Reviewerly/exHarmony huggingface dataset:
Towards a corpus for credibility assessment in software practitioner blog articles

Blogs are a source of grey literature which are widely adopted by software practitioners for disseminating opinion and experience. Analysing such articles can provide useful insights into the state-of-practice for software engineering research. However, there are challenges in identifying higher quality content from the large quantity of articles available. Credibility assessment can help in identifying quality content, though there is a lack of existing corpora. Credibility is typically measured through a series of conceptual criteria, with 'argumentation' and 'evidence' being two important criteria. We create a corpus labelled for argumentation and evidence that can aid the credibility community. The corpus consists of articles from the blog of a single software practitioner and is publicly available. Three annotators label the corpus with a series of conceptual credibility criteria, reaching an agreement of 0.82 (Fleiss' Kappa). We present preliminary analysis of the corpus by using it to investigate the identification of claim sentences (one of our ten labels). We train four systems (Bert, KNN, Decision Tree and SVM) using three feature sets (Bag of Words, Topic Modelling and InferSent), achieving an F1 score of 0.64 using InferSent and a Linear SVM. Our preliminary results are promising, indicating that the corpus can help future studies in detecting the credibility of grey literature. Future research will investigate the degree to which the sentence level annotations can infer the credibility of the overall document.
Dense generic well-rounded lattices

It is well-known that the densest lattice sphere packings also typically have the largest kissing numbers. The sphere packing density maximization problem is known to have a solution among well-rounded lattices, of which the integer lattice $\mathbb{Z}^n$ is the simplest example. The integer lattice is also an example of a generic well-rounded lattice, i.e., a well-rounded lattice with a minimal kissing number. However, the integer lattice has the worst density among well-rounded lattices. In this paper, the problem of constructing explicit generic well-rounded lattices with dense sphere packings is considered. To this end, so-called tame lattices recently introduced by Damir et al. are utilized. Tame lattices came to be as a generalization of the ring of integers of certain abelian number fields. The sublattices of tame lattices constructed in this paper are shown to always result in either a generic well-rounded lattice or the lattice $A_n$, with density ranging between that of $\mathbb{Z}^n$ and $A_n$. Further, explicit deformations of some known densest lattice packings are constructed, yielding a family of generic well-rounded lattices with densities arbitrarily close to the optimum. In addition to being an interesting mathematical problem on its own, the constructions are also motivated from a more practical point of view. Namely, the constructed lattices provide good candidates for lattice codes used in secure wireless communications.
Design and Development of Alumni Tracking Information System

This study aims to produce a web-based alumni tracking information system at SMKN 1 Garut, test the alumni tracking information system based on functional aspects, and see user responses to the alumni tracking information system. This alumni tracking information system was developed using a waterfall model consisting of analysis, design, code, and test. The functional aspect testing through blackbox testing and expert validation was carried out by two experts, then the results were calculated using the matrix feature completeness formula (ISO 9126), while user responses were carried out by giving a questionnaire using a Likert scale to 10 respondents, namely alumni of SMKN 1 Garut. The results of the research are the web-based alumni tracing system at SMKN 1 Garut with the results of blackbox testing and expert validation, namely X is worth 1 then based on the feature completeness matrix (ISO 9126), the alumni tracking system at SMKN 1 Garut is "feasible" in terms of functional aspects. While the results of the calculation for user responses obtained a score of 462 in the range of 400 - 500 score intervals, it is interpreted as "Very Positive".
Towards the Effective Requirements for Building a Cloud Computing Infrastructure

In order to be competitive enterprises are rapidly changing in terms of technology and moving towards emerging technology trends.Recently cloud computing has emerged as a revolutionary trend and become an integral part of business organizations.However, it is important for organizations to consider a best cloud computing infrastructure that may best fit to the business needs.In this study different types of cloud computing infrastructures have been discussed.The study discusses and analyzes the essential requirements for effective cloud computing infrastructure and concludes the best infrastructure requirements for business organizations before they build a cloud computing infrastructure.
An Empirical Study on the Status Quo of Higher Vocational Teachers’ Informatization Teaching Ability – A Case Study of Hainan Province

