Reviewerly/exHarmony huggingface dataset:
LEARNERS’ DEMOGRAPHICS, HOME-BASED SUPPORT SYSTEM AND READINESS AS CONTRIBUTING COMPONENTS TO THE MOST ESSENTIAL LEARNING COMPETENCIES (MELCS) IN ENGLISH 6

The sudden shutdowns of schools in the country brought by COVID-19 pandemic has become a great challenge for the Department of Education (DepEd).The department adhered to continue delivering quality education despite the challenges the country is facing through the implementation of distance learning.This study seeks to determine the components contributing to the first quarter Most Essential Learning Competencies (MELCs) in English 6 under the new normal.The respondents of this study were one hundred seventy-seven grade 6 pupils of Pila Elementary School both from online and modular classes.It employed the descriptive research design to describe a population, situation, or phenomenon concisely and orderly.Also, it used the quantitative research method to collect and analyze numerical data about the components related to the first Quarter MELCs in English 6.The weighted mean, percentage and standard deviation were used as statistical treatment of the study.The findings indicated that most of the learners involved in the study were females and those who are of in the average age or the maximum age category for grade 6 pupils which are estimated to be in the age category ranging from 10 years six months to 11 years five months.Though not highly educated, most of the respondents' parents are still capable of assisting their children in their distance learning.The results also showed that learners get adequate home-based support system.Parents and older siblings and other adult relatives attend to the needs of assisting them in learning their lessons and performing their tasks.In terms of readiness, respondents were found out to be technologically ready since using a computer in learning makes them feel excited in these new normal school days.However, the findings indicated that the respondents were not physically, psychologically and emotionally ready since learning in the new normal has become stressful for them.Some of the learners also experience physical health issues like frequent headaches and eye problems while attending online classes or answering my modules.The results of the study revealed that parents' educational attainment, the adequacy of home-based support system, learners' readiness as to physical, psychological-emotional, technological aspect and learning delivery modalities are contributing components to the most essential learning competencies.However, gender and age of learners are not significant to MELCs.
Parallelization of Machine Learning Algorithms Respectively on Single Machine and Spark

With the rapid development of big data technologies, how to dig out useful information from massive data becomes an essential problem. However, using machine learning algorithms to analyze large data may be time-consuming and inefficient on the traditional single machine. To solve these problems, this paper has made some research on the parallelization of several classic machine learning algorithms respectively on the single machine and the big data platform Spark. We compare the runtime and efficiency of traditional machine learning algorithms with parallelized machine learning algorithms respectively on the single machine and Spark platform. The research results have shown significant improvement in runtime and efficiency of parallelized machine learning algorithms.
A NON-TECHNICAL GUIDE TO NATURAL LANGUAGE PROCESSING AND ITS APPLICATIONS

In this ever-changing world, Artificial Intelligence (AI) is a constantly evolving field of Computer Science and Natural Language Processing (NLP) is a branch of AI which is extensively concerned with the ability of a computer to seamlessly understand human text and speech like a real human could. This could revolutionize the way we interact with computers and robots. This paper will explore some of the potential applications of NLP, including text summarization, sentiment analysis, chatbots, Question Answering Systems , and virtual assistants. We will also discuss how the use of NLP can help elevate businesses to the next level.
Attacks on Perception-Based Control Systems: Modeling and Fundamental Limits

We study the performance of perception-based control systems in the presence of attacks, and provide methods for modeling and analysis of their resiliency to stealthy attacks on both physical and perception-based sensing. Specifically, we consider a general setup with a nonlinear affine physical plant controlled with a perception-based controller that maps both the physical (e.g., IMUs) and perceptual (e.g., camera) sensing to the control input; the system is also equipped with a statistical or learning-based anomaly detector (AD). We model the attacks in the most general form, and introduce the notions of attack effectiveness and stealthiness independent of the used AD. In such setting, we consider attacks with different levels of runtime knowledge about the plant. We find sufficient conditions for existence of stealthy effective attacks that force the plant into an unsafe region without being detected by any AD. We show that as the open-loop unstable plant dynamics diverges faster and the closed-loop system converges faster to an equilibrium point, the system is more vulnerable to effective stealthy attacks. Also, depending on runtime information available to the attacker, the probability of attack remaining stealthy can be arbitrarily close to one, if the attacker's estimate of the plant's state is arbitrarily close to the true state; when an accurate estimate of the plant state is not available, the stealthiness level depends on the control performance in attack-free operation.
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin &amp; Eosin-Stained Histological Images

