Reviewerly/exHarmony huggingface dataset:
To What Extent Cognitive-Driven Development Improves Code Readability?

Cognitive-Driven Development (CDD) is a coding design technique that aims to reduce the cognitive effort that developers place in understanding a given code unit (e.g., a class). By following CDD design practices, it is expected that the coding units to be smaller, and, thus, easier to maintain and evolve. However, it is so far unknown whether these smaller code units coded using CDD standards are, indeed, easier to understand. In this work we aim to assess to what CDD improves code readability. To achieve this goal, we conducted a two-phase study. We start by inviting professional software developers to vote (and justify their rationale) on the most readable pair of code snippets (from a set of 10 pairs); one of the pairs was coded using CDD practices. We received 133 answers. In the second phase, we applied the state-of-the art readability model on the 10-pairs of CDD-guided refactorings. We observed some conflicting results. On the one hand, developers perceived that seven (out of 10) CDD-guided refactorings were more readable than their counterparts; for two other CDD-guided refactorings, developers were undecided, while only in one of the CDD-guided refactorings, developers preferred the original code snippet. On the other hand, we noticed that only one CDD-guided refactorings have better performance readability, assessed by state-of-the-art readability models. Our results provide initial evidence that CDD could be an interesting approach for software design.
Peningkatan Aktivitas dan Hasil Belajar Peserta Didik Tema Tugasku Sehari-Hari Materi Uang Melalui Model Discovery Learning dengan Metode Pemberian Tugas di Kelas II SD N 02 Gurun

This study aims to see and analyze the Increase in Student Activities and Learning Outcomes the Theme of My Daily Tasks Material Money Through the Discovery Learning Model with the Assignment Method in Class II SDN 02 Gurun. This research was carried out using the Classroom Action Research method which consisted of two cycles, where each cycle consisted of: planning, action, observation and reflection. It is carried out from August to October 2021. Data is processed in each cycle and then analyzed using percentages. The results obtained from this study are the discovery learning model, the assignment method can improve student learning outcomes. This is evident from the increase in the learning activities of pre-cycle students = 63%, cycle I = 75%, and cycle II = 88%. For teacher activity in pre-cycle = 61%, cycle I = 72%, and cycle II = 92%, for mastery learning knowledge increased from pre-cycle 65.29 = 47%, cycle I 71.17 = 59%, and cycle II 80 ,00 = 82% For mastery learning skills, namely pre-cycle, pre-cycle 68.23 = 59%, cycle I 74.11 = 65% and cycle II 80.58 = 82%. The conclusion obtained from this research is that the use of the discovery learning model of the assignment method can increase the activities and learning outcomes of students.
Autonomous Vehicle System

Abstract: This paper is on a project an autonomous car is a vehicle capable of sensing its environment and operating without human involvement . A human passenger is not required to take control of then vehicle at any time , nor is a human passenger required to be present in the vehicle at all. Autonomous cars are the future smart cars anticipated to be driver less, efficient and crash avoiding ideal urban car of the future. In this regard the first challenge would be to customize and imbibe existing technology in conventional vehicle to translate them to a near expected autonomous car.
Concept and Dependencies Enhanced Graph Convolutional Networks for Short Text Classification

Abstract Short text classification task is a special kind of text classification task in that the text to be classified is generally short, typically generating a sparse text representation that lacks rich semantic information. Given this shortcoming, scholars worldwide have explored improved short text classification methods based on deep learning. However, existing methods cannot effectively use concept knowledge and long-distance word dependencies. Therefore, based on graph neural networks from the perspective of text composition, we propose concept and dependencies enhanced graph convolutional networks for short text classification. First, the co-occurrence relationship between words, the inclusion relationship between documents and words, long-distance word dependencies, and the association relationship between external concepts and words are defined. Then, a text graph is constructed for an entire text corpus based on the four relationships. Finally, the text graph is input into graph convolutional neural networks, and the category of each document node is predicted after two layers of convolution. Experimental results show that our proposed method overall best on multiple classical English text classification datasets.
Collective Victimhood Narratives in Far-right Communities on Telegram

