Reviewerly/exHarmony huggingface dataset:
Ways to build text collections for training classifiers

We report on solving the problem of forming a Russian-language text collection (dataset) consisting of bibliographic descriptions of scientific articles for training classifiers. Various approaches to creating such collections are considered. The expediency of using expert estimates for assigning class labels is assessed. The known datasets are analyzed, the requirements for the generated text array are formulated, and the choice of the subject area (Computer Science) is justified. We propose a technology of forming collection in conditions of the shortage of Russian-language articles. To do this we use automated translation of publications (bibliographic descriptions) from available English-language electronic libraries (ACM digital library, IEEE Xplore digital library, CiteSeerX) with additional expert quality control of the translation. The bibliographic collection thus formed was studied using methods of clustering (Latent Semantic Analysis) and visualization (Principal Component Analysis). Training and test samples were compiled and «standard» classifiers (K-Nearest Neighbor Method, Logistic Regression, Random Forest) were used. Then we calculated standard quality measures (accuracy, precision, recall). The rigid and soft classification were carried out. For rigid and soft classification all calculated measures (for the studied classifiers) ranged within [0.79; 0.87], and [0.91; 0.95], respectively. The experiments showed almost identical results for Russian and English bibliographic descriptions (the difference did not exceed 2%). The proposed method of forming text collections reduces the complexity of the labeling process compared to the expert approach, solves the problem of the lack of Russian-language documents, allows formation of sufficiently large balanced bibliographic datasets for training and testing classifiers.
Social Media Content Analysis and Classification Using Data Mining and ML

Students' natural conversations on social media such as Twitter and Whatsapp are useful to understand their learning experiences feelings. Collecting and analyzing data from such media can be a difficult task. However, the large scale of data is required for automatic data analysis techniques to classify Twitter data. The proposed new system is a combination of qualitative analysis and large-scale data mining and ML techniques. This system focuses on engineering students' Twitter posts, which are collected from engineering colleges, to understand issues and problems in their learning. The authors first conduct a qualitative analysis using ML studio on tweets collected from engineering colleges using term #DStudentsproblems, engineeringProblem, Aluminisuggestions, and ladyEngineer. Collected tweets are related to engineering students' college lives. In the proposed system, a multi-label classification algorithm to classify tweets reflecting students' problems such as soft skill issues, heavy study load, lack of social engagement, and sleep problems is used.
An Analysis of the Teachers’ Strategies in Teaching English Vocabulary at the Seventh Grade of Mts N 7 Kebumen in the Academic Year 2019/2020

Teaching vocabulary is one way to increase students' knowledge so they can make grammatical sentences. Teaching vocabulary must also be equipped with strategies that are suitable for students. Many strategies can be used by teachers. In vocabulary, there are many things that must be equated and good handling must be taken so that students can understand what vocabulary is. This research was conducted at MTs N 7 Kebumen. The subjects of this study were the English teachers in seventh grade. The purpose of this research is to find the strategies used by English teachers in teaching vocabulary. This research find out students' responses toward the application of teachers' strategies in teaching vocabulary to seventh grade students of MTs N 7 Kebumen. In the interview with an English teacher at MTs N 7 Kebumen, the researcher found out some obstacles experienced by the teacher. One of the obstacle was students have little vocabulary. Therefore the teacher used strategies which easily accepted by students. The teacher also felt not easy to teaching English. This research discusses about analysis the teachers’ strategies in teaching English vocabulary. This research method used descriptive qualitative to describe the strategy of English teachers in teaching vocabulary. The research focused on the strategies of the English teacher in teaching vocabulary in the classroom. The researcher used interview, docummentation, and questionnaire for data collection techniques. First, the researcher interviewed with English teacher. Second, the researcher conducted data analysis using pictures, lesson plans and syllabus as data docummentation. Third, the researcher also gave questionnaires to students in seventh grade MTs N 7 Kebumen using Google Forms. Based on the analysis, the results of the research are three findings that can be taken. First, there are some strategies used by teachers to teach vocabulary, namely presentation, translation, memorization, playing games, reading, pantomime, action, and body movements. Second, this research found out the media used to convey the material, the media used are real things, videos, pictures, English books of students. Third, the researcher found out students' responses with strategies used by the teacher. The researcher concluded that students can enjoy teaching and learning that the teacher gives. By using presentations, translations, memorization, playing games, words, mime, actions, and movements. Students also can remember new words more easily.&#x0D; Keyword: Strategies, Students, Teachers, Teaching, Vocabulary
Compositional RL Agents That Follow Language Commands in Temporal Logic

