Reviewerly/exHarmony huggingface dataset:
Video Based Person Re Identification Methods, Datasets, and Deep Learning

Video Analytics applications like security and surveillance face a critical problem of person re-identification abbreviated as re-ID. The last decade witnessed the emergence of large-scale datasets and deep learning methods to use these huge data volumes. Most current re-ID methods are classified into either image-based or video-based re-ID. Matching persons across multiple camera views have attracted lots of recent research attention. Feature representation and metric learning are major issues for person re-identification. The focus of re-ID work is now shifting towards developing end-to-end re-Id and tracking systems for practical use with dynamic datasets. Most previous works contributed to the significant progress of person re-identification on still images using image retrieval models. This survey considers the more informative and challenging video-based person re-ID problem, pedestrian re-ID in particular. Publicly available datasets and codes are listed as a part of this work. Current trends which include open re-identification systems, use of discriminative features and deep learning is marching towards new applications in security and surveillance, typically for tracking.
Software restructuring models for object oriented programming languages using the fuzzy based clustering algorithm

Advances in the domain of software-based technology pave the way for widespread use of object-oriented programs.There is a need to develop a well-established software system that will reduce maintenance costs and enhance the usability of the component.While designing a software system, its internal structure deteriorates due to prolonged or delayed maintenance activities.In such situations, restructuring of the software is a superior approach to improve the structure without changing external behaviour of the system.One approach to carry out restructuring is to use refactoring on the existing source code for improving the internal structure of the code.Code refactoring is an effective technique for software development that improves the software's internal structure without changing its external behaviour.The purpose of refactoring is to improve the cohesion of existing code and minimize coupling in the existing module of a software system.Among numerous methods, clustering is one of the effective approaches to increase the cohesion of the system.Hence in this paper, the authors suggest to extract member functions and member variables and propose to find their similarity by Frequent Usage Pattern approach.Next, the proposed fuzzy based clustering algorithm perform effective code refactoring.The proposed method utilizes multiple refactoring methods to increase the cohesion of the component without any change in the meaning of the software system.The proposed system will offer automated support to change low-cohesive to high-cohesive functions.Finally, the proposed model has been experimentally tested with object-oriented programs.
The application of digital tools in listening to music in music culture education

Listening to music is a dominant activity in the Music Culture teaching in which students develop critical thinking about aesthetic value of music. Teachers mostly use CDs and DVDs as important tools during the music listening activity despite the development of technology. On today’s classes we meet digitally literate students born in a completely digitalized world, which represents a great challenge for teachers. The students and the system expect a wide range of digital contents – audio tracks, videos, animations, photos, etc. Fortunately, teachers can choose digital tools which will be used for improvement of the teaching activity – music listening. Research has been conducted within this paper and its aim is to determine the way in which the teachers implement the music listening activity, as well as the influence of the use of digital tools that help the students with learning and motivation. 675 students from four general education schools from fifth to eighth grade and their teachers of Music Culture from Osijek-Baranya County, as well as Vukovar-Srijem County took part in this research. The research is experimental and it is divided into two phases – before and after the use of digital tools. The research results show that the digital tool Kahoot! helps students to learn the matter easily and that they are much more motivated during music listening. Also, both students and teachers have developed digital competences.
BNPdensity: Bayesian nonparametric mixture modelling in R

Summary Robust statistical data modelling under potential model mis‐specification often requires leaving the parametric world for the nonparametric. In the latter, parameters are infinite dimensional objects such as functions, probability distributions or infinite vectors. In the Bayesian nonparametric approach, prior distributions are designed for these parameters, which provide a handle to manage the complexity of nonparametric models in practice. However, most modern Bayesian nonparametric models seem often out of reach to practitioners, as inference algorithms need careful design to deal with the infinite number of parameters. The aim of this work is to facilitate the journey by providing computational tools for Bayesian nonparametric inference. The article describes a set of functions available in the R package BNPdensity in order to carry out density estimation with an infinite mixture model, including all types of censored data. The package provides access to a large class of such models based on normalised random measures, which represent a generalisation of the popular Dirichlet process mixture. One striking advantage of this generalisation is that it offers much more robust priors on the number of clusters than the Dirichlet. Another crucial advantage is the complete flexibility in specifying the prior for the scale and location parameters of the clusters, because conjugacy is not required. Inference is performed using a theoretically grounded approximate sampling methodology known as the Ferguson &amp; Klass algorithm. The package also offers several goodness‐of‐fit diagnostics such as QQ plots, including a cross‐validation criterion, the conditional predictive ordinate. The proposed methodology is illustrated on a classical ecological risk assessment method called the species sensitivity distribution problem, showcasing the benefits of the Bayesian nonparametric framework.
Effect of Geological Characteristics on Landslides and Floods in Karangtengah