Feelings of collective victimhood have been demonstrated to have a strong effect on intergroup bias, outgroup hostility and support for violence. The use of narratives stirring these feelings in far-right communications is especially concerning given their inclusion in the manifestos of several mass killers across Europe and North America. However, scholars still have little knowledge on the reach of such narratives as well as the extent to which major salient events increase attention to collective victimhood messaging among far-right followers. To address these gaps, we analyze the use of collective victimhood narratives on the popular secure instant messaging service, Telegram, which has exploded in popularity in response to mainstream platforms attempts to moderate speech. we develop a supervised machine learning algorithm to predict the presence of these discourses in text from over 18.5 million messages that were extracted from 1,870 far-right Telegram channels. We then use these data to test what impact the George Floyd protests and the storming of the U.S. Capitol had on the frequency of collective narrative discussions on far-right Telegram. Our findings suggest that both events coincided with a significant increase in the use of victimhood narratives, thus providing insight into the radicalization process of far-right communities online.
Perception, Motivation, and Communicative Self-confidence of Indonesian Students On Willingness to Communicate in L2 By Using Facebook

The present study aims to investigate direct and indirect factors affecting willingness to communicate (WTC) in L2 by using Facebook. Second semester English students (n=156) from private and state universities in three cities in Indonesia; Surabaya, Bali, and Malang participated in this study. A set of questionnaire with a 5-point Likert-scale encompassing students’ perception, motivation, communicative selfconfidence (CSC), and Willingness to Communicate (L2 WTC) was used to collect the data. The data were then analyzed using a software package, AMOS 20, to gauge the magnitude of the factors affecting L2 WTC by using FB platform. The finding reveals that there are considerable effects on perception and motivation mediated by communicative self-confidence to WTC. While, communicative self-confidence shows the strongest predictor on L2 WTC.
Deconstructing the linguistic features of hybrid text-based online social network communication among Algerian Facebook users: A case study

Abstract This paper aims at dissecting the foremost innovative linguistic features that characterize the hybrid text-based online social network communication of some Algerian Facebook users. It also seeks to emphasize the role of computer-mediated communication, mainly social networking sites (SNSs), in the modernization of stigmatized informal language varieties as in the case of Algerian Dialectal Arabic (ADA). To achieve this, a qualitative research approach, in which both the researcher’s long-term participant observation and a descriptive content analysis of a corpus of 30 hybrid text-based Facebook posts, is adopted for investigation. Moreover, the relevant data was collected from a virtual Facebook group community that was chiefly created by some younger Algerian EFL students at Ibn Khaldoun University of Tiaret in Algeria in order to share information related to their studies. Interestingly, the findings of our case study revealed that the ADA is linguistically accommodated to some nonstandard and unconventional spellings of hybrid informal writing such as transliteration in its two forms, the use of distinctive signs such as numbers for some letters, the partial and/or full removal of vowel sounds for shortenings, the occurrence of written code switching/mixing and loanwords, and deficiency in using punctuation marks. Ultimately, the research ends with some recommendations for future research in the domain of text-based online communication with reference to the sociolinguistic situation in Algeria.
Aplikasi R Biblioshiny dalam Mengungkap Trend Riset Pembelajaran Berbasis ICT : Kajian Scientometrik

This study aims to determine the development of ICT Literacy research trends published by leading journals. The data analyzed consisted of 1778 indexed research publications. The data is then processed and analyzed using the R Bibliometric application to find out the bibliometric map of the ICT Literacy. The results showed that the number of publications on the ICT Literacy had increased significantly per year. The most types of documents that analyze ICT Literacy are journaled articles with 1068. The most popular author is Scherer R, and the most popular keywords are ICT, Literacy dan Digital. The trend analysis shows that ICT Literacy need to be developed considering the need for adapted with the demands of a technological world.
Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos

&lt;p&gt;Since the era of learning to write by human beings, mistakes made in typing words have occupied a privileged place in linguistic studies, integrating new disciplines into school curricula such as spelling and dictation. According to exhaustive studies that we have done in the field of spellchecking errors made in typing Arabic texts, very few research works that deal with typographical errors specifically caused by the insertion or missing of the blank-space in words. On the other hand, spelling correction software remains ineffective for handling this type of errors. Failure to process errors due to the insertion/missing of blankspace between and in words leads and brings us back to situations of ambiguity and incomprehension of the meaning of the typed text. To remedy this limitation of correction, we propose in this article an ad-hoc probabilistic method which is based jointly on two approaches. The first approach treats the errors due to deletion or missing of blank-space between or inside words, while the second puts emphasis in correcting space insertion errors in a word of course in addition to other kinds of elementary editing errors (addition, deletion, permutation of characters). Our new approach combines edit distance with n-gram language models to correct the errors already mentioned. Our new approach gave an accuracy rate that reaches 98,14% for missing blank-space errors (noted MBSE) and 89,5% for insertion blank-dpace errors (noted IBSE), which gives an average correction rate of around 95,26%. These results are very encouraging and show the interest and the importance of our approach.&lt;/p&gt;
THE USE OF SURVEY APPLICATION TO CREATE LEARNING MODULES IN THE PANDEMIC ERA

