Reviewerly/exHarmony huggingface dataset:
Learner Experiences Related to Digital Education Schedules in Light of Empirical Data

In this work, I briefly introduce the theoretical foundations and main aspects of electronic learning, in order to explore and evaluate the instruction schemes implemented as a result of the digital transformation.I base my findings on the analysis of a questionnaire-based survey administered to a sample of students.My research aims at examining the efficiency of the implementation of digital instruction and the accompanying challenges at various levels of the education sphere.I was interested in students' Information and Communications Technology (ICT) preferences and usage habits and whether or not they possess vital digital competences in their own view.The digitally scheduled education programs introduced in response to the COVID-19 pandemic could not fully reach a level required for effective learning according to research findings.The responses revealed that several schools did not implement uniform systems, the educational materials were not suitable for independent learning, the given materials did not have a modular structure, the respective texts did not reflect a system oriented perspective, and it was proven that neither students nor teachers possessed digital literacy skills considered to be currently vital.The most important result of this statistical analysis entails that those students were more successful in the digital home-based education, who spent more time with Information and Communications Technology (ICT) use, regularly played with or used experience-oriented software and relied on smart devices for most of their studies.
TRANSFER LEARNING ON DEEP NEURAL NETWORK: A CASE STUDY ON FOOD-101 FOOD CLASSIFIER

—In the era of the ‘Big-Data’ we hear a lot about machine learning for working on this big data. Machine learning helps us to predict and analyze data with better accuracy and least human intervention. Machine learning is autonomous but susceptible to errors. This is due to biased prediction when previously trained on small data. This leads to chain of errors that can`t be determined easily for long period of time. And when recognized takes lot time to recognize source. There comes the idea of deep learning which achieves the flexibility by using use nested hierarchy of concept to define the world. But deep learning has setback of taking very long time to train data which could be reduced by using transfer learning.
A Framework for Improving Fault Localization Effectiveness Based on Fuzzy Expert System

Many spectrum-based fault localization (SBFL) techniques have been proposed in order to improve debugging efficiency. These SBFL techniques were designed according to different underlying assumptions and then adopt different fault locator functions to evaluate the likelihood of each statement being faulty, called suspiciousness. So far no single SBFL technique claims that it can outperform all of the others under every scenario. That is, the effectiveness of fault localization may vary considerably by just adopting a single SBFL technique. Due to the aforementioned reasons, this study presents a framework for improving fault localization effectiveness by using a Fuzzy Expert System (FES) to integrate different SBFL techniques. In the presented framework, the outputs of several SBFL techniques will be put into the fuzzification and then transferred to fuzzy input sets. After undergoing the fuzzy inference based on the given fuzzy rules, the fuzzy input sets will be transferred to a fuzzy output set. Finally, the fuzzy set will be transferred to a crisp output (called a weighted suspiciousness value). The code statements will then be ranked according to their weighted suspiciousness values. In other words, no additional instrumentations and analyses on the source code and the test suite are necessary for our approach. Our experiment results indicate that our FES-based framework is effective at combining the SBFL techniques from different equivalent groups and achieves high effectiveness on the nine subject programs. It is also noted that in the literature, most of the approaches that combine multiple SBFL techniques are learning-based and they are suitable for long-term projects with sufficient historical data. Since our approach does not reference historical data for model training, it can be applied to new software projects. Thus, the application scenarios of our approach should be complementary to those of the state-of-the-art learning-based approaches.
Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events

Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process.Firstly, we propose to formulate this task as a question answering problem.This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding.Secondly, since the transformerbased language models cannot encode the flow of events by themselves, we propose a Time-Stamped Language Model (TSLM model) to encode event information in LMs architecture by introducing the timestamp encoding.Our model evaluated on the Propara dataset shows improvements on the published stateof-the-art results with a 3.1% increase in F1 score.Moreover, our model yields better results on the location prediction task on the NPN-Cooking dataset.This result indicates that our approach is effective for procedural text understanding in general.
DEVELOPMENT OF SIGIL-BASED ADDITIVES AND ADDICTIVE SUBSTANCES TEACHING CHART FOR JUNIOR HIGH SCHOOL STUDENTS: ALTERNATIVE LEARNING RESOURCES DURING A PANDEMIC

