Reviewerly/exHarmony huggingface dataset:
The Analysis of Student Responses to the Use of Google Classroom in Physics Learning

The purpose of this study was to analyze the responses of students in the use of google classroom in physics learning. This study uses descriptive research with a quantitative approach. The data collection technique is through a questionnaire distributed to students of class XI MIPA odd semester for the 2021/2022 academic year at SMA Muhammadiyah Maumere using google form. Sampling in this study using simple random sampling. There were 20 students who participated in filling out the questionnaire so that all of them were used as samples. The research data were then analyzed descriptively quantitatively with the help of SPSS 25 software and Microsoft Excel to find the average student response to each question, while the percentage used Microsoft Excel. The results showed that on average, students responded to questionnaire questions with very positive criteria with an average of 84%, while an average of 70% with positive criteria, and 0 for negative and very negative criteria. The results of the analysis concluded that the use of google classroom media has a very positive impact on online physics learning.
Analysis of Dependency Networks of Package Managers Used in iOS Development

&lt;p&gt;Using third-party libraries is common practice when writing software. Nevertheless, vulnerabilities can be found in even very popular and well-tested libraries. Once detected, these vulnerabilities are often fixed quickly in the library code. The easiest way to include these fixes in a dependent software application, is to update the used library version. Package managers provide automated solutions to updating library dependencies, which make this process easier. Using a package manager, it is easy to declare as many dependencies as needed. However, library dependencies can have dependencies to other libraries creating a dependency network with several levels of indirections. Assessing vulnerability risks induced by dependency networks is a non-trivial task for developers of software applications.&lt;/p&gt; &lt;p&gt;The library dependency network in the Swift ecosystem encompasses libraries from CocoaPods, Carthage and Swift Package Manager. These three package managers are used when developing, for example, iOS or Mac OS applications in Swift or Objective-C. Recently, we created a library dependency network dataset for the Swift ecosystem. In this article, we explain in full detail how we built this dataset. We provide the first analysis of the library dependency network evolution in the Swift ecosystem and present one example of how the dataset can be used to analyze the spread of known vulnerabilities through the library dependency network. &lt;/p&gt; &lt;p&gt;We found that, although CocoaPods is the package manager with the biggest set of libraries, the difference to other package managers is not as big as expected. The youngest package manager and official package manager for Swift, Swift PM, is becoming more and more popular, resulting in a gradual slow-down of the growth of the other two package managers. &lt;/p&gt; &lt;p&gt;When analyzing direct and transitive dependencies, we found that the mean total number of dependencies is lower in the Swift compared to many other ecosystems. This results in less libraries being affected by vulnerable dependencies through transitive dependencies. We found that only 5.9% of connected libraries had a direct or transitive dependency to a vulnerable library. &lt;/p&gt;
Chatbot Application for Tourism Using Deep Learning

Abstract—Chatbot is broadly known now a days which is intended to expand the communication and connection with human users. Generally, it works by question from the user and response by a software program. The user can enter the question by means of text. When the user enters the query, the bot uses TensorFlow’s tokenizer to assign a unique token to each distinct word and send it to RNN layer. This chatbot will essentially respond to the queries and relevant information on all the tourist places and facilities in Andaman and Nicobar island.
Development of digital economy in Russia in context of global trends

Development of the digital economy has been defined as one of the national priorities of the Russian Federation, because the level of the digital economy in Russia is somewhat behind the global leaders now, when the widespread use of digital technologies is beyond doubt. To achieve the goals in the field of digitalization facing Russian business and government bodies, a scientific and methodological base is needed to take into account the actual theoretical approaches and concepts of the digital economy based on the analysis of the world experience in digitalization, the achievements of leading countries and the problems of Russia identified by the Russian and world experts. Constructing the base is a topical area of scientific research. Development of digital business transformation are of particular importance, since the efficiency of the functioning of enterprises and the results obtained directly determine a whole range of factors of the development of the national economy and social sphere. Theoretical approaches to the concept of the digital economy are generalized and the prospects for its development in Russia are outlined, taking into account the global trends that must be applied to control digitalization processes at the micro and macro levels. There have been used the statistical data, results of scientific researches of the Russian and foreign authors and organizations. The concepts of the digital economy are considered and the evolutionary relationship of their development is revealed; there has been analyzed the level of digital competitiveness of Russia in the context of factors and sub-factors and taken into account the global trends; the trends in the development of the digital economy in Russia have been identified through SWOT analysis; the conceptual scheme of digital transformation has been illustrated, its main elements and the relationship between them have been established.
Exploring the Moderation Effects of Gender Differences and Self-Efficacy in the Adoption of E-Learning Systems

