Reviewerly/exHarmony huggingface dataset:
PEDAGOGICAL CONDITIONS FOR TEACHING BACHELORS OF FOREIGN PHILOLOGY IN THE INFORMATION-EDUCATIONAL ENVIRONMENT AT HEI

The research purpose is to determine, theoretically substantiate and experimentally verify the organizational and pedagogical conditions for teaching Bachelors of Foreign Philology in the information-educational environment at higher educational institutions.To achieve the purpose the following methods have been used: theoretical (a comprehensive analysis of philosophical, psychological, pedagogical and methodological literature; theoretical and critical analysis of official documents of Ministry of Education and Science of Ukraine, analysis of curricula, programs and textbooks, manuals and recommendations; induction, deduction, synthesis, generalization, abstraction and generalization), empirical (direct and indirect observation, diagnostic methods (questionnaires, testing, surveys, interviews), experimental methods (ascertaining, formative and final stages of pedagogical experiment)) and methods of mathematical statistics (ranking, comparison, quantitative and qualitative analysis of experimental results and their statistical processing by the Pearson's criterion χ².The organizational and pedagogical conditions for the future Bachelors of Foreign Philology professional training have been identified, theoretically substantiated and experimentally verified.It has been found out that organizational and pedagogical conditions enable significant improvements in the educational process, create a special mechanism of students' thinking capacity, promoting the accumulation of knowledge and life experience.It has been stated that only a complex implementation of suggested organizational and pedagogical conditions contributes to the successful professional training of the future Bachelors of Foreign Philology.The hypothesis has been confirmed: after the pedagogical experiment the future Bachelors' of Foreign Philology readiness level for professional activity at the formative stage of the experiment in the experimental group is higher than in the control group.
Matching Forensic Composite Sketches with Digital Face Photos: A Bidirectional Local Binary Pattern-Based Approach

Facial sketches are extensively used by investigators in order to identify the suspects involved in criminal activities. The manual method of identifying suspects is slow and complex. To make the process automated, proposed method attempts to map the computer created composite sketches to face photos automatically. This research work focuses on searching for missing and wanted persons who are involved in criminal activities that in turn assist investigative agencies in locating suspects in a timely manner. Proposed method attempts to address the challenge of mapping composite sketch to facial photos using bidirectional local binary pattern (BLBP). In the proposed method, Viola–Jones algorithm is used to detect composite sketch; feature extraction is done using BLBP; classification and recognition are done using two-dimensional convolution neural networks (2D-CNNs). The experimental results show that BLBP and 2D-CNN combined approach achieves recognition accuracy of 90% in comparison with other existing methods (Han et al. in IEEE Trans. Inf. Forensics Secur. 8, 191–204, 2013; Hochreiter et al. in Neural Comput. 9, 1735–1780, 1997; Paritosh et al.: in International Conference on Biometrics, Phuket, Thailand, pp. 251–256, 2015; Roy, H., Bhattacharjee, D.: Adv. Intell. Syst. Comput. 883, 2019).
Chapter 7 Data aggregation and anonymization for mathematical modeling and epidemiological studies

Abstract An important secondary purpose of the Smittestopp development was to provide aggregated data sets describing mobility and social interactions in Norway’s population. The data were to be used to monitor the effect of government regulations and recommendations, provide input to advanced computational models to predict the pandemic’s spread, and provide input to fundamental epidemiology research. In this chapter we describe the challenges and technical solutions of Smittestopp’s data aggregation, as well as preliminary results from the time period when the app was active.We first give a detailed overview of the requirements, specifying the types of data to be collected and the level of spatial and temporal aggregation. We then proceed to describe the concepts for anonymization via :-anonymity and Y-differential privacy (Y-DP ), and the technical solutions for collecting and aggregating data from the database. In particular, we present details of how GPS- and Bluetooth events were mapped to geographical regions and points of interest, and the solutions employed for efficient data retrieval and processing. The preliminary results demonstrate how the recorded GPS- and Bluetooth events match with expected temporal and spatial variations in mobility and social interactions, and indicate the usefulness of the aggregated data as a tool for pandemic monitoring and research. One of the main criticisms of Smittestopp concerns the centralized storage of individuals’ movements, even if such data were used and presented only at an aggregated and anonymized level. In this chapter, we also outline a completely different approach, where the GPS data do not leave the user’s phone but are, instead, pre-processed to a much higher level of privacy before being dispatched to a server-side data aggregation algorithm. This approach, which would make the app significantly less intrusive, is made possible by recent advances in determining close contacts from Bluetooth data, either by a revised Smittestopp algorithm or by means of the Google/Apple Exposure Notification framework.
The Effect of Using Loose Parts Media on Critical Thinking Ability in Children Aged 5-6 Years in Learning in Kindergarten

