Reviewerly/exHarmony huggingface dataset:
DESICOM as Metaheuristic Search

Decomposition into Simple Components (DESICOM) is a constrained matrix factorization method to decompose asymmetric square data matrices and represent them as combinations of very sparse basis matrices as well as dense asymmetric affinity matrices. When cast as a least squares problem, the process of finding the factor matrices needs special attention as solving for the basis matrices with fixed affinities is a combinatorial optimization problem usually requiring iterative updates that tend to result in locally optimal solutions. Aiming at computing globally optimal basis matrices, in this work we show how we can cast the problem of finding optimal basis matrices for DESICOM as a metaheuristic search and present an algorithm to factorize asymmetric data matrices. We empirically evaluate our algorithm on synthetic datasets and show that it can not only find interpretable factors but also, compared to the existing approach, can better represent the data and escape locally optimal solutions.
TinyVIRAT: Low-resolution Video Action Recognition

The existing research in action recognition is mostly focused on high-quality videos where the action is distinctly visible. In real-world surveillance environments, the actions in videos are captured at a wide range of resolutions. Most activities occur at a distance with a small resolution and recognizing such activities is a challenging problem. In this work, we focus on recognizing tiny actions in videos. We introduce a benchmark dataset, TinyVIRAT, which contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging. We propose a novel method for recognizing tiny actions in videos which utilizes a progressive generative approach to improve the quality of low-resolution actions. The proposed method also consists of a weakly trained attention mechanism which helps in focusing on the activity regions in the video. We perform extensive experiments to benchmark the proposed TinyVIRAT dataset and observe that the proposed method significantly improves the action recognition performance over baselines. We also evaluate the proposed approach on synthetically resized action recognition datasets and achieve state-of-the-art results when compared with existing methods. The dataset and code is publicly available at this https URL.
A Multi-Component Analysis of CPTED in the Cyberspace Domain

The visual fidelity of a virtual environment lacks the exceedingly complex layers from the physical world, but the continuous improvements of image rendering technology and computation powers have led to greater demands for virtual simulations. Our study employs Crime Prevention through Environmental Design (CPTED) as a risk control measure and utilizes two principles: Access Control and Natural Surveillance. We conducted an experiment with (n-sample: 100) graduate students. For the experiment, we utilized the Factor Analysis of Information Risk (FAIR) to quantitatively analyze the risk. Furthermore, we adopted the lme4 package for R to estimate the mixed effect of the 6,242,880 observations retrieved from Kaggle. Based on the two experiments, we were able to critically evaluate the contributions of CPTED through a multi-component analysis. Our study investigates how spatial syntax and territorial demarcation may translate in the cyberspace realm. We found that the corollaries of the mophology in the virtual environment effects the distribution of crime. The results of our study discusses how to determine the criminogenic designs and capacity in the cyberspace realm.
Alleviating Over-segmentation Errors by Detecting Action Boundaries

We propose an effective framework for the temporal action segmentation task, namely an Action Segment Refinement Framework (ASRF). Our model architecture consists of a long-term feature extractor and two branches: the Action Segmentation Branch (ASB) and the Boundary Regression Branch (BRB). The long-term feature extractor provides shared features for the two branches with a wide temporal receptive field. The ASB classifies video frames with action classes, while the BRB regresses the action boundary probabilities. The action boundaries predicted by the BRB refine the output from the ASB, which results in a significant performance improvement. Our contributions are three-fold: (i) We propose a framework for temporal action segmentation, the ASRF, which divides temporal action segmentation into frame-wise action classification and action boundary regression. Our framework refines frame-level hypotheses of action classes using predicted action boundaries. (ii) We propose a loss function for smoothing the transition of action probabilities, and analyze combinations of various loss functions for temporal action segmentation. (iii) Our framework outperforms state-of-the-art methods on three challenging datasets, offering an improvement of up to 13.7% in terms of segmental edit distance and up to 16.1% in terms of segmental F1 score. Our code will be publicly available soon.
The Optimization of Folklore in Literature Learning as a Strengthener of Student Literacy Culture

