Reviewerly/exHarmony huggingface dataset:
Mindfulness Coaching Education Program: A Preliminary Study

Happiness could increase by both external factors, and exerting self-potential when internal growth is accompanied. In this respect, the importance of ‘mindfulness’ is emphasized. Mindfulness was originally based on the Buddhist practice as a states of self-awareness by nonjudgmental acceptance and awareness, but it is accepted as an important concept about happiness and leads to practical education and research in various areas. In this context, this study developes the mindfulness coaching education program by applying the coaching technique based on the Mindfulness-Based Stress Reduction (MBSR) program, a popularized mindfulness program, for the purpose of improving the happiness of modern people who live busy daily lives. The mindfulness coaching education program in this study is 6step ReMindfulness, consists of six steps and eight sessions. The main features are as follows. First, the process of restoring the original image is the process of planting the intention of going to a desired figure while looking around from the past to the present at each stage. Second, the program consists of open questions, using a coaching's question technique, which leads to conscious change and growth by expanding one's point of view and raising inner thoughts, emotions and patterns. Third, the program is composed of the GROW model of coaching, and there are a series of processes that build self-goals and action plans at each stage. The mindfulness coaching education program in this study has two advantages. Directional awareness is achieved quickly and creating a practical goal and will is generated for each individual, which can be compared to existing mindfulness education programs. Therefore, developing concrete education plans for various adult groups and preparing and implementing universal education programs through the validation of the effectiveness of education will contribute to restore modern people's mindfulness and to pursue their own happiness.
Relevance-Promoting Language Model for Short-Text Conversation

Despite the effectiveness of sequence-to-sequence framework on the task of Short-Text Conversation (STC), the issue of under-exploitation of training data (i.e., the supervision signals from query text is \textit{ignored}) still remains unresolved. Also, the adopted \textit{maximization}-based decoding strategies, inclined to generating the generic responses or responses with repetition, are unsuited to the STC task. In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation. To enhance generation performance, we design a relevance-promoting transformer language model, which performs additional supervised source attention after the self-attention to increase the importance of informative query tokens in calculating the token-level representation. The model further refines the query representation with relevance clues inferred from its multiple references during training. In testing, we adopt a \textit{randomization-over-maximization} strategy to reduce the generation of generic responses. Experimental results on a large Chinese STC dataset demonstrate the superiority of the proposed model on relevance metrics and diversity metrics.\footnote{Code available at https://ai.tencent.com/ailab/nlp/dialogue/.
Semantic Data Integration Service for eHealth Applications.

Technologies for health have been receiving considerable attention with the popularization of devices for internet access. The Internet can be seen as a repository of knowledge due to its large amount of available information; however, on the other hand, in the midst of this vast amount of content, there is information either scientifically inaccurate or incomplete. This work presents a semantic integration service to provide information of diabetes from medical databases to eHealth applications.
Hybrid Decision Trees: Longer Quantum Time is Strictly More Powerful

In this paper, we introduce the hybrid query complexity, denoted as $\mathrm{Q}(f;q)$, which is the minimal query number needed to compute $f$, when a classical decision tree is allowed to call $q'$-query quantum subroutines for any $q'\leq q$. We present the following results: $\bullet$ There exists a total Boolean function $f$ such that $\mathrm{Q}(f;1) = \widetilde{\mathcal{O}}(\mathrm{R}(f)^{4/5})$. $\bullet$ $\mathrm{Q}(f;q) = Ω(\mathrm{bs}(f)/q + \sqrt{\mathrm{bs}(f)})$ for any Boolean function $f$; the lower bound is tight when $f$ is the ${\rm O{\small R}}$ function. $\bullet$ $\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log n};1) = \widetildeΩ(\sqrt{n})$ for some sufficiently large constant $C$, where $g := {\rm B{\small OOL}S{\small IMON}}_n$ is a variant of Simon's problem. Note that $\mathrm{Q}(g\circ {\rm X{\small OR}}_{C \log n}) = \mathcal{O}(\mathrm{polylog}\; n)$. Therefore an exponential separation is established. Furthermore, this open the road to prove the conjecture $\forall k,\,\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log^{k+1} n};\log^{k} n) = \widetildeΩ(\sqrt{n})$, which would imply the oracle separation $\mathsf{HP}(\mathsf{QSIZE}(n^α))^\mathfrak{O} \subsetneq \mathsf{BQP}^\mathfrak{O}$ for any $α$, where $\mathsf{HP}(\mathsf{QSIZE}(n^α))$ is a complexity class that contains $\mathsf{BQTIME}(n^α)^{\mathsf{BPP}}$ and $\mathsf{BPP}^{\mathsf{BQTIME}(n^α)}$ in any relativized world.
Quantum computation for early universe cosmology

