Reviewerly/exHarmony huggingface dataset:
The Image of Tourist Attraction in Bali Based on Big Data Analytics and Sentiment Analysis

Tourism development is currently very rapid due to advances in information technology. The growth of various digital tourism platforms makes it easy to obtain tourism information. Almost every time tourists leave traces of their travel experiences on digital platforms, one of them is a review. Until now, the review is only greatly beneficial to prospective tourists in terms of supporting decision-making about the consumption of tourism products and services to minimize the risk of failure. This research analyzed large amounts of reviews, known as big data, to benefit stakeholders. This research aimed to describe the image of Bali tourism that originated from tourist attraction reviews on TripAdvisor. Sentiment analysis was carried out using the Vader Lexicon method to obtain the image clarified using term frequency, bigrams, and topic-based trigrams. The analysis obtained several positive images of tourist attractions in Bali, including beautiful beaches, amazing temples, and friendly locals. Meanwhile, we recommend improvements to several negative images that we found such as dirty tourist objects, plastic waste, and disturbances from hawkers in order to get a better image of Bali tourism.
THE RESULT OF INTRODUCING THE MODEL OF FORMATION OF INFORMATION AND COMMUNICATION COMPETENCE OF PEDAGOGICAL STAFF

This scientific article summarizes the results of a scientific study conducted in the field of training modern, at the same time, professional teaching staff in accordance with the requirements of modernity. Not only the ability to use information and communication tools currently used in society, but also the importance of their use in education and training is noted. Here we focused not only on the results of scientific research to improve the information and communication competence of the teacher but also on the work done to achieve a good result. The training was conducted with the teaching staff divided into two groups, called control and experimental. Where possible, the results were presented in percentage terms for clarity.
Synthesis regarding the general aspects of studying visual programming in pre-university education

The tools and platforms for visual programming have become popular worldwide, and many educational institutions are integrating them into the school curriculum to familiarize students with programming and computational thinking. This contributes to preparing students for the constantly evolving digital world and helps them become active and competent citizens in the technology era. The proposed research aims to conduct an analysis of educational policies regarding the implementation of visual programming, both in schools in the Republic of Moldova and in education systems of other countries such as Romania, the United Kingdom, Estonia, or Spain. Aspects to be analyzed include established competencies, educational levels at which visual programming is introduced, programming languages and platforms used, as well as available resources. The goal of this analysis is to identify the experiences, approaches, and best practices used in the reference countries regarding the integration of visual programming in pre-university education. It also aims to propose concrete suggestions for improving the study of visual programming in the Republic of Moldova. These suggestions include recommendations for the curriculum, educational resources, and teacher training to ensure proper preparation of students in the field of visual programming. Visual programming plays a significant role in developing digital skills and computational thinking in students, providing them with the opportunity to express their creativity, learn essential technological skills, and develop logical and systematic thinking.
Using a Convolutional Neural Network to Classify Subvocalized Letters Collected from the Brain by EEG

Communication is essential to human behavior. Individuals with amyotrophic lateral sclerosis and severe traumatic brain injury encounter enormous challenges in communicating via speaking, writing or typing with others. Such lack of communication severely limits their independence and quality of life. Recent research employing invasive and expensive surgical implants of neuroprosthesis shows some degree of success in reconstructing language or images. However, these procedures are not user-friendly and not available for all affected individuals. To address these limitations, the researcher created a convolutional neural network model to classify individually thought letters – i.e., subvocalization. Using a commercially available simple electroencephalography (EEG) device data were collected from a study participant. All collected data was formatted for computer interpretation and uploaded to a Python notebook. The data was then augmented and preprocessed to make it easier for the neural network model to make predictions. The convolutional neural network model with the best performance had 63.33% classifying 38/60 samples correctly with a statistically significant p value (p&lt;0.00001). Limitations include the reliability of the EEG headset used and time restrictions for collecting data. For future research, better hyperparameters will be investigated using additional data. Finally, the researcher plans to conduct experiments to make real-time predictions with the model once higher accuracies are achieved.
A Study on the Effects of Different Intensity Musical Stimulation on Learners' Attention and Meditation