The existence of folklore as oral literature has been potentially eliminated of students.Student interest in learning and preserving has decrease.However, folklore as a cultural wealth of nations contains of noble values that should be inherited and preserved to the younger generation.The problem in this research is how to optimize folklore in literary learning as supporter of cultural literacy among students.The research method is a qualitative descriptive.The data of this study are folklore from Kudus.The data collection techniques and the research instruments are interview guides, observation sheets, data cards, and documentation files.The data analysis technique was carried out by qualitative techniques, namely reduction, data processing, data presentation, and conclusion.The introduction and preservation of folklore can be conducted by optimizing in literary learning as a learning resource.This optimization can be a reinforcement of the younger generation character and cultural literacy (reading and writing).This condition focuses on the declining of cultural literacy of students.Folklore as a fictional story can be presented with interesting language and not boring so that it can foster readers' interest to write and read.The strengthening of cultural literacy begins with having interest in reading then continues with writing.Thus, the optimizing of folklore is needed especially in the literary learning as a supporter of cultural literacy for young generations who are superior, creative and innovative.
Simulations in virtual reality: assessment of firefighters' decision-making competence

This study focuses on the use of virtual reality simulations for the training of portuguese firefighters in emergency operations management and the assessment of decision-making competence. The research was carried out through the action-research method. In the first cycle a pilot training course was carried out in a group of eight trainers from the Portuguese National Fire Service School, in which data were collected on the performance of trainees and in the evaluation of training. In the last step, corrections and improvements to the training course were identified. In the second cycle of action-research the steps were repeated with the formal implementation of the training course for eight fire officers of several fire brigades in the country. Every trainee played the role of incident commander in two different training scenarios and their performance was assessed at both times using a common evaluation tool. The evaluation of the impact of the changes introduced in the second cycle was carried out through a comparative analysis of the learning assessments, the evaluations of the trainees of the two groups and the evaluation of the team of trainers. The results of the trainees' performance and the training evaluation allow to conclude that the training course promoted the development of decision-making competence in the management of emergency operations using virtual reality simulations.
THE IMPLEMENTATION OF TEAM PAIR SOLO STRATEGY IN INCREASING THE STUDENTS’ ABILITY IN WRITING TEXT

The research deals with the implementation of team pair solo strategt in increasing the students’ ability in writing text. The objectives of study are to find out: 1. The students’ ability in writing text before and after implementation of Team Pair Solo Strategy, 2. The students’ ability in writing text can be increased by implementing Team Pair Solo Strategy. This research was conducted by implementing two cycles. Each cycle consists of planning, acting, observing and reflecting. By implementing two cycles can observe the increasing of the students’ ability. The subject of this study is one class of the eleventh students (XI – 1 IPS) of SMA Swasta YPIS Maju Binjai consists of 35 students.The data of this study were a sentence in writing analytical exposition text. The data were collected through test. The finding of this study revealed thatnthe students’ ability in writing text before implementing the strategy was still low, there was not students who got score point up 65 (English passing grade). After implementing the strategy the students’ ability was increased from the first cycle to the second cycle. Even, the second cycle is higher than the first cycle. And by implementing Team Pair Solo Strategy can increase significantly the students’ ability in writing text especially analytical exposition text.
Tracking the Untrackable.

Although short-term fully occlusion happens rare in visual object tracking, most trackers will fail under these circumstances. However, humans can still catch up the target by anticipating the trajectory of the target even the target is invisible. Recent psychology also has shown that humans build the mental image of the future. Inspired by that, we present a HAllucinating Features to Track (HAFT) model that enables to forecast the visual feature embedding of future frames. The anticipated future frames focus on the movement of the target while hallucinating the occluded part of the target. Jointly tracking on the hallucinated features and the real features improves the robustness of the tracker even when the target is highly occluded. Through extensive experimental evaluations, we achieve promising results on multiple datasets: OTB100, VOT2018, LaSOT, TrackingNet, and UAV123.
LibGuides: Data Visualization: News & Magazines

This guide is for the subject Data Visualization. It contains resources available in the NCU Library.
Chapter 3 Big Data Outlook, Tools, and Architectures