With the emergence of quantum computing we can, in theory, run complex simulations with many variables. For the field of cosmology, this could lend itself to applications in modeling the early universe as a quantum system to better understand large scale properties of the universe today. Here we utilize the hybrid variational quantum eigensolver (VQE) algorithm applied to an anharmonic oscillator system using QISKit to examine it's effectiveness for these cosmological questions. We then analyze a 1+1 dimensional ring of scalar bosons acting on a lattice as a naive model of the early universe. We chose to focus on rings of 3 and 5 massive scalars in 1+1 dimensions for our study to test the effectiveness of our approach.
Correction to: Optimal image compression via block-based adaptive colour reduction with minimal contour effect

After the publication of this work [1], it was noticed that the co-author Pejman Rasti was omitted as the first author following an institutional investigation. The first author’s name is inserted in this erratum article.
IMPROVING THE USABILITY TESTING: FUZZY BASED EXPERT SYSTEM FOR TEST CASE GENERATION ON WEB GRAPHICAL USER INTERFACE

Usability testing (UT) technique is used to evaluate the user-friendliness of a website or of its interface without involving the actual users of the website. UT is performed either manually or by using an automated tool. The manual process of usability testing is time-consuming and costly. Manual work requires more resources (testers) and there is a considerable chance to get inconsistent results. The objective of this research is to improve the efficiency and reliability of test cases’ (TC) generation whereas, the testing process is carried using automatic testing tools. Automated testing (AT) can be efficient and can provide accurate results. There are many automated tools available for software testing, with limited availability for TC automation. In this research, the systematic literature review (SLR) was conducted to find out the gap(s) in existing AT and challenges in TC generation. Secondly, the survey was conducted for identifying the main issues faced by different local testers during the process of generating TC manually. Fuzzy logic expert system was used to generate TC according to the selected suitable test cases. Fuzzy logic can emphasize non-probabilistic, uncertainty issues and multi-valued logic. The Data analysis was performed for the login page or registration page code and test cases were generated based on the GUI events through fuzzy logic. The system separated the keywords, attributes, and conditions from data analysis code and the output was displayed in the form of test cases. The comparative analysis was performed between manual TC generation processes with the fuzzy-based expert system for evaluation. The evaluation results obtained by statistical analysis showed that the proposed system is more efficient and reliable to generate test cases than the manual system.
STUDENTS AWARENESS OF PLAGIARISM IN PARAPHRASING ENGLISH TEXT

This study was conducted to find out the students awareness of plagiarism while they’re paraphrasing the English text. Researchers took 30 samples from private universities namely Universitas Muhammadiyah Aceh and the state university UIN Ar-Raniri Banda Aceh. This study uses questionnaire to investigate this research which contain 13 questions. The result of this study shows that more than seventy percent of the samples taken by researchers understand what paraphrase and what plagiarism are. However they still faced some problems how to avoid plagiarism in their writing. 10 percent of the students said in order to avoid plagiarism is by providing references even in their personal notes, 16,7 percent said that when using the ideas of others, it must clear who the original author was, then 33,3 percent said that to avoid plagiarism is only by writing notes in their own words and the last 40 percent of the students choose all the answers to answer all the way to avoid plagiarism.  Keywords: Paraphrasing, Plagiarism, Student’s Ability, English Text, Writing
Conclusion-Supplement Answer Generation for Non-Factoid Questions

