Reviewerly/exHarmony huggingface dataset:
A pilot study to analyze descriptive questionnaire responses with natural language processing techniques

The main purpose of the research reported here is to show that the questionnaire responses in text format can be processed and statistically analyzed to some extent by computer programs. We employ unigram, bigram and trigram frequency calculations with the assistance of Python programming in order to analyze the questionnaire responses in text format.Questionnaire responses chosen from options can be easily statistically analyzed whether they are on a ratio, interval, ordered or nominal scale. In contrast, descriptive questionnaire responses have long been read and interpreted by researchers themselves. They have been analyzed by human labor alone. This could to some extent lead to arbitrary and subjective interpretations and analyses. We demonstrate in this paper that natural language processing techniques in Python could help analyze questionnaire responses in text format more objectively and accurately.
UNDERSTANDING EMOJI ATTRIBUTES IN INSTANT MESSAGING APPS

Emoji are pictographs that represent emotion, opinions, ideas, feelings, and reactions concerning a matter or an event. Unlike the traditional text-based system, Emoji possesses anthropomorphic attributes which are widely used in Instant Messaging (IM) apps. In spite of this, the fundamental questions of how the anthropomorphic attributes of Emoji can influence emotion and why users prefer them as a medium of communication are rarely discussed in the design context. Therefore, the objective of this study is twofold: first, to analyze Emoji characters, and secondly, to evaluate the user's response. Eight categories of Emoji characters, Happy, Angry, Sad, Unwell, Love, Fear, Shy and Funny, were selected as samples. The findings show that the anthropomorphism perception of the users towards the selected samples is unique and distinctive. In regard to the user’s response, the results show a higher combined mean in terms of Perceived Anthropomorphism and Perceived Usefulness. The study concluded that basic anthropomorphic attributes and graphic elements of Emoji characters crucially play a significant role in representing the intended expression. Having said that its practicality rests on the aspect of familiarity and usefulness. It is hoped that this study would help enhance knowledge regarding the anthropomorphic attributes and the development of future Emoji characters in IM apps.
Auxiliary Loss Adaptation for Image Inpainting.

Auxiliary losses commonly used in image inpainting lead to better reconstruction performance by incorporating prior knowledge of missing regions. However, it usually requires a lot of effort to fully exploit the potential of auxiliary losses, or otherwise, improperly weighted auxiliary losses would distract the model from the inpainting task, and the effectiveness of an auxiliary loss might vary during the training process. Hence the design of auxiliary losses takes strong domain expertise. To mitigate the problem, in this work, we introduce the Auxiliary Loss Adaptation for Image Inpainting (ALA) algorithm to dynamically adjust the parameters of the auxiliary loss. Our method is based on the principle that the best auxiliary loss is the one that helps increase the performance of the main loss most through several steps of gradient descent. We then examined two commonly used auxiliary losses in inpainting and used ALA to adapt their parameters. Experimental results show that ALA induces more competitive inpainting results than fixed auxiliary losses. In particular, simply combining auxiliary loss with ALA, existing inpainting methods can achieve increased performances without explicitly incorporating delicate network design or structure knowledge prior.
Deep Distilling: automated code generation using explainable deep learning.

Human reasoning can distill principles from observed patterns and generalize them to explain and solve novel problems. The most powerful artificial intelligence systems lack explainability and symbolic reasoning ability, and have therefore not achieved supremacy in domains requiring human understanding, such as science or common sense reasoning. Here we introduce deep distilling, a machine learning method that learns patterns from data using explainable deep learning and then condenses it into concise, executable computer code. The code, which can contain loops, nested logical statements, and useful intermediate variables, is equivalent to the neural network but is generally orders of magnitude more compact and human-comprehensible. On a diverse set of problems involving arithmetic, computer vision, and optimization, we show that deep distilling generates concise code that generalizes out-of-distribution to solve problems orders-of-magnitude larger and more complex than the training data. For problems with a known ground-truth rule set, deep distilling discovers the rule set exactly with scalable guarantees. For problems that are ambiguous or computationally intractable, the distilled rules are similar to existing human-derived algorithms and perform at par or better. Our approach demonstrates that unassisted machine intelligence can build generalizable and intuitive rules explaining patterns in large datasets that would otherwise overwhelm human reasoning.
An application of the mean value theorem to study numerical series