Big data is a persistent phenomena, the data is being generated and processed in a myriad of digitised scenarios. This chapter covers the history of ‘big data’ and aims to provide an overview of the existing terms and enablers related to big data. Furthermore, the chapter covers prominent technologies, tools, and architectures developed to handle this large data at scale. At the end, the chapter reviews knowledge graphs that address the challenges (e.g. heterogeneity, interoperability, variety) of big data through their specialised representation. After reading this chapter, the reader can develop an understanding of the broad spectrum of big data ranging from important terms, challenges, handling technologies, and their connection with large scale knowledge graphs.
PENCEMARAN TANAH AKIBAT LIMBAH INDUSTRI

The existence of industry in an area can help improve the economy of the local community.However, due to industrial processes, the industry will produce by-products in the form of waste.Waste if not managed properly, will certainly cause problems in the future.The purpose of this paper is to find out what is meant by soil pollution due to industrial waste, is there a legal basis that regulates soil pollution due to industry, what are the types of industrial waste, what is the government's role related to land pollution due to industrial waste, what are the the constraints faced and control measures in industrial waste.The method used in this research is normative juridical, using qualitative analysis with the resulting data in the form of secondary data obtained from previous research, laws and regulations and other sources.The results of this study are that soil pollution is a foreign object added to a land area which causes the quality of the soil in that land area to decrease in quality or endanger living things that use the land.This can be caused by the problem of non-existent processing units due to various considerations, such as land availability, high costs and low awareness of small industrial business actors.
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data

Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science. In this paper, we propose using a generalized sigmoid function to model the distance similarity in both high- and low-dimensional spaces. In particular, the parameter b is introduced to the generalized sigmoid function in low-dimensional space, so that we can adjust the heaviness of the function tail by changing the value of b. Using both simulated and real-world data sets, we show that our proposed method can generate visualization results comparable to those of uniform manifold approximation and projection (UMAP), which is a newly developed manifold learning technique with fast running speed, better global structure, and scalability to massive data sets. In addition, according to the purpose of the study and the data structure, we can decrease or increase the value of b to either reveal the finer cluster structure of the data or maintain the neighborhood continuity of the embedding for better visualization. Finally, we use domain knowledge to demonstrate that the finer subclusters revealed with small values of b are meaningful.
LibGuides: Data Visualization: Websites

This guide is for the subject Data Visualization. It contains resources available in the NCU Library.
Dynamic Visual Few-Shot Learning Through Parameter Prediction Network

Though machine learning algorithms have achieved great performance when adequate amounts of labeled data is available, there has been growing interest in reducing the volume of data required. While humans tend to be highly effective in this context, it remains a challenge for machine learning approaches. The goal of our work is to develop a visual learning based few-shot system that achieves good performance on novel few shot classes (with less than 5 samples each for training) and does not degrade the performance on the pre-trained large scale base classes and has a fast inference with little or zero training for adding new classes to the existing model. In this paper, we propose a novel, computationally efficient, yet effective framework called Param-Net, which is a multi-layer transformation function to convert the activations of a particular class to its corresponding parameters. Param-Net is pre-trained on large-scale base classes, and at inference time it adapts to novel few shot classes with just a single forward pass and zero-training, as the network is category-agnostic. Two publicly available datasets: MiniImageNet and Pascal-VOC were used for evaluation and benchmarking. Extensive comparison with related works indicate that, Param-Net outperforms the current state-of-the-art on 1-shot and 5-shot object recognition tasks in terms of accuracy as well as faster convergence (zero training). We also propose to fine-tune Param-Net with base classes as well as few-shot classes to significantly improve the accuracy (by more than 10% over zero-training approach), at the cost of slightly slower convergence (138 s of training on a Tesla K80 GPU for addition of a set of novel classes).
How Do Machines Learn?

A key question in machine learning is how learning is accomplished. Humans learn through experience in our lives, and it would be extremely beneficial to have machines that can do the same thing. Therefore, we should try to develop them, especially as machines are far better at some tasks than humans, such as large-scale mathematical calculations and high-capacity data storage. But how to find a way to make a machine learn? This is an entirely different problem, which this chapter addresses.
COMPETENCE APPROACH AND PROBLEMS OF ITS IMPLEMENTATION IN HIGHER EDUCATION

