Reviewerly/exHarmony huggingface dataset:
Accuracy of The Level of Critical Water Catchment Area for Flood Mitigation Around Bengkulu City, Indonesia

Disaster mitigation activities require the availability of a potentially flooded area (PFA) map. One of the causes of flooding is the criticality of water catchment areas; the higher the criticality level, the higher the flooding potential. This study aims to determine the accuracy of the model for determining the PFA around Bengkulu City, which was derived from the Level of Critical Water Catchment Area (LCWCA) model developed by the Ministry of Forestry. After obtaining the LCWCA Map, another analysis was performed in order to obtain the PFA Map. Furthermore, the overlaying was carried out with the Existing Flood Map in such a way that the level of accuracy is known. The threshold values from Justice are used to justify the level of accuracy in three categories, namely Good (&gt; 85%), Moderate (70 - 85%), and Poor (&lt;70%). The results showed that in the eight sub-watersheds around the city of Bengkulu, there were two sub-watersheds with reasonable accuracy (&gt; 85%), which means that there was &gt; 85% overlap between areas on the Potentially Flooded Area Map as a result of the analysis of The LCWCA with the area on the Existing Flood Map. There are three sub-watersheds with Moderate accuracy (70 - 85%) and three sub-watersheds with Poor accuracy (&lt;70%)
Which Factors Affect Q-Learning-based Automated Android Testing? – A Study Focusing on Algorithm, Learning Target, and Reward Function –

With the spread of smartphones, the importance of automated testing of mobile applications has increased.However, many current approaches are inadequate, as they are not able to test functions that are available only on hard-to-reach GUI, which is a screen that can be reached only through a specific sequence of input events.To solve this problem, there has been an increase in testing research based on reinforcement learning, specifically Qlearning.Each research uses different learning targets and reward function.Testing research has also been done using Deep Q-Network, which extends reinforcement learning in a "deep" way.Although each work has conducted their own evaluation, it is not clear how the combination of learning algorithm, learning target, and reward function affects the result.To bridge this gap, we have conducted an empirical study comparing eight possible combinations.Our study found that the combination of Deep Q-Network as the learning algorithm, component as the learning target, and GUI change ratio as the reward function had the highest test quality in terms of code coverage.
Europe Needs to Integrate Immersive Learning Quickly at All Education Levels – But How? What to Learn From 25 EU Projects in This Field

Manifold technical developments have allowed immersive technologies - virtual (VR) and augmented (AR) realities and their fusion to mixed or extended reality (MR/XR) - to find their way into many areas of life and work. Given their almost infinite creative possibilities, they are also of great interest for pedagogy. But what are the possibilities and where are the limits, and where does Europe stand in this respect at all? To find answers, we have evaluated 25 EU projects dealing with VR/AR/XR-based learning.
Search by lackadaisical quantum walk with symmetry breaking

The lackadaisical quantum walk is a lazy version of a discrete-time, coined quantum walk, where each vertex has a weighted self-loop that permits the walker to stay put. They have been used to speed up spatial search on a variety of graphs, including periodic lattices, strongly regular graphs, Johnson graphs, and the hypercube. In these prior works, the weights of the self-loops preserved the symmetries of the graphs. In this paper, we show that the self-loops can break all the symmetries of vertex-transitive graphs while providing the same computational speedups. Only the weight of the self-loop at the marked vertex matters, and the remaining self-loop weights can be chosen randomly, as long as they are small compared to the degree of the graph.
Multi-Fusion with Attention Mechanism for 3D Object Detection

Artificial intelligence gradually plays the essential role in automatic driving, such as 3d object detection.Many state-ofthe-art 3d detection frameworks fuse point cloud data and image data to perceive the surrounding environment of the vehicle.However, these approaches focus more on vehicle detections, and for objects with less point cloud sampling, such as pedestrians and cyclists, the performance is moderate.In this paper, we propose the multi-fusion framework with two kinds of attention mechanisms to solve the above problem and improve the detection accuracy of 3d objects.The proposed 3d attention mechanism with voxel sparse information is utilized in the framework.This framework contains two important modules: point fusion with 2d attention and voxel fusion with 3d attention.These modules firstly obtain the image features by projecting the lidar point or 8 vertices of the voxel to image feature maps.Then, these modules perform attentive fusion on the voxelized image features, point-wise image features and lidar data.Our evaluation on the challenging KITTI dataset, including 3d and bird's eye view metrics, demonstrates great improvements, especially at objects with less point cloud sampling.
Streaming access in a fractured world: Designing LibGuides with student users in mind

