices. However, there are still many parents who are overwhelmed to control the safe links for their children to explore. As a result, many cases of children watching pornographic videos without strict security from the application level. For this reason, the site block feature has been implemented using the NSFWJS library initiated by Infinity Red. The detection system will run according to the API request from the main server which will be sent to the NSFW server. The test results have shown that for each image labeled with a certain ground truth, it is in accordance with the actual detection results. Tests on the full URL have also passed the test by directly blocking the URL if it finds the first image that contains negative content. In conclusion, with the development of the blocking feature using NSFWJS on the backend server, all access attempts that will be made on the child's device will be immediately blocked so that the blocking runs more effectively and optimally. &lt;/p&gt;
Multiclass Optimal Classification Trees with SVM-splits

In this paper we present a novel mathematical optimization-based methodology to construct tree-shaped classification rules for multiclass instances. Our approach consists of building Classification Trees in which, except for the leaf nodes, the labels are temporarily left out and grouped into two classes by means of a SVM separating hyperplane. We provide a Mixed Integer Non Linear Programming formulation for the problem and report the results of an extended battery of computational experiments to assess the performance of our proposal with respect to other benchmarking classification methods.
Technology in the Pandemic: Rupturing the Aura of Higher Education

Framed by Benjamin’s (2008) concepts of aura, technological reproduction, and miniaturization, this paper argues that technology provides students with greater opportunities to take control of their learning in ways that were not possible when education took place largely on-campus, in the higher education classroom. Post pandemic, digital technology has influenced, and will further impact, the way in which we teach and communicate with our students. Educators emerging from pandemic pedagogies need a deeper understanding, and more judicious and effective use of technologies for learning. By understanding and using technology in these ways, educators can help students become more empowered, collaborative, and critically reflective. A lack of readiness for emergency online learning has been replaced with a sense of acceptance that educators must incorporate digital tools into their practice for both educational and communicative purposes. This means that our intellectual and social attachment to the physical classroom as a place for learning and communicating is not as strong as before. Far from being a eulogy for the physical classroom, this paper aims to interrogate the proliferation of technology, and the broad implications for higher education. It argues that the use of technology may help diminish the historical power and aura of higher education and intellectual pursuits, thus undermining the traditional overt and covert control of the institution as an ideological state apparatus. Nevertheless, this shifting educational landscape brings new challenges and questions about the historical aura of higher education, and the concomitant evolution of teacher student relationships and power structures through technology implementation. While maintaining optimism about educational technology, this paper urges educators to consider the broader context of our educational settings, and argues for prioritising critical engagement with technology that aims to utilise its potential for collaboration, empowerment and critically reflective inquiry.
An automated system for the assessment and grading of adolescent delinquency using a machine learning-based soft voting framework

Abstract Adolescent (or juvenile) delinquency is defined as the habitual engagement in unlawful behavior of a minor under the age of majority. According to studies, the likelihood of acquiring a deviant personality increases significantly during adolescence. As a result, identifying deviant youth early and providing proper medical counseling makes perfect sense. Due to the scarcity of qualified clinicians, human appraisal of individual adolescent behavior is subjective and time-consuming. As a result, a machine learning-based intelligent automated system for assessing and grading delinquency levels in teenagers at an early stage must be devised. To solve this problem, a soft voting-based ensemble classification model has been developed that includes a Decision Tree, Multi-layer Perceptron, and Support Vector Machine as base classifiers to accurately classify teenagers into three groups based on severity levels, viz., low, medium, and high. Over the normalized structured behavioral data, the proposed soft voting-based model outperforms all other individual classifiers with 87.50% accuracy, an AUC of 0.94, 0.81 Kappa value, and an F -score of 0.88.
Engagement challenges in a hybrid classroom: Reflections of a higher education tutor