In this note we show an application of the mean value theorem to derive the convergence/divergence of numerical series. In particular, our analysis allows us to derive the congervence of p-series and to derive the classical Cauchy condensation test.
Compression of image using multi-wavelet techniques

Digital compression of images is a topic that has appeared in a lot of studies over the past decade to this day. As wavelet transform algorithms advance and procedures of quantization have helped to bypass current compression of image standards such as the JPEG algorithm. To get the highest effectiveness in compression of image transforms of wavelet need filters which gather a desirable character's number i.e., symmetry and orthogonally. Nevertheless, wave design capabilities are restricted due to their ability to have all of such desirable characters at the same time. The multi-wavelet technology removes a few of the restrictions of the wavelet play more than the options of design and thus able to gather all desired Characters of transforming. Wavelet andmulti-wave filter banks are tested on a larger scale with images, providing more useful analysis. Multiple waves indicate energy-compression efficiency (a higher compression ratio usually indicates a higher mean square error, MSE, in the compressed image). Filter bank Characters such as orthogonal and compact support, symmetry, and phase response are important factors that also affect MSE and professed quality of the image. The current work analyzes the multi-wave Characters effect on the performance of compression of images. Four multi-wavelength Characters (GHM, CL, ORT4) were used in this thesis and the compression of image performance of grayscale images was compared with common scalar waves (D4). SPIHT quantification device in stress chart and use of PSNR and subjective quality measures to assess performance. The results in this paper point out those multi wave characteristics that are most important for the compression of images. Moreover, PSNR results and subjective quality show similar performance to the best scalar and multi-waves. The analysis also shows that a programmer based on multi-band conversion significantly improves the perceived image quality.
Motorcyclists: The Conceptual Basis

Motorcyclists on the street or in a training course make choices, set goals and create activities. The reality of a motorcycle training course, therefore, is structured around assumed but seldom-questioned relationships amongst students, between students and instructors, and between students and machines, roadways and physical obstacles. Motorcycle rider characteristics from a mid-range perspective may be pictured as a grey area between the emic and etic. According to C. Geertz, an emic analysis is closely tied to concrete social events and occasions. Whereas emic corresponds to the micro world of human activities, the mid-range extends from the micro to the macro worlds, the etic relates entirely to the macro world of social events. The etic perspective on motorcycle riders consists of analytical techniques for translating precoded data into findings which have statistical rigor and precision. The chapter also presents an overview of the key concepts discussed in this book.
Voice Query Auto Completion

Query auto completion (QAC) is the task of predicting a search engine user’s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing methods fails because the intermediate transcriptions often don’t form prefixes or even substrings of the final transcription. To address this issue, we propose to condition QAC approaches on intermediate transcriptions to complete voice queries. We evaluate our models on a speech-enabled smart television with real-life voice search traffic, finding that this ASR-aware conditioning improves the completion quality. Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.
MagNet: A Neural Network for Directed Graphs

The prevalence of graph-based data has spurred the rapid development of graph neural networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets naturally modeled as directed graphs, including citation, website, and traffic networks, the vast majority of this research focuses on undirected graphs. In this paper, we propose MagNet, a spectral GNN for directed graphs based on a complex Hermitian matrix known as the magnetic Laplacian. This matrix encodes undirected geometric structure in the magnitude of its entries and directional information in their phase. A charge parameter attunes spectral information to variation among directed cycles. We apply our network to a variety of directed graph node classification and link prediction tasks showing that MagNet performs well on all tasks and that its performance exceeds all other methods on a majority of such tasks. The underlying principles of MagNet are such that it can be adapted to other spectral GNN architectures.
Application of Fuzzy Concept in Fuzzy Group Redefined

The concept of fuzzy set introduced by L.A. Zadeh was applied by Rosen Feld in 1971 to generalize some of the basic concept of groupoids and groups. The definitions of fuzzy sub groupoid and fuzzy groups where extended by him to cover other so-called non-deterministic structure which are similar to ordinary algebraic concepts like- Ideals- fuzzy ideals- homomorphism image fuzzy homomorphism image etc. But, some mathematical structure which seems to be “fuzzy” do not satisfied the definition of Rosen Feld. Hence it would be proper to modify these definitions. Anthony and Sherwood in 1979 observe that the use of minimum in the definition of fuzzy sub groupoid and fuzzy subgroup given by Rosen Feld restricts the notion rendering it not useful in various fuzzy situations, and hence they introduced t-norm place in of minimum and re-define fuzzy algebraic structure and have given independent proof of some theorems there in.
Region Oriented Image Forgery Detection (ROIFD) with FAST

