Reviewerly/exHarmony huggingface dataset:
Perceptions and Challenges Regarding Cyberbullying during the Covid-19 Pandemic

The increased use of the Internet and digital communication platforms has facilitated the emergence and development of the phenomenon known in the literature as cyberbullying. This research aims to examine the perception of the interviewees about the phenomenon of cyberbullying in the period marked by the Covid 19 pandemic. In this research the method of sociological survey based on questionnaire was used. The research was attended by 541 people, mostly young people (83.5% of the interviewees are under 30 years old). The findings showed that the interviewees know to a large extent the phenomenon of cyberbullying; respondents believe that this phenomenon spread during the Covid 19 pandemic; half of those surveyed witnessed the phenomenon of cyberbullying, while 44.7% say they know people who have been victims of cyberbullying. The present study analyzes the way in which the interviewees position themselves in relation to different acts specific to cyberbullying. The study also highlights the opinions of the interviewees on effective action strategies to combat cyberbullying.
Why a Logic is not only its Set of Valid Inferences

The main idea that we want to defend in this paper is that the question of what a logic is should be addressed differently when structural properties enter the game. In particular, we want to support the idea according to which it is not enough to identify the set of valid inferences to characterize a logic. In other words, we will argue that two logical theories could identify the same set of validities (e.g. its logical truths and valid inferences), but not be the same logic.
Threshold based Support Vector Machine Learning Algorithm for Sequential Patterns

Now a days the pattern recognition is the major challenge in the field of data mining. The researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., Here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. Here the proposed research work uses sequential pattern mining approach using classification technique of Threshold based Support Vector Machine learning (T-SVM) algorithm. The pattern mining is to give the variable according to the user’s interest by statistical model. Here this proposed research work is used to analysis the gene sequence datasets. Further, the T-SVM technique is used to classify the dataset based on sequential pattern mining approach. Especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. Because this makes deeper understanding about sequential input data and classify the result by providing threshold values. Therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, False Positive rate, True Positive rate and it also reduces operating time. This proposed model is performed in MATLAB in the adaptation of 2018a.
A Central Limit Theorem for Predictive Distributions

Let S be a Borel subset of a Polish space and F the set of bounded Borel functions f:S→R. Let an(·)=P(Xn+1∈·∣X1,…,Xn) be the n-th predictive distribution corresponding to a sequence (Xn) of S-valued random variables. If (Xn) is conditionally identically distributed, there is a random probability measure μ on S such that ∫fdan⟶a.s.∫fdμ for all f∈F. Define Dn(f)=dn∫fdan−∫fdμ for all f∈F, where dn&gt;0 is a constant. In this note, it is shown that, under some conditions on (Xn) and with a suitable choice of dn, the finite dimensional distributions of the process Dn=Dn(f):f∈F stably converge to a Gaussian kernel with a known covariance structure. In addition, Eφ(Dn(f))∣X1,…,Xn converges in probability for all f∈F and φ∈Cb(R).
Research On Human-computer Dialogue Based On Improved Seq2seq Model

With the constant maturity of deep learning technology, human-computer dialogue has become a research hotspot in natural language processing. People in academia and industry are very concerned about it. The extensive use of artificial intelligence and deep learning technology in the human-machine dialogue system and the deep neural network modeling for text semantics are of great significance in promoting human-computer dialogue technologies and the application of human-computer dialogue to serve humanity better. Based on the above background, this paper focuses on the research of the human-computer dialogue system based on the improved seq2seq model, using the pre-trained Bert improved model as the codec modeling, and addressing the lack of Q&A data sets, the imbalance of category distribution, and the robustness of the model. These problems can be solved by adding disturbance structure adversarial sample training.
ANALISIS REGRESI COUNT DATA UNTUK PEMODELAN JUMLAH KASUS PENYAKIT TUBERKULOSIS DI KABUPATEN BANYUMAS