This paper tackles the goal of conclusion-supplement answer generation for non-factoid questions, which is a critical issue in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI), as users often require supplementary information before accepting a conclusion. The current encoder-decoder framework, however, has difficulty generating such answers, since it may become confused when it tries to learn several different long answers to the same non-factoid question. Our solution, called an ensemble network, goes beyond single short sentences and fuses logically connected conclusion statements and supplementary statements. It extracts the context from the conclusion decoder’s output sequence and uses it to create supplementary decoder states on the basis of an attention mechanism. It also assesses the closeness of the question encoder’s output sequence and the separate outputs of the conclusion and supplement decoders as well as their combination. As a result, it generates answers that match the questions and have natural-sounding supplementary sequences in line with the context expressed by the conclusion sequence. Evaluations conducted on datasets including “Love Advice” and “Arts & Humanities” categories indicate that our model outputs much more accurate results than the tested baseline models do.
Histogram Transform Ensembles for Density Estimation

We investigate an algorithm named histogram transform ensembles (HTE) density estimator whose effectiveness is supported by both solid theoretical analysis and significant experimental performance. On the theoretical side, by decomposing the error term into approximation error and estimation error, we are able to conduct the following analysis: First of all, we establish the universal consistency under $L_1(\mu)$-norm. Secondly, under the assumption that the underlying density function resides in the H\"{o}lder space $C^{0,\alpha}$, we prove almost optimal convergence rates for both single and ensemble density estimators under $L_1(\mu)$-norm and $L_{\infty}(\mu)$-norm for different tail distributions, whereas in contrast, for its subspace $C^{1,\alpha}$ consisting of smoother functions, almost optimal convergence rates can only be established for the ensembles and the lower bound of the single estimators illustrates the benefits of ensembles over single density estimators. In the experiments, we first carry out simulations to illustrate that histogram transform ensembles surpass single histogram transforms, which offers powerful evidence to support the theoretical results in the space $C^{1,\alpha}$. Moreover, to further exert the experimental performances, we propose an adaptive version of HTE and study the parameters by generating several synthetic datasets with diversities in dimensions and distributions. Last but not least, real data experiments with other state-of-the-art density estimators demonstrate the accuracy of the adaptive HTE algorithm.
Effect of Audience Response System on Morbidity and Mortality Conference Engagement

This article was migrated. The article was marked as recommended. Objectives: Morbidity and mortality (M & M) conference is a central part of emergency medicine residency training. While audience response systems have become popular in traditional didactic teaching, little research has looked at effects in the unique M & M conference setting. We aimed to evaluate the effects of an audience response system on engagement in emergency medicine morbidity and mortality conference. Methods: An SMS text and internet-based audience response system was integrated into the M & M conference at one site in our emergency medicine residency. Anonymous, quantitative data about respondents use of the system was collected. Conference attendees were also surveyed to assess their evaluation of the ease of engagement in conference, effects of audience response system on engagement in conference and on perceived audience distractions. Results: The number of participants varied by conference and ranged from 37 to 63 respondents who responded from 1 to 21 times per conference (median 2 responses per respondent per conference). Subjects who used the audience response system were significantly more likely to report improved engagement in conference (p = .002). Subjects with more seniority (Assistant, Associate and Full Professors) reported easier engagement with M&M conference in general (p = .003). The audience response system did not result in significant reduction in audience distractions. Unexpected benefits of the audience response system included increased opportunity for engagement as well as quality of feedback for speakers. Conclusions: The integration of an audience response system into our M&M conference resulted in increased engagement and improved quality of speaker feedback. Further research is needed to evaluate effects on learning retention and clarify effects on audience distractions and behavior.
Sistem Informasi Monitoring Siswa Menggunakan SMS Gateway