Faculty in the University of Baltimore Division of Legal, Ethical, and Historical Studies incorporate multiple modes of learning into their course materials. For many professors, this means that films are shown as part of the curriculum. As we know from professional experience, streaming access to films through platforms like Kanopy has become financially untenable for many academic libraries. While the RLB Library often loans physical copies of films for use in the classroom, the COVID-19 pandemic changed the way that we were able to provide access to physical copies and streaming media. Our Access Services department noted that reserve requests for August 2020 exceeded the number of reserve requests made in August for the last three years. All three authors have fielded more faculty requests for content, facilitated troubleshooting sessions when technology failed, and have educated additional faculty about streaming content since March 2020.
Using Kids Coloring Fun Application to enhance Children Vocabulary Mastery at Tumbuh Kembang Kindergarten

The research was mainly discussing the use of Kids Coloring Fun Application to enhance the vocabulary mastery of children in the Tumbuh Kembang Kindergarten. The researchers selected the sample from the population by using total sampling technique. The number of the sample was 40 children. This research was a pre-experimental design with one group pretest and posttest. The group was tested after treatment, the test included pretest and posttest that consisted of 25 items. The result of data analysis showed that there was a significant difference between the mean scores of pretest and posttest. The mean score of posttest was greater than the mean score of pretest. The means score of pretest was 5.06 and the mean score of posttest was 8.07. Then, the T-test value was 25.5 and T-table value was 2.042 with df = 39 and α = 0.05. It can be concluded that the use of Kids Coloring Fun Application can enhance children vocabularies. It means that there is a significant difference (H1) of the children vocabularies before and after treatment by using Kids Coloring Fun Application.
Comparison of American Sign Language Use Identification using Multi-Class SVM Classification, Backpropagation Neural Network, K - Nearest Neighbor and Naive Bayes

Communication is essential in conveying information from one individual to another. However, not all individuals in the world can communicate verbally. According to WHO, deafness is a hearing loss that affects 466 million people globally, and 34 million are children. So it is necessary to have a non-verbal language learning method for someone who has hearing problems. The purpose of this study is to build a system that can identify non-verbal language so that it can be easily understood in real-time. A high success rate in the system needs a proper method to be applied in the system, such as machine learning supported by wavelet feature extraction and different classification methods in image processing. Machine learning was applied in the system because of its ability to recognize and compare the classification results in four different methods. The four classifications used to compare the hand gesture recognition from American Sign Language are the Multi-Class SVM classification, Backpropagation Neural Network Backpropagation, K - Nearest Neighbor (K-NN), and Naïve Bayes. The simulation test of the four classification methods that have been carried out obtained success rates of 99.3%, 98.28%, 97.7%, and 95.98%, respectively. So it can be concluded that the classification method using the Multi-Class SVM has the highest success rate in the introduction of American Sign Language, which reaches 99.3%. The whole system is designed and tested using MATLAB as supporting software and data processing.
Implementasi Big Data Analytical Untuk Perguruan Tinggi Menggunakan Machine Learning

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance
Multiple Object Tracking by Bounding Boxes Without Using Texture Information and Optical Flow

Object tracking is a key task in many applications using video analytics. While there is a huge number of algo-rithms to track objects, there is still a need for new methods to solve the correspondence problem under certaincircumstances. In our article, we assume a very typical but still open scenario: a still image object detector hasalready identified the objects to be tracked; thus, we have object labels, confidence values, and bounding boxes ineach video frame captured at a low sampling rate. That is, optical flow methods difficult to be applied (also dueto bad lighting conditions, cluttered or homogeneous areas and strong ego-motion), and moreover, many objectslook similar (having the same category labels). Our proposed approach is based on the Hungarian method andincorporates the above information into the cost function evaluating the possible pairings of objects. To considerthe uncertainty of the detector, the elements of the confusion matrix also contribute to the cost of pairs, as wellas the probability of spatial translations based on prior observations. As a use case, we apply the algorithm to adata-set, where images were captured from onboard cameras and traffic signs were detected by RetinaNet. Weanalyze the performance with different parameter settings.
Management of Literacy Culture Development in Elementary Schools