Bantul regency becomes an area that has a high potential for flooding in 2019 with each percentage of 27%, Bantul regency becomes an area that has a high potential for landslide disasters with a percentage of 43% or there has been a landslide as much as 220 times, landslide-prone points in Bantul districts are in some places there are 5 districts that are very vulnerable, one of the landslides and floods was in the Karangtengah village, research in this Karangtengah to see the most influential geological character as a trigger and cause of landslides and floods in the area of ??Karangtengah, The method used in this research is a field survey method assisted by GIS software, in field survey activities, by looking at the condition of the slopes, lithology on the slopes and structures that develop on the slopes, this analysis is carried out to determine the influence of geology on landslide disasters in the area. the upstream part which is the slope, while the analysis for the flood disaster is in the form of studies including infiltration at several points including residential areas and riverbanks and analysis of rainfall data, infiltration is carried out in the river boar area which is the downstream part, the geological aspects of landslides and floods in the Karangtengah area are quite dominant considering the results of the infiltration analysis, geomorphology and developing structures, besides the geological aspects of hydrometeorology and the use of a fairly large influence on the occurrence of the disaster.
Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing

Unsupervised cross-domain dependency parsing is to accomplish domain adaptation for dependency parsing without using labeled data in target domain.Existing methods are often of the pseudo-annotation type, which generates data through self-annotation of the base model and performing iterative training.However, these methods fail to consider the change of model structure for domain adaptation.In addition, the structural information contained in the text cannot be fully exploited.To remedy these drawbacks, we propose a Semantics-Structure Adaptative Dependency Parser (SSADP), which accomplishes unsupervised cross-domain dependency parsing without relying on pseudo-annotation or data selection.In particular, we design two feature extractors to extract semantic and structural features respectively.For each type of features, a corresponding feature adaptation method is utilized to achieve domain adaptation to align the domain distribution, which effectively enhances the unsupervised crossdomain transfer capability of the model.We validate the effectiveness of our model by conducting experiments on the CODT1 and CTB9 respectively, and the results demonstrate that our model can achieve consistent performance improvement.Besides, we verify the structure transfer ability of the proposed model by introducing Weisfeiler-Lehman Test.
Test Harder than You Train: Probing with Extrapolation Splits

Previous work on probing word representations for linguistic knowledge has focused on interpolation tasks.In this paper, we instead analyse probes in an extrapolation setting, where the inputs at test time are deliberately chosen to be 'harder' than the training examples.We argue that such an analysis can shed further light on the open question whether probes actually decode linguistic knowledge, or merely learn the diagnostic task from shallow features.To quantify the hardness of an example, we consider scoring functions based on linguistic, statistical, and learning-related criteria, all of which are applicable to a broad range of NLP tasks.We discuss the relative merits of these criteria in the context of two syntactic probing tasks, part-of-speech tagging and syntactic dependency labelling.From our theoretical and experimental analysis, we conclude that distance-based and hard statistical criteria show the clearest differences between interpolation and extrapolation settings, while at the same time being transparent, intuitive, and easy to control.
Penggunaan Google Classroom Kombinasi WhatsApp sebagai Media Pembelajaran Daring di Tengah Covid-19

The outbreak of the coronavirus outbreak has disrupted the world of education, so learning activities are carried out online. So, this study aims to determine the effectiveness of the use of google classroom combined with WhatsApp as an online learning medium in the midst of Covid-19. The method used is descriptive quantitative and data collection using a questionnaire. From the results of the distributed questionnaires, data on the use of google classroom combined with WhatsApp was quite effective for online learning media. The use of google classroom and WhatsApp is quite effective for delivering material and evaluating learning with a confidence level of 64.33% of respondents being able to understand lecture material through google classroom and WhatsApp. Likewise, the use of internet data with a confidence level of 85.21% of respondents believed that google classroom and WhatsApp used less internet data.&#x0D; &#x0D; Merebaknya wabah corona virus membuat dunia pendidikan terganggu sehingga aktivitas pembelajaran dilakukan secara daring. Oleh karena itu, penelitian ini bertujuan untuk mengetahui efektivitas pemanfaatan google classroom kombinasi WhatsApp sebagai media pembelajaran daring di tengah covid-19. Metode yang digunakan yaitu kuantitatif deskriptif dan pengumpulan data menggunakan angket. Berdasarkan hasil angket yang disebar, diperoleh data pemanfaatan google classroom kombinasi WhatsApp cukup efektif untuk media pembelajaran daring. Pemanfaatan google classroom dan WhatsApp cukup efektif untuk menyampaikan materi serta melakukan evaluasi pembelajaran dengan tingkat kepercayaan sebesar 64,33% responden dapat memahami materi kuliah melalui google classroom dan WhatsApp. Begitu juga dengan penggunaan data internet dengan tingkat kepercayaan sebesar 85,21% responden percaya bahwa google classroom dan WhatsApp lebih sedikit menggunakan data internet.
Impact of Organizational Commitment on Accounting Professionals’ Performance: Case of Accounting Firms

Purpose–This study examines the impact of organizational commitment(OC)on job performance. Two hundredforty professionals participated in this study.&#x0D; Design/methodology/approach–Our methodology has been applied toof 240 professional accountants who work in accounting offices in Tunisia.&#x0D; Findings–The results show that both affective and continuity dimensions have a positive and significant impact on performance, whereas the normative commitment has a positive but not significant effect.&#x0D; Originality/value–our knowledge, very little research has been conducted to investigate the accounting profession. our study aimed to fill this gap by studying the impact of OC on job performance of accounting professionals in the Tunisian context.
Real-time gas image enhancement algorithm based on guided filter simplification kernel

