Reviewerly/exHarmony huggingface dataset:
The effectiveness of videoscribe as innovative media to increase students reading comprehension

The purpose of this study was to find out the effect of using videoscribe media learning on students' reading comprehension. We can create the content of reading use videoscribe with combine text and image in one video, so teaching learning proses doesnt monotonous and student can enjoyed to read. So it can help improved students reading comprehension. The design of this study was Pre-Experimental design. Thus, one classrooms containing the total of 15 students of eight grade at SMP AN NUR FUADI Bangkalan were selected as the sample. In collecting the data, pre-test and post-test were utilized to assess students’ comprehension abilities before and after the treatment. In analyzing the data, the t-tests (paired sample, and independent sample) were run in SPSS. The result is score post-test students is higher than pre-test score of studets. So the researcher conclude videoscribe media can applied in teaching reading to help teacher improved students reading comprehension.
Conversational Flow in Human-Robot Interactions at the Workplace: Comparing Humanoid and Android Robots

This article deals with the conversational flow during human-robot interactions at the workplace. We conducted an experimental field study in a company setting. Drawing on computer-as-social-actor (CASA) paradigm, we argue that employees might mindlessly apply social rules in conversations with service robots which is reflected in the degree of conversational flow. We examined to which extent the structure of the Conversational Skills Rating Scale (CSRS) can be replicated with real-life human-robot interactions and whether there are differences in conversational flow between humanoid robots, android robots and humans. We could show that the conversational flow in such service settings at the workplace is based on the factors expressiveness, coordination and composure. The results show that android robots can evoke a higher level of expressiveness in employees than humanoid robots. To our knowledge, our study is the first to compare different aspects of conversational flow towards service robots in a real workplace setting. Since conversational flow can create social bonds and satisfy social needs, companies should use this knowledge when considering using robots in customer or employee contact.
Additional Git Features

In this chapter, I have a lovely amalgam of Git features for you – features that I could not find any place to put. The reason they ended up here might be that where they would have originally fit, we had not established the right mental models, or that they are slightly tangential to the rest of the content in this book. These are features that might help you in your work but should not come into play on an everyday basis. Being aware of their existence might key you in for those dire situations where they are just the right thing for you. We cover figuring out what specific commit introduced a discrepancy using Git bisect. We use Git Submodules to manage dependencies between repositories. And we are going to use Git Large File Storage or Git LFS for short. If you made it this far, congratulations. You have completed the Practical Git curriculum and mastered the foundations. The rest is the icing on the cake.
Dampak Penggunaan Konten Youtube terhadap Minat Belajar Bahasa Inggris Mahasiswa Universitas Pamulang

Students often face many problems in learning process; one of the problem is attentiveness of learning. Students’ attention is relatively low when they face difficult subject, such as English. Student attentiveness can be formed by interesting learning media. The more interesting learning media used, the more interested students will be learnt. YouTube is social media that is often used as learning media. The Video tutorials containing various learning topics are widely uploaded on YouTube and the contents are attracted by the public. The English learning video is one of the learning video found on YouTube, which can be accessed by all levels of society, including students. The purpose of this research is to find out and analyze the effect of using YouTube content as teaching media toward students’ attentiveness in learning English in Bachelor Degree of Accounting Program in Pamulang University. Descriptive statistics and causality were the method of the research. Quantitative research in looking at variable relationships to the more researched object is the cause and effect (causal), Therefore, in the research, there are independent variable and dependent variable. The result shows the use of YouTube content as teaching media has a positive and significant influence on learning English in undergraduate student of Bachelor Degree of Accounting Program in Pamulang University.
Don't Panic! Better, Fewer, Syntax Errors for LR Parsers.