We demonstrate how a reinforcement learning agent can use compositional recurrent neural networks to learn to carry out commands specified in linear temporal logic (LTL). Our approach takes as input an LTL formula, structures a deep network according to the parse of the formula, and determines satisfying actions. This compositional structure of the network enables zero-shot generalization to significantly more complex unseen formulas. We demonstrate this ability in multiple problem domains with both discrete and continuous state-action spaces. In a symbolic domain, the agent finds a sequence of letters that satisfy a specification. In a Minecraft-like environment, the agent finds a sequence of actions that conform to a formula. In the Fetch environment, the robot finds a sequence of arm configurations that move blocks on a table to fulfill the commands. While most prior work can learn to execute one formula reliably, we develop a novel form of multi-task learning for RL agents that allows them to learn from a diverse set of tasks and generalize to a new set of diverse tasks without any additional training. The compositional structures presented here are not specific to LTL, thus opening the path to RL agents that perform zero-shot generalization in other compositional domains.
Designing and Development of Framework for Supply Chain AI Bots

Supply chain has various use cases and it’s a huge area where it involved lot of operations in Packings, Manufacturing, Shipping and Picking. Human-Computer Speech is getting vitality as a technique of PC collaboration. There has been a later upsurge in talk-based look engines and colleagues, for example, Alexa, Siri, Google Home and Cortana. NLP methodologies, for example, NLTK for Python can be associated with dismember talk, and intelligently responses can be found by arranging an engine to supply fitting human like responses. This kind of program is known as a Chatbot, which is the focal point of this contemplate. We accept that a coordinated and effective inventory network framework will convey consistent worth, helping customers to convey the correct item or administration at the correct cost. It has been demonstrated certain that the inventory network represents 80 percent of the complete operational expense, and thus, making efficiencies on this front is at the center of improved benefit and business advantage for any maker.
Hubungan Persepsi Peserta Didik Tentang Pola Asuh Orang Tua, Kebiasaan Belajar dengan Hasil Belajar

The purpose of this research was to find out the significant and positive correlation between student perception about parenting parents and learning achievement, learning habits and learning achievement, and student perception about parenting parents and learning habits together with learning achievement thematic of fourth grade students SD Muhammadiyah Metro Central. The type of research was ex-post facto correlation. Population was 230 and sample was 70 students. Data collection techniques used observation, questionnaire and documentation studies. Data analysis technique used was product moment correlation and multiple correlation. The results showed that there was a significant and positive correlation between student perception about parenting parents and learning achievement indicated at low level, there was a significant and positive correlation between learning habits and learning achievement indicated at low level, and there was a significant and positive correlation between student perception about parenting parents and learning habits together with learning achievement indicated at moderate level. Keywords: Learning achievement, study habits, parenting parents
From GWT to Angular: An Experiment Report on Migrating a Legacy Web Application

Because Google Web Toolkit (GWT) is no longer actively maintained, Berger-Levrault decided to migrate its applications to Angular. We designed a semiautomated migration approach that helps developers migrate applications’ front ends from GWT to Angular and a tool that performs the migration.
Cyberbullying Severity Detection Using Deep Learning Techniques

Internet and social media networks have become a defining factor for modern socialization, communication, and collaboration. At the same time, they have also become a massive platform for antisocial behavior such as cyberbullying, hate speech, and aggressive behavior, among others. This paper examines the automatic detection and classification of cyberbullying incidents in social networks. Most of the current work focuses on the detection of cyberbullying as a binary problem. However, some incidents are more severe than others and demand immediate attention and intervention from the stakeholders such as parents, government, and other relevant agencies. This paper presents the detection and classification of cyberbullying events based on their severity using various deep learning techniques – CNN, recurrent neural networks (RNN-BiLSTM), and HLSTM (HAN-BiLSTM). We curated three datasets from different social networks of varying balances to study the impact of class balance in a multi-class classification problem such as cyberbullying severity detection. Experimental results reveal higher efficiency of HLSTM and RNN-BiLSTM over CNN using all significant metrics such as k-score, MAP, MAR, and F1-Score. Similarly, results demonstrate that the deep learning techniques generally work well with moderately balanced datasets. The study reveals that deep learning techniques effectively detect severity in cyberbullying incidents, a multi-class classification problem.
A better loss for visual-textual grounding