In the current digital era, Images served as a major source for any work. In an innovative improvement ofdigital device technologies, these digital images  has beencaptured quickly at no cost, and to store them easily onmany of digital formats, or share them on the Internet. Atthe same time, with the wide availability of advanced imageediting multimedia tools (e.g. Adobe Photoshop, Coral Draw) modifying a digital image, with little or no obvious signs of modification. This leads to discarding the image authenticity. Thesetampering are not visible to the naked eye; Region oriented forgeries are common image tamperingmanipulations. Which makes a serious issues in the current era. In this paper the region oriented forgery detection has implemented by SURF with FAST Corner detector framework.
Statistical Discussion on Future Music Teaching-Learning Changes and Improvements in terms of Content, Method, and Environment

The purpose of this study is to explore the changes in the content and method of school music education in consideration of the changes in the future. The main research contents are analysis of previous studies on the trend of future education, extraction of key items and contents related to future education, gathering opinions on the direction and practice of future music education, and important improvement of music education. In methods, literature research, expert councils, questionnaires and interviews were conducted. The main results of this study are as follows. First, in future music education, personal choice education related to student life becomes important. Second, a communication environment between students and a collaborative interactive learning environment is needed. Third, individual cognitive and practical competency-based education is emphasized. Fourth, interdisciplinary learning and network learning, using technology, are important. Based on these findings, future music education should focus on sensory experiences, musical performances, and understanding of musical concepts.
ОЛИЙ ТАЪЛИМ МУАССАСАЛАРИ ТАЛАБАЛАРИДА “МЕН БЎЛАЖАК БОШЛАНҒИЧ ТАЪЛИМ ЎҚИТУВЧИ-ТАДҚИҚОТЧИСИМАН” МОТИВАЦИЯСИНИНГ РИВОЖЛАНТИРИШНИНГ ЎЗИГА ХОС ЖИҲАТЛАРИ

The article highlights the features of the development of research skills of future primary education teachers. The author gave his opinion and recommendations on the development of research competencies among students of higher educational institutions.
SISTEM INFORMASI PEMETAAN DAN PENYULUHAN COVID-19 PADA DINAS KOMUNIKASI DAN INFORMATIKA KABUPATEN LABUHANBATU UTARA

The spread of the Corona virus or what is called COVID-19 has caused hundreds of thousands of victims to die around the world. The need for fast and accurate information is a top priority for the community to deal with a pandemic situation like this, especially in North Labuhanbatu district. In this case, the delivery of information by the North Labuhanbatu Regency Communication and Information Office is carried out using the socialization method. This method is considered right on target, but with the increasing spread of Covid-19 in the North Labuhanbatu district, a faster and more effective method is needed in conveying information to the public to prevent additional casualties. The internet and the web as new breakthroughs in information media are expected to make it easier for people to get fast and accurate information. Apart from that, the presence of the internet and the web has also given rise to new media in geospatial mapping and WebGIS. WebGIS is a web-based Geographical Information System which consists of several interrelated components. WebGIS is a combination of mapping graphic design, digital maps with geographic analysis, computer programming, and a database that is connected to one part of web design and web mapping. The existence of WebGIS can help the Office of Communication and Informatics in North Labuhanbatu regency in delivering fast and accurate information to the people of North Labuhanbatu district.
"Functional Equivalence" Cases in E-C Translation of Long Sentences in EST Text and Their Implications for Translation Teaching

EST (English for science and technology) translation plays an increasingly significant role in scientific field. Compared with the general Non-tech English style, EST has its own typical features which were intensively represented by the extensive use of long sentences, as they contain enormous amount of information and complex structures, and there are great differences in sentence patterns between English and Chinese. Through case studies, this paper explores the emerging “Functional Equivalence” Cases in E-C Translation of Long Sentences in EST by analyzing the text hypotaxis-prominent English sentences, which is proved to be compact in structure whereas parataxis-prominent Chinese sentences are loose in structure. The paper induces that as one of the information type texts, EST’s effect to reader is more univocal than other texts for it aims to convey information to reader and requires high accuracy. The paper also described the functional equivalence theoruy's implications for  applied translation teaching by arguing that Functional equivalence theory lays emphasize on target reader’s response and it is more suitable to translate EST under the guidance of functional equivalence theory.
Structured Reordering for Modeling Latent Alignments in Sequence Transduction

