Reviewerly/exHarmony huggingface dataset:
NetAC, An Automatic Classifier of Online Hate Speech Comments

Nowadays in many linguistic and social areas researchers collect the reaction of people to someone’s statements (newspaper articles or social network posts) with the intention of analyzing the speech style. From that analysis different conclusions can be inferred giving rise to a large number of social impact attitudes. However it is not enough to create a huge corpus of texts. It is necessary to process the collected statements and comments and resort to appropriate tools to extract the relevant terms from the texts and analyze their occurrences. This paper is about a statistical framework, NetAC, built in the context of NetLang Project to study prejudice discourse aiming at individual or group discrimination. Given a categorization table the tools included in NetAC search for frequency of occurrence of the keywords in each category and, based on the greatest frequency, propose a classification for each comment and for the overall text. Besides the main classifier, other features will be presented.
ADVANTAGES AND DISADVANTAGES OF ELECTRONIC TEXTBOOK

Objective of the article is to describe the advantages of the Interactive content in Moodle in comparison with the traditional textbook as a means of teaching foreign language for mastering listening skills for Civil Engineering students. Methods . Analysis. Deduction. Induction. Observation. Results . Electronic textbook has a number of advantages over a traditional one: teachers’ time saver, availability of the audiovisual material, greater interest in learning by students. Conclusion . E-learning enhances motivation and effectiveness of educational process due to the interactive technology and edutainment.
A Systematic Review on Automatic Detection of Plasmodium Parasite

Plasmodium parasite is the main cause of malaria which has taken many lives. Some research works have been conducted to detect the Plasmodium parasite automatically. This research aims to identify the development of current research in the area of Plasmodium parasite detection. The research uses a systematic literature review (SLR) approach comprising three stages, namely planning, conducting, and reporting. The search process is based on the keywords which were determined in advance. The selection process involves the inclusion and exclusion criteria. The search yields 45 literatures from five different digital libraries. The identification process finds out that 28 methods are applied and mainly categorizes as machine learning algorithms with performance achievements between 60% and 95%. Overall, the research of Plasmodium parasite detection today has focused on the development with artificial intelligence specifically related to machine and deep learning. These approaches are believed as the most effective approach to detect Plasmodium parasites.
The Role of Analytics and Robo-Advisory in Investors' Financial Decisions and Risk Management

The global financial world has been experiencing increasing complexities in the design and construct of financial instruments, wherein the average retail investors need to be well informed and aware of the risks about the evolution of financial products and financial system. The global financial crisis of 2008-2009 is evident to the fact that traditional methods of financial advisory involving the use of human intermediaries and financial advisors is not an infallible method to determine one's financial decision or risk management. Almost a decade since the crisis, the advancement of financial technology in the form of robo-advisors and the use of data and visual analytics have completely transformed the way the retail investors make their financial decisions and also in the sphere of risk management of these investors. The study qualitatively analyses the use of the use of analytics and robo-advisory in determining retail investors' financial decisions and risk management by doing a systematic and an exhaustive literature review on the latest and the past studies on the topic.
Further Results on Bent–Negabent Boolean Functions

Bent functions are optimal combinatorial objects having a lot of applications, in particular, in cryptography. Since their introduction, substantial efforts have been directed toward their study in the last three decades. In this paper, we investigate two families of functions possessing properties related to bentness: the so-called negabent and bent–negabent functions, and derive several results on their constructions and characterizations.
Improved image denoising via RAISR with fewer filters

Abstract In recent years, accurate Gaussian noise removal has attracted considerable attention for mobile applications, as in smart phones. Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time. Therefore, we propose a rapid post-processing method for Gaussian noise removal in this paper. Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise. Although these nonlocal image denoising methods have quantitatively high performance, some fine image details are lacking due to the loss of high frequency information. To tackle this problem, an improvement to the pioneering RAISR approach (rapid and accurate image super-resolution), is applied to rapidly post-process the denoised image. It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost, preserving important image structures well. Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements: geometric conversion and reduction of the strength classes. In addition, following RAISR, the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results. Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods, efficiently and with low memory requirements.
Bulmaca Temelli Öğrenme Yaklaşımının Ortaokul Öğrencilerinin İngilizce Dersine Yönelik Tutumlarına ve İngilizce Öz-Yeterlik İnançlarına Etkisi