The Covid-19 pandemic required a shift that brought many hybrid classrooms to the doors of tutors. Online video tools were one method utilised in a way to keep classrooms safe due to the need for social distancing. This paper explores the term hybrid learning and how previous studies have implemented online tools within this mode of teaching. The paper will explore some of the common challenges a hybrid learning environment presents when considering student engagement and belonging. A clearer understanding of good practice in using online tools such as chat boxes to encourage student participation is discussed. The challenge of working with students both physically and remote is explored and reflected on.&#x0D; The benefits of a hybrid classroom environment are examined from previous studies and how utilising positive teaching methods can encourage students to stay connected. A holistic reflection on the quick movement towards hybrid is considered. The challenges of hybrid have been considered including how to keep students engaged as lockdowns were eased due to the pandemic.
A Bidirectional DNN Partition Mechanism for Efficient Pipeline Parallel Training in Cloud

Abstract Recently, deep neural networks (DNNs) have shown great promise in many fields while their parameter sizes are rapidly expanding. To break through the computation and memory limitation of a single machine, pipeline model parallelism is proposed for large-scale DNN training by fully utilizing the computation and storage power of the distributed cluster. Cloud data centers can also provide sufficient computing, storage and bandwidth resources. However, most existing approaches apply layer-wise partitioning, which is difficult to obtain an even model partition result because of the large computational overhead discrepancy between DNN layers, resulting in degraded efficiency. To tackle this issue, we propose "Bi-Partition", a novel partitioning method based on bidirectional partitioning for forward propagation (FP) and backward propagation (BP), which improves the efficiency of the pipeline model parallelism system. By deliberated designing distinct cut positions for FP and BP of DNN training, workers in the pipeline get nearly equal computational loads, and the balanced pipeline fully utilizes the computing resources. Experiments on various DNN models and datasets validate the efficiency of our mechanism, e.g., the training efficiency achieving up to 1.9x faster than the state-of-the-art method PipeDream.
Comparing Student high and low Reading Performance with Differentiated Digital Reading Materials

The present study investigates eight Grade-4 classes (9- to 12-year-olds, 52.1% girls) who worked on at least 10 lessons using project RegioDiff material. The study focuses on one of these lessons (including nine text passages and corresponding tasks) and on students with low (19 students, percentile &lt;15) and high reading skills (29 students, percentile &gt; 70). While students were working with the material, screencasts were recorded (30h). The construct “task performance” (processing time, response accuracy, and task engagement) was then analysed using the screencasts.&#x0D; The analysis revealed that the two groups differed significantly in the processing time of two tasks, but not in the total time spent on all nine tasks. Significant differences were revealed also for general task engagement. Task engagement was highly correlated with processing time. Participants with higher reading skills spent more time on the tasks and were more engaged than participants with lower reading skills. However, we did not find any differences in terms of answer accuracy. This indicates that task difficulty and student reading skills were well matched.&#x0D; The study also shows how tasks may be adapted or augmented in order to match the learning environment more closely to student learning needs.
How to Design Virtual Video Production for Augmented Student Presentations

e-Learning environments have been developed and used by teachers and learners for decades. However, it is well known that sending, recording or meeting online can have a lack of presence and immersion. Furthermore, the configuration of a studio environment typically depends on physical props and technologies, which can be time consuming and hard to use for teaching purposes where each session may need a different configuration. Virtual Video Production (VVP) is a relatively new technology that builds on advances in extended reality (XR), supported by game engines and computer-controlled camera equipment. Camera data (pan, tilt, zoom, position) can be sent to a virtual camera in the game engine. The scene can be rendered via a green screen or with large LED displays. This provides an immersive presence with virtual 3D objects positioned in the room. Light settings can be mixed into the scene with remote control of LED lights to be in sync with virtual lights. Thus, VVP opens many opportunities for more immersive e-learning experiences. The challenge is how to apply these opportunities that involve syncing of several technical components and layers, designed to be useful within limited course resources. The question in this paper is how VVP can be designed and set up in an easy way for teachers and students to use it for presentations in courses? This paper presents results from a course in Immersive Environments where students first developed mobile Augmented Reality app prototypes and then used VVP to present their final work together with the authors. The authors documented preparation, wrote instructions for students and observed with notes taken during recording sessions. The results show how VVP can be designed and set up for course presentations that goes beyond a plain video recording in a lecture room or at home, but also beyond what was previously possible in a video studio at the university. This includes e.g., technical setup, direction of students, synopsis, concepts and virtual 3D props. Finally, the authors draw conclusions of what challenges remain for future research and suggestions of how to overcome them.
What Previous Research Says About Virtual and Augmented Reality in Higher Education