Learning in early childhood education today should adapt to technological advances or the competency framework of the 21st century.The application of current learning that prioritizes High Order Thinking Skills as the main capital to compete in the 21st Century.Critical thinking in children is a very important ability and is needed so that Children are able to solve problems with a careful and thorough attitude and have different points of view.The current phenomenon in recent years is that asurvey has been conducted Program for International Student Assessment (PISA), the results show that Indonesia occupies 64th position consisting of 72 countries that have been reviewed by the Organization for Economic Cooperation and Development (OECD).Therefore, learning must be able to hone children's critical thinking skills in learning.one way is with the support of learning media, namelymedia loose parts.Media loose parts is a medium which is suitable and readily available in the environment around the child without the cost of expensive.media loose parts come from natural materials or materials that can be recycled.This study uses a quantitative approach to the type of pre-experimental research.The research design used one group pretest-posttest.
A Quantum Annealing Approach for Solving Hard Variants of the Stable Marriage Problem

The Stable Marriage Problem (SMP) describes the problem, of finding a stable matching between two equally sized sets of elements (e.g., males and females) given an ordering of preferences for each element. A matching is stable, when there does not exist any match of a male and female which both prefer each other to their current partner under the matching. Finding such a matching of maximum cardinality, when ties and incomplete preference lists are allowed, is called MAX-SMTI and is an NP-hard variation of the SMP. In this work a Quadratic Unconstrained Binary Optimization (QUBO) formulation for MAX-SMTI is introduced and solved both with D-Wave Systems quantum annealing hardware and by their classical meta-heuristic QBSolv. Both approaches are reviewed against existing state-of-the-art approximation algorithms for MAX-SMTI. Additionally, the proposed QUBO problem can also be used to count stable matchings in SMP instances, which is proven to be a #P-complete problem. The results show, that the proposed (quantum) methods can compete with the classical ones regarding the solution quality and might be a relevant alternative, when quantum hardware scales with respect to the number of qubits and their connectivity.
A Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation

Question Answering (QA) is a Natural Language Processing (NLP) task that can measure language and semantics understanding ability, it requires a system not only to retrieve relevant documents from a large number of articles but also to answer corresponding questions according to documents.However, various language styles and sources of human questions and evidence documents form the different embedding semantic spaces, which may bring some errors to the downstream QA task.To alleviate these problems, we propose a framework for enhancing downstream evidence retrieval by generating evidence, aiming at improving the performance of response generation.Specifically, we take the pre-training language model as a knowledge base, storing documents' information and knowledge into model parameters.With the Child-Tuning approach being designed, the knowledge storage and evidence generation avoid catastrophic forgetting for response generation.Extensive experiments carried out on the multi-documents dataset show that the proposed method can improve the final performance, which demonstrates the effectiveness of the proposed framework.
Canonical Extensions of Conditional Probabilities and Compound Conditionals

In this paper we show that the probability of conjunctions and disjunctions of conditionals in a recently introduced framework of Boolean algebras of conditionals are in full agreement with the corresponding operations of conditionals as defined in the approach developed by two of the authors to conditionals as three-valued objects, with betting-based semantics, and specified as suitable random quantities. We do this by first proving that the canonical extension of a full conditional probability on a finite algebra of events to the corresponding algebra of conditionals is compatible with taking subalgebras of events.
M-BAD: A Multilabel Dataset for Detecting Aggressive Texts and Their Targets

Recently, detection and categorization of undesired (e. g., aggressive, abusive, offensive, hate) content from online platforms has grabbed the attention of researchers because of its detrimental impact on society.Several attempts have been made to mitigate the usage and propagation of such content.However, most past studies were conducted primarily for English, where low-resource languages like Bengali remained out of the focus.Therefore, to facilitate research in this arena, this paper introduces a novel multilabel Bengali dataset (named M-BAD) containing 15650 texts to detect aggressive texts and their targets.Each text of M-BAD went through rigorous two-level annotations.At the primary level, each text is labelled as either aggressive or non-aggressive.In the secondary level, the aggressive texts have been further annotated into five fine-grained target classes: religion, politics, verbal, gender and race.Baseline experiments are carried out with different machine learning (ML), deep learning (DL) and transformer models, where Bangla-BERT acquired the highest weighted f 1 -score in both detection (0.92) and target identification (0.83) tasks.Error analysis of the models exhibits the difficulty to identify contextdependent aggression, and this work argues that further research is required to address these issues.
Online Cultural Comparison between China and the US: A Research on the Connotation of Pepe the Frog Memes