The overall aim of this research was to examine the effectiveness of puzzle-based learning approach on secondary school students' attitudes and their self-efficacy beliefs in English lesson.The research employed the mixed model and quasi-experimental design with pre-test/post-test was applied.The working group was consisted of the 8th grade students who were selected from two classes and who learned at Elbistan Secondary School during the 2017-2018 academic year.The study was conducted with two groups; experimental group and control group.While lessons were taught based on puzzle-based learning approach in experimental group, in control group the lessons were taught according to the suggested methods of English lesson curriculum.At the end of the study, experimental group students' attitudes and self-efficacy beliefs in English lesson scores increased within groups and between groups.The activities which were carried out in the context of puzzle-based learning were considered as effective applications by students.Observations done during the implementation process of puzzle-based learning in experimental group revealed that the students had fun and learned, and their active participation increased during lessons.
Endek Classification Based On GLCM Using Artificial Neural Networks with Adam Optimization

Not many people know that endek cloth itself has 4 known variances. .Nowadays. Computing and classification algorithm can be implemented to solve classification problem with respect to the features data as input. We can use this computing power to digitalize these endek pattern. The features extraction algorithm used in this research is GLCM. Where these data will act as input for the neural network model later. There is a lot of optimizer algorithm to use in back propagation phase. In this research we prefer to use adam which is one of the newest and most popular optimizer algorithm. To compare its performace we also use SGD which is older and popular optimizer algorithm. Later we find that adam algorithm generate 33% accuracy which is better than what SGD algorithm give, it is 23% accuracy. Longer epoch also give affect for overall model accuracy.
New musical interfaces for older adults in residential care: assessing a user-centred design approach

Purpose:For older adults in aged-care, group music-making can bring numerous physical and psychological benefits, ultimately improving their quality of life. However, personalising music-making to optimise these benefits is often difficult given their diverse ages, experiences, abilities, cognitive and motor skills, and their experience with music technology.Materialsandmethods:In this study, we conducted a 10-week group music-making intervention with twenty participants in an aged-care home, using a prototype digital musical instrument that we iteratively refined by following a user-centred design approach from direct resident feedback. The prototype instrument adopted a novel method for errorless learning in music-making settings, which we also refined, by increasing the difficulty level of the instrument's operation. We also assessed the residents' engagement with the sessions by obtaining feedback from caregivers and facilitators.Results:Results show that residents' enjoyment decreased as the complexity (difficulty) of our errorless learning implementation increased. We also found that resident engagement increased when changes to the prototype digital musical instrument were provided, but not when residents were giving feedback. Results also found that participation over the course of the intervention, and the number of songs played during each session also enhanced engagement.Conclusions: Overall, our results show the intervention was beneficial to residents, although we note some areas of enhancement for further interventions in designing prototype musical instruments for group music-making in aged-care settings. IMPLICATIONS FOR REHABILITATIONOlder adults positively engage with novel music technology, and do so increasingly over subsequent sessions. Repeated sessions may have the potential to enhance longer-term adoption of technologies as well as any rehabilitative effects of the group music-making activity.There is significant potential for residents with different abilities to all make music together, although to maximise the sustainability of the devices, the sessions, and the subsequent rehabilitative benefits, residents must be given the right adaptation for individual interfaces that balances ambition and ability.Rapid DMI prototyping positively enhances engagement among older adults, suggesting that in the case of a custom DMI, an upgrade schedule should be aligned with key rehabilitative milestones. Similarly, in the case of pre-developed digital music systems, resident exposure to new features or functionality should be strategically introduced, so as to maximise engagement for key phases of resident rehabilitation.
MS-Ranker: Accumulating evidence from potentially correct candidates via reinforcement learning for answer selection

Answer selection (AS) aims to select correct answers for a question from an answer candidate set. Conventional AS methods generally address this task by independently matching the question and each candidate. However, since the matching information between the question and a single candidate is usually limited, it is not enough to use the question as the only evidence to estimate the correctness of each candidate. To address this problem, we propose a novel reinforcement learning (RL) based multi-step ranking model, named MS-Ranker, which accumulates candidate. In specific, we explicitly consider the potential correctness of candidates when accumulating information and update the evidence with a gating mechanism. Moreover, as we use a listwise ranking reward, our model learns to pay more attention to the overall performance. Experiments on three benchmarks, namely WikiQA, SemEval-2016 CQA and SelQA, show that our model significantly outperforms existing methods that do not rely on external resources.
Criterion of Blockchain Vulnerability to Majority Attack Based on Hashing Power Distribution Assessment

