Reviewerly/exHarmony huggingface dataset:
Development of Distance-Measuring System for Person-Following Robot

A person-following robot can be an immensely useful assistive device for elderly people. It can support them to carry their belongings or other load to nearby places with zero effort. This paper reports the development details of a distance-measuring system for a person-following assistive robot (PFAR). The principle of the distance-measuring technique used and circuit details of the measurement and control system (MCS) developed around an ATmega-2560 microcontroller and an alarm and indication unit (AIU) is explained. The distance-measuring system has been combined with a direction-sensing system and tested to evaluate its performance in static mode. The results, as presented here, are very satisfactory. The system has also been tested in different lighting environments and found be unaffected. The work can form a basis of future research on other non-visual and visual sensors for target tracking by robots.
Story segmentation for news broadcast based on primary caption.

In the information explosion era, people only want to access the news information that they are interested in. News broadcast story segmentation is strongly needed, which is an essential basis for personalized delivery and short video. The existing advanced story boundary segmentation methods utilize semantic similarity of subtitles, thus entailing complex semantic computation. The title texts of news broadcast programs include headline (or primary) captions, dialogue captions and the channel logo, while the same story clips only render one primary caption in most news broadcast. Inspired by this fact, we propose a simple method for story segmentation based on the primary caption, which combines YOLOv3 based primary caption extraction and preliminary location of boundaries. In particular, we introduce mean hash to achieve the fast and reliable comparison for detected small-size primary caption blocks. We further incorporate scene recognition to exact the preliminary boundaries, because the primary captions always appear later than the story boundary. Experimental results on two Chinese news broadcast datasets show that our method enjoys high accuracy in terms of R, P and F1-measures.
Optimal Group Formulation Using Machine Learning

Group formation itself a perplexing process. Over the decade of time education and others disciple has improved imminently but optimal group formation in educational system is still struggling. Our research focus on to create optimal group in a class of any institute. In this research we use Simulated Annealing (SA) for best group formation based on the previous academic record. We generally create an arbitrary cluster first then optimise using SA. Our model has significant success rate over a large number of datasets. This research will play a pioneer role in group formations in the academic and related researches.
CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network

In this paper, we propose a new deep image compression framework called Complexity and Bitrate Adaptive Network (CBANet), which aims to learn one single network to support variable bitrate coding under different computational complexity constraints. In contrast to the existing state-of-the-art learning based image compression frameworks that only consider the rate-distortion trade-off without introducing any constraint related to the computational complexity, our CBANet considers the trade-off between the rate and distortion under dynamic computational complexity constraints. Specifically, to decode the images with one single decoder under various computational complexity constraints, we propose a new multi-branch complexity adaptive module, in which each branch only takes a small portion of the computational budget of the decoder. The reconstructed images with different visual qualities can be readily generated by using different numbers of branches. Furthermore, to achieve variable bitrate decoding with one single decoder, we propose a bitrate adaptive module to project the representation from a base bitrate to the expected representation at a target bitrate for transmission. Then it will project the transmitted representation at the target bitrate back to that at the base bitrate for the decoding process. The proposed bit adaptive module can significantly reduce the storage requirement for deployment platforms. As a result, our CBANet enables one single codec to support multiple bitrate decoding under various computational complexity constraints. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of our CBANet for deep image compression.
Academic motivation-achievement cycle and the behavioural pathways: a short-timeframe experiment with manipulated perceived achievement

The present research investigates the relations between motivation and academic achievement in educational contexts. The first main question is whether motivation is reciprocally related to achievement and a cyclic loop of motivation and achievement is formed over time. The second objective is to study a possible behavioural mediating pathway between motivation and achievement by measuring objective effort spent on learning. Third, we examine the causality of these relations by investigating the dynamics between motivation and achievement (rather than the individual constructs) when perceived achievement is experimentally manipulated. The study is a short-timeframe experiment in which undergraduate Dutch students learn new English vocabulary and their motivation, effort, and achievement is measured at multiple time points within one hour. Roughly halfway in the experiment, participants receive rigged feedback that their achievement has dropped, which is expected to influence their subsequent motivation, effort, and actual achievement. To study these dynamics, we will model how one construct affects another construct over time under a random-intercept cross-lagged panel framework. Planned analyses, expected (narrative) results, and simulated data are provided.
LOS DESAFÍOS DE LA UNIVERSIDAD BOLIVIANA EN LA MEJORA DE LA CALIDAD EDUCATIVA DE LA EDUCACIÓN SUPERIOR