The demand for knowledge plays a major role in the reading and writing ability of individuals towards an advanced generation. This study aims to describe the planning, organization, implementation, evaluation, and follow-up of literacy culture at SDN SN Sungai Miai 5 and 7 (National Standardized State Elementary Schools) in Banjarmasin. Data were collected through in-depth interviews, participatory observation, and documentation techniques, with its analysis carried out qualitatively. The results show that both schools have a good literacy culture and work together with residents to achieve the desired goals. It also shows a big influence on the achievements generated by the schools. It is hoped that the results of this study is used as material for consideration in implementing literacy culture development management in other schools.
Implementation of the Reading Write Literacy Program to Elementary School Students About the Impact of the Natural Disaster of Mount Merapi

The implementation of reading and writing literacy programs for elementary school students is needed because in the modern era there is a lack of interest in reading and writing owned by elementary school students. The purpose of this research is to implement literacy regarding the mitigation of the natural disaster of Mount Merapi for elementary school students. Because there are still many elementary school students who do not know the mitigation of the natural disaster of Mount Merapi. The method used in this study is a qualitative descriptive method in the form of an instrument used in distributing pre-test and post-test questionnaires. Contains 10 questions regarding the literacy program on the impact of the volcanic natural disaster, which is directed to elementary school. This is evidenced through a pre-test questionnaire at the beginning and the average result of 10 students before being given material on natural disaster mitigation is 45%. After students were given material through online learning media, the average results obtained increased by 80%. It is proven that based on the data obtained, most of the students have good knowledge about natural disaster mitigation. The impact felt by students related to the literacy program regarding the natural disaster of Mount Merapi, students are more concerned about natural disaster preparedness.
WORK SCHEDULING PROGRAM DESIGN FOR LECTURE ATTENDANCE CONTROL AND LECTURER WORKLOAD CALCULATION WITH FINGERPRINT AND RFID IDENTIFICATION DEVICE

The essential factor in achieving a good learning process is discipline in the lecture process. Students and lecturers are encouraged to be disciplined and active participants in the lecture process, allowing the Lecturer's principal duty of transforming and disseminating science and technology to become more concentrated. Furthermore, to eliminate overlapping lecture schedules, lecture hours, and lecture venues that do not meet needs, it is easier for stakeholders to monitor and regulate lecturers' and students' lecture attendance so that quick and accurate decision-making can be done. To establish discipline in school utilizing biometric identification technology (fingerprints) and Radio Frequency Identification (RFID) technology, developing application software to make it easier to manage work schedules is required. The Work Scheduling program is an online application built with the PHP programming language, MySQL database, and fingerprint and RFID identification support devices that are expected to be effective and efficient tools in the process of scheduling lectures and recording lecture attendance of students and lecturers, making it easier to publish lecture schedules.
Intuitive Contrasting Map for Antonym Embeddings

This paper shows that modern word embeddings contain information that distinguishes synonyms and antonyms despite small cosine similarities between corresponding vectors. This information is implicitly encoded in the geometry of the embeddings and could be extracted with a straightforward manifold learning procedure or a contrasting map. Such a map is trained on a small labeled subset of the data and can produce new embeddings that explicitly highlight specific semantic attributes of the word. The new embeddings produced by the map are shown to improve the performance on downstream tasks.
Fashion Retail Recommendation System by Multiple Clustering

Fashion retail has a large popularity and relevance, allowing customers to buy anytime finding the best offers and providing nice experiences in the shops.Consequently, Customer Relationship Management solutions have been enhanced by Information and Communication Technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their buying experience, as well as increasing the retailers' profitability.Current solutions on marketing provide a too general approach, based on most popular or most purchased items, losing the focus on the customer centricity.In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users' profiles in online and on physical stores.The proposed solution relies on association rules mining techniques, allowing to predict the purchase behavior of newly acquired customers, thus solving the cold start problems which is typical of current state of the art systems.The presented work has been developed in the context of the Feedback project founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark.The recommendation system has been validated in store, as well as online.
EYE-TRACKING-BASED DESIGN OF MIXED REALITY LEARNING ENVIRONMENTS IN STEM