Sad Frog is popular on social media in China as part of Sang culture, but its original name is Pepe the frog and now a culture of hate in the US.Cyberculture and cyberplace provide fertile environment for the reconstruction and spread of memes.By comparing and analyzing the differences in Chinese and American political systems from the perspective of media ecology proposed by Postman, this study finds that memes in the US are politically polarized, while Chinese memes are prone to individual emotional release.There used to be politicalized memes with negative implications, but memes now are dominated by Sang subculture instead of political polarization.
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification

Emotion is the essential attribute of human beings.Perceiving and understanding emotions in a human-like manner is the most central part of developing emotional intelligence.This paper describes the contribution of the LingJing team's method to the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Emotion Classification.The participants are required to predict seven emotions from empathic responses to news or stories that caused harm to individuals, groups, or others.This paper describes the continual pre-training method for the masked language model (MLM) to enhance the DeBERTa pre-trained language model.Several training strategies are designed to further improve the final downstream performance including the data augmentation with the supervised transfer, child-tuning training, and the late fusion method.Extensive experiments on the emotional classification dataset show that the proposed method outperforms other state-of-the-art methods, demonstrating our method's effectiveness.Moreover, our submission ranked Top-1 with all metrics in the evaluation phase for the Emotion Classification task.
Wisconsin Breast Cancer Classification Using Noisy Training Data Augmentation and Multi-Layer Perceptron (Mlp)

Breast cancer is really a public health reality for woman and earlier detection increases the chances of it being cure. Even for specialists, diagnosing breast cancer at an early stage can be challenging because the symptoms are subtle, variable and Computer-Aided Diagnosis systems can be extremely useful. Machine learning techniques are available for decision support system. However, improving the performance of such system is a challenging task. In this study, we have setup a machine learning technique for breast cancer classification. The proposed approach uses noisy training data augmentation through nearest neighbour interpolation with noisy class label. This model was tested on the Wisconsin Breast Cancer (original) dataset and Multi-layer Perceptron (MLP). The 99.56 % maximal accuracy obtained with maximal random noisy class label was equal to 0.112, Fourier feature and one hidden layer of ten neurons is at the state of art, showing that the proposed technique is challenging and deserve to be tested on other dataset.
Building Heuristics and Ensembles for the Travel Salesman Problem

The Travel Salesman Problem (TSP) is one of the most studied optimization problems due to its high difficulty and its practical interest. In some real-life applications of this problem the solution methods must be very efficient to deal with dynamic environments or large problem instances. For this reasons, low time consuming heuristics as priority rules are often used. Even though such a single heuristic may be good to solve many instances, it may not be robust enough to take the best decisions in all situations so, we hypothesise that an ensemble of heuristics could be much better than the best of those heuristic. We view an ensemble as a set of heuristics that collaboratively build a single solution by combining the decisions of each individual heuristic. In this paper, we study the application of single heuristics and ensembles to the TSP. The individual heuristics are evolved by Genetic Programming (GP) and then Genetic Algorithms (GA) are used to build ensembles from a pool of single heuristics. We conducted an experimental study on a set of instances taken from the TSPLIB. The results of this study provided interesting insights about the behaviour of rules and ensembles.
A new effective denoising filter for high density impulse noise reduction

Today, thanks to the rapid development of technology, the importance of digital images is increasing.However, sensor errors that may occur during the acquisition, interruptions in the transmission of images and errors in storage cause noise that degrades data quality.Salt and pepper noise, a common impulse noise, is one of the most well-known types of noise in digital images.This noise negatively affects the detailed analysis of the image.It is very important that pixels affected by noise are restored without loss of image fine details, especially at high level of noise density.Although many filtering algorithms have been proposed to remove noise, the enhancement of images with high noise levels is still complex, not efficient or requires very long runtime.In this paper, we propose an effective denoising filter that can restore the image effectively in terms of quality and speed with less complexity for high density noise level.In the experimental studies, we compare the denoising results of the proposed method with other state-of-the-art methods and the proposed algorithm is quantitatively and visually comparable to these algorithms when the noise intensity is up to 90%.
Hand Gesture Recognition Using Convolutional Neural Networks and Computer Vision