SMK Telkom Bandung is one of the educational institutions under the Telkom Education Foundation called Telkom Schools. As a school that has a mission to implement information system based on Information and Communication Technologies (ICT) for learning media, SMK Telkom Bandung must have an information system that can process student data related to student absences and grades. The management of absence and grade of student is not efficient because in once assesment process still requires a long time, there are a lot of errors in the sum result and data loss. Where these data are used to measure of the progress of student achievement. Therefore, it is necessary to develop a monitoring system for students who can deliver absent report and student grades to parents quickly through SMS Gateway notifications to help monitor the teaching and learning process in schools. This information system was developed using the SDLC prototype and PIECES analysis approach by integrating Gammu SMS Gateway as a notification feature. In the testing phase using black box testing the results show that the system can documentation absent and grades in a structured manner, be able to convey student attendance report information and grades quickly, teachers can present and evaluate attendance report and reports and grades accurately, as well as with the SMS Gateway notification parents can help monitor the teaching and learning process at school.
Emergent Structures and Lifetime Structure Evolution in Artificial Neural Networks

Motivated by the flexibility of biological neural networks whose connectivity structure changes significantly during their lifetime, we introduce the Unstructured Recursive Network (URN) and demonstrate that it can exhibit similar flexibility during training via gradient descent. We show empirically that many of the different neural network structures commonly used in practice today (including fully connected, locally connected and residual networks of different depths and widths) can emerge dynamically from the same URN. These different structures can be derived using gradient descent on a single general loss function where the structure of the data and the relative strengths of various regulator terms determine the structure of the emergent network. We show that this loss function and the regulators arise naturally when considering the symmetries of the network as well as the geometric properties of the input data.
Model Pembelajaran Find Someone Who dan Timed Pair Share untuk Meningkatkan Motivasi dan Hasil Belajar IPS

&lt;p class="Abstrak"&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This study aims to describe the application of the Find Someone Who and Timed Pair Share learning models to improve social studies motivation and learning outcomes. The type of research used is Class Action Research. The subject of this study was the VB class of SDN Pakunden 2 with 37 students. The results of this study that the application of the Find Someone Who and Timed Pair Share learning models can increase the motivation and learning outcomes of social studies. At the end of the second cycle, all indicators assessed were able to exceed the specified success criteria.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Penelitian ini bertujuan untuk mendeskripsikan penerapan model pembelajaran &lt;em&gt;Find Someone Who&lt;/em&gt; dan &lt;em&gt;Timed Pair Share&lt;/em&gt; untuk meningkatkan motivasi dan hasil belajar IPS. Jenis penelitian yang digunakan adalah Penelitian Tindakan Kelas. Subjek penelitian ini adalah kelas VB SDN Pakunden 2 dengan jumlah 37 siswa. Hasil dari penelitian ini bahwa penerapan model pembelajaran &lt;em&gt;Find Someone Who&lt;/em&gt; dan &lt;em&gt;Timed Pair Share &lt;/em&gt;dapat meningkatkan motivasi dan hasil belajar IPS. Pada akhir siklus II, seluruh indikator yang dinilai mampu melampaui kriteria keberhasilan yang ditetapkan.
Fiction Sentence Expansion and Enhancement via Focused Objective and Novelty Curve Sampling

We describe the task of sentence expansion and enhancement, in which a sentence provided by a human is expanded in some creative way. The expansion should be understandable, believably grammatical, and optimally meaning-preserving. Sentence expansion and enhancement may serve as an authoring tool, or integrate in dynamic media, conversational agents, or variegated advertising. We implement a neural sentence expander trained on sentence compressions generated from a corpus of modern fiction. We modify an MLE objective to support the task by focusing on new words, and decode at test time with controlled curve-like novelty sampling. We run our sentence expander on sentences provided by human subjects and have humans evaluate these expansions. We show that, although the generation methods are inferior to professional human writers, they are comparable to, and as well liked as, our subjects' original input sentences, and preferred over baselines.
PERANCANGAN APLIKASI KOMPRESI FILE VIDEO MENGGUNAKAN ALGORITMA INTERPOLATIVE CODING