Virtual reality is a computer-generated environment with scenes and objects that appear to be real, making the user feel they are immersed in their surroundings. It is not a recent technology, nor is its application to education. Despite its popularity, it is not widely used in education because of its limitations including required technologies, the cost and etc. In order to gain a better understanding of how it is used in higher education so far and what the trends regarding courses and teaching methods in which virtual and augmented realities are integrated are, it is thought that it may be helpful to conduct a systematic review of the use of virtual reality in higher education.&#x0D; This paper aims at contributing to an understanding of using virtual reality in higher education in the context of technology enhanced learning. So, it is designed as a systematic literature review with five steps. Those steps are description of research questions, searching for relevant published research, screening of those research, coding of them, and lastly analysing and mapping the data. Based on the data, it can be said that virtual and augmented realities in higher education have been mostly implemented in the medicine courses and simulations have been mostly implemented in virtual and augmented realities in higher education as teaching strategies.
FPGA-based ANFIS linearizer for measurement systems

Nonlinear sensors and digital solutions are used in many data acquisition system designs. As the input/output characteristic of most sensors is nonlinear in nature, obtaining data from a nonlinear sensor by using an optimized device has always been a design challenge. This chapter aims to present a new Adaptive Neuro-Fuzzy Inference System (ANFIS) digital architecture based Field Programmable Gate Array (FPGA) to linearize the sensor’s characteristic.
Comparative Analysis of Deep Learning Approaches for Twitter Text Classification

Abstract—Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly. Sentiment analysis aims to extract opinion automatically from data and classify them as positive and negative. Twitter widely used social media tools, been seen as an important source of information for acquiring people’s attitudes, emotions, views, and feedbacks. Within this context, Twitter sentiment analysis techniques were developed to decide whether textual tweets express a positive or negative opinion. In contrast to lower classification performance of traditional algorithms, deep learning models, including Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), have achieved a significant result in sentiment analysis. Keras is a Deep Learning (DL) framework that provides an embedding layer to produce the vector representation of words present in the document. The objective of this work is to analyze the performance of deep learning models namely Convolutional Neural Network (CNN), Simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), bidirectional Long Short-Term Memory (Bi-LSTM), BERT and RoBERTa for classifying the twitter reviews. From the experiments conducted, it is found that RoBERTa model performs better than CNN and simple RNN for sentiment classification. Keywords—Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Deep Learning, Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach (RoBERTa).
Specific Factors in the Formation of Research Competence of Cadets of the Ministry of Internal Affairs of Russia

At the present stage of the development of society, new, more complex challenges are faced with police officers. Criminals, to realize their criminal intent, use the latest technological achievements, improve the methods and means of committing crimes. More and more offenses are committed in the field of information security, using computer technology and the Internet. In order to adequately respond to the challenges of modernity and keep up with the times, law enforcement agencies must pay attention to the process of training cadets in educational organizations of the Ministry of Internal Affairs of Russia. The learning process in such organizations is mainly built on the transfer of knowledge from the teacher to students. However, knowledge is only an integral part of the competence that, according to the Federal State Educational Standard, should become the result of training. In this article, attention is drawn to a different component of competence, the formation of which is also provided for in the Federal State Educational Standard – Research Competence. The purpose of the article is to determine the specific factors influencing the process of formation of the research competence of cadets of educational organizations of the Ministry of Internal Affairs of Russia, to develop their component composition. In the article, based on the analysis of literature on the issues of research activities of students of higher educational establishments, revealing the features of the factors acting in the process of forming the research competence of cadets. Giving definitions of the concepts “research competence” and “factor”. Considering the factors of formation of research competence of students of higher educational establishments. Concretizing the structure and specificity of the action of factors influencing the process of formation of the research competence of cadets. The novelty of the study is expressed in the development of the component composition of factors that act in the process of training cadets and influence the formation of research competence. As a result of the research, it is concluded that the influence of the considered factors on the formation of research competence and professional development of cadets is of a dual nature and can be both positive and negative, depending on the created conditions.