Recently, pruning deep neural networks (DNNs) has received a lot of attention for improving accuracy and generalization power, reducing network size, and increasing inference speed on specialized hardwares. Although pruning was mainly tested on computer vision tasks, its application in the context of medical image analysis has hardly been explored. This work investigates the impact of well-known pruning techniques, namely layer-wise and network-wide magnitude pruning, on the nuclei instance segmentation performance in histological images. Our utilized instance segmentation model consists of two main branches: (1) a semantic segmentation branch, and (2) a deep regression branch. We investigate the impact of weight pruning on the performance of both branches separately and on the final nuclei instance segmentation result. Evaluated on two publicly available datasets, our results show that layer-wise pruning delivers slightly better performance than networkwide pruning for small compression ratios (CRs) while for large CRs, network-wide pruning yields superior performance. For semantic segmentation, deep regression and final instance segmentation, 93.75 %, 95 %, and 80 % of the model weights can be pruned by layer-wise pruning with less than 2 % reduction in the performance of respective models.
Attention, stimulus and Augmented Reality for urban daily-life education in a social peripheral setting: the Streets that tell stories AR app

This paper aims to identify which design strategies can be considered when exploring different levels of stimulation of cognition and attention of the user in the development of educational location-based applications used in public urban space. We proposed the Stimulus Level Framework, divided into linear, circular and spiral stimuli, based on a theoretical reflection. It is a tool to help designers to think about educational and contextual digital experiences. Afterwards, we made a practical reflection about the proposed framework in a real context. An Augmented Reality project integrated with urban furniture of a historical street in the city of Fortaleza, Brazil, was proposed. Thinking about the design decisions based on the levels of the framework, helped in the process of data collection. It also helped in the proposition of elements that could stimulate the user's attention in different levels, having the technology as an element that unifies the whole experience.
FORMATION OF PROFESSIONAL QUALITIES OF THE FUTURE STUDENT LEADER MUSICAL AND INSTRUMENTAL BANDS

The author of the article develop the complex of professional qualities of future leader of student's orchestras and instrumental bands. His constituents - personality qualities, capabilities , knowledges, skills, abilities are analysedю
A Multi-stage deep architecture for summary generation of soccer videos

Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the field of video analytics, due to the massive popularity of the game and the emergence of new markets. Previous state-of-the-art methods on soccer matches video summarization rely on handcrafted heuristics to generate summaries which are poorly generalizable, but these works have yet proven that multiple modalities help detect the best actions of the game. On the other hand, machine learning models with higher generalization potential have entered the field of summarization of general-purpose videos, offering several deep learning approaches. However, most of them exploit content specificities that are not appropriate for sport whole-match videos. Although video content has been for many years the main source for automatizing knowledge extraction in soccer, the data that records all the events happening on the field has become lately very important in sports analytics, since this event data provides richer context information and requires less processing. We propose a method to generate the summary of a soccer match exploiting both the audio and the event metadata. The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries which are similar enough but with relevant variability to provide different options to the final editor. Furthermore, we show the generalization capability of our work since it can transfer knowledge between datasets from different broadcasting companies, different competitions, acquired in different conditions, and corresponding to summaries of different lengths
Optimizing Slimmable Networks for Multiple Target Platforms

In this work, we extend platform-aware adaptive training to the weighted average of multiple target platforms, where the weighting is determined e.g. by the market share of the target platform. To simulate different market regimes, we generate different weight settings by a Chinese restaurant process to benchmark optimization strategies. We use a neural architecture search framework based on Markov Random Fields to efficiently find the optimal channel configurations for each platform, and investigate different sampling strategies to train a single slimmable network that can be deployed to multiple platforms at the same time. Empirical results on CIFAR-100 demonstrate improved performance over the original slimmable network across different weight settings, while maintaining efficient training.
"Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction

Dimensionality reduction is a popular preprocessing and a widely used tool in data mining. Transparency, which is usually achieved by means of explanations, is nowadays a widely accepted and crucial requirement of machine learning based systems like classifiers and recommender systems. However, transparency of dimensionality reduction and other data mining tools have not been considered in much depth yet, still it is crucial to understand their behavior -- in particular practitioners might want to understand why a specific sample got mapped to a specific location. In order to (locally) understand the behavior of a given dimensionality reduction method, we introduce the abstract concept of contrasting explanations for dimensionality reduction, and apply a realization of this concept to the specific application of explaining two dimensional data visualization.
CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization

The quest for seeking health information has swamped the web with consumers' health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other healthcare needs, contributing to the challenges of natural language understanding. One way to address this challenge is to summarize the questions and distill the key information of the original question. To address this issue, we introduce a new dataset, CHQ-Summ that contains 1507 domain-expert annotated consumer health questions and corresponding summaries. The dataset is derived from the community question-answering forum and therefore provides a valuable resource for understanding consumer health-related posts on social media. We benchmark the dataset on multiple state-of-the-art summarization models to show the effectiveness of the dataset.
Approaching the Norns Shield as a Laptop Alternative for Democratizing Music Technology Ensembles

Music technology ensembles-often consisting of multiple laptops as the performers' primary instrument-provide collaborative artistic experiences for electronic musicians.In an effort to remove the significant technical and financial barriers that laptops can present to performers looking to start their own group, this paper proposes a solution in the form of the Norns Shield, a computer music instrument (CMI) that requires minimal set-up and promotes immediate music-making to performers of all skill levels.Prior research centered on using alternative CMIs to supplant laptops in ensemble settings is discussed, and the benefits of adopting the Norns Shield in service of democratizing and diversifying the music technology ensemble are demonstrated in a discussion centered on the University of Texas Rio Grande Valley New Music Ensemble's adoption of the instrument.A description of two software packages developed by the author showcases an extension of the instrument's abilities to share collaborative control data between internet-enabled CMIs and to remotely manage script launching and parameter configuration across a group of Norns Shields, providing resources for ensembles interested in incorporating the device into their ranks.
Trends and patterns of gender research in Indonesian folktales: Implications for primary education

The increasing awareness of researchers on the importance of gender equality in various fields influences the field of Indonesian literature. Folktales that have been considered as a source of noble values ​​began to be re-evaluated. Various forms of depiction that allegedly contain gender injustice are questioned. However, attention to gender issues in Indonesian folktales and their significance to education has not received serious attention. Using the Google Scholar database regarding the publication of scientific journals of Indonesian researchers in 2015-2021, this paper identifies patterns and trends of Indonesian researchers in analyzing gender issues in Indonesian folktales. Furthermore, a discussion of the implications of these findings for education is presented. This paper argues that the study of gender issues in folktales is still primarily focused on textual analysis. Applied research is needed that focuses on efforts to reconstruct stories from a gender perspective and use them in students’ reading texts, especially in primary education. In Indonesia, at the primary education level, folktales are often used as children’s reading material. Through these studies, an understanding of gender equality can be taught from an early age.
Fault Tolerance for Composite Cloud Services: A MAS-based Novel Approach

Several Cloud services may be composed in order to respond quickly to the users’ needs. Unfortunately, when running such a service some faults may occur. The outcome of fault control is a big challenge. In this paper, the authors propose a new approach based back recovery and multi-agent planning methods. The proposed architecture-based MAS (Multi-Agent System) is composed of two main types of Agents: a Composition Manager Agent (CMA) and a Supervisor Agent (SA). The role of the CMA is to create a set of plans as an oriented graph where the nodes are the Cloud services and the valued arcs represent the composition order of these services. This agent saves checkpoints (nodes) in a stable memory so that there is at least one possible path. However, the SA ensures that the running plan is working properly; otherwise, it informs the CMA to select another sub-plan. Experimental results show the performance of the proposed approach in terms of different metrics such as response time, cost, and total cost of different alternatives of cloud services.
Text-Aware End-to-end Mispronunciation Detection and Diagnosis

Mispronunciation detection and diagnosis (MDD) technology is a key component of computer-assisted pronunciation training system (CAPT). In the field of assessing the pronunciation quality of constrained speech, the given transcriptions can play the role of a teacher. Conventional methods have fully utilized the prior texts for the model construction or improving the system performance, e.g. forced-alignment and extended recognition networks. Recently, some end-to-end based methods attempt to incorporate the prior texts into model training and preliminarily show the effectiveness. However, previous studies mostly consider applying raw attention mechanism to fuse audio representations with text representations, without taking possible text-pronunciation mismatch into account. In this paper, we present a gating strategy that assigns more importance to the relevant audio features while suppressing irrelevant text information. Moreover, given the transcriptions, we design an extra contrastive loss to reduce the gap between the learning objective of phoneme recognition and MDD. We conducted experiments using two publicly available datasets (TIMIT and L2-Arctic) and our best model improved the F1 score from $57.51\%$ to $61.75\%$ compared to the baselines. Besides, we provide a detailed analysis to shed light on the effectiveness of gating mechanism and contrastive learning on MDD.
Parameterized Complexity Results for Bayesian Inference