This paper analyzes the challenges of the public university in Bolivia in improving the quality of education. To this end, the author proposes achieving this objective by analyzing the rankings of Bolivian universities by Webometrics , and also by counting the number of Bolivian academic journals related to the public university, as well as exploring the student participation indicators using official quantitative data.
Assessment of the effectivness of vocational guidance at the university

The changes occurring in the socioeconomic structure impact the development of higher education and the transition from industry 3.0 to industry 4.0 determined the goal of the education as the transformation from the educational process into a business process. The formation of a new approach to the processes of the university allows evaluating in details the results of the university. The ultimate goal of the study is to increase the effectiveness of the university. From the point of view of the process approach, educational activity is the main business process. In the case of statefinanced places, the main clients of this business process are the state and society. The state directly (through financing) and society indirectly (through paid taxes) invest in training future personnel who, in accordance with their educational direction, must participate in the creation of the gross product. Currently, according to various surveys, less than 70 percent of graduates are employed.
An introduction to the current debate on central bank digital currency (CBDC)

This paper provides an overview of the concept of central bank digital currency (CBDC) that may serve as a basis for an ordered discussion and an in-depth analysis of the different relevant aspects in the ongoing debate about this digital financial asset. The primary objective is to review the grounds that could warrant issuing CBDC and undertake a preliminary analysis of its main implications, especially those linked to the CBDC models that seem most likely to be adopted, judging by the motivations of the central banks whose issuance plans are currently most advanced.
Implementasi Aplikasi Quizizz Untuk Meningkatkan Kehadiran dan Hasil Belajar IPS

The Covid 19 Pandemic period provides a challenge for teachers to be creative in choosing the right learning media so that students are not bored in participating in Teaching and Learning Activities (KBM). The purpose of this study was to describe the implementation of the Quizizz application to increase attendance and analyze social studies learning outcomes. The research method used is evaluation research. This research comes from Best Practices experience when using the Quizizz application while teaching social studies. The Quizizz application has interesting features such as playing games so that students are motivated to learn. The results showed that students who wanted to fill in attendance before the Quiziz were 50%. After using Quizizz increased by 84%. Besides, the IPS scores of students who meet the KKM increased by 22%. This shows that implementing Quizizz can improve social studies attendance and learning outcomes.
AI supported Topic Modeling using KNIME-Workflows

Topic modeling algorithms traditionally model topics as list of weighted terms. These topic models can be used effectively to classify texts or to support text mining tasks such as text summarization or fact extraction. The general procedure relies on statistical analysis of term frequencies. The focus of this work is on the implementation of the knowledge-based topic modelling services in a KNIME workflow. A brief description and evaluation of the DBPedia-based enrichment approach and the comparative evaluation of enriched topic models will be outlined based on our previous work. DBpedia-Spotlight is used to identify entities in the input text and information from DBpedia is used to extend these entities. We provide a workflow developed in KNIME implementing this approach and perform a result comparison of topic modeling supported by knowledge base information to traditional LDA. This topic modeling approach allows semantic interpretation both by algorithms and by humans.
Digital ID Generation and Management Framework Using Blockchain

In the age of digitization and Internet, one of the most compelling need is to have a user identity management system that is decentralized, secure, and interoperable across different online service domains. The success of cryptographically secured, decentralized ledger system, called blockchain, as the underlying platform for Bitcoin cryptocurrency system, has made it a promising candidate for research and development in the identity management domain as well. One of the challenges in designing blockchain-based digital identity management solutions is to balance the degree of decentralization and self-sovereignty in order to maximize security, credibility of personal data mapped with the digital identity (ID), and increase usability. In this work, a novel digital ID generation and management framework over blockchain is proposed that balances centralized digital ID issuance process with a decentralized peer verification of the issued ID on the blockchain before the ID is confirmed and ready to be shared. The proposed work has been simulated on the Hyperledger-Sawtooth blockchain framework to demonstrate its feasibility. The results show the efficacy and usability of the proposed work. The work is concluded by showing future research directions for improving privacy of the proposed identity management system.
Improved Exposition Text Reading Ability By Using Problem Based Learning Students In SMP Negeri 3 Lahewa In 2020

