Reviewerly/exHarmony huggingface dataset:
Getting into Learners’ Pace: QR Coded Supplemental Materials for SHS Learners in the Division of Laguna, Philippines

QR" stands for "Quick Response", which refers to the instant access to the information hidden in the code.It is a two-dimensional version of the barcode, typically made up of black and white pixel patterns.Now, it has found its way into mobile marketing with the widespread adoption of smartphones.This study explored the use of QR Codes in educational setting.The QR-coded supplemental materials were used both in modular and online distance learning since both modality use weekly home-learning plan (WHLP) being distributed to students in a regular basis.They were attached to WHLP beside the General Mathematics subject for the students to scan using their smartphones.If they don't have the scanning application in their phones, they could just encode the affixed link in WHLP through Google engines and the teacher-made brochure will automatically pop out.Upon scanning or encoding the affixed link, the learners were able to have a glance of the supplemental material.Fifteen schools, from the Department of Education, Division of Laguna, Philippines, including the home school of the author, participated.The study made use of explanatory sequential mixed method of quantitative and qualitative approaches.Findings revealed that there is a significant increase in the academic performance of Grade 11 learners in the General Mathematics when QR-coded supplemental materials were used.Both learners and teachers highly agree that QR-coded supplemental materials have various benefits in lesson preparation and understanding of key concepts and competency in General Mathematics.Themes developed are (1) Easy access is a key to understanding and learning, and (2) Less time to browse, more time to help and teach.Reflection and challenge on continuing to make ways for knowledge to be accessible was given at the end of the study.
Applications of Information Communication Technology (ICT) in Academic Libraries: An Overview of Turkana University College Library

Information is considered as need of human in the recent undertakings. In this paper the study has been done to research on application of Information communication Technology (ICT) and uses of ICT tools in Academic Libraries, with a focus on Turkana University College Library, to find out the application of ICT skills in Library Information Science professionals and to modernize the libraries. The study has been done in the School of Science and Technology and School of Education and Social Sciences, with the aims: (1) how students in these schools apply ICT in the library, and (2) how ICT tools are applied to provide innovative services to the users. This paper shows that the application of ICT tools is increasing in academic libraries especially from the two schools of Turkana University College, due to the development of technologies and according to the respondent ranking mobile phone is making it inevitable, due to easy access at any time anywhere.
An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures

Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents.The comparison of these models is usually limited because the hyperparameters are held fixed.In this paper, we present an empirical analysis and comparison of Neural Topic Models by finding the optimal hyperparameters of each model for four different performance measures adopting a singleobjective Bayesian optimization.This allows us to determine the robustness of a topic model for several evaluation metrics.We also empirically show the effect of the length of the documents on different optimized metrics and discover which evaluation metrics are in conflict or agreement with each other.
Leveraging e-Learning technology to enhance pre-service training for healthcare trainees in Ghana: evidence from a pilot project and pointers to policy reforms

E-Learning solutions are increasingly being advocated to augment existing traditional teaching and learning efforts in health training institutions. Unfortunately, these emerging technologies rarely focus on health sciences education within the context of public universities, hence the need for this study. This project evaluated experiences of healthcare trainees with a pilot e-Learning project (SMART e-Learning project) initiated in one of Ghana's public universities in 2017. The study used a mixed methods cross-sectional approach among 363 healthcare trainees. Data collection was between 17th October, 2019 to 3rd December, 2019. Data was analysed descriptively and test for variable differences done using Pearson Chi-square and Fisher's Exact for categorical variables. Wilcoxon Mann-Whitney test was used to test for differences in the Likert scale items. Additionally, rotated varimax factor analysis was performed for the health trainees' rated satisfaction factors.Out of 446 respondents who consented to participate in the study, 363 responses were complete and valid, representing a response rate of 81 %. Most frequently used e-learning facilities by healthcare trainees were: writing interim assessments (IAs) (82 %) while the least used function was live chating with faculty (5 %). Challenges associated with the e-learning pilot project were: limited workspace in the pilot computer laboratory (33 %), slow internet/intranet speed (29 %) and limited capacity of teaching and ICT staff to support users (28 %).There is the need to engage relevant stakeholders at the University, ministries of health and education through policy dialogues to develop statutory e-Learning guidelines for health educational institutions of higher learning to complement existing traditional teaching and learning approaches.
Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction

Background: Machine learning models have been developed for numerous medical prognostic purposes. These models are commonly developed using data from single centers or regional registries. Including data from multiple centers improves robustness and accuracy of prognostic models. However, data sharing between multiple centers is complex, mainly because of regulations and patient privacy issues. Objective: We aim to overcome data sharing impediments by using distributed ML and local learning followed by model integration. We applied these techniques to develop 1-year TAVI mortality estimation models with data from two centers without sharing any data. Methods: A distributed ML technique and local learning followed by model integration was used to develop models to predict 1-year mortality after TAVI. We included two populations with 1,160 (Center A) and 631 (Center B) patients. Five traditional ML algorithms were implemented. The results were compared to models created individually on each center. Results: The combined learning techniques outperformed the mono-center models. For center A, the combined local XGBoost achieved an AUC of 0.67 (compared to a mono-center AUC of 0.65) and, for center B, a distributed neural network achieved an AUC of 0.68 (compared to a mono-center AUC of 0.64). Conclusion: This study shows that distributed ML and combined local models techniques, can overcome data sharing limitations and result in more accurate models for TAVI mortality estimation. We have shown improved prognostic accuracy for both centers and can also be used as an alternative to overcome the problem of limited amounts of data when creating prognostic models.
Compression using Matrix Folding Algorithm

Space optimization is a particular phenomenon which has puzzled the computer programmers worldwide. More and more data needs to be stored into ever decreasing space. Several techniques are utilized to either decompress the files or better manage the memory. The first aspect involves the mathematics behind the conversion and the second aspect involves physics to store more data on to smaller chips. The proposed formulation i.e. The Matrix Folding Algorithm serves the first aspect of Space optimization by making use of mathematical calculations to reduce the size of a matrix. Matrix Folding Algorithm is a technique to reduce the space occupied by a square matrix to a level of 25% of the original size. The algorithm is based on mathematical calculations applied on the original values in order to double fold the matrix first from right to left and then from bottom to top. Like many other algorithms this one also has limitations that the original matrix should have numbers ranging from 0 through 15 only when we consider a system where the integer takes 2 bytes of space. And the matrix should have numbers ranging from 0 through 255 only when we consider a system where the integer takes 4 bytes of space.
Classification of Scale Items with Exploratory Graph Analysis and Machine Learning Methods

In exploratory factor analysis, although the researchers decide which items belong to which factors by considering statistical results, the decisions taken sometimes can be subjective in case of having items with similar factor loadings and complex factor structures. The aim of this study was to examine the validity of classifying items into dimensions with exploratory graph analysis (EGA), which has been used in determining the number of dimensions in recent years and machine learning methods. A Monte Carlo simulation was performed with a total number of 96 simulation conditions including average factor loadings, sample size, number of items per dimension, number of dimensions, and distribution of data. Percent correct and Kappa concordance values were used in the evaluation of the methods. When the findings obtained for different conditions were evaluated together, it was seen that the machine learning methods gave results comparable to those of EGA. Machine learning methods showed high performance in terms of percent correct values, especially in small and medium-sized samples. In all conditions where the average factor loading was .70, BayesNet, Naive Bayes, RandomForest, and RseslibKnn methods showed accurate classification performances above 80% like EGA method. BayesNet, Simple Logistic and RBFNetwork methods also demonstrated acceptable or high performance under many conditions. In general, Kappa concordance values also supported these results. The results revealed that machine learning methods can be used for similar conditions to examine whether the distribution of items across factors is done accurately or not.
AutoAspect: Automatic Annotation of Tense and Aspect for Uniform Meaning Representations

We present AutoAspect, a novel, rule-based annotation tool for labeling tense and aspect.The pilot version annotates English data.The aspect labels are designed specifically for Uniform Meaning Representations (UMR), an annotation schema that aims to encode crosslingual semantic information.The annotation tool combines syntactic and semantic cues to assign aspects on a sentence-by-sentence basis, following a sequence of rules that each output a UMR aspect.Identified events proceed through the sequence until they are assigned an aspect.We achieve a recall of 76.17% for identifying UMR events and an accuracy of 62.57% on all identified events, with high precision values for 2 of the aspect labels.
Penerapan Model Mind Mapping Berbantuan Media Pohon Ajaib Pada Siswa Tema 6 Cita-Citaku Kelas IV Sekolah Dasar