Given a textual phrase and an image, the visual grounding problem is the task of locating the content of the image referenced by the sentence. It is a challenging task that has several real-world applications in human-computer interaction, image-text reference resolution, and video-text reference resolution. In the last years, several works have addressed this problem by proposing more and more large and complex models that try to capture visual-textual dependencies better than before. These models are typically constituted by two main components that focus on how to learn useful multi-modal features for grounding and how to improve the predicted bounding box of the visual mention, respectively. Finding the right learning balance between these two sub-tasks is not easy, and the current models are not necessarily optimal with respect to this issue. In this work, we propose a loss function based on bounding boxes classes probabilities that: (i) improves the bounding boxes selection; (ii) improves the bounding boxes coordinates prediction. Our model, although using a simple multi-modal feature fusion component, is able to achieve a higher accuracy than state-of-the-art models on two widely adopted datasets, reaching a better learning balance between the two sub-tasks mentioned above.
Evaluation of 360° Image Projection Formats; Comparing Format Conversion Distortion Using Objective Quality Metrics

Currently available 360° cameras normally capture several images covering a scene in all directions around a shooting point. The captured images are spherical in nature and are mapped to a two-dimensional plane using various projection methods. Many projection formats have been proposed for 360° videos. However, standards for a quality assessment of 360° images are limited. In this paper, various projection formats are compared to explore the problem of distortion caused by a mapping operation, which has been a considerable challenge in recent approaches. The performances of various projection formats, including equi-rectangular, equal-area, cylindrical, cube-map, and their modified versions, are evaluated based on the conversion causing the least amount of distortion when the format is changed. The evaluation is conducted using sample images selected based on several attributes that determine the perceptual image quality. The evaluation results based on the objective quality metrics have proved that the hybrid equi-angular cube-map format is the most appropriate solution as a common format in 360° image services for where format conversions are frequently demanded. This study presents findings ranking these formats that are useful for identifying the best image format for a future standard.
Reply to “Comment on ‘Practical quantum key distribution with geometrically uniform states' ”

In reply to the Comment by D. A. Kronberg [Phys. Rev. A 104, 026401 (2021)], we would like to reassure readers that none of the critiques actually apply to our final result of the security analysis, namely, Eq. (30). The major part of the Comment, as well as the conclusions, are derived from the consideration of the multiphoton component, which does not appear in our final result. At the same time, we admit that our original interpretation of multiphoton terms in Eq. (26) as well as the potential extension of Eq. (30) with such terms were not correct. The single-photon component plays an essential role in quantum key distribution and was the only one to appear in the final key rate equation, which is valid against the most general collective attack. The Comment concludes that Eq. (30) ``is incorrect ... and the security proof ... is not complete,'' which, as we show, is not the case.
Prospective Reading Teachers’ Digital Reading Habit: A Cross-sectional Design

This empirical investigation intended to determine the digital reading habit of prospective reading teachers with age range of 17 to 40, and mean age equals to 20.46 (Standard Deviation [SD] = 2.95). Moreover, this cross-sectional investigation employed a descriptive-quantitative-correlational design to determine whether a gender difference exists on the digital reading habit of the respondents and whether a significant relationship could be drawn between the polychotomous variable socioeconomic status (SES) and the digital reading habit (DRH), the main contruct investigated in this research. The data drawn from 328 respondents were collected through the utilization of an adapted survey-questionnaire with determined reliability of Cronbach’s alpha = 0.97. Analyses of the data evidenced that the respondents are of ‘satisfactory’ digital reading habit. In addition, it is disclosed that there significant difference on the DRH of the respondents of the study across the dichotomous variable gender with male to be noted to have better DRH. Moreover, the investigation established that there is a positive and significant relationship between the DRH of the respondents and their SES. Implications are provided.
A Stacking Model for Aviation High Risk Event Decision Making

Abstract Aviation accidents are one of the most causes of severe injuries and death worldwide. With the increase in inter-countries movement of the peoples around the world, air transportation is increasing at a much faster rate. As result possibilities of air accidents have also increased which may be due to inter-air collision, technical fault in airplane systems and pilot faults, etc. Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts, the research community to look into the factors behind these accidents by applying data analysis techniques based on an advanced machine learning algorithm. This analysis would be beneficial in forecasting the high-risk events based on the available historical accident data. So, in this paper, we classify all aviation abnormal events into three groups with regards to their associated risk: low-risk events, medium-risk events, high-risk events and then deployed ML algorithms like Random Forest, Support Vector Machine (SVM), XGBoost, Logistic Regression, etc., to make forecasts about high-risk events. After analyzing the results, the performance of the hybrid model that is formed by stacking of best-performing algorithms is found to best in quantifying the high-risk events.
Development of a Thermo-Computing Platform