Hand Gesture Recognition is meant for communication of the people who are deaf and dumb. Communicating with others is always a difficult task for them. The sign language becomes a boon to those people who helps in communication with others knowing the sign language. Here, the sign language used is American sign language (ASL) which is widely used globally. Hand Gestures are a bit confusing and difficult for the people who have never learnt this. In this study, the user captures the images of hand gestures using web camera and this model detects the captured image and displays the corresponding symbol. For detecting hand gesture, we use OpenCV. Here, we used the Background Subtraction technique to segment the hand region. Convolutional Neural Networks (CNN) are used for training and to classify the images. For reducing the loss rate and increase in accuracy, we used optimization algorithms. This model achieved a remarkable accuracy of above 96%.
Mordell Elliptic Curve Based Design of Nonlinear Component of Block Cipher

Elliptic curves (ECs) are deemed one of the most solid structures against modern computational attacks because of their small key size and high security. In many well-known cryptosystems, the substitution box (S-box) is used as the only nonlinear portion of a security system. Recently, it has been shown that using dynamic S-boxes rather than static S-boxes increases the security of a cryptosystem. The conferred study also extends the practical application of ECs in designing the nonlinear components of block ciphers in symmetric key cryptography. In this study, instead of the Mordell elliptic curve (MEC) over the prime field, the Galois field has been engaged in constructing the S-boxes, the main nonlinear component of the block ciphers. Also, the proposed scheme uses the coordinates of MEC and the operation of the Galois field to generate a higher number of S-boxes with optimal nonlinearity, which increases the security of cryptosystems. The proposed S-boxes resilience against prominent algebraic and statistical attacks is evaluated to determine its potential to induce confusion and produce acceptable results compared to other schemes. Also, the majority logic criteria (MLC) are used to assess the new S-boxes usage in the image encryption application, and the outcomes indicate that they have significant cryptographic strength.
Virtual Team Collaboration: How the Empathy Tendency Influences User Experience?

The subjective experiences and satisfaction of using technology to collaborate remotely may differ due to the individual differences of personal characteristics. The present study aims to investigate the influence of empathy tendency on user experience. Twelve groups of three participants completed a decision-making task in the virtual environment. The results revealed a significant correlation between personal traits (i.e., empathy and the big five personalities), user experience (i.e., social presence), and satisfaction. The level of cognitive empathy has a positive effect on the feeling of social presence, social immersion, and outcome satisfaction in the virtual environment, while is not associated with media satisfaction. The findings of this study suggest that the cognitive ability of empathy, namely the ability to identify with and understand the views of others may increase one's experience and satisfaction in remote collaboration. This study provides an empirical exploration of team interactions in virtual environments and advances user research by identifying the relationship between user's traits (empathy), user experience, and satisfaction.
On Building Fine-Grained One-Way Functions from Strong Average-Case Hardness

Constructing one-way functions from average-case hardness is a long-standing open problem. A positive result would exclude Pessiland (Impagliazzo '95) and establish a highly desirable win-win situation: either (symmetric) cryptography exists unconditionally, or all $$\mathsf {NP}$$ problems can be solved efficiently on the average. Motivated by the lack of progress on this seemingly very hard question, we initiate the investigation of weaker yet meaningful candidate win-win results of the following type: either there are fine-grained one-way functions (FGOWF), or nontrivial speedups can be obtained for all $$\mathsf {NP} $$ problems on the average. FGOWFs only require a fixed polynomial gap (as opposed to superpolynomial) between the running time of the function and the running time of an inverter. We obtain three main results: Construction. We show that if there is an $$\mathsf {NP} $$ language having a very strong form of average-case hardness, which we call block finding hardness, then FGOWF exist. We provide heuristic support for this very strong average-case hardness notion by showing that it holds for a random language. Then, we study whether weaker (and more natural) forms of average-case hardness could already suffice to obtain FGOWF, and obtain two negative results: Separation I. We provide a strong oracle separation for the implication ( $$\exists $$ exponentially average-case hard language $$\implies $$ $$\exists $$ FGOWF). Separation II. We provide a second strong negative result for an even weaker candidate win-win result. Namely, we rule out a black-box proof for the implication ( $$\exists $$ exponentially average-case hard language whose hardness amplifies optimally through parallel repetitions $$\implies $$ $$\exists $$ FGOWF). This separation forms the core technical contribution of our work.
TEXT SUMMARIZATION USING NLP

This Project represents the work related to Text Summarization. In this paper, we present a framework for summarizing the huge information. The proposed framework depends on highlight extraction from the internet, utilizing both morphological elements and semantic data. Presently, where huge information is available on the internet, it is most important to provide improved ways to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large document of text. There are plenty of text materials available on the internet. So, there is a problem of searching for related documents from the number of documents available and absorbing related information from it. In essence to figure out the previous issues, automatic text summarization is very much necessary. Text Summarization is the process of identifying the most important and meaningful information in an input document or set of related input documents and compressing all the inputs into a shorter version while maintaining its overall objectives.