Research and development of sigil-based teaching materials based on facts in yhe field, namely students rarely read textbooks in printed form because the display is less attractive, additive and addictive substances have not been delivered as a whole so thaht astudents miss lessons and lack insight. When in class students tend not to play attention because the learning used by teachers is often convensional and rarely uses media. Teaching materials are needed by students in learning activities that can be used anytime and anywhere so that learning activities can be efficient. The purpose of the study was to describe the results of validation and student responses to sigil-based teaching materials for junior high schools. This type of research is the research and development of R&amp;D (Research and development). The development model in this study uses a model developed by Thiagarajan 4-D. The product that has been developed is then validated by 2 (two) lecturers from the Faculty of Tarbiyah and teacher Training consisting of material experts and media experts as well as one science teacher of SMP Plus Darus Shlolah as user expert. After being validated, the product is then field tested to determine student responses. The results of the materials expert assessment are 98.75%, 91.42% media expert and 92.30% user expert, all three have a high percentage so that the developed digital teaching material has the criteria of “very valid”. The small-scale trial results showed a percentage of 78.4% so they included the criteria of “interesting”. Large-scale trial results the percentage of results is 83.3% so it includes the criteria of “very interesting”.
Spiritual Awakening of the Personality as key to Spiritual Security in the Context of Postmodernism

The article presents a new view on the problem of educating the spiritual security of the personality from the positions of axiological, culturological, civilizational, systemic, and anthropological approaches, on which the research methodology is based. The article describes the basic concepts of research: spirituality, culture, spiritual awakening, spiritual security, and presents the author’s definition of the concept of spiritual security of the personality. The article describes the cultural ideals of coziness in different countries of the world, which allowed us to highlight the features and prove the importance of leading a spiritual way of life, the essence of which is a person’s acquiring of a state of inner peace, coziness, security and which is accompanied by the achievement of pleasure from life. The principles of educating the spiritual security of the personality have been defined. They include the principle of spiritualization, the principle of cooperation, the principle of “live” dialogue based on spiritual and cultural values. The authors have identified the main threats to the spiritual security of the personality and described the ways to overcome them, among which special attention is paid to the importance of reviving the spiritual and cultural educational component, which will be the first step towards educating a positive personality with a human face, the humane, altruistic, creative personality focused on creation, not destruction.
Software Quality Prediction Method Using Fuzzy Logic

: Software Quality is the key priority of today’s marketplace and software development organization to which a system, technique, or factor meets particular requirements and conditions. Soft computing techniques play a vital role in developing software engineering applications. In this paper, we have identified five parameters: Reliability, Efficiency, Usability, Maintainability, and Portability for accessing the level of quality of software. A fuzzy logic-based intelligent identification methodology has been proposed to access the quality of particular software-based on five parameters. The proposed identification scheme takes these five parameters as input and predicts the quality of the software using the fuzzy rule base which is generated using various studies. As this scheme takes five inputs and each input is divided into three regions i.e. ‘Low’, ‘Medium’, ‘High’ and thus a total of 35 i.e. 243 rules has been generated to analyze the software quality. Furthermore, Mamdani fuzzy model has been used as the reference model. To show the effectiveness of the proposed methodology, simulation results have been performed in MATLAB, which shows that the software's quality closely matches with the actual one.
Satellite On-Orbit Anomaly Detection Method Based on a Dynamic Threshold and Causality Pruning

It is difficult for existing deep learning-based satellite on-orbit anomaly detection methods to define the residual-based detection threshold and identify false anomalies.To solve the above problems, this paper proposes both a detection threshold determination and dynamic correction method and a causality-based false anomaly identification and pruning method.We use the GRU (Gated Recurrent Unit) to model and predict the telemetry parameters to obtain the residual vector; determine and dynamically correct the threshold according to the prescribed false positive rate; propose an improved multivariate transfer entropy method to identify the causal relationships between the telemetry parameters; and, based on the causality, determine whether the detected parameter anomalies are false.Experiments show that the precision, recall, and F1-score of the method proposed in this paper are superior to the current typical method, and the false positive rate is significantly reduced, demonstrating the effectiveness of the proposed method.
On the Relations among Object-Oriented Software Metrics:A Network-Based Approach