Current and exponential developments concerning information and communication technologies have triggered significant shifts in the education area, resulting in individuals being exposed to the new advancement systems such as e-learning.With regards to this issue, the objective of this study was to investigate adoption of e-learning in higher education among undergraduates accounting students.Perceived usefulness and perceived ease of use are the two independent variables tested in this study.The moderating effect of gender differences and self-efficacy on the adoption of e-learning additionally has been examined in this study to fill the gap.All accounting students in UiTM Tapah were selected to be the respondents of the study.In terms of data collection, questionnaires were distributed through an online platform.The findings revealed that perceived usefulness and perceived ease of use have positive and significant associations with e-learning adoption among accounting students.For the moderating relationship, the outcomes of gender differences and self-efficacy were proved to have no moderating effect on the e-learning adoption.Thus, the findings of this study provide some useful implementations to the academic institutions and governing authorities to design further practical and adaptable e-learning approaches to ensure successful implementation of virtual learning for the upcoming years.
DEEP LEARNING BASED MALAYSIAN COINS RECOGNITION FOR VISUAL IMPAIRED PERSON

Currency recognition has been widely developed using various types of techniques and able to assist people who have a visual impairment. Machine learning is one of the methods implemented where deep learning architecture is one of them. The deep learning approach is reliable and can be used in detection and recognition of objects based on images. As currency recognition has been developed for other currencies, thus in this project, currency recognition using Malaysian coins has been developed by modeling Convolutional Neural Network (CNN) in recognizing coin images. Malaysian coins dataset was developed consist of 2400 images of four classes of coins, 5 sen, 10 sen, 20 sen, and 50 sen. In this study, pretrained CNN which are AlexNet, GoogleNet, and MobileNetV2 were formulated in recognizing such coins. Performance of each trained model was evaluated using confusion matrix and GoogleNet obtained the best performance with 99.2% testing accuracy, 99.2% precision, 99.18% recall, and 99.19% F1 score. From the trained model, it can be further developed and implemented in assisting visually impaired persons by producing a prototype using Raspberry Pi and FPGA before it can be clinically tested on the subject.
OPPORTUNITIES AND VERSATILITY OF ONLINE LEARNING IN HIGHER EDUCATION INSTITUTIONS

The article discusses the solution of learning problems based on statistical studies of transformed learning methods due to the Covid - 19 World Pandemic. Everyone knows that the entire "educational world" (and not only) has switched to the distance learning method. For this, various electronic platforms were used: faсebook, skype, google, other social networks, Zoom, TIMS, etc. Teaching methods, the form and assessment criteria, student activity, their feedback, a different approach to the intensity of student attendance have changed. It is no secret to anyone that the form of education used is still a way out of the current situation, and not the goal of teaching. More than two years have passed, and with confidence you can evaluate the process, draw conclusions, share experience. The importance of distance learning is confirmed by some changes in the legislative part of the States. The Law of Georgia on Higher Education has a whole chapter 474 - "e-learning" and chapter 477 - "distance learning" [1]. Based on the assessment of the statistical information studied by us, conclusions and recommendations for the use of "online learning" as one of the methods are given. Along with the strengths, there are also weaknesses, the overcoming of which would make the educational process more attractive and focused on the quality of the learning outcome.
Convolutional Neural Network-Based Virtual Reality Real-Time Interactive System Design for Unity3D

In this paper, the algorithm of the convolutional neural network is used for in-depth research and analysis of real-time interactivity, and a virtual reality real-time interactive system is designed based on Unity3D. Aiming at the problems of high computation and low efficiency of existing high-precision models, based on the lightweight network model, this paper proposes a real-time semantic segmentation method based on an asymmetric codec. The encoder part of the network adopts a newly designed bottleneck residual module based on depth-separable convolution, null convolution, and decomposition convolution to extract local and contextual information without increasing the computational effort. At the same time, channel rearrangement is introduced in the module to facilitate the interaction of information between channels. The approach proposed in this paper can present the architectural system simply and clearly and is more flexible to match various complex business relationships. The system design technology is based on the Spring MVC framework technology, using visualization technology design and implementation. The combination of the Spring MVC framework and Unity3D achieves the separation of the front and back ends of the system, which makes the system stable, real-time, visible, and efficient. Meanwhile, a newly designed global attention guidance module based on the attention mechanism is introduced at the jump connection between codecs to guide the low-level features of encoder structure and high-level features of decoder structure for better integration and accuracy improvement.
Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation AI Models

For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These foundation models or 'Jacks of All Trades' (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this paper conducts an initial exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, we formulate the Set of Sets to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively 'Masters of All Trades'.
Key Challenges and Proposed Solutions to Design Sentiment Analysis System