Despite success in many domains, neural models struggle in settings where train and test examples are drawn from different distributions. In particular, in contrast to humans, conventional sequence-to-sequence (seq2seq) models fail to generalize systematically, i.e., interpret sentences representing novel combinations of concepts (e.g., text segments) seen in training. Traditional grammar formalisms excel in such settings by implicitly encoding alignments between input and output segments, but are hard to scale and maintain. Instead of engineering a grammar, we directly model segment-to-segment alignments as discrete structured latent variables within a neural seq2seq model. To efficiently explore the large space of alignments, we introduce a reorder-first align-later framework whose central component is a neural reordering module producing {\it separable} permutations. We present an efficient dynamic programming algorithm performing exact marginal inference of separable permutations, and, thus, enabling end-to-end differentiable training of our model. The resulting seq2seq model exhibits better systematic generalization than standard models on synthetic problems and NLP tasks (i.e., semantic parsing and machine translation).
Internet of Thing-Based Monitoring Systems and Their Applications

In this chapter, it is described that the monitoring systems have a major role in preventing critical problem-solving. Now most of the applications and devices are based on wireless technology only. The main advantage of this system is that it is very easy to communicate with the device and control the overall activity of the system and even to take necessary and timely decisions. The monitoring systems are used to control the activities and decisions based on the observed data. The sensors are used to sense the data whatever users are required. Most of the contactless devices are made by using all types of processors and devices. The main problem of this kind of wireless technology is data transmission and data analysis for future references. The decisions are based upon the data analysis report. For that reason, the blowfish algorithm is used to send the compressed packet as data. The algorithms take the data from the cloud and the trained data results process it.
Transformer Feed-Forward Layers Are Key-Value Memories

Feed-forward layers constitute two-thirds of a transformer model’s parameters, yet their role in the network remains under-explored. We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary. Our experiments show that the learned patterns are human-interpretable, and that lower layers tend to capture shallow patterns, while upper layers learn more semantic ones. The values complement the keys’ input patterns by inducing output distributions that concentrate probability mass on tokens likely to appear immediately after each pattern, particularly in the upper layers. Finally, we demonstrate that the output of a feed-forward layer is a composition of its memories, which is subsequently refined throughout the model’s layers via residual connections to produce the final output distribution.
Effective Phishing Emails Detection Method

The term “phishing” is a type of cyber-attack where the attacker sends fraudulent emails, then asks the user to follow an embedded link, where the user is asked to enter private information. As a social engineering attack, a phishing attack causes huge financial losses to the recipients. Therefore, there is an urgent need for high-accuracy phishing detection.In this paper, we propose a phishing email detection model based on two classification algorithms that are discussed and compared to detect and classify phishing attacks, such as; Multi-Layer Perceptron (MLP) and Random Forest (RF) Classification algorithms with publicly available datasets for both phishing and benign emails, with the main objective is to develop the phishing email classification with the greatest accuracy and least features. From a dataset of 4600 phishing and benign emails messages, we extract three feature tess from the header, and hyperlinks, The features are extracted using a well-known scheme called TermFrequency-Inverse Document Frequency (TF-IDF) principle to weight the features in each email message. Furthermore, we selected 25 of the most important features using Information Gain (IG) based feature selection. Ten-fold cross-validation was applied for training, testing, and validation. The best experimental result was achieved by using 25 out of 32 features and applying them to the classification algorithms. The model achieved an accuracy of 99.46% for the Random Forest (RF) algorithm, the highest recorded so far for a validated data set.
Convergence and divergence in the expression of partitvity: a cross-linguistic experimental study

This paper focusses on the so-called partitive pronoun in French and Dutch, and the corresponding data in German, a language which is assumed not to have partitive pronouns in its standard, in contrast to certain dialectal varieties. Taking the diverse uses of the French partitive pronoun en as a starting point, we investigate the corresponding constructions in Dutch and German. The ultimate aim of the paper is to develop an analysis accounting for the similarities and differences between these languages in relation to the presence/absence of the partitive pronoun. To reach our objectives, we rely on data collected in a Grammaticality Judgment Test taken by native speakers of French, Dutch and German. We put together theory and experimental data in a crosslinguistic perspective investigating three languages that differ in their uses of partitive elements and formalize the results in a model in which the partitive pronoun can replace different portions of the nominal structure.Keywords: partitivity; (non-)referentiality; DP-structure; partitive pronoun