In order to understand the status quo of teachers’ informatization teaching ability in Hainan higher vocational colleges, this study took teachers in Hainan higher vocational colleges as the survey samples and the survey was conducted from five perspectives including the teachers’ awareness of informatization teaching, the design ability of informatization teaching, the implementation ability of informatization teaching, the teachers’ demands for informatization training and their satisfaction with informatization environment. Then an empirical research was conducted through SPSS. The results showed that the teachers’ satisfaction with the school’s informatization environment would positively affect their awareness of informatization teaching, which would then positively affect their design ability of informatization teaching and demands for informatization training. Meanwhile, the teachers’ design ability of informatization teaching would positively affect their implementation ability of informatization teaching. In this sense, higher vocational colleges should intensify the school’s informatization environment, stipulate related policies to encourage information-based teaching, and provide informatization technology training in diversified manners to improve the teachers’ initiative in informatization learning.
Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction

The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which brings challenge in taking machine learning approaches for academic performance prediction. To this end, inspired by the recent advancement of pre-training method in natural language processing community, we propose DPA, a transfer learning framework with Discriminative Pre-training tasks for Academic performance prediction. DPA pre-trains two models, a generator and a discriminator, and fine-tunes the discriminator on academic performance prediction. In DPA's pre-training phase, a sequence of interactions where some tokens are masked is provided to the generator which is trained to reconstruct the original sequence. Then, the discriminator takes an interaction sequence where the masked tokens are replaced by the generator's outputs, and is trained to predict the originalities of all tokens in the sequence. Compared to the previous state-of-the-art generative pre-training method, DPA is more sample efficient, leading to fast convergence to lower academic performance prediction error. We conduct extensive experimental studies on a real-world dataset obtained from a multi-platform ITS application and show that DPA outperforms the previous state-of-the-art generative pre-training method with a reduction of 4.05% in mean absolute error and more robust to increased label-scarcity.
The Effect of Personality Education through Taekwondo Training of American Adolescents on Emotional Intelligence, Self-regulation Ability, and Problem Solving Ability

The purpose of this study is to investigate whether personality education through Taekwondo training has an effect on emotional intelligence, self-regulation, and problem-solving ability of American youth. The subjects of this study analyzed the data of 233 adolescents who participated in personality education through Taekwondo in the United States. SPSS 21.0 and AMOS 22.0 were used for structural equation model analysis. The results were as follows. First, in the personality education through Taekwondo training of American adolescents, emotional intelligence had a positive effect on self-regulation ability. Second, American youth
Exploring struggling readers of English as a second language’s experiences in the Malaysian primary classroom

Children are stakeholders in education, yet the young students’ voices pertinent to their learning experience with ESL reading, have been under-studied. Therefore this study aimed to explore the way struggling readers engaged with their English as a second language (henceforth ESL) reading. This study adopts a qualitative approach and data analysis was performed inductively involving co-construction of meaning between the participants’ expressions and the researcher’s own understanding. This paper discusses two themes discovered in the study which were “challenges” and “perceptions on classroom learning experiences”. The study revealed that all students had difficulties in reading English and perceived themselves as non-readers as well as had particular preferences on teaching methods of the teacher. The findings suggest that the Ministry of Education needs to assist educators to deal with the particular needs of young students and set attainable goals for the students to achieve in their ESL reading to boost their self-confidence.
몽골 초중고등학교의 스마트교실을 활용한 한국어교육

Journal of the International Network for Korean Language and Culture 17-2, 185-216. This paper aims to provide an overview of Korean language education in Mongolian elementary and secondary schools that use smart learning and to examine the usefulness of smart learning in Korean language education in the KFL environment as a new teaching-learning paradigm. This study reviewed theoretical studies of smart learning and discussed its main components and learning effectiveness in the Korean language in Mongolian elementary and secondary schools. As components of smart learning in the Korean language, this study focused on smart classrooms, which are composed of smart learning devices, smart learning contents of the Korean language, teaching methods, and teachers in charge of Korean language education. It also surveyed 15 teachers, and the results were analyzed to discuss the effectiveness of smart classrooms. To summarize these discussions, Korean language education in Mongolian elementary and secondary schools has proper components, and a relatively satisfactory learning effect. However, we are compelled to point out the limitations of this study which did not include a comparison of learning effects between the experimental and comparative groups owing to the special recent circumstances. Smart learning of the Korean language in Mongolian elementary and secondary schools has contributed to the expansion of Korean language education in Mongolia. It is considered a new educational paradigm that can be applied to the field of Korean language education in elementary and secondary schools abroad.(Sangmyung University)
VolNet: Estimating Human Body Part Volumes from a Single RGB Image