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Banyumas Regency is one of the districts with quite high Tuberculosis cases in Central Java. This study aims to analyze the factors that affect the number of tuberculosis cases in Banyumas Regency using regression analysis of count data. Poisson regression is the simplest count data regression model that has the assumption of equidispersion, that is, the mean value equal to the variance. However, in its application, these assumption is often not fulfilled, for example, there are cases of overdispersion (variance value is greater than the mean). In this study, to overcome the case of overdispersion, an approach was used using Generalized Poisson Regression (GPR) and negative binomial regression. The results showed that the data on the number of tuberculosis cases in Banyumas Regency in 2019 was overdispersion. The data modeling of the number of tuberculosis cases in Banyumas Regency with the negative binomial regression model is better than the GPR model. Meanwhile, the only predictor variable that affects the number of tuberculosis cases in Banyumas Regency is the sex ratio of productive age (15-49 years).
Web Application based Image Geolocation Analysis to Detect Human Trafficking

Migrant smuggling is a dangerous phenomenon threatening international peace and security. This global concern for many countries is mainly due to the high use of multimedia devices and social media. There are great demands to specify an in depth gathering of information about a suspect’s mobile device while investigating cases of human trafficking and migrant smuggling. The primary purpose of this study is to help resolve the human trafficking problem that currently faces Sudan. In this paper we cover the steps used to geolocate images captured from suspects and victims’ mobile devices cameras. The obtained images are then analyzed using our specialized developed web application. This latter is built using open-source tools such as the Laravel framework and a Google Maps API which is considered as the main component of the web application. The results of the developed web application on a real case in Sudan has proven its usefulness in easing and speeding up the digital investigation process
Analysis and Design of Prototype Application of Caregivers’ Supportive System Phase 1 Alzheimer’s Based on a Design Thinking Process

There are 7 million Alzheimer’s patients in Thailand, and 10% of people of 65 years of age or more are in the early stages of this pernicious disease. Alzheimer’s reduces the quality of daily life in several ways, as well as having a negative impact on the work performance of the patient’s family. Alzheimer's’ patients cannot receive proper care at home, despite the good intentions and efforts of family members, but this is the only option available to many families due to the cost of care in an external setting. The tendency for Alzheimer's patients to wander away from home and become lost is even more problematic for their families and this tendency is estimated to worsen as time goes by. Therefore, the purpose of this research is to alleviate the concern of families by preventing their loved ones who are suffering from Alzheimer's from wandering away from home and becoming lost by designing a prototype system that can track and monitor their movements.Design thinking was used in this research as a creative problem-solving approach to produce an application with the core functions of tracking and monitoring the movement of Alzheimer's patients. The application’s accuracy was validated by a scenario test, the results of which were discussed based on user feedback.
Cultivating Polyculturalism in Higher Education: Reflective Approach

Abstract. The paper considers study areas for formation of the poly-/ multicultural personality of the future teacher who is ready to deliver high-class philological study and instructional value in the area of higher education. That is seen possible with application of reflexive approach as methodology for the educational process, accompanied as well by axiological, culturological, communicative, and environmental approaches for fostering high cultural and linguistic values with a future teacher of foreign languages in a poly- / multicultural classroom. Establishing, or fostering, or cultivating poly- / multiculturalism in higher education for foreign language teachers is inextricably linked with the processes of comprehension, awareness, and the formation of such teachers’ own personal values to knowledge and culture, their own activities, especially with the process of reflection (reflexion). In this regard, reflective thinking comes to the fore. Next, both formation and development of such reflective thinking become important tasks at all levels of (higher) education in general and instruction and acquisition of foreign languages, in particular. At the same time, it is critical to note that reflective thinking is at the foundation of applying coaching oriented technologies as a pool of highly efficient tools to achieve the set goals of the coach (classroom teacher, mentor, supervisor, foreign language instructor, trainer, or expert) and a coachee, student in a poly- / multicultural classroom. It is also recommended under the research to distinguish between the adjective terms reflexive’ and reflective’. To be more precise, reflective’ under the study results means natural / characteristic of something which reflects, or redirects back to the source’ while reflexive’ stipulates referring back to the subject, or having an object equal to the subject’. The latter is of prime value for the subject matter of the research inasmuch connects to coaching-based portfolio of methodology and ideology, largely speaking.
An Empirical Study Based on the Impact of Smart Sensor System on Rural Relative Poverty