Currently, the vocational education system is based on a competency-based approach, which considers the person»s ability to act productively in a professional situation rather than the amount of information learned as the result of education. The term «competency», as an indicator of the result of training, was introduced in the Russian Federation in 2009, with the introduction of the state educational standards for higher education of the first generation (Federal State Educational Standard), at the same time as the graduate students» competency model was introduced. Competence-based education implies competence-based orientation of both the content of the educational process and the methods of its implementation, as well as assessment procedures, means of assessing the quality of students» training, since the goals and the content of education, the organization of the educational process that are provided by educational standards, determine the results of education in the form of competencies. The implementation of the standards of competencies set in the Federal State Educational Standard of Higher Education in the actual learning process proved to be not so simple. Difficulties arise when relating the goals of education with the expected results, i.e., competencies. The difficulties are associated with the mismatch between the practical orientation of the competence-based approach and the existing subject-based orientation of the pedagogical practice. In the course of studying a specific discipline, the opportunity to fully form a speicfic competency, not a set of competencies specified by the Federal State Educational Standard, but also a specific competence, is small. However, the main difficulty is associated with the assessment of the level of competence formation. A competence-based approach requires measuring not only specific knowledge and skills of students, but an assessment of the qualities of the person him- or herself. However, the existing system for assessing the quality of education does not have the necessary assessment tools for this. This article discusses the issues of competence-based education in higher education, analyzes its features, and problems raleted to the assessment of the level of competence formation.
MorphWorld: A State Transition Simulator

Digital simulation enables a wide variety of research and applications underlying the study of artificial life. In evolutionary robotics applications, the focus is often on maximizing performance o...
LibGuides: Science for First Grade: Molecular Biology

This science based libguide is for first grade teachers and homeschool partnerships working with ages 5-7. It is specific to the North Carolina Common Core for science but is easily adaptable for other science curricula studies.
A novel dowscaling procedure for compositional data in the Aitchison geometry with application to soil texture data

In this work, we present a novel downscaling procedure for compositional quantities based on the Aitchison geometry. The method is able to naturally consider compositional constraints, i.e. unit-sum and positivity. We show that the method can be used in a block sequential Gaussian simulation framework in order to assess the variability of downscaled quantities. Finally, to validate the method, we test it first in an idealized scenario and then apply it for the downscaling of digital soil maps on a more realistic case study. The digital soil maps for the realistic case study are obtained from SoilGrids, a system for automated soil mapping based on state-of-the-art spatial predictions methods.
Preventive care for asthma and multidisciplinary team social work: art and education experience

Objective: The purpose of this article is to describe the experience in art education of Social Work in a multiprofessional team, in the preventive care for asthma, and the changes felt by the participants of the "Choir Voices in Overcoming", through the (inter) experiences and experiences of singing and living with asthma. Methods: Qualitative, empirical, bibliographical and phenomenological research. Using field diary, semi-structured interview and content analysis. A total of 14 women and a man between the ages of 23 and 75 participated in the Asthma Reference Center. Results: Art education, through music, is a resource for the social worker to enhance the self-esteem of people in asthma treatment. Conclusion: People with asthma socializing while engaged in art education, influences their quality of life.
Meningkatkan Hasil Belajar Pada Materi Struktur dan Keseimbangan Pasar Dengan LKPD Berbasis Teams Games Tournament (TGT)

Teaching material is a supporting factor for students in the learning process. However, the irrelevance of teaching materials used will result in not achieving learning objectives. The purpose of this research is to describe the feasibility of LKPD with the Team Games Tournament model, to analyze the practicality and effectiveness of using LKPD with the TGT model. The research method used is Research &amp; Development using the 4D model from Thiagarajan. The research procedure consisted of 4 phases, define, design, develop, and disseminate. Instruments of product validity were obtained from material experts, graphic experts, evaluation experts and students' post-test results. The results of this study indicate that (1) The development of TGT-based LKPD is considered Eligible, based on the average validation of experts at 77.92%. (2) LKPD is considered effective in learning in terms of learning outcomes as well as the students' skills in working on HOTS questions increased with the final result 82.14% so that students are declared to have passed completeness. (3) LKPD is considered practical in learning because the development carried out is able to increase the interest and relaxation of students in the learning process according to the results of research students' responses where an interest in the product as much as 80.60% is said to be feasible. Based on these results it can be said that TGT based LKPD is declared to be very feasible and good for use in the learning process because it shows an increase in learning outcomes after using this LKPD.