There is an increasing demand for the recognition of context from sensor data. This is presently achieved through running complicated statistical signal processing algorithms with significant computing and memory requirements. In order to reduce the complexity and power requirements, unconventional computing platforms are being considered, which rely on the responses of the materials or devices instead of digitizing information and processing them. Herein, for the first time to the best of our knowledge, we propose a thermo-computing platform, which can shift much of the complex computations to the sensors. The proposed platform employs an entirely passive network of thermistors for processing temporal data. We present results that confirm the capability of the thermo-computer in processing data. A thermo-computer was then used for processing benchmark data, and its results are compared against algorithmic programming. The proposed platform, in addition to its use as a thermal computer, can lay the foundation for the development of cognizant sensor that utilize thermistor-like devices, such as MOX multi-gas sensors.
Representing Continuous Functions between Greatest Fixed Points of Indexed Containers

We describe a way to represent computable functions between coinductive types as particular transducers in type theory. This generalizes earlier work on functions between streams by P. Hancock to a much richer class of coinductive types. Those transducers can be defined in dependent type theory without any notion of equality but require inductive-recursive definitions. Most of the properties of these constructions only rely on a mild notion of equality (intensional equality) and can thus be formalized in the dependently typed language Agda.
Evaluating reliability of tree-patterns in extreme-<i>K</i> categorical samples problems

Exploratory Data Analysis (EDA) approaches are adopted to address the difficult extreme-K categorical sample problem. Due to observed data's categorical nature, all comparisons among populations are performed by comparing their distributions in the form of a histogram with symbolic bins. A distance measure is designed to evaluate the discrepancy between two symbol-based histograms to facilitate Hierarchical Clustering (HC) algorithms. The resultant binary HC-tree then serves as the basis for our EDA task of discovering tree-patterns of interest. Since each population-leaf's location within a binary HC-tree's geometry is expressed through a binary code sequence, a binary code segment characterizes all commonly shared tree-patterns for all members. We then generate a large ensemble of mimicries of the observed dataset based on multinomial distributions and construct a large ensemble of binary HC-trees. Upon each identified tree-pattern which we determined based on the observed dataset, we evaluate its reliability and uncertainty through two histograms.
Blockchain-based E-tendering Evaluation Framework

Blockchain-based E-tendering Evaluation Framework Xingbo Gong, Xingyu Tao, Moumita Das, Yuhan Liu and Jack Cheng Pages 115-121 (2021 Proceedings of the 38th ISARC, Dubai, UAE, ISBN 978-952-69524-1-3, ISSN 2413-5844) Abstract: Tendering & Bidding generally involve many construction units participating in a project bidding. The purchaser selects the best bidder with a short construction period, low cost, high quality, and good reputation. There are huge economic benefits and points of concern behind it. Tendering suffers an increased risk of data tampering due to the centralized database architecture, resulting in huge financial losses and unnecessary consumption of human resources. Blockchain technology has gained increasing attention in the construction industry because of its high transparency, information traceability, non-repudiation, and non-tampering characteristics. Initial explorations have been carried out to discuss blockchain benefits in the e-tendering process. However, existing studies are limited to conducting qualitative analysis or proposing conceptual frameworks. A workable blockchain-based that supports basic functions in the e-tendering process still lacks. Therefore, as an initial exploration, this paper presents a secure blockchain-based framework for tender evaluation. Besides, technical components such as tendering smart contracts are developed to support framework functionalities. The framework feasibility is validated in a tendering evaluation example. Results show that the proposed framework guarantees secure tender evaluation by keeping data traceability and immutability. Keywords: E-Tendering & Bidding; Blockchain; Smart contract; Tender transaction DOI: https://doi.org/10.22260/ISARC2021/0018 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support

In the twenty years since Dr. Leo Breiman's incendiary paper Statistical Modeling: The Two Cultures was first published, algorithmic modeling techniques have gone from controversial to commonplace in the statistical community. While the widespread adoption of these methods as part of the contemporary statistician's toolkit is a testament to Dr. Breiman's vision, the number of high-profile failures of algorithmic models suggests that Dr. Breiman's final remark that "the emphasis needs to be on the problem and the data" has been less widely heeded. In the spirit of Dr. Breiman, we detail an emerging research community in statistics - data-driven decision support. We assert that to realize the full potential of decision support, broadly and in the context of precision health, will require a culture of social awareness and accountability, in addition to ongoing attention towards complex technical challenges.