Recent years have witnessed a great revolution in software applications.The quality of the software is important insofar as it contributes to providing better services for users.Software metrics are mainly used to obtain feedback on the quality of software design.These metrics enable developers to identify the potential weaknesses of their designs.Furthermore, software metrics may have correlations with each other and impact the outcome of each other.This specific case may cause a misleading interpretation of the design, which eventually affects the quality of software and waste the time and effort during the design phase.Therefore, selecting the appropriate metric during this phase should be carefully performed.In this work, we use network science concepts for deeply investigating the relations among object-oriented software metrics.The analysis approach is based on network visualization and network measurements.The dataset of this work was collected from accredited references in the field of Software Engineering.This study involves the main 104 metrics that are basically used during software design.The findings demonstrated interesting facts on the relations among metrics.Besides, this work is considered as a comprehensive analysis and assessment that takes into account different dimensions of the relations among software metrics.We believe that the analysis and the results can make it easy for developers in selecting the appropriate metrics during the design phase.
LOP Game Development to Improve Early Childhood Mathematical-Logic Learning Ability

This study aims to develop a learning media product, namely the LoP Game to improve the Mathematical-Logic Learning Ability of Early Childhood Beginning. This type of research is a Research and Development (R&amp;D) using a model developed by Borg and Gall. There are 3 stages used in this study from 10 stages proposed by Borg and Gall. These stages include: research and information collecting, planning, and develop preliminary form of product. Data validity analysis was performed using the Aiken formula. The data is obtained from the results of the instrument assessment in the form of a questionnaire from material experts and media experts. The feasibility assessment is tested by 3 material experts and 3 media experts. From the results of the validity test, it can be seen that the expert judgment for the media in the LoP game is all valid with all aspects that have been previously described. Thus, it can be said that the LoP game is appropriate and suitable for the development of early childhood abilities, especially mathematical-logic abilities.
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

We investigate the use of natural language to drive the generalization of control policies and introduce the new multi-task environment Messenger with free-form text manuals describing the environment dynamics. Unlike previous work, Messenger does not assume prior knowledge connecting text and state observations $-$ the control policy must simultaneously ground the game manual to entity symbols and dynamics in the environment. We develop a new model, EMMA (Entity Mapper with Multi-modal Attention) which uses an entity-conditioned attention module that allows for selective focus over relevant descriptions in the manual for each entity in the environment. EMMA is end-to-end differentiable and learns a latent grounding of entities and dynamics from text to observations using only environment rewards. EMMA achieves successful zero-shot generalization to unseen games with new dynamics, obtaining a 40% higher win rate compared to multiple baselines. However, win rate on the hardest stage of Messenger remains low (10%), demonstrating the need for additional work in this direction.
Artificial Intelligence in Electric Government; Ethical Challenges and Governance in Jordan

Artificial intelligence (AI) is rapidly being applied to a wide range of fields, including e-government. However, AI also raises several challenges and ethical concerns. This article presents the evolution of research on AI in business over time, highlighting seminal works and leading publication venues. It presents several main developmental trends and the resulting challenges. The field was founded on the assumption that can be accurately described to the degree that a machine can be simulated. There is a controversy about what intelligence and types, the human intelligence possesses, and how to simulate it. Artificial intelligence research is highly specialized. AI is the most prominent ethical issue that arises in e-government implementation. Jordan aims to participate in and lead the global governance of AI Research on AI ethics in e-government lacking.
Neural Sequence Segmentation as Determining the Leftmost Segments