This study aims to describe the application of the Mind Mapping model assisted by magic tree media in increasing learning motivation, students' critical thinking and teacher skills. Theme 6 My Idea for Class IV Elementary School. Data collection techniques used were observation, interviews, documentation and tests. Data analysis in this study consisted of quantitative and qualitative. The results showed an increase in students' learning motivation by using the observation results of the first cycle with an average of 65 with criteria needing guidance, experiencing an increase in the second cycle that is 79 with good criteria, besides learning motivation was also seen from the questionnaire sheet with results 88 with good criteria. Critical thinking of students in the first cycle reaches an average of 65 with the criteria needing guidance, experiencing an increase in the second cycle that is equal to 77 with sufficient criteria.Teacher's skills also experienced an increase in cycle I reaching 88 with good criteria and in cycle II it reached an average of 97 with very good criteria.This proves that the application of the Mind Mapping model assisted by magical tree media can increase motivation to learn, critical thinking of students and teacher skills.Theme 6 "My future" for grade IV of Elementary School.
ROLoad: Securing Sensitive Operations with Pointee Integrity

Sensitive operations (e.g. control-flow transfers) are attractive targets for attackers. To protect them from being hijacked, we propose a new solution ROLoad to guarantee the integrity of their operands, which are loaded from (potentially corrupted) memory. We extend the RISC-V instruction set, implement an FPGA-based prototype of ROLoad, and then demonstrate two specific defense applications. Results show that this solution only costs few extra hardware resources (< 3.32%). However, it could enable many lightweight (e.g. with overheads less than 0.31%) defenses, and provide broader and stronger security guarantees than existing hardware solutions, e.g. ARM BTI and Intel CET.
Bismuth, lead-bismuth and lead-antimony sulfosalts from the granite-hosted hydrothermal quartz veins at the Elisabeth mine, Gemerská Poloma, Spišsko-gemerské rudohorie Mts., Slovakia

An interesting assemblage of bismuth and complex lead-bismuth and lead-antimony sulfosalts have been identified in samples from hydrothermal quartz veins hosted in S-type granitic rocks at the Elisabeth mine near Gemerská Poloma, Slovakia.We provide the first detailed study of the chemical composition of sulfosalts from the hydrothermal veins directly related to the specialized (Sn-W-F enriched) Gemeric granites.Bismuthinite derivates (bismuthinite and phases with n aik ranging from 21.3 to 23.7 and 30.3), minerals of the kobellite-tintinaite series (with Sb/(Sb + Bi) atomic ratio ranging considerably between 0.13 and 0.71), giessenite-izoklakeite series (with Sb/(Sb + Bi) from 0.26 to 0.33) as well as Pb-Sb sulfosalts (mainly jamesonite, boulangerite, robinsonite and their Bi-rich varieties) are common.Rare Bi-enriched rouxelite, bournonite and minerals of the tetrahedrite group were also observed.The two distinct types of sulfosalts associations were distinguished, each related to the different type of host rock and with variable Bi/Sb ratio.The first is represented predominantly by Bi-rich sulfosalts (bismuthinite derivates, kobellite, giessenite-izoklakeite) and occurs in the quartz veins hosted in P-enriched leucogranite.The second association is developed only in hydrothermal quartz veins hosted in porphyric granites and except of Bi (bismuthinite derivates) also significant amounts of Sb-rich sulfosalts (tintinaite, boulangerite, robinsonite, jamesonite, rouxelite, bournonite and tetrahedrite-(Zn) to tetrahedrite-(Fe)) are present.
Automating Claim Construction in Patent Applications: The CMUmine Dataset

Intellectual Property (IP) in the form of issued patents is a critical and very desirable element of innovation in high-tech.In this position paper, we explore the possibility of automating the legal task of Claim Construction in patent applications via Natural Language Processing (NLP) and Machine Learning (ML).To this end, we first create a large dataset known as CMUmine™and then demonstrate that, using NLP and ML techniques the Claim Construction in patent applications, a crucial legal task currently performed by IP attorneys, can be automated.To the best of our knowledge, this is the first public patent application dataset.Our results look very promising in automating the patent application process.
Cretaceous Geology, Age-Differences and Economic Geology of Gidan Alfarma Environ, Northwestern Nigeria