We present completeness results for inference in Bayesian networks with respect to two different parameterizations, namely the number of variables and the topological vertex separation number. For this we introduce the parameterized complexity classes $\mathsf{W[1]PP}$ and $\mathsf{XLPP}$, which relate to $\mathsf{W[1]}$ and $\mathsf{XNLP}$ respectively as $\mathsf{PP}$ does to $\mathsf{NP}$. The second parameter is intended as a natural translation of the notion of pathwidth to the case of directed acyclic graphs, and as such it is a stronger parameter than the more commonly considered treewidth. Based on a recent conjecture, the completeness results for this parameter suggest that deterministic algorithms for inference require exponential space in terms of pathwidth and by extension treewidth. These results are intended to contribute towards a more precise understanding of the parameterized complexity of Bayesian inference and thus of its required computational resources in terms of both time and space.
Secret dance and bring light: Enhancing user autonomy with directional ambiguity in positive emotion regulation interventions

This case study summarizes the development of a self-administered behavioral intervention for positive emotion regulation (PER) that gives users the freedom to choose their own positive behaviors.In contrast to the predominant practice of prescribing positive behaviors (e.g., "keep a gratitude journal"), we explore whether -and how -open-ended instructions could permit users to self-select such behaviors themselves.Employing a research-through-design approach over 4 iterations, our product interactions utilize ambiguous prompts (e.g., 'secret dance' and 'bring light') intended to inspire users to first envision and then perform self-selected, positive behaviors during their day-to-day lives.The authors engage in self-reflective exercises and/or collect user feedback with each iteration to inform purposeful design choices intended to enhance user autonomy, interest, and subjective well-being.The case study concludes with a discussion of process-focused lessons for designers along with future research directions for enhancing user autonomy in PER-focused behavioral interventions.
Sistem Informasi Pengelolaan Manajemen Hotel pada Hotel Planet Holiday Berbasis Web Mobile

This study aims to design a mobile web-based hotel management information system at Planet Holiday Hotels, the background of this research is for problems that often occur in the management structure of Planet Holiday Hotels, namely the placement and compilation of data collection that is less than optimal at the time of the recap of employee data collection in position or department data so that the positions that are already full are entered into the Planet Holiday Hotel data collection which has various departments where there will be a data crosscheck process, they have to wait for the next data collection which will take a long time. The stages are literature study, data collection, data processing, system design, implementation, testing and deployment. Meanwhile, the method and modeling used is the Rational Unfield Process (RUP) and the modeling is using the Unfield Modeling Language (UML). This information system is designed based on a mobile web made with Process Hypertext Preprocessor (PHP), Cascading Style Sheet (CSS) technology, which was built using Xampp as a web server. With the existence of a management structure information system at Hotel Planet Holiday, it is hoped that the results of the research carried out can be concluded that it can help media information management become easier, system managers such as Human Resources can easily manage data collection and also employee data can be viewed in a timely manner. The management structure information system at Planet Holiday Hotel is expected to be able to collect data on the employee structure of the mobile web-based management structure information system so that it has a wider coverage and can help employees for efficient work programs.
A Survey : Neural Networks for AMR-to-Text

AMR-to-text is one of the key techniques in the NLP community that aims at generating sentences from the Abstract Meaning Representation (AMR) graphs. Since AMR was proposed in 2013, the study on AMR-to-Text has become increasingly prevalent as an essential branch of structured data to text because of the unique advantages of AMR as a high-level semantic description of natural language. In this paper, we provide a brief survey of AMR-to-Text. Firstly, we introduce the current scenario of this technique and point out its difficulties. Secondly, based on the methods used in previous studies, we roughly divided them into five categories according to their respective mechanisms, i.e., Rules-based, Seq-to-Seq-based, Graph-to-Seq-based, Transformer-based, and Pre-trained Language Model (PLM)-based. In particular, we detail the neural network-based method and present the latest progress of AMR-to-Text, which refers to AMR reconstruction, Decoder optimization, etc. Furthermore, we present the benchmarks and evaluation methods of AMR-to-Text. Eventually, we provide a summary of current techniques and the outlook for future research.