Prior methods to text segmentation are mostly at token level.Despite the adequacy, this nature limits their full potential to capture the long-term dependencies among segments.In this work, we propose a novel framework that incrementally segments natural language sentences at segment level.For every step in segmentation, it recognizes the leftmost segment of the remaining sequence.Implementations involve LSTM-minus technique to construct the phrase representations and recurrent neural networks (RNN) to model the iterations of determining the leftmost segments.We have conducted extensive experiments on syntactic chunking and Chinese part-of-speech (POS) tagging across 3 datasets, demonstrating that our methods have significantly outperformed previous all baselines and achieved new stateof-the-art results.Moreover, qualitative analysis and the study on segmenting long-length sentences verify its effectiveness in modeling long-term dependencies.
Fuzzy inverse logic: part-1. introduction and bases

In almost all deterministic and artificial intelligence techniques, for the solution of the scientific problems such as design and control problems, the output estimations are performed depending on manuplations on the values of input variables.With the other words, lots of different values derived from input parameters are tried in order to obtain desired output(s).Contrary to these conventional estimation methods, this study consists of two parts in which a new artificial intelligence method called fuzzy inverse logic(FIL) is developed to determine or estimate the value of the input parameters that give the targeted problem output.In the first part of this study, after providing a brief overview about the method of classical fuzzy logic(FL), the solution approaches and calculation details about FIL are given.In the second part of the study, fuzzy inverse logic method was used to solve one simple mathematical problem and one simple civil engineering problem.After the validity of the developed method was demonstrated by graphics and tables.some evaluations were made about the method.
Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers

Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches.Previous work focuses on using or probing source linguistic features in the encoder.To date, the way word translation evolves in Transformer layers has not yet been investigated.Naively, one might assume that encoder layers capture source information while decoder layers translate.In this work, we show that this is not quite the case: translation already happens progressively in encoder layers and even in the input embeddings.More surprisingly, we find that some of the lower decoder layers do not actually do that much decoding.We show all of this in terms of a probing approach where we project representations of the layer analyzed to the final trained and frozen classifier level of the Transformer decoder to measure word translation accuracy.Our findings motivate and explain a Transformer configuration change: if translation already happens in the encoder layers, perhaps we can increase the number of encoder layers, while decreasing the number of decoder layers, boosting decoding speed, without loss in translation quality?Our experiments show that this is indeed the case: we can increase speed by up to a factor 2.3 with small gains in translation quality, while an 18-4 deep encoder configuration boosts translation quality by +1.42 BLEU (En-De) at a speed-up of 1.4.
The Problem Field of Professional (Vocational) Education: Innovations and Ways to Improve

The need for systemic changes in vocational education is considered since the sphere of higher education is in dire need of professionals capable of developing their students, mastering new educational technologies and new pedagogical and administrative tools. The authors reviewed the main methodological approaches and substantiated the use of the synergetic approach to finding ways to improve vocational education. As such a path, the authors proposed a synergy of a personal learning environment along with the correct motivation of students. The authors clearly showed the mechanism of the personal learning environment and the place of each of its participants and proposed a possible variant of the task for the student in this environment and the criteria for its assessment.
Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.
Retraction Note: Evaluation of mangrove wetland potential based on convolutional neural network and development of film and television cultural creative industry

Convolutional neural network is a deep learning network, which is more suitable for image and speech processing. Image classification is one of the main research areas of visual artificial intelligence, with many applications, such as scene detection and robot vision enhancement. This paper proposes a new convolutional neural network to evaluate the potential of mangrove wetlands and discusses the development of the network in the film and television cultural and creative industries. Mangrove wetland is a special type of marine wetland centered on mangrove plant communities in tropical and subtropical coasts and intertidal zones of estuaries. They play an important role and play an important role in providing food, protecting wild germplasm resources, and population survival. Over the years, due to population pressure and economic development, the quality of mangrove wetlands has declined, the area has shrunk, and the restoration of mangrove wetlands has become a global hot spot. The development of the film and television industry is an important way to promote China’s cultural innovation. It is essential for strengthening the country’s soft power and improving China’s international image. However, due to the influence of many factors, the development of China’s film and television cultural creation industry is slow, and the development process is lagging behind. In recent years, the country has considered the development level of the cultural industry as an important development strategy, and has been taking various measures to promote the development of the film and television cultural industry. The creative film and television industry should be aware of its responsibility for cultural dissemination, actively learn from foreign industry development experience, and explore a scientific development system that meets China’s development needs and actual national conditions.