Learning to Read Exposition Text with Problem Based Learning Model in Junior High School. This study aims to describe the process and results of learning to read exposition text using problem-based learning mode. Research designed with classroom action research. The data used in this study are data on the implementation process in the form of observations, field notes, and documentation, as well as evaluation data which includes the results of the reading test for the exposition text using a problem-based learning model. The data is sourced from grade VIII students of SMP Negeri 3 Lahewa. The results of this study indicate that the reading skills of the exposition text have increased significantly
Revisiting the Negative Data of Distantly Supervised Relation Extraction

Distantly supervision automatically generates plenty of training samples for relation extraction. However, it also incurs two major problems: noisy labels and imbalanced training data. Previous works focus more on reducing wrongly labeled relations (false positives) while few explore the missing relations that are caused by incompleteness of knowledge base (false negatives). Furthermore, the quantity of negative labels overwhelmingly surpasses the positive ones in previous problem formulations. In this paper, we first provide a thorough analysis of the above challenges caused by negative data. Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. Thirdly, we propose a pipeline approach, dubbed \textsc{ReRe}, that performs sentence-level relation detection then subject/object extraction to achieve sample-efficient training. Experimental results show that the proposed method consistently outperforms existing approaches and remains excellent performance even learned with a large quantity of false positive samples.
A Study imbalance handling by various data sampling methods in binary classification

The purpose of this research report is to present the our learning curve and the exposure to the Machine Learning life cycle, with the use of a Kaggle binary classification data set and taking to explore various techniques from pre-processing to the final optimization and model evaluation, also we highlight on the data imbalance issue and we discuss the different methods of handling that imbalance on the data level by over-sampling and under sampling not only to reach a balanced class representation but to improve the overall performance. This work also opens some gaps for future work.
Pengaruh Penggunaan LKS Terhadap Prestasi Belajar Siswa pada Mata Pelajaran Pendidikan Agama Islam

This study aims to determine the effect of using Student Worksheets on student achievement in Islamic subjects. This study uses quantitative research methods using correlation techniques. The population as well as the sample in this study were all students of SMP Laut Dendang, totaling 40 students. Data collection techniques using interview techniques, questionnaires, and observations. Correlation calculation is done by testing the hypothesis with the Product Moment correlation formula. The results showed that there was a positive influence between the use of student worksheets and student achievement in Islamic subjects, this is indicated by the results of calculations where r­­count= 0.719 while rtable= 0.302. This calculation also means that the teaching and learning process of teachers and students can make the most of the Student Worksheets so that student learning achievement will increase significantly.
Minimizing Cognitive Load in Cyber Learning Materials – An Eye Tracking Study

Cybersecurity education is critical in addressing the global cyber crisis. However, cybersecurity is inherently complex and teaching cyber can lead to cognitive overload among students. Cognitive load includes: 1) intrinsic load (IL- due to inherent difficulty of the topic), 2) extraneous (EL- due to presentation of material), and 3) germane (GL- due to extra effort put in for learning). The challenge is to minimize IL and EL and maximize GL. We propose a model to develop cybersecurity learning materials that incorporate both the Bloom's taxonomy cognitive framework and the design principles of content segmentation and interactivity. We conducted a randomized control/treatment group study to test the proposed model by measuring cognitive load using two eye-tracking metrics (fixation duration and pupil size) between two cybersecurity learning modalities – 1) segmented and interactive modules, and 2) traditional-without segmentation and interactivity (control). Nineteen computer science majors in a large comprehensive university participated in the study and completed a learning module focused on integer overflow in a popular programming language.
All-optical Quantum State Engineering for Rotation-symmetric Bosonic States

Continuous-variable quantum information processing through quantum optics offers a promising platform for building the next generation of scalable fault-tolerant information processors. To achieve quantum computational advantages and fault tolerance, non-Gaussian resources are essential. In this work, we propose and analyze a method to generate a variety of non-Gaussian states using coherent photon subtraction from a two-mode squeezed state followed by photon-number-resolving measurements. The proposed method offers a promising way to generate rotation-symmetric states conventionally used for quantum error correction with binomial codes and truncated Schrödinger cat codes. We consider the deleterious effects of experimental imperfections such as detection inefficiencies and losses in the state engineering protocol. Our method can be readily implemented with current quantum photonic technologies.
Collection of Thoughts Using Text Evidence