A blockchain distributed ledger is an actively developed technology that goes beyond its original cryptocurrency application and integrates to IoT, smart manufacturing, smart cities, logistics and more. Therefore, the blockchain security is of the current interest. The article considers a blockchain security issue caused by imbalanced hashing power distribution between nodes: the majority attack. The majority attack (the 51% attack) is a technique which intends to fork the blockchain in order to cancel confirmed transactions and violate data integrity. Despite the attack cost, it has become frequent in current blockchains. The existing security mechanisms against the majority attack have significant weaknesses and limitations. This paper proposes a novel criterion of the blockchain vulnerability to the majority attack. The criterion allows to detect vulnerable states of the distributed ledger based on hashing power distribution assessment. The blockchain hashing power time series are constructed and analyzed in order to evaluate the portion of total hashing power that is controlled by attacker. This estimate is used as a foundation of the proposed criterion. The criterion may be used to in conjunction with other security mechanisms to enhance protection of distributed ledgers in vulnerable states.
Scouting the Path to a Million-Client Server

To keep up with demand, servers will scale up to handle hundreds of thousands of clients simultaneously. Much of the focus of the community has been on scaling servers in terms of aggregate traffic intensity (packets transmitted per second). However, bottlenecks caused by the increasing number of concurrent clients, resulting in a large number of concurrent flows, have received little attention. In this work, we focus on identifying such bottlenecks. In particular, we define two broad categories of problems; namely, admitting more packets into the network stack than can be handled efficiently, and increasing per-packet overhead within the stack. We show that these problems contribute to high CPU usage and network performance degradation in terms of aggregate throughput and RTT. Our measurement and analysis are performed in the context of the Linux networking stack, the the most widely used publicly available networking stack. Further, we discuss the relevance of our findings to other network stacks. The goal of our work is to highlight considerations required in the design of future networking stacks to enable efficient handling of large numbers of clients and flows.
Myanmar POS Resource Extension Effects on Automatic Tagging Methods

Part-of-speech (POS) tagging is the process of assigning the part-of-speech tag or other lexical class marker to each word in a sentence. It is also one of the most important steps in Natural Language Processing (NLP) task pipeline. There are several research works in Myanmar POS tagging implemented with different approaches. However, there is only one publicly available tagged corpus named myPOS corpus. The size of this corpus is only 11 thousand sentences. It is not enough to train downstream NLP tasks, such as machine learning. For this reason, we manually extended the original myPOS corpus as myPOS version 2.0 and the size of the extended corpus becomes approximately triple size of the original myPOS corpus. To evaluate the effects of the extended corpus versus the original corpus, the accuracies of four supervised tagging algorithms, namely, Conditional Random Fields (CRFs), Hidden Markov Model (HMM), Ripple Down Rules based (RDR), and neural sequence labeling approach of Conditional Random Fields (NCRF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> ) are compared. The results showed that the extended myPOS version 2.0 improved the accuracies of automatic POS tagging methods compared with the original myPOS.
Few-Shot Action Recognition with Compromised Metric via Optimal Transport

Although vital to computer vision systems, few-shot action recognition is still not mature despite the wide research of few-shot image classification. Popular few-shot learning algorithms extract a transferable embedding from seen classes and reuse it on unseen classes by constructing a metric-based classifier. One main obstacle to applying these algorithms in action recognition is the complex structure of videos. Some existing solutions sample frames from a video and aggregate their embeddings to form a video-level representation, neglecting important temporal relations. Others perform an explicit sequence matching between two videos and define their distance as matching cost, imposing too strong restrictions on sequence ordering. In this paper, we propose Compromised Metric via Optimal Transport (CMOT) to combine the advantages of these two solutions. CMOT simultaneously considers semantic and temporal information in videos under Optimal Transport framework, and is discriminative for both content-sensitive and ordering-sensitive tasks. In detail, given two videos, we sample segments from them and cast the calculation of their distance as an optimal transport problem between two segment sequences. To preserve the inherent temporal ordering information, we additionally amend the ground cost matrix by penalizing it with the positional distance between a pair of segments. Empirical results on benchmark datasets demonstrate the superiority of CMOT.
Multiclass Vehicle Classification Across Different Environments