Human body volume estimation from a single RGB image is a challenging problem despite minimal attention from the research community. However VolNet, an architecture leveraging 2D and 3D pose estimation, body part segmentation and volume regression extracted from a single 2D RGB image combined with the subject's body height can be used to estimate the total body volume. VolNet is designed to predict the 2D and 3D pose as well as the body part segmentation in intermediate tasks. We generated a synthetic, large-scale dataset of photo-realistic images of human bodies with a wide range of body shapes and realistic poses called SURREALvols. By using Volnet and combining multiple stacked hourglass networks together with ResNeXt, our model correctly predicted the volume in ~82% of cases with a 10% tolerance threshold. This is a considerable improvement compared to state-of-the-art solutions such as BodyNet with only a ~38% success rate.
Pressured or Pleasure Reading: A Survey of Reading Preferences of Secondary School Students during COVID-19 Lockdown

The study was a descriptive cross-sectional survey which investigated the reading preferences of secondary school students in Nigeria during the lockdown occasioned by COVID-19 pandemic. A hundred secondary school students in Ile- Ife and Ilesa in Osun State, Nigeria were the study respondents. Close ended self-administered questionnaire was used to obtain data. Of the one hundred questionnaire which were applied to the study respondents, ninety-three were retrieved. Data obtained were analyzed using simple frequencies and percentages. The study established that majority of respondents were not pressured to read but read for pleasure. Preference for a wide range of fiction and non-fiction books which was read for purposes ranging from academic, social, relaxation and information was indicated by respondents. Availability of reading materials determined reading preference of majority of respondents as many could not afford to access electronic books whether free or not. The researchers recommended that different types of reading materials that suit the reading preferences and meet the diverse purpose of reading should be provided for the secondary school students during lockdown to improve their reading habits. Public libraries should function during this lockdown period within the guidelines of the appropriate health authorities, while school libraries should be equipped to offer virtual services during the lock down and the students should be exposed to e- books for their reading pleasure. URL:  http://ijkcdt.net/xml/29427/29427.pdf
Neural Monocular 3D Human Motion Capture with Physical Awareness

We present a new trainable system for physically plausible markerless 3D human motion capture, which achieves state-of-the-art results in a broad range of challenging scenarios. Unlike most neural methods for human motion capture, our approach, which we dub physionical, is aware of physical and environmental constraints. It combines in a fully differentiable way several key innovations, i.e., 1. a proportional-derivative controller, with gains predicted by a neural network, that reduces delays even in the presence of fast motions, 2. an explicit rigid body dynamics model and 3. a novel optimisation layer that prevents physically implausible foot-floor penetration as a hard constraint. The inputs to our system are 2D joint keypoints, which are canonicalised in a novel way so as to reduce the dependency on intrinsic camera parameters -- both at train and test time. This enables more accurate global translation estimation without generalisability loss. Our model can be finetuned only with 2D annotations when the 3D annotations are not available. It produces smooth and physically principled 3D motions in an interactive frame rate in a wide variety of challenging scenes, including newly recorded ones. Its advantages are especially noticeable on in-the-wild sequences that significantly differ from common 3D pose estimation benchmarks such as Human 3.6M and MPI-INF-3DHP. Qualitative results are available at http://gvv.mpi-inf.mpg.de/projects/PhysAware/
ENNS: Variable Selection, Regression, Classification and Deep Neural Network for High-Dimensional Data

High-dimensional, low sample-size (HDLSS) data problems have been a topic of immense importance for the last couple of decades. There is a vast literature that proposed a wide variety of approaches to deal with this situation, among which variable selection was a compelling idea. On the other hand, a deep neural network has been used to model complicated relationships and interactions among responses and features, which is hard to capture using a linear or an additive model. In this paper, we discuss the current status of variable selection techniques with the neural network models. We show that the stage-wise algorithm with neural network suffers from disadvantages such as the variables entering into the model later may not be consistent. We then propose an ensemble method to achieve better variable selection and prove that it has probability tending to zero that a false variable is selected. Then, we discuss additional regularization to deal with over-fitting and make better regression and classification. We study various statistical properties of our proposed method. Extensive simulations and real data examples are provided to support the theory and methodology.
Quantum state driving: measurements versus pulses

The quantum Zeno effect is well-known for fixing a system to an eigenstate by frequent measurements. It is also known that applying frequent unitary pulses induces a Zeno subspace that can also pin the system to an eigenspace. Both approaches have been studied as means to maintain a system in a certain subspace. Extending the two concepts, we consider making the measurements/pulses dynamical so that the state can move with the motion of the measurement axis/pulse basis. We show that the system stays in the dynamical eigenbasis when the measurements/pulses are slowly changing. Explicit bounds for the apply rate that guarantees a success probability are provided. In addition, both methods are inherently resilient against non-Markovian noise. Finally, we discuss the similarities and differences between the two methods and their connection to adiabatic quantum computation.
Management of Resource at the Network Edge for Federated Learning.

Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we describe the recent work on resource management at the edge, and explore the challenges and future directions to allow the execution of federated learning at the edge. Some of the problems of this management, such as discovery of resources, deployment, load balancing, migration, and energy efficiency will be discussed in the paper.
Recovering the Unbiased Scene Graphs from the Biased Ones

Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects. Recently, more efforts have been paid to the long tail problem in SGG; however, the imbalance in the fraction of missing labels of different classes, or reporting bias, exacerbating the long tail is rarely considered and cannot be solved by the existing debiasing methods. In this paper we show that, due to the missing labels, SGG can be viewed as a "Learning from Positive and Unlabeled data" (PU learning) problem, where the reporting bias can be removed by recovering the unbiased probabilities from the biased ones by utilizing label frequencies, i.e., the per-class fraction of labeled, positive examples in all the positive examples. To obtain accurate label frequency estimates, we propose Dynamic Label Frequency Estimation (DLFE) to take advantage of training-time data augmentation and average over multiple training iterations to introduce more valid examples. Extensive experiments show that DLFE is more effective in estimating label frequencies than a naive variant of the traditional estimate, and DLFE significantly alleviates the long tail and achieves state-of-the-art debiasing performance on the VG dataset. We also show qualitatively that SGG models with DLFE produce prominently more balanced and unbiased scene graphs.
Internet user and economic selected Southeast Asia nations: a panel data analysis / Noorhamizah Abdul Wahab, Sabri Nayan and Yong Kang Cheah

There has been an increasing interest among academicians about the relationship between the internet user and economic growth. The adoption of the internet creates an effective platform of business trade that provides expediency and convenience in doing business globally and thus results in tremendous dependency on technology to expand business locally and internationally. Hence, the use of the internet plays a crucial role in the economic development of a country and undoubtedly, to a certain extent, affects the economic growth of a nation. This paper aims to examine the relationship between the internet user and economic growth in seven selected Southeast Asia countries. Panel Autoregressive Distributed Lag (ARDL) (PMG) model is employed in examining the relationship between the internet user and economic growth from the year 1997 to 2013. Consistent with the endogenous growth theory proposed by Romer (1986), this study reveals that internet user and trade positively and significantly affect the economic growth in selected Southeast Asia countries. The increase in the use of internet thus is philanthropic to the users and can lead to faster economic growth. Internet improves the standard of living of an individual.
Readability of Aksaraand Budaya Lampung Text Book With Fry and Raygor Graph

Abstract: The aim of this research is to measure the readability level of Aksara and Budaya Lampung textbooks. To measure readability, the author used two readability formulas, namely Fry graph and Raygor graph. In this book, the authors took all the discourse to measure the level of readability. Using Fry graphs, 72% of discourse was not appropriate. Whereas measurements with raygor charts showed that 57% did not match the level of the readers. This difference was caused by linguistic factors. Compared with the number of sentences, the vocabulary used was still too easy for this level.
The Effect of Archives Infrastructure and Competency of Archives Officers on the Effectiveness of Work in the Development of Marga and Construction Development of North Sumatra Province

The aims of this study to find out the effect of archives infrastructure and competency of archives officers on the effectiveness of work in the development of marga and construction development of North Sumatra Province. The type of research used in this study is a causal relationship research with a quantitative approach. The quantitative approach is a systematic scientific study of the parts and phenomena and their relationships. The result shows that the relationship between the arrangement of archives, infrastructure and the competence of archiving officers on the effectiveness of work in the Department of Highways and Construction of North Sumatra Province that the R Square value obtained is 0.218 or 21.8%. This shows the ability of the Archives Arrangement variable (X1), Infrastructure (X2) and Archive Officer Competence (X3) simultaneously or together to affect Work Effectiveness by 21.8%.