We prescribe learning the term contained in this thing with a specific feeling, presented word inserting learning calculations as a rule as it utilized words, but disregard enthusiastic messages. Since the words with comparative settings but inverse feeling extremity are connected to neighboring word vectors such as great or awful, a wistful examination is tricky. This issue is managed by scrambling data with content sentiments (e.g. sentences and words) at the side word settings in outstanding inserting.The near neighbors in space of feeling are related in a pioneering manner, integrating proof of meaning and degree of feeling.We create neuro networks with evolving loss roles to reliably interpret the embedded feeling and instantly gather broad texts with emoticon-like signals. Sentiment integration can actually be used in a variety of sentiment analyzes without a plug-in as word-features. We use inbuilt stimuli to understand words, to recognize the sentence of emotions and to create lexicons of emotions. Experimental results show that emotions are regularly incorporated into several bench-marking data sets. This work provides information on neural networks design in other natural ways for the study of word embedding.
Unsupervised learning of co-occurrences for face images retrieval.

Despite a huge leap in performance of face recognition systems in recent years, some cases remain challenging for them while being trivial for humans. This is because a human brain is exploiting much more information than the face appearance to identify a person. In this work, we aim at capturing the social context of unlabeled observed faces in order to improve face retrieval. In particular, we propose a framework that substantially improves face retrieval by exploiting the faces occurring simultaneously in a query's context to infer a multi-dimensional social context descriptor. Combining this compact structural descriptor with the individual visual face features in a common feature vector considerably increases the correct face retrieval rate and allows to disambiguate a large proportion of query results of different persons that are barely distinguishable visually. To evaluate our framework, we also introduce a new large dataset of faces of French TV personalities organised in TV shows in order to capture the co-occurrence relations between people. On this dataset, our framework is able to improve the mean Average Precision over a set of internal queries from 67.93% (using only facial features extracted with a state-of-the-art pre-trained model) to 78.16% (using both facial features and faces co-occurrences), and from 67.88% to 77.36% over a set of external queries.
Analisis Sentimen Multi-Class Pada Sosial Media Menggunakan Metode Long Short-Term Memory (LSTM)

Technological developments, especially in the internet and social media, could be a very important research subject in obtaining information, because of the large amount of information in a text found on social media. In recent years, there has been an increase in research about sentiment analysis on text reviews and tweets in order to determine the polarity generated by social media.  There are still few studies that apply the deep learning method with the Long Short-Term Memory (LSTM) algorithm to analyze multiclass sentiments in Indonesian-language texts. This study aims to analyze positive and negative emotions in social media texts using the information classification approach in the text and dividing them into 8 different classes using the LSTM method. The dataset is directly taken and collected from users' posts on social media. In testing the LSTM method, the calculation of the accuracy, exactness, review, f-measure values is generated. The results of the processing of the LSTM method show quite well with 5 trials with the highest accuracy value of 91.9% and the average value of multiclass getting 89.45% results.
KEMAMPUAN MENULIS CERPEN PADA SISWA KELAS X SEMESTER GENAP SMK TRISAKTI BANDAR LAMPUNG TAHUN PELAJARAN 2018/2019

The problem examined in this study is the students' ability to write short stories. Through the ability to write short stories, it is expected to improve students' abilities in the field of literature in general. To achieve the objectives of the study, descriptive qualitative methods were used with performance test data collection techniques because students were given the task of writing short stories. Scores obtained based on each indicator are as follows: the suitability of the story with the theme of entering the category very well with a frequency of 50%, creativity in developing the story entered in the good category with a frequency of 62%, completeness of the story in the category is quite good with a frequency of 62%, and the presentation of elements in the form of figures, plot, and setting in the category is quite good with a frequency of 43%. The ability of students to write short stories get an average value of 72 and enter into a fairly good category.
sparta: Sparse Tables and their Algebra with a View Towards High Dimensional Graphical Models

A graphical model is a multivariate (potentially very high dimensional) probabilistic model, which is formed by combining lower dimensional components. Inference (computation of conditional probabilities) is based on message passing algorithms that utilize conditional independence structures. In graphical models for discrete variables with finite state spaces, there is a fundamental problem in high dimensions: A discrete distribution is represented by a table of values, and in high dimensions such tables can become prohibitively large. In inference, such tables must be multiplied which can lead to even larger tables. The sparta package meets this challenge by implementing methods that efficiently handles multiplication and marginalization of sparse tables. The package was written in the R programming language and is freely available from the Comprehensive R Archive Network (CRAN). The companion package jti, also on CRAN, was developed to showcase the potential of sparta in connection to the Junction Tree Algorithm. We show, that jti is able to handle highly complex graphical models which are otherwise infeasible due to lack of computer memory, using sparta as a backend for table operations.