Solving the problem of rural poverty is a difficult problem for the country to enter a well-off society in an all-round way. Therefore, this paper conducts an experimental analysis based on the impact of smart sensor systems on the relative poverty in rural areas. This article is aimed at studying the related factors of rural poverty and improving relative poverty in rural areas. In this regard, this article proposes an intelligent processing function based on smart sensors. Multiple sensors work together to process relatively complex things. Then, through the experimental analysis of GH efficiency, the data collected in the experimental area is used as the data. Combining the Lorentz curve and Gini coefficient analysis to determine the influencing factors of rural relative poverty. This article also selected 10 areas for experimentation. The experimental results show that the proportion of middle school education from 2014 to 2020 is between 24.3% and 34%, and the number of poor people has also declined, indicating that education level is a factor affecting rural poverty. Therefore, based on the intelligent sensor system, the factors of relative poverty in rural areas can be found, and related measures can be analyzed. By implementing the rural poverty alleviation strategy, the relative poverty situation in rural areas can be effectively improved.
Automatic Face Shape Classification Via Facial Landmark Measurements

This paper tackles the sensitive subject of face shape identification via near neutral-pose 2D images of human subjects. The possibility of extending to 3D facial models is also proposed, and would alleviate the need for the neutral stance. Accurate face shape classification serves as a vital building block of any hairstyle and eye-wear recommender system. Our approach is based on extracting relevant facial landmark measurements and passing them through a naive Bayes classifier unit in order to yield the final decision. The literature on this subject is particularly scarce owing to the very subjective nature of human face shape classification. We wish to contribute a robust and automatic system that performs this task and highlight future development directions on this matter.
Mini-Batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Al-Quran is the primary text of Muslims’ religion and practise. Millions of Muslims around the world use al-Quran as their reference guide, and so knowledge can be obtained from it by Muslims and Islamic scholars in general. Al-Quran has been reinterpreted to various languages in the world, for example, English and has been written by several translators. Each translator has ideas, comments and statements to translate the verses from which he has obtained (Tafseer). Therefore, this paper tries to cluster the translation of the Tafseer using text clustering. Text clustering is the text mining method that needs to be clustered in the same section of related documents. The study adapted (mini-batch k-means and k-means) algorithms of clustering techniques to explain and to define the link between keywords known as features or concepts for Al-Baqarah chapter of 286 verses. For this dataset, data preprocessing and extraction of features using Term Frequency-Inverse Document Frequency (TF-IDF) and Principal Component Analysis (PCA) applied. Results showed that two/three-dimensional clustering plotting assigning seven cluster categories (k = 7) for the Tafseer. The implementation time of the mini-batch k-means algorithm (0.05485s) outperformed the time of the k-means algorithm (0.23334s). Finally, the features ‘god’, ‘people’, and ‘believe’ was the most frequent features.
Introduction to Pandas

In the previous chapter, you learned many Matplotlib techniques. You will now learn how to use another library that is common in data science and data visualization.
Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition

There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.
Sentiment Analysis on Twitter Hashtag Datasets

Abstract: Sentiment Analysis has improvement in online shopping platforms, scientific surveys from political polls, business intelligence, etc. In this we trying to analyse the twitter posts about Hashtag like #MakeinIndia using Machine Learning approach. By doing opinion mining in a specific area, it is possible to identify the effect of area information in sentiment analysis. We put forth a feature vector for classifying the tweets as positive, negative and neutral. After that applied machine learning algorithms namely: MaxEnt and SVM. We utilised Unigram, Bigram and Trigram Features to generate a set of features to train a linear MaxEnt and SVM classifiers. In the end we have measured the performance of classifier in terms of overall accuracy. Keywords: Sentiment analysis, support vector machine, maximum entropy, N-gram, Machine Learning
Facial Emotion Characterization and Detection using Fourier Transform and Machine Learning