Aiming at the problem of low signal-to-noise ratio and weak imaging in infrared imaging of toxic and harmful gases, this paper proposes an algorithm for real-time enhancement of gas images based on the gain mask of the simplified kernel of the guided filter to improve the visual effects of gas infrared images. First, based on the gas movement between the two frames, the difference information between the two frames is amplified. Then, the differentially amplified image is decomposed into a detailed layer containing gas texture details and noise information and a base layer containing gas contour parts through a guided filter. The kernel of the guide filter is simplified to identify edges, textures and flat areas in the detail layer. A gain mask based on the guide filter simplification kernel is designed for the detail layer, which does not filter out the gas texture information and retains the edge information of the image while suppressing noise. Then the enhanced detail layer and the base layer are recombined to obtain an enhanced image. Finally, a segmentation algorithm based on the Gaussian mixture model is performed on the enhanced image to extract the gas area, and an adaptive connected domain is designed to remove misjudged front sights, so as to separate the gas area from the background. The experimental results show that the gas enhancement algorithm proposed in this paper can segment the gas area to a large extent, has strong robustness to the environment and exhibits good realtime performance.
3D spatial priors for semi-supervised organ segmentation with deep convolutional neural networks

Fully Convolutional neural Networks (FCNs) are the most popular models for medical image segmentation. However, they do not explicitly integrate spatial organ positions, which can be crucial for proper labeling in challenging contexts.In this work, we propose a method that combines a model representing prior probabilities of an organ position in 3D with visual FCN predictions by means of a generalized prior-driven prediction function. The prior is also used in a self-labeling process to handle low-data regimes, in order to improve the quality of the pseudo-label selection.Experiments carried out on CT scans from the public TCIA pancreas segmentation dataset reveal that the resulting STIPPLE model can significantly increase performances compared to the FCN baseline, especially with few training images. We also show that STIPPLE outperforms state-of-the-art semi-supervised segmentation methods by leveraging the spatial prior information.STIPPLE provides a segmentation method effective with few labeled examples, which is crucial in the medical domain. It offers an intuitive way to incorporate absolute position information by mimicking expert annotators.
Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.
Improving Evidence Retrieval with Claim-Evidence Entailment

Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim.Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant.We propose a novel model that uses the entailment score to express the relevancy.Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence.
PERAN LITERASI DIGITAL DALAM MODEL PEMBELAJARAN BLENDED LEARNING MAHASISWA PGMI

This study aims to determine the application, implementation and role of digital literacy in the blended learning model of PGMI students. The research method is qualitative, using descriptive research. His research background is in one of the private universities in Bangil, namely STAIPANA Bangil, especially students of PGMI semester I and semester V. As for the results of the research, in its application there are three things that need to be considered, namely: digital competence, application of digital competence in the context of the blended learning model, and digital transformation. In addition, its implementation requires educational staff to provide information, guide students or students, provide training and conduct assessments in the digital literacy process in the blended learning model. So that its role is to provide motivation so that the enthusiasm for learning follows the online and offline learning process, increase the digital abilities of students, open a wide discussion space, increase digital literacy skills, increase student soft skills, increase the national literacy movement and increase the ability to interact at the same time without obstructed by distance and space.&#x0D; Keywords: Digital Literacy, Blended Learning
Determinants of Africa’s Development: An Exploratory Study

The increasing sustainability and the causes of economic growth in African countries, provide a set of new research questions for development scholars around the World.The possible determinants of this phenomenon are usually considered to be of economic, social, and institutional natures.In this work is assessed which economic, social, and institutional determinants of economic development are important to the development of Africa countries, for the years 1996 and 2014.The similarities amongst countries and the evolution between the two years are also analyzed.A principal components analysis for categorical data to examine the inter-relationships between the indicators in 1996 and also in 2014.An agglomerative clustering algorithm was used through two different methods: ward's method and complete linkage method (also called furthest neighbor).The Hierarchical Cluster Analysis (HCA) was suited by a k-means algorithm, to obtain an optimal solution and a typology of countries was identified for each year.The main contribution of the work is to make a joint analysis of the three determinants of economic growth and developmenteconomic, social, and institutional, in which the literature is extremely scarce.Results indicate a positive association amongst institutional, economic, and social determinants of development, which means that countries that exhibit a good performance in institutional indicators also have a good performance in economic and social indicators, and vice-versa, although results are not as clear for 2014 as they are for 1996.Additionally, a higher concentration of countries in the two clusters in which these three indicators are better in 2014 (31 countries in 1996 and 49 countries in 2014), seems to indicate a positive evolution for development of African countries from 1996 to 2014.Results show that policy makers should take an integrated view regarding development and economic growth policies and take in consideration both the economic, social, and institutional characteristics of each country.If an economic, social, or institutional policy is designed independently of the other two, this policy will probably fail in reaching its development or economic growth goal, since all these three factors are interconnected.