Vehicle detection and classification are necessary components in a variety of useful applications related to traffic, security, and autonomous driving systems.Many studies have focused on recognizing vehicles from the point of view of a single perspective, such as the rear of other cars from the driving seat, but not from all possible perspectives, including the aerial view.In addition, they are usually given prior knowledge of a specific kind of vehicle, such as the fact that it is a car, as opposed to being a bus, before deducing other information about it.One of the popular classification techniques used is boosting, where weak classifiers are combined to form a strong classifier.However, most boosting applications consider complex classification problems to be a combination of binary problems.This paper explores in detail the development of a multi-class classifier that recognizes vehicles of any type, from any view, without prior information, and without breaking the task into binary problems.Instead, a single multi-class application of the GentleBoost algorithm is used.This system is compared to a similar system built from a combination of separate classifiers that each classifies a single vehicle.The results show that a single, multi-class classifier clearly outperforms a combination of separate classifiers, and proves that a simple boosting classifier is sufficient for vehicle recognition, given any type of vehicle from any perspective of viewing, without the need of representing the problem as a complex 3D model.
Structure of the Golets Vysochaishy gold deposit (Northern Transbaikalia)

The article describes the fold-thrust structure of the Golets Vysochaishy deposit located at the Baikal-Patom Upland in the Marakan-Tunguska megasyncline. The latter is composed of terrigenous-carbonate carbonaceous rocks metamorphosed in greenschist facies conditions. The deposit is detected in the hanging wing of the asymmetric Kamenskaya anticline. In a cross section, the anticline is an S-shaped structure extending in the latitudinal direction. The main feature of the Golets Vysochaishy deposit is the development of interlayer sulfidization zones (pyrite, pyrrhotite), including gold-bearing ones. Its gold-ore zones tend to occur in layered areas of interlayer sliding in the rocks of the Khomolkhinskaya suite. Four structural markers revealed within the deposit area are indicative of repeated deformation processes: (1) sublatitudinal folding, cleavage of the axial surface and its subsequent transformation into schistosity; (2) crenulation cleavage; (3) interlayer sliding and rock breakdown with interlayer drag folds, parallel microfractures and polished slickensides; (4) large quartz veins and veinlets that cross cut the main structural elements in plan.
Summary Strategies for Literary Texts in English

Abstract One of the problems when students go to university is that they are faced with insufficient skills (reading, summarizing and writing). These skills are not just an option for students – they are a necessity. One of these skills is text summarizing. Summarizing strategies may be called the gist of the literary text. Different summarization strategies may be required for different text types and lengths. The ability to summarize well means your reading comprehension and writing skill should be excellent. Summarization is a high-level comprehension strategy which is effective in improving reading achievement and text summary quality. Several approaches have been developed to analyse summarizing skills which are required to teach and learn English at all levels especially at university level. The research was conducted among students of the second year English department at University of Vlora. The summaries reflect the major differences among the strategies used by skilled and unskilled students. The skilled students produce coherent linguistic and syntactic structures. Skilled (high-proficiency) students use semantic and prepositional phrases more than unskilled students (low-proficiency). Thus, the skilled students do not distort ideas of the original text but distort subject-verb order. When they summarize, we can easily distinguish the way in which they combine idea units across two or more paragraphs. The summaries reflect that students write very long sentences. The margin of errors is considerable due to the mother tongue influence and their level of English.
Distance Learning as an Educational Trend in Higher Education

The case study research strategy describes variations in the educational and professional trajectories of part-time students in comparison with their attitude to distance learning, the goal of higher education, and their professional future of the individual. This research strategy makes it possible to present distance learning as a modern educational trend. It has been determined that in the system of continuing education, distance learning is (1) a tool for changing a profession, (2) part of a well-thought-out career strategy, (3) a guarantee of employment, (4) a way to legitimize a professional status, (5) a form that facilitates obtaining a diploma of higher education, (6) “future education”, education for the future. The subjective meanings of distance education in connection with motivation, the purpose of training, the attitude of students to it are revealed. For students receiving higher education, this is “self-education,” for those whose goal is a diploma, it is “formality”, “superficial mastering of disciplines.” The authors conclude that the consideration of correspondence education in the context of continuity allows us to identify trends in individualization, diversification (the diversity and variability of educational programs, creating opportunities for choice), informatization, and individualization. An assessment of the prospects for extramural higher education based on the experience of forced distance learning (March-June 2020) is given.