Syntax errors are generally easy to fix for humans, but not for parsers in general nor LR parsers in particular. Traditional mode error recovery, though easy to implement and applicable to any grammar, often leads to a cascading chain of errors that drown out the original. More advanced error recovery techniques suffer less from this problem but have seen little practical use because their typical performance was seen as poor, their worst case unbounded, and the repairs they reported arbitrary. In this paper we introduce the CPCT+ algorithm, and an implementation of that algorithm, that address these issues. First, CPCT+ reports the complete set of minimum cost repair sequences for a given location, allowing programmers to select the one that best fits their intention. Second, on a corpus of 200,000 real-world syntactically invalid Java programs, CPCT+ is able to repair 98.37%±0.017% of files within a timeout of 0.5s. Finally, CPCT+ uses the complete set of minimum cost repair sequences to reduce the cascading error problem, where incorrect error recovery causes further spurious syntax errors to be identified. Across the test corpus, CPCT+ reports 435,812±473 error locations to the user, reducing the cascading error problem substantially relative to the 981,628±0 error locations reported by panic mode.
Novel Descriptors for Effective Recognition of Face and Facial Expressions

In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.
Do Pipe Cleaners Help Software Engineers to Understand Agile Mindset?

Agile practices are widely introduced to projects these days. However, merely using processes/tools in the practices does not necessarily result in “achieving agility”, especially for those who have been using a waterfall model. They tend to think that the waterfall model is the only way. This mindset is the main obstacle to make the introduction of agile practices successful. To derive a true value in using them, understanding the underlying mindset in the agile practices is essential. This report introduces a novel workshop-based learning course to understand the agile mindset. The course participants create pipe cleaner arts for experiencing pseudo software development in two different models: waterfall and agile. A questionnaire result from total 201 participants shows that they perceived a clear difference between the two models. About 87% of them answered that the experience is useful for their future development works.
On Two Characterizations of Feature Models

Software-intensive systems can have thousands of interdependent configuration options across different subsystems. Feature models allow designers to organize the configuration space by describing configuration options using interdependent features: a feature is a name representing some functionality and each software variant is identified by a set of features. Different representations of feature models have been proposed in the literature. In this paper we focus on the propositional representation (which works well in practice) and the extensional representation (which has been recently shown well suited for theoretical investigations). We provide an algebraic and a propositional characterization of feature model operations and relations, and we formalize the connection between the two characterizations as monomorphisms from lattices of propositional feature models to lattices of extensional features models. This formalization sheds new light on the correspondence between the extensional and the propositional representations of feature models. It aims to foster the development of a formal framework for supporting practical exploitation of future theoretical developments on feature models and software product lines.
Volume Measurements of Post-transplanted Liver of Pediatric Recipients Using Workstations and Deep Learning

Purpose: The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant recipients on digital imaging and communications in medicine (DICOM) -format images using U-Net and three-dimensional (3-D) workstations (3DWS) . Method: For segmentation accuracy assessments, Dice coefficients were calculated for the graft liver and spleen. After verifying that the created DICOM-format images could be imported using the existing 3DWS, accuracy rates between the ground truth and segmentation images were calculated via mask processing. Result: As per the verification results, Dice coefficients for the test data were as follows: graft liver, 0.758 and spleen, 0.577. All created DICOM-format images were importable using the 3DWS, with accuracy rates of 87.10±4.70% and 80.27±11.29% for the graft liver and spleen, respectively. Conclusion: The U-Net could be used for graft liver and spleen segmentations, and volume measurement using 3DWS was simplified by this method.
Expert heuristic tuning design for the FRIQ-learning

The conventional reinforcement learning (RL) methods (e.g. Q-learning, SARSA, Fuzzy Q-learning) are searching for the solution starting from an empty initially empty knowledgebase, which is then expanded and filled by the problem related knowledge through iterations incrementally. These traditional RL systems do not have any additional external knowledge about the solution, therefore the learning phase may be a long process. Many methods exist which is able to inject external information into the RL system. This RL area is called heuristically accelerated reinforcement learning. The heuristically accelerated version of the fuzzy rule interpolation based Q-learning (FRIQ-learning) is able to incorporate the external expert knowledge in form of fuzzy rule-base into its knowledgebase. In this FRIQ-learning system the expert knowledge is static, it does not change during the learning phase. In the case if the external knowledge is not entirely correct, it can have a negative influence on the system efficiency (e.g. low convergence rate). Thus a methodology is needed, which is able to optimize (tune) the external knowledge rule-base (Q-function) during the learning phase too. The main goal of this paper is to suggest a method for the FRIQ-learning system which may be suitable for optimizing the injected expert knowledgebase (Q-function) too.
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF BERBASIS ADOBE FLASH CS6 UNTUK MENINGKATKAN MOTIVASI DAN HASIL BELAJAR PADA MATERI KESETIMBANGAN KIMIA