Effective learning needs to be supported by adequate facilities and infrastructure that are approved in finding relevant references and materials. In the field, learning is quite difficult in the learning process because of limited referrals/material. Therefore, it is necessary to design a module that is by following the syllabus (RPS) that helps students learn independently. The purpose of the research is to find the needs and desires that are expected in the course modules in linguistic/literature research methods. The research method used was conducting a survey using questionnaires and conducting interviews. The questionnaire analyzes the needs of students and lecturers who are then analyzed by identifying based on their needs. The questionnaire for needs analysis is published with SurveyMonkey media application to facilitate the process of filling out the questionnaire. The results of the interview activities will be described qualitatively based on field observations. The subjects of the study were 100 students in the 6th semester and three lecturers who were lecturers parallel to this course. The results of this study consisted of an analysis of student needs and the views of the lecturers regarding the design content of the module, which contained; dimension of content, learning
Application of Blockchain in Artificial Intelligence

Abstract: Artificial intelligence (AI) refers to the simulation of human intelligence in machines designed in such a way that machines can think like humans and imitate their actions. It combines sub-fields for machine learning and in-depth learning, using data-trained AI algorithms to make predictions or categories, and become smarter over time. The benefits of AI include automated repetitive tasks, improved decision-making and better customer experience. Blockchain is a shared, unchanging platform that provides fast, shared and transparent data exchange at the same time across multiple groups as they begin and complete transactions. The blockchain network can track orders, payments, accounts, production, and more. Because members are allowed to share a common vision, they gain confidence and trust in their work and other businesses, as well as new efficiency and opportunities. AI can successfully mine with a large database and create new environments and discover patterns based on data behaviour. Blockchain helps to effectively remove bugs and counterfeit data sets. The two technologies complement each other as Blockchain can reduce the risk to AI, and AI can improve Blockchain performance. Much research is currently being done on the use of Blockchains to detect intelligent applications in key areas such as health care, finance, power, government, and defense.
Exploring the Application of the Random Matrix Thinking Model in Teaching English Predicate Constructions

This paper, the random matrix thinking model, studies the grammatical features of verbs on random matrix and logical relations and designs a model for teaching English predicate constructions by combining random matrix thinking and arithmetic rules. In this paper, an attention mechanism-based English predicate verb recognition method and a BERT-based English predicate verb recognition method are proposed for the characteristics of predicate verbs as the focal point of sentences. Since the attention mechanism can obtain the long-distance semantic dependency information in the sentence, the attention mechanism-based method can effectively improve the recognition performance of the predicate verb compared with the traditional way. The BERT-based English predicative verb recognition model improves the former approach by taking full advantage of the input corpus and improving the model performance. A random matrix-based predicate verb uniqueness discrimination method is proposed for the uniqueness of predicate verbs in sentences. By setting the classification fitting conditions, the data can be optimized to output and fully fit the global identity of the predicate verb in the penalty during the training process, and better results are achieved.
Environmental Role Against Indonesian Language Learning_Syahri Ramadhona/20016117

The environment is very influential in all aspects of life, not least in the world of education. In the learning process, especially learning Indonesian, the environment also has a role in influencing the quality of student learning. The application of environmental-based learning is felt to increase student interest in learning. In addition, it is also able to reduce student boredom in the teaching and learning process. Not only as a supporter of the learning process in the classroom, the environment also has a huge influence on students' Indonesian language skills. The influence of the environment on the Indonesian language skills of these students also plays an important role in learning Indonesian.
Behavior Transformers: Cloning $k$ modes with one stone

While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large, human-generated datasets. Human behaviors have wide variance, multiple modes, and human demonstrations typically do not come with reward labels. These properties limit the applicability of current methods in Offline RL and Behavioral Cloning to learn from large, pre-collected datasets. In this work, we present Behavior Transformer (BeT), a new technique to model unlabeled demonstration data with multiple modes. BeT retrofits standard transformer architectures with action discretization coupled with a multi-task action correction inspired by offset prediction in object detection. This allows us to leverage the multi-modal modeling ability of modern transformers to predict multi-modal continuous actions. We experimentally evaluate BeT on a variety of robotic manipulation and self-driving behavior datasets. We show that BeT significantly improves over prior state-of-the-art work on solving demonstrated tasks while capturing the major modes present in the pre-collected datasets. Finally, through an extensive ablation study, we analyze the importance of every crucial component in BeT. Videos of behavior generated by BeT are available at https://notmahi.github.io/bet
Two families of optimal ternary cyclic codes with two zeros