Having good concentration can help people acquire more knowledge and information in a limited time, therefore, how to use music to enhance the concentration level of online learning to improve learning efficiency has been paid attention to by the majority of educators. Electroencephalogram(EEG) is a comprehensive representation of neuronal activity in the human brain and contains rich physiological neural information. This paper studies the effects of musical stimulation on learners' attention and meditation in online learning environment based on EEG and explores the differences between different types of learners. In this experiment, it’s found that: 1) learners' attention reaches the highest value of 58.3 when the music intensity is 56 dB ± 3 dB, while meditation reaches the best value of 64.8 when the intensity is 56 dB ± 3 dB. 2) the optimal intensity for active learners' attention is 5 dB lower than that of passive learners, but the attention is 8.2 higher than that of passive learners. The mean and maximum of attention for female learners and male learners are approximately the same, but the optimal value of music intensity of attention for male is higher than that of female by 6 dB, indicating that the optimal attention that can be achieved by male and female is not related to gender, but to music intensity. 3) Meanwhile, this paper finds that the meditation of active learners is 4.8 lower than that of passive learners when the music intensity is 56 dB ± 3 dB, which indicates that active learners are more tense and their attention is not easy to keep stable; while on the whole, the meditation of female learners is higher than that of male learners by 4.2 on average, which indicates that women are more relaxed and easier to stay focused in soft music.
DETECTION OF FRAUDULENT WEBSITES USING MACHINE LEARNING

Phishing is an online scam when a perpetrator sends out phoney messages that appear to be from a reliable source. The email will contain a link or file that, when clicked, can steal personal data or infect a machine with malware. In the past, phishing attacks were carried out through massive spam operations that indiscriminately targeted large populations of people. To get as many individuals to open a malicious file or click on a malicious link was the objective. There are numerous methods to find this category of attack. The utilization of machine learning is one strategy. The URLs that the user receives will be entered to the machine learning model, which will then process the input and present the results, indicating whether they are phishing or not. These URLs can be categorized by means of a collection of machine learning (ML) approaches, together with SVM, Neural Networks, Random Forest, Decision Tree, XG boost, etc. The Gradient Boost classifier, Random Forest, Decision Tree classifier, and seven more classifiers are all covered by the suggested method. The proposed method successfully distinguished between legitimate and fraudulent URLs with a Gradient Boost Classifier accuracy of 97.4%
APLIKACIJA ZA DETEKCIJU CEPANJA MREŽE I RASPADA SISTEMA

This paper presents an application for the detection of system split and blackout. The described application was developed as part of the Emergency and Restoration (ER) module of the HORIZON 2020 TRINITY project (H2020-863874 Transmission system enhancement of regioNal borders by means of IntelligenT market technologY). First, a brief overview of the relevant unit of the ER module is given, then a functional description of the application software for detecting system split, as well as a description of the software technologies used. Also, modeling software (database editor) is shown. At the end, an overview of the further development plan is briefly described. The application for detection of system split and blackout was developed on the basis of a topology processor which is a standard component of network applications, specifically variants intended primarily for energization detection, i.e. „SCADA topology“ and „line staining“. The application was developed on the Linux platform, using the C ++ programming language. A relational MySQL database is used as a platform for storing system models and other data. The user interface of the database editor was developed using the Java programming language
A Time Efficient Approach to Data Compression for LZW Algorithm

This paper introduces an efficient data compression algorithm, which performs better then available LZW algorithm. Here, we propose a modified way to update the dictionary for LZW algorithm. First, we identify the different characters in the file and count the number of times each character is repeated. Then we arrange them in descending order based on the repetition count, and delete all remaining unused characters from the dictionary till 255. The proposed algorithm has shown to reduce the compression time by almost six times of the original time taken by conventional Lempel-Ziv-Welch (LZW) algorithm. The Proposed algorithm can compress data faster than conventional LZW in a single level-based operation, so it leaves an opportunity for future research by adding one more level of compression to further improve the compression rate. Its time performance in single level is better than conventional LZW.
A construction and optimization method of a highly available intelligent domain name system

The Domain Name System (DNS) is the infrastructure and crucial resource of the Internet. Self-built DNS systems commonly face management issues such as availability and security. Firstly, we thoroughly analyze the current status and problems of DNS construction, and propose a domain name system construction framework that has high reliability and flexibility. Then, in practical applications, an optimized DNS resolution method is proposed for a multi-operator network environment. Finally, through empirical verification, it has been demonstrated that this method enables users to obtain the optimal CDN service node, thereby improving network service quality.
CenterDA: Center-Aware Unsupervised Domain Adaptation Regularized by Class Diversity for Distracted Driver Recognition