This study aims to determine: (1) the feasibility of interactive learning media based on Adobe Flash CS6; (2) differences in motivation and learning outcomes using interactive learning media based on Adobe Flash CS6 and using chemistry learning media on the internet; (3) the influence of motivation on learning outcomes. This study uses R&amp;D ADDIE (Analysis, Design, Development, Implementation, and Evaluation) method. The population was all students of second grade at SMA Negeri 1 Padangsidimpuan. The samples were taken by using purposive sampling, there were 60 students from two classes at the second grade. The data collection instruments were tests and questionnaires. The results: (1) the feasibility of interactive learning media based on Adobe Flash CS6 shows the mean is 4.5, which is very suitable to use; (2) motivation and learning outcomes using interactive learning media based on Adobe Flash CS6 shows 94.7% and 87.17 which is higher than the motivation and learning outcomes using chemistry learning media on the internet, namely 88.5% and 79.67; (3) the effect of learning motivation in learning outcomes using interactive learning media based on Adobe Flash CS6 is 18.6%. The results of this study is used as an input for teachers and prospective teachers in using interactive learning media to create fun learning and increase motivation that will have an impact on learning outcomes.
Answering Poetic Verses' Thematic Similarity Multiple-Choice Questions with BERT

We define the task of answering poetic verses' thematic similarity multiple-choice questions (MCQs), which can prove useful for estimating the difficulty of this type of question in the Iranian university entrance exam. Using the pre-trained, multilingual Google BERT language representation on 100 such questions, we have attempted to establish a baseline for the task by attaining an accuracy of 32%. The model's performance was shown to depend heavily on semantic similarity at the word level, and the model struggled with more abstract verses. These particular verses required an interpretation of the verse and contained little or no semantic hints that the model could exploit. As a result, it appears that the model has answered these questions randomly. While the 32% accuracy of the established baseline has much room for improvement, the current results may be used to find a correlation between the performance of the model and that of the test-takers. This correlation may later be used for the task of MCQ difficulty estimation. Furthermore, training the model on a dataset of Persian poetry as well as fine-tuning BERT could also yield better results.
Application Specific Statistical Analysis of Fully Homomorphic Crypto Systems for Cybersecurity

Cryptography has taken its roots since ages and has been evolving with each generation. From early day crypto-systems which were based on substitutions, to modern day elliptic-curve based systems, cryptography has come a long way. Generally, cryptographic systems are non-mutable in nature. This means that once the cryptographic data is generated, then in order to get perfect re-construction, we must not change it. This has been a major challenge with software-security-architects. Because, this immutability renders the data non-shareable. For instance, if Amazon wants to perform some complex computations on some confidential data, then the encryption and decryption keys for that data had to be shared with the analysis team. This team, would first decrypt the data, perform the computations, and then encrypt it back and send it over to Amazon. Which lead to chances of a major security breach from the analysis team. In order to avoid such breaches, homo-morphic systems were designed. Using homo-morphic encryption, security-engineers can encrypt data, and allow third parties to perform all kind of computational analysis on that data. Once the computations are done, then security-engineers decrypt the data, and obtain the same computations reflected on the original data. In this paper, we analyse various homo-morphic crypto-systems, and compare their performances in order to find which systems are best suited for what kind of applications. We also evaluate these systems, to find out certain advantages and drawbacks of these systems and provide in-depth analytical conclusions about them. This paper concludes with some interesting observations about the said algorithms, and also proposes ways to improve them.
INTERREGIONAL RESEARCH PROJECT «PSYCHOLOGICAL COMFORT OF EDUCATION IN MODERN RURAL SCHOOL» AS BASIS FOR MAKING A MODERN CONCEPT OF RURAL SCHOOLS DEVELOPMENT IN RUSSIA