Sentiment analysis (SA) is one of the most important tasks in the natural language processing (NLP) field.Many researchers have been trying to build an efficient SA system for many applications such as detection of terrorist activities, customer support management, analyzing customer feedback, market research, competitive research, and many others.Almost all these researches deal with a classification task, and they tried to solve one or two of the challenges that face this task.In this research, a complete SA system was constructed for processing five main challenges in SA and studying the effect of each one on the system.These challenges are the processing of negation, multi-polarity of words, text multi-polarity, semantically ambiguous words (exact meaning), and sarcasm.Some solutions were introduced for these challenges for getting high accuracy.It is the first work for studying the effect of these five challenges collectively with novel solutions to some of them.For aspect-based SA, three different classifiers were used; support vector machine (SVM), maximum entropy (MaxEnt), and long short-term memory (LSTM).The best results for f-measure were 0.833, 0.852, and 0.875 using the LSTM method on Foursquare ABSA, SemEval2014 (laptop and restaurant) datasets, respectively.We induced the most effective negation processing on the SA system from many test scenarios, followed by multi-polarity of words, semantically ambiguous words, text multi-polarity, and sarcasm, respectively.Also, the proposed sarcasm processing technique was evaluated on two annotated corpora with sarcasm, iSarcasm, and Rillof datasets, before applying it on SA.
Peculiarities of Professional Training of Educational Managers in Conditions of Transformation Processes

The article is devoted to the study of theoretical aspects of professional training of educational managers within the requirements of modern transformational society. In the conditions of the prevailing postmodernism new requirements to the manager of education as the specialist and as the person are allocated, according to what the emphasis on the influence of such processes as globalization, informatization, and innovative development on the preparation of modern experts in management is made. Changes in the educational paradigm indicate the need for a specialist with universal abilities, able to quickly adapt to new socio-economic and social changes and effectively perform their professional duties. The analysis of the newest tendencies in pedagogical education proves the growth of requirements to professionally significant qualities of the head of a modern educational institution. Based on a thorough analysis, the features of professional competence of education managers, their professionally important and personal qualities that determine their professional activity are determined. Among the main professionally significant qualities of an education manager are such as strategic thinking, readiness for self-development, ability to make operational decisions, special and general abilities, positive motivation of professional activity, cross-cultural competence, emotional intelligence and cognitive empathy, effective time management. It is established that the effectiveness of management activities of a modern education manager is determined by his ability to properly assess the business environment, set complex but achievable goals, organize staff for their implementation.
PPDMIT: A Lightweight Architecture for Privacy-Preserving Data Aggregation in the Internet of Things

Abstract Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preserving. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been caused a big challenge in this field. To improve this trafe-off efficiency rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages homomorphic paillier encryption, K-means, a one-way hash chain, and the Chinese Remainder Theorem. We have found that the proposed privacy-preserving architecture achieves a more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted 8.096% improvement over LPDA and 6.508% over PPIOT.
Development of CNN-LSTM Hybrid Deep Learning Network for the Joint Detection of Non-Orthogonal Multiple Access Signals in 5G Uplink Receivers

This paper presents a novel approach for signal detection in non-orthogonal multiple access (NOMA) uplink receivers.We propose converting incoming packets into a stream of 2D image-like vectors.Thereby, converting a signal detection problem into a video classification problem.Our approach is true end-to-end learning where no manual feature engineering and/or pre-processing is required.Detection is done blindly in a joint fashion with no explicit channel estimation and equalization steps required.Successive interference cancellation (SIC) is the default method for detecting NOMA packets, but it requires perfect channel estimation to be maintained.Deep learning approaches have shown great promise.However, they suffer from overfitting and/or poor performance.We show how to improve performance by a better training dataset generation procedure and hyperparameter optimization along with the use of CNN as a feature filter.The CNN-LSTM hybrid network has registered a training and testing accuracies of 88.61 and 85.36 which are higher than the state-of-the-art approaches and its symbol error rate (SER) vs signal-to-noise ratio (SNR) performance is higher by about 9dB than the LSTM approach, maximum likelihood and other standard SIC based approaches like the minimum mean square error (MMSE) and least square (LS).This suggests deep learning-based receivers as strong candidates for the upcoming generations of wireless communication systems.
Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation

Abstract Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.
Metalanguages for source document analysis

A detailed analysis of a source document (SD) forms crucial sources for decision making in the core translation process. It contributes to both an improved quality of translation and a better explanation of the translation process. Terminologies, or metalanguages, for SD analysis that have been used in the literature are often mutually inconsistent, which may hinder their practical application as well as smooth communication among various players involved in the translation project. This chapter explains the role and definition of SD analysis process and its components. It describes the scope and procedures of the review-based approach for metalanguage development. The chapter presents the resulting systematic metalanguages of document properties and elements, respectively. It further discusses the effective application of the formulated metalanguages, and presents future avenues for metalanguage development and application.
BayCon: Model-agnostic Bayesian Counterfactual Generator

Generating counterfactuals to discover hypothetical predictive scenarios is the de facto standard for explaining machine learning models and their predictions. However, building a counterfactual explainer that is time-efficient, scalable, and model-agnostic, in addition to being compatible with continuous and categorical attributes, remains an open challenge. To complicate matters even more, ensuring that the contrastive instances are optimised for feature sparsity, remain close to the explained instance, and are not drawn from outside of the data manifold, is far from trivial. To address this gap we propose BayCon: a novel counterfactual generator based on probabilistic feature sampling and Bayesian optimisation. Such an approach can combine multiple objectives by employing a surrogate model to guide the counterfactual search. We demonstrate the advantages of our method through a collection of experiments based on six real-life datasets representing three regression tasks and three classification tasks.