Unsupervised domain adaptation (UDA) suffers from misleading by misclassified samples when aligning the labeled source domain and the unlabeled target domain, which causes unreliable alignment and class overlap. In this paper, we propose a center-aware UDA algorithm for inter-domain alignment, along with class diversity regularization for target intra-domain separability. First, we align the class centers of the source and target domain by weighting the sample confidence to corresponding centers, to denoise the mislabeled samples. Second, we scatter the predictions of the target domain by minimizing the entropy of intra-class predictions and decreasing the similarity of inter-class distributions, which tackles the class overlap. The proposed method not only aligns the two domains but also increases the separability of the target domain, which results in improved target data classification performance. We validate the proposed method on behavior recognition and object recognition datasets, and the experimental results show that the method greatly alleviates the domain shift compared to the latest UDA methods. Code is available at: https://github.com/seizetheday00/Center-Aware-DA.
Novel Preprocessing and Data Augmentation Method for Bearing Fault Diagnosis

To avoid financial and life risks, early diagnose of bearing faults is particularly important, and this has made intelligent fault diagnosis popular. Intelligent fault diagnosis faces challenges such as the need for sufficient data and suitable performance for noisy data. This paper presents a preprocessing and data augmentation method to improve the performance of a few-shot learning method based on these two challenges. In this method first vibration signal is converted to two-dimensional images by means of mel spectrogram, then two image processing methods, namely, histogram equalization and gamma correction are applied. Finally, a data augmentation method based on gamma correction method is used. By using the Case Western Reserve University dataset, it is shown that the proposed method can improve the fault diagnosis process.
Self-supervised Deep Learning Based End-to-End Face Verification Method using Siamese Network

Achieving state-of-the-art results in face verification systems typically hinges on the availability of labeled face training data, a resource that often proves challenging to acquire in substantial quantities. In this research endeavor, we proposed employing Siamese networks for face recognition, eliminating the need for labeled face images. We achieve this by strategically leveraging negative samples alongside nearest neighbor counter-parts, thereby establishing positive and negative pairs through an unsupervised methodology. The architectural framework adopts a VGG encoder, trained as a double branch siamese network. Our primary aim is to circumvent the necessity for labeled face image data, thus proposing the generation of training pairs in an entirely unsupervised manner. Positive training data are selected within a dataset based on their highest cosine similarity scores with a designated anchor, while negative training data are culled in a parallel fashion, though drawn from an alternate dataset. During training, the proposed siamese network conducts binary classification via cross-entropy loss. Subsequently, during the testing phase, we directly extract face verification scores from the network's output layer. Experimental results reveal that the proposed unsupervised system delivers a performance on par with a similar but fully supervised baseline.
How can we organise team building and brainstorming online in times of corona?

&lt;strong&gt; Online or offline? &lt;/strong&gt; Where to begin? In the spring of this year – right as Europe’s “first wave” hit – we started thinking about how to organise our second-ever research retreat. We considered several options.
A Pandemic-sized Effect on Writing and Editing

At the beginning of my time as Editor of JAACAP Connect in 2019, I was excited for the opportunity to expand the tent of writers to Connect by involving those who may have never thought of an academic journal as a forum for their interests, such as those participating in advocacy. The ability to collaborate with the American Academy of Child and Adolescent Psychiatry’s Advocacy Committee to bring in new writers to Connect was the exciting first step in this mission.
Modeling the Water Temperature Profile of Lakes: A Physics Informed Neural Network (PINN) Approach

Water temperature plays an important role in our environment and is applicable to nearly all limnology research, as the temperature of a body of water affects biological activity and growth of organisms such as algae and bacteria. Certain organisms have a preferred temperature range within which they can survive, while others become dormant or die when the water reaches extreme temperatures. The temperature of water also governs the maximum dissolved oxygen concentration of water. Dissolved oxygen in water is important for aquatic life because of its vital role in cellular respiration. Predicting water temperature is also an important factor in determining whether a body of water is acceptable for human use. Warm bodies of water may contain pathogens that can be dangerous to humans.&#x0D; &#x0D; Our research presents a computational method of determining lake water temperature through a novel technique known as physics-informed neural networks (PINNs). PINNs can be used to model and forecast the temperature of water over a specific time period by training a neural network using the data points derived from the discrete form of a partial differential equation and taking into account the boundary conditions. Several factors such as wind, precipitation, and solar energy effects on water temperature were investigated. By using a computer simulation in place of an analytical mathematical model, a tremendous increase in run time speed can be achieved. The results can be used to determine the patterns in water temperature throughout a year, demonstrating the advantages of a PINN over an analytical model.
Motivational Asymmetry in the Prospective and Retrospective Mode of Value: Going from the Third Person Logic of the Non-Involved External Observer to the First-Person Ego-Logic of the Motivated Actor Himself