The purpose of this article is to present the author's interpretation of the phenomenon of the education psychological comfort in rural school and some results on the analysis of its actual state, recorded in the framework of the empirical part of the study. The research methodology is integrative-differentiated, value, comparative, statistical and inductive approaches. The main methods are analysis of a few theoretical domestic and foreign sources on the problem of psychological comfort of education in a general educational organization; a survey method conducted in the format of a Google questionnaire for four groups of participants in the educational process – teachers teaching in basic and high schools, their parents and school administrators (the questionnaire used the diagnostic technique of I. A.»); content analysis of answers to open-ended questionnaires; method of primary qualitative analysis of empirical arrays obtained in the study; secondary analysis using methods of mathematical statistics. The content of the article presents the following research results: the author's interpretation of the phenomenon «psychological comfort of education in modern rural school»; the results obtained in 10 regions of the Russian Federation in the form of a) defining the mission of modern rural school based on an analysis of the answers of representatives of the administration of rural schools; b) the calculated complex characteristics of education in rural school «integral coefficient of psychological comfort in education». All results are of a comparative nature: data on rural schools correlate with similar parameters of urban schools in the regions. These results determine the scientific novelty of the research being carried out. The materials of the article will be useful to the pedagogical community; scientists interested in both the problems of rural schools and comparative pedagogy; managers and graduate students; possibly to social institutions collecting statistics on the education system. We should add that this article starts series of articles in which the results of the study on individual problems and regions will be presented in more detail.
CDS: Contextual Spoken Language Understanding Model Based on Dual Semantics

Abstract Spoken language understanding (SLU) in multi-turn task-oriented dialogue systems usually needs to take historical contextual information into account. To achieve this, current approaches encode dialogue utterances with contextual information to improve the accuracy of slot filling and intent detection. However, this kind of approach ignores the different influences of contextual information on dialogue utterances. Some utterances with independent context will be misunderstood due to the addition of noisy contextual information. In this paper, we propose a Dual Semantics model(CSD) that is based on dual semantics. Each dialogue utterance is encoded based on independent semantics and contextual semantics, where the contributions of these two semantic information are dynamically decided. A large number of experiments on two datasets verify the effectiveness of our approach. Specifically, the accuracy of slot filling at the KVRET dataset increases by 3.3% compared to the baseline model. A dataset of psychological service appointments(PSA), is further collected for the verification of our approach. Compared with existing methods, the accuracy is improved by 4.9% on this dataset.
Domain-Transferable Method for Named Entity Recognition Task

Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.
Bringing industry into the university experience

A large percentage of students in any computing program will graduate and take jobs in industry. Part of the challenge in the college curriculum is creating real-world experiences for students whil...
The Surveillance State: Privacy and Criminal Investigation in India: Possible Futures in a Post-Puttaswamy World

Indian law, as it stands, arms the State with disproportionate and unchecked power to extract and utilise private information. First, executive discretion to authorise and monitor surveillance is not subject to any independent inter-branch oversight. Second, illegally obtained evidence is usually admissible in courts as long as it is ‘relevant’. In this article, we examine the possible impact on this status quo flowing from the judgments of the Supreme Court of India in KS Puttaswamy v Union of India, (2017) 10 SCC 1 (dealing with the right to privacy) and KS Puttaswamy v Union of India (II), (2019) 1 SCC 1 (dealing with the constitutional challenge to the centralised, biometric-based, unique identity scheme ‘Aadhaar’). We unpack the doctrinal toolkit that these two judgments provide us with, including the standard of proportionality applied, and argue that they offer a framework for testing the existing system of laws governing surveillance. The thread running through our analysis is the judiciary’s role in preserving the rule of law and providing a counterweight to the executive’s power to deploy the State’s surveillance infrastructure. By focusing on individual liberty as a counterweight to State power, we argue that there is now a starting point for insisting on judicial oversight of surveillance action and exclusion of illegally obtained evidence.