We present a Fourier-based machine learning technique that characterizes and detects facial emotions. The main challenging task in the development of machine learning (ML) models for classifying facial emotions is the detection of accurate emotional features from a set of training samples, and the generation of feature vectors for constructing a meaningful feature space and building ML models. In this paper, we hypothesis that the emotional features are hidden in the frequency domain; hence, they can be captured by leveraging the frequency domain and masking techniques. We also make use of the conjecture that a facial emotions are convoluted with the normal facial features and the other emotional features; however, they carry linearly separable spatial frequencies (we call computational emotional frequencies). Hence, we propose a technique by leveraging fast Fourier transform (FFT) and rectangular narrow-band frequency kernels, and the widely used Yale-Faces image dataset. We test the hypothesis using the performance scores of the random forest (RF) and the artificial neural network (ANN) classifiers as the measures to validate the effectiveness of the captured emotional frequencies. Our finding is that the computational emotional frequencies discovered by the proposed approach provides meaningful emotional features that help RF and ANN achieve a high precision scores above 93%, on average.
Few-Shot Out-of-Domain Transfer Learning of Natural Language  Explanations

Recently, there has been an increasing interest in models that generate natural language explanations (NLEs) for their decisions. However, training a model to provide NLEs requires the acquisition of task-specific NLEs, which is time- and resource-consuming. A potential solution is the out-of-domain transfer of NLEs from a domain with a large number of NLEs to a domain with scarce NLEs but potentially a large number of labels, via few-shot transfer learning. In this work, we introduce three vanilla approaches for few-shot transfer learning of NLEs for the case of few NLEs but abundant labels, along with an adaptation of an existing vanilla fine-tuning approach. We transfer explainability from the natural language inference domain, where a large dataset of human-written NLEs exists (e-SNLI), to the domains of (1) hard cases of pronoun resolution, where we introduce a small dataset of NLEs on top of the WinoGrande dataset (small-e-WinoGrande), and (2) commonsense validation (ComVE). Our results demonstrate that the transfer of NLEs outperforms the single-task methods, and establish the best strategies out of the four identified training regimes. We also investigate the scalability of the best methods, both in terms of training data and model size.
Quantum absentminded driver problem revisited

Abstract The aim of the paper is to study the problem of absentminded driver in the quantum domain. In the classical case, it is a well-known example of a decision problem with imperfect recall that exhibits lack of equivalence between mixed and behavioral strategies. The optimal payoff outcome is significantly lower than the maximum payoff appearing in the game. This raises the question whether a quantum approach to the problem can increase the strategic position of the decision maker. The results that we present in the paper clearly reveal the benefits from playing the absentminded problem with the aid of quantum objects. Through appropriately chosen initial quantum state, the unitary strategies enable the decision maker to obtain the maximum possible payoff. At the same time, our scheme comes down to the classical problem with a suitable restriction of unitary strategies.
Comparative Analysis of Brute Force and Boyer Moore Algorithms in Word Suggestion Search

The development of information that continues to develop causes an explosion of information which certainly has a very complex impact on information storage management. This also impacts on companies that have several data that continues to grow every day. Therefore, there is a needs to have a search engine algorithm that can do a search system quickly with the development of information that continues to increase every day. Search engine applications or search engines in a computer system make it easy for users to find a variety of information. To facilitate its use, search engines add search features or better known as word suggestion, which in designing this application requires string matching algorithms that can be used to solve these problems. Many strings matching algorithms are available and therefore, the need for an analysis of the search algorithm to be able to help determine which search system is appropriate for use in word suggestion search. The result comparing brute force and boyer moore algorithm, it was found that as much as 79.05% showed that the Boyer Moore algorithm has a better time efficiency compared to the Brute Force.
Representation and Display of Digital Images of Cultural Heritage: A Semantic Enrichment Approach

Digital images of cultural heritage (CH) contain rich semantic information. However, today’s semantic representations of CH images fail to fully reveal the content entities and context within these vital surrogates. This paper draws on the fields of image research and digital humanities to propose a systematic methodology and a technical route for semantic enrichment of CH digital images. This new methodology systematically applies a series of procedures including: semantic annotation, entity-based enrichment, establishing internal relations, event-centric enrichment, defining hierarchy relations between properties text annotation, and finally, named entity recognition in order to ultimately provide fine-grained contextual semantic content disclosure. The feasibility and advantages of the proposed semantic enrichment methods for semantic representation are demonstrated via a visual display platform for digital images of CH built to represent the Wutai Mountain Map, a typical Dunhuang mural. This study proves that semantic enrichment offers a promising new model for exposing content at a fine-grained level, and establishing a rich semantic network centered on the content of digital images of CH.