The paper compares two models of motivational asymmetry, one developed by Rijsman in his earlier work on social comparison (1970, 1974), and one developed by Kahneman and Tversky in their later work on prospect theory (1979). Although both models agree on motivational asymmetry, in the sense of “losses looming larger than gains”, they differ greatly in terms of the motivational value they assign to equality and to extreme outcomes, and differ also in terms of the temporal modality in which they are formulated. To better understand these differences, the first model was carefully reviewed in terms of its analytic construction and it’s application to behavioural economics, and contrasted with the second model, in which there was basically no conceptual analysis of motivational value, but intuitive transformation of the functional relation between outcomes and value on the normative balance itself. It was concluded that when we incorporate Self-involvement of the actor in our concept of prospective outcomes, as is automatically the case in the retrospective mode of value, we actually obtain the same type of motivational asymmetry as obtained for Self-involving outcomes in the past, as proposed in the first model. This was further elaborated in terms of the difference between the illusion of third-person logic (typical for models in normative economics), versus the first-person Ego-logic which is necessary to describe the actual decision making of the so called “motivated” actors themselves.
From Unstructured Data to Insights: Understanding the Role of ChatGPT in the Rising Trend of AI Chatbots in Web Publications

In this research, we delve into the expanding presence of AI chatbots in online media. Leveraging data mining and natural language processing, we dissected unstructured data from web publications to track the trajectory of chatbot popularity, focusing on OpenAI’s ChatGPT. Our findings reveal a pronounced uptrend in AI chatbot integration and user interaction, with ChatGPT emerging as a pivotal figure. This uptick indicates a broader acceptance and integration of AI in digital interactions. The study underscores the pivotal role of ChatGPT in shaping user experience and offers foresight into future AI chatbot applications in the digital media realm.
Designing and Investigating the Effect of Single-Intermediate and 2-Intermediate Layers of Vanilla Neural Networks in Electromagnetic Path Loss Prediction

Impact of two designed architectural compositions of Vanilla Neural Networks (VNNs): the single- intermediate layer of VNN and two (2)- intermediate layers of VNN, in the prediction of signal power loss were examined in this work using measured data from a Long Term Evolution (LTE) network collected from a micro-cell built up environment. The effect of different values of learning rate hyper-parameter were also examined on the VNN architectural models. Early stopping method at the ratio of 75%:15%:15% was adopted during the network training process to avoid over-fitting and Bayesian Regularization (BR) mathematical training algorithm was employed for the training process to ensure good network generalization. Both the intermediate layer neurons for the single- intermediate layer of the VNN and 2-intermediate layers of the VNN were carefully selected to ensure adequate and robust training with excellent predictive performance. Statistical performance metrics, the Correlation Coefficient (R), the Standard Deviation (SD) and Root Mean Squared Error (RMSE) were employed for result analysis of the performances of the two VNN architectural structures while the Mean Squared Error (MSE) and Coefficient of Regression (R) were employed for examining the effect of the various selected values of learning rates on the prediction performances of the two VNN models.The overall experimental results under similar circumstances for training both the VNN models while considering their architectural structures and the effect of learning rates show that for an efficient neural network training and prediction of signal power loss, a well-trained single-intermediate layer of VNN architectural structure with an appropriate neuron numbers gives more efficient and optimal prediction results as the results of the actual output is closest to the desired output showing optimal prediction in comparison with training using 2-intermediate layers of VNN model. The best neural networks training result outputs of a single intermediate layer VNN with 52neurons in the intermediate layer gives R of 0.9728, SD of 1.2758 and RMSE of 1.7435 while best training result of the 2-intermediate layers gives R of 0.9531, SD of 1.5407 and RMSE of 2.2754 on application of [16, 20] neurons which gives the highest prediction results of the considered neuron numbers. Training the single-intermediate layer VNN with small learning rate of 0.002 shows very high R of 0.9903 while with 2-intermediate layers VNN gives R of 0.8810 The training time required for training the single-intermediate layer VNN is considerable low in comparison to training time required to train 2-intermediate layers VNN for best prediction results. These prediction results are of utmost importance in the planning, design and upgrading of wireless network for optimal performance especially when applied in similar environment to the environment of data collection as VNNs results show adaptability and robustness.