Cyclic code is an interesting topic in coding theory and communication systems. In this paper, two families of optimal ternary cyclic codes with parameters $[3^m-1,3^m-2m-1,4]$ are presented. The first family of cyclic codes with two zeros $\pi$ and $\pi^v$ is constructed by using multivariate method. The second family of cyclic codes with two zeros $\pi^2$ and $\pi^v$ is obtained by analyzing irreducible factors of certain polynomials with finite degrees over the finite field $\mathbb{F}_{3^m}$, where $\pi$ is a generator of $\mathbb{F}_{3^m}^*$.
Review of Koshelev, Alexey. 2020. On the Genesis of Thought and Language. Translated by Alexander Kravchenko with Jillian Smith. Moscow and Boston: Academic Studies Press

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Theory for the good of practice. To discussion on the origins of documentology

The author responds to the considerations expressed mainly by E. A. Plesh-kevich in his article “On documentology and its methodology”. The author praises Pleshkevich’s acknowledging the concept of “documentology”. Indeed, the term “document” differs pertaining to the subject (substantially) and object (functional-ly), and this representation makes the foundation for relativeness of this concept in practical applications. Meanwhile, the author points to the disruptiveness of other Pleshkevich’s arguments: alleged unscientific nature of differentiated ap-proach to the concept of “document”, validity of pure theorizing regardless of practice, and denial of Paul Otlet’s methodology. The latter thought that there was the overdue need to develop the general theory of document similar to that in biology, sociology, physics and the so-called big sciences. The author demonstrates the wrongfulness of abandoning of the wider definition of the document by the International Standardization Organization (ISO). According to ISO, the document is an object that may be regarded as an element of documented quality management system. The practical effectvenesss of the method of rising from the abstract to the concrete which enables to narrow the concept of document down to the needs of individual libraries, is substantiated.
Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effects

We propose a novel system for action sequence planning based on a combination of affordance recognition and a neural forward model predicting the effects of affordance execution. By performing affordance recognition on predicted futures, we avoid reliance on explicit affordance effect definitions for multi-step planning. Because the system learns affordance effects from experience data, the system can foresee not just the canonical effects of an affordance, but also situation-specific side-effects. This allows the system to avoid planning failures due to such non-canonical effects, and makes it possible to exploit non-canonical effects for realising a given goal. We evaluate the system in simulation, on a set of test tasks that require consideration of canonical and non-canonical affordance effects.
A Survey on Computational Intelligence-based Transfer Learning

The goal of transfer learning (TL) is providing a framework for exploiting acquired knowledge from source to target data. Transfer learning approaches compared to traditional machine learning approaches are capable of modeling better data patterns from the current domain. However, vanilla TL needs performance improvements by using computational intelligence-based TL. This paper studies computational intelligence-based transfer learning techniques and categorizes them into neural network-based, evolutionary algorithm-based, swarm intelligence-based and fuzzy logic-based transfer learning.
Agent-based Graph Neural Networks

We present a novel graph neural network we call AgentNet, which is designed specifically for graph-level tasks. AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size. The architecture of AgentNet differs fundamentally from the architectures of traditional graph neural networks. In AgentNet, some trained \textit{neural agents} intelligently walk the graph, and then collectively decide on the output. We provide an extensive theoretical analysis of AgentNet: We show that the agents can learn to systematically explore their neighborhood and that AgentNet can distinguish some structures that are even indistinguishable by 2-WL. Moreover, AgentNet is able to separate any two graphs which are sufficiently different in terms of subgraphs. We confirm these theoretical results with synthetic experiments on hard-to-distinguish graphs and real-world graph classification tasks. In both cases, we compare favorably not only to standard GNNs but also to computationally more expensive GNN extensions.
Developing vocabulary of the hoteliers through web-based learning and mobile based learning

The ability to receive and transmit information through words, is the most important mode of communication. It is a significant factor in both the transmission and reception of academic knowledge because teachers deliver subject matter content through general vocabulary and their courses' specialized vocabulary. In this modern technological trend there are numerous mobile applications related to learning and teaching to enhance ones speaking skill. The study involves in developing the basic vocabulary proficiency though a mobile application called Word Up and a website named Vocabulary.com, among the students of hotel management to develop the speaking skill. In this advanced education there are various portable websites identified with learning and educating to upgrade ones talking ability. The samples were 36 under graduates from SRM University, Kattangulathur campus. They will work on both vocabularysize.com and Word up mobile application. Gathered information were involved with SPSS measurable apparatus and the outcomes show that the Vocabulary.com versatile website rouses and engages the understudy to learn, recall and edge sentences with further developed vocabularies.