With the advent of commercially available Mixed-Reality(MR)-headsets in recent years MR-assisted learning started to play a vital role in educational research, especially related to STEM (science, technology, engineering and mathematics) education. Along with these developments it seems viable to further frameworks and structured design processes for MR-based learning environments. Instead of a widely applicable framework for designing educational MR applications, we here consider the case of virtually enhancing physical hands-on experiments in STEM, where students are given a certain problem to solve, and how to design these. For this focused realm, we suggest an empirically driven problemand user-centred design process for MR applications to get novices to act more like experts and exemplify it for a specific experiment and problem set containing a non-trivial electric circuit with capacitors and coils.
Evidence-based Smartphone Use among Engineering Students in an Academic Writing Course

The role of smartphones is vital in academia as interconnectivity in the classroom promotes learning autonomy, increases motivation, and enhances teaching and learning mobility. Using classroom research design, this study aimed to investigate the perspectives of Engineering students of smartphone use in an academic writing course. The data were collected from students enrolled in a writing course in a top-ranked Science and Technology university in Thailand. Fifty students voluntarily submitted reflections towards the end of the semester. The study was qualitative, in which inductive coding was used. The findings elicited specific situations of smartphone use in an academic writing course, for example, knowing and looking at the meaning of words, knowing the word form, finding information, taking notes, brainstorming with friends, using translation, and others. Two roles of smartphone use were coded. The first role is facilitative, which has the following functions: resource-based, cognitive-based, memory-based, output-based, collaborative-based, entertainment-based, and communicative-based. Another is the debilitative role indicating two functions, such as sources of cognitive distraction and undesirable behaviors. Interestingly, self-regulation of smartphone use in class was coded. Implications on how smartphones can be used in teaching writing were also discussed.
Drone interactions within the field of Augmented Reality

Using drones and augmented reality paradigm, new forms of interactive algorithms has been created and proposed.We start with a first person view interaction where the drone mimics the movement of one person’s head wearing aHMD so that movements of the head can be mapped to actions by the drones. We then provide two novel AR/VRapplications of drones to create something similar to third person view in 2D and 3D. To get started, our firstidea is to control a drone using head movements. The second application which we implemented is to provide animplementation where tangible platforms are used by the drone to react to the movements of the character. Finallyour third implementaton if to create and AR world using real outdoor scenery and asking a drone to mimic a thirdperson view combining the real scenery with a synthetic actor so that based on the synthetic actor movement thedrone changes its behavior correctly in the real-word trying to provide a synchronized view of the real and syntheticword. There are three novel ideas providing a new form of interactions which will improve with drones functionalityin future. Our implementation shows the feasibility of our idea as discussed in the paper.
Rules Ruling Neural Networks – Neural vs. Rule-Based Grammar Checking for a Low Resource Language

We investigate both rule-based and machine learning methods for the task of compound error correction and evaluate their efficiency for North Sámi, a low resource language.The lack of error-free data needed for a neural approach is a challenge to the development of these tools, which is not shared by bigger languages.In order to compensate for that, we used a rulebased grammar checker to remove erroneous sentences and insert compound errors by splitting correct compounds.We describe how we set up the error detection rules, and how we train a bi-RNN based neural network.The precision of the rule-based model tested on a corpus with real errors (81.0%) is slightly better than the neural model (79.4%).The rule-based model is also more flexible with regard to fixing specific errors requested by the user community.However, the neural model has a better recall (98%).The results suggest that an approach that combines the advantages of both models would be desirable in the future.Our tools and data sets are open-source and freely available on GitHub and Zenodo.
Professional competence characteristics of information and communication technology specialists

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
On a Huge Family of Non-Schurian Schur Rings

In his famous monograph on permutation groups, H. Wielandt gives an example of a Schur ring over an elementary abelian group of order $p^2$ ($p&gt;3$ is a prime), which is non-schurian, that is, it is the transitivity module of no permutation group. Generalizing this example, we construct a huge family of non-schurian Schur rings over elementary abelian groups of even rank.