In this present study, the geology and economic aspect of lithological units of Sokoto Basin, sheet 4 South West Sokoto, part of Nigerian Sedimentary Basins were studied. It is bounded by latitudes N 13° 36' 30"- N 13° 40' 45" and longitudes E 05° 38' 00"- E 05° 40' 44"to gently undulating plain which was mapped on the scale of 1:25,000. The methodology adopted was mainly primary data which involved reconnaissance survey and actual or full field survey. The economic deposits include clays/ shale, lateritic ironstones, limestone, phosphate and gypsum, which are mined by the local artisanal miners.
The Multilingual Corpus of Survey Questionnaires Query Interface

The dawn of the digital age led to increasing demands for digital research resources, which shall be quickly processed and handled by computers.Due to the amount of data created by this digitization process, the design of tools that enable the analysis and management of data and metadata has become a relevant topic.In this context, the Multilingual Corpus of Survey Questionnaires (MCSQ) contributes to the creation and distribution of data for the Social Sciences and Humanities (SSH) following FAIR (Findable, Accessible, Interoperable and Reusable) principles, and provides functionalities for end-users that are not acquainted with programming through an easy-to-use interface.By simply applying the desired filters in the graphic interface, users can build linguistic resources for the survey research and translation areas, such as translation memories, thus facilitating data access and usage.
Some Features of Mail Traffic in online working in 2020-2021

The features of е-mailing during the mass transition to onlile working at the beginning of the COVID-19 pandemic are formulated. Using the example of a scientific organization, the quantitative characteristics of the components of e-mail traffic are considered and the proposals of their change in comparison with usual mail traffic are named.
Distant Teaching: A Way to Face COVID-19 Pandemic in a Public University

In México, first COVID-19 patient was identified in February and from March 23 on confinement measures were taken by the government, including the suspension of activities at universities. This paper objective is to explore the challenges that professors of a public university faced while moving into a distant teaching practice in the pandemic context. Universidad Autónoma Metropolitana is located in México City and up to the pandemic, most of its faculty lacked experience with online education, so the Emerging Distant Education Project was implemented. A qualitative design was developed and in-depth interviews were made to gather information from members of the Business Administration Faculty. Main findings show that in a short time, professors managed to redesign the courses and to develop materials, while trainning themselves in the use of educational platforms, videoconference and communication devices. Among positive experiences they mentioned widening their teaching skills, the positive and helpful attitudes from students, technical support from the institution and the achievement of learning objectives; while on the negative side they mentioned that math contents demanded an extra effort in order to secure students comprehension. Technical problems with the internet and the lack of a proper learning environment were cited as barriers to students’ optimal academic achievement. Most of professors defined their experience as “a challenge”. In conclusion, professors took on and overcame the challenges in order to continue classes and to achieve the academic objectives of the term.
Wireless Sensor Network-Based Mathematical Modeling for Communication Intelligent Monitoring System

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large-scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long-time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t-SNE) algorithm to expose the clustering features of large-scale cell phone users in the low-dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering

Relation detection in knowledge base question answering, aims to identify the path(s) of relations starting from the topic entity node that is linked to the answer node in knowledge graph.Such path might consist of multiple relations, which we call multi-hop.Moreover, for a single question, there may exist multiple relation paths to the correct answer, which we call multi-label.However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance.Therefore, in this paper, we propose a novel divide-and-conquer approach for multi-label multi-hop relation detection (DC-MLMH) by decomposing it into head relation detection and conditional relation path generation.In specific, a novel path sampling mechanism is proposed to generate diverse relation paths for the inference stage.A majority-vote policy is employed to detect final KB answer.Comprehensive experiments were conducted on the FreebaseQA benchmark dataset.Experimental results show that the proposed approach not only outperforms other competitive multi-label baselines, but also has superiority over some state-of-art KBQA methods.
Online Shaming - a New Challenge for Criminal Justice

Online social media platforms set the agenda and structure for public and private communication in our age. Their influence and power is beyond any traditional media empire. Their legal regulation is a pressing challenge, but currently, they are mainly governed by economic pressures. There are now diverse legislative attempts to regulate platforms in various parts of the world. The European Union and most of its Member States have historically relied on soft law, but are now looking to introduce regulation.