The size of the video files is relatively large, long duration and has a good top kulithen it requires a large storage space. With a very large video file size, at the time of the move process could be the process of moving failed because the media storage space exceeds the limit and consume very much quota at the time of transfer. The solution to this problem is how the video file can be compressed to accelerate the process of transferring and saving the video files and saving the quota. In this research, the algorithm used is Interpolative Coding, using the different compression methods-the difference of each value, and the compression results will be profitable, because the delivery will be easier and saves your quota and saves storage space.Keywords: Video File compression, Interpolative Coding algorithm
The Effectiveness of Online Dictionary in Teaching Reading Descriptive Text ( A Quasi Experimental Research at Tenth Grade of Students in SMAN 1 Ciomas).

Elsha Septiyaningsih. 152301877.2019. “The Effectiveness of Online Dictionary in Teaching Reading Descriptive Text ( A Quasi Experimental Research at Tenth Grade of Students in SMAN 1 Ciomas).This research investigates The Effectiveness of Online Dictionary in Teaching Reading Descriptive Text. This research used experimental research which was conducted base on the main problems, 1) how is student’s reading descriptive in SMAN 1 Ciomas? 2) is there any significant effect in using online dictionary on students’ comprehension of the text in SMAN 1 Ciomas? To answer the research question, the researcher collects the data from 60 students as a sample in two classes. One class as control class which consist of 30 students and another classes as experimental class which consist of 30 students. The researcher used of quasi experimental design with three stages are pre-test, treatment, post-test. The sample of the research used class X IPA 3 as experimental class and X IPA 4 as control class. Based on the result statistic calculation, it is obtained that the score of to is = 7,45> tt = 1,68 in degree of significant 5%. The score of to is = 7,45> tt = 2,39 in degree of significant 1%. It means that Ha : to>tt. it means there is significant influence of using online dictionary to improve student’s reading skill at ten grade of SMAN 1 Ciomas.Key words: online dictionary, experiment, reading comprehension.
Snagging on the new website theme is ...

Snagging on the new website theme is progressing nicely, although some things do take a while for the cache to empty. I think I'll go
Identity-Based Threshold Key-Insulated Proxy Signature in the Standard Model

Abstract Loss of secret keys is the deadly attack in public key cryptosystems. We present an identity-based threhold key-insulated proxy signature (IBTKIPS) scheme to settle the problem of key exposure in the identity-based proxy signature system. The proposed IBTKIPS scheme is secure in the standard model. In the IBTKIPS scheme, we use at least k out of n helper keys to refresh the temporary secret keys. The IBTKIPS scheme is more flexible and securer than the identity-based proxy signature scheme.
A Comparative Evaluation of SGM Variants (including a New Variant, tMGM) for Dense Stereo Matching

Our goal here is threefold: [1] To present a new dense-stereo matching algorithm, tMGM, that by combining the hierarchical logic of tSGM with the support structure of MGM achieves 6-8\% performance improvement over the baseline SGM (these performance numbers are posted under tMGM-16 in the Middlebury Benchmark V3 ); and [2] Through an exhaustive quantitative and qualitative comparative study, to compare how the major variants of the SGM approach to dense stereo matching, including the new tMGM, perform in the presence of: (a) illumination variations and shadows, (b) untextured or weakly textured regions, (c) repetitive patterns in the scene in the presence of large stereo rectification errors. [3] To present a novel DEM-Sculpting approach for estimating initial disparity search bounds for multi-date satellite stereo pairs. Based on our study, we have found that tMGM generally performs best with respect to all these data conditions. Both tSGM and MGM improve the density of stereo disparity maps and combining the two in tMGM makes it possible to accurately estimate the disparities at a significant number of pixels that would otherwise be declared invalid by SGM. The datasets we have used in our comparative evaluation include the Middlebury2014, KITTI2015, and ETH3D datasets and the satellite images over the San Fernando area from the MVS Challenge dataset.