Miracle-dz/UniFAR-FTUs huggingface dataset:
A Style-Aware Collaborative Filtering-Based Recommender System

Online shopping for clothing products is growing rapidly. In order to avoid choice overload and match consumers with the most suitable products, retailers use recommender systems. However, unlike other products, recommending clothes can be challenging. Most customers not only search a clothes by their popularity or price but also by style. We present a Collaborative Filtering recommender system based on the traditional Matrix Factorization which incorporates items’ contextual information in order to discover users’ aesthetic preferences. We apply a style-aware recommender model in a real-world dataset of Amazon for experimental evaluation, demonstrating that our algorithm outperforms the state-of-the-art CF-based recommender approach. Keywords: Recommender Systems, E-commerce, Collaborative Filtering
Heterogeneous information knowledge construction based on Ontology

Describing and representing multi-source and heterogeneous knowledge is a popular research topic in recent years. After investigating knowledge forming process based on multi-source heterogeneous information resources, we present a new approach in which different information resources are put into a mutual RDF(S) data model, and semantic reasoning of RDF(S) is conducted. Moreover, a knowledge base construction framework for multi-source heterogeneous information source with combination of Ontology knowledge model is put forward, and an algorithm of knowledge base construction is also proposed, in which the core issues are knowledge inclusion and updating. Then the time complexity of our algorithm is analyzed. Finally, in order to solve the heterogeneous, and uneven horizontal of geographical distribution of ethnic minority information resources in Yunnan Province, we use the proposed method to construct a domain knowledge base for ethnic minority information resources, and use this model to evaluate the efficiency for the knowledge inclusion algorithm in responding time and indexing responding time for different data resources in our experiments.
Role of K-Means Algorithm in Disease Prediction

Disease Prediction has always been a matter of research due to increasing number of health risks. Modern Medicine System produce huge amount of data which needs to be organized. Medical Data Mining plays a vital role in generating efficient results when it comes to prediction based analytics. Data Mining turns data into patterns which are useful for analyzing the diseases. This research paper focuses on role of K-Means Algorithm in disease prediction.
PERFORMANCE ANALYSIS AND PROBABILITIES BY GENDER IN JUDO: COMBAT PHASES, TECHNIQUES AND BIOMECHANICAL LEVERS

The aim of the current study was to compare and verify factors associated with gender in combat phases, techniques and biomechanical leversused during judo matches, and performance probabilities in those same matches. We evaluated high-level judo athletes from each weight division who qualified for the Olympic Games. 773 male and 638 female bouts were analyzed based on the motor actions during approach, gripping, attack, defense, groundwork and biomechanics of techniques. The current results demonstrated significant differences between male and female bouts in pause frequencies [7(4;12); 9(4;13); p≤0.05], and approach with displacements [1(0; 6); 3(0;9); p≤0.05]. Female athletes used higher frequencies of techniques with different biomechanical levers for attacks (i.e. trunk leg lever attempts; waist lever variable attempts, waist lever variable effective and attempts, and maleolo lever effective and attempts; p≤0.05), while men used more variations of gripping (i.e. left collar, left collar and sleeve, both collars, right sleeve, left sleeve and both sleeves; p≤0.05) and groundwork attacks (i.e. Osae-waza, kansetsu-waza and shime-waza attempts; p≤0.05). The regression analysis demonstrated that female performance probabilities are associated positively with the use of Osae-waza, Variable/medium length attempted and scored, Arm/foot lever, Minimum lever attempted and scored and negatively with the grip on both collars, while male performance probabilities are associated positively with the use of Variable/medium length scored, Arm/foot lever scored, Minimum lever attempted and negatively with the grip on the right sleeve. The present data can be applied by coaches and athletes to elaborate the training programs focused on competitive strategies that increase the chances of winning.
Simulation of 3D Garment Based on Improved Spring-Mass Model

Some problems still exist in 3D garment modeling and simulation, including that the model is complex, the efficiency is low, and the structural characteristics of garment are ignored. An improved spring-mass model is proposed, by which 2D→3D transformation and simulation of garment are formulated unifiedly. The dynamic system of modeling and simulation is derived and solved using time differentiate method, with given composition and expression of internal and external cloth forces in the formulation. And then the simulation algorithm is described. Overcoming the serious weakness representing cloth properties simply of previous model, the improved mass-spring model considers mechanical properties of cloth such as stretching, shearing and bending. Structural characteristics of garment such as dart and pleat are took into account too, thus complex garment pattern can be modeled and simulated. Compared the efficiency and the effect of simulation with other simulation systems, the resulting simulation system is faster and more realistic. The technique has been used in some garment enterprises and gets well responses.
Analysis and Design of Optimum Path Algorithm Based on Raster Data

The optimum path analysis is one of basic space analyses of GIS.So far,there are lots of studies at home and abroad on the algorithm of shortest path/ optimum path based on vector or network data models.But there are few studies on algorithm of optimum path based on raster data model despite of it peer importance as vector ones.The algorithm of optimum path based on raster data model is the basis of special related application models,such as accessibility to road model,gravitational mode of cities,floodwater development model and so on.This paper introduced a new algorithm to realize raster optimum path based on of the former researches,analyzed and described the detailed computing process,and finally,made a summary about the advantages and shortcomings according to the experimental results with computation.
Compiling for a Multithreaded Horizontally-microcoded Soft Processor Family

Compiling for a Multithreaded Horizontally-Microcoded Soft Processor Family Ilian Tili Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2013 Soft processing engines make FPGA programming simpler for software programmers. TILT is a multithreaded soft processing engine that contains multiple deeply pipelined and varying latency functional units. In this thesis, we present a compiler framework for compiling and scheduling for TILT. By using the compiler to generate schedules and manage hardware we create computationally dense designs (high throughput per hardware area) which make compelling processing engines. High schedule density is achieved by mixing instructions from different threads and by prioritizing the longest path of data flow graphs. Averaged across benchmark kernels we can achieve 90% of the theoretical throughput, and can reduce the performance gap relative to custom hardware from 543x for a scalar processor to only 4.41x by replicating TILT cores up to a comparable area cost. We also present methods of quickly navigating the design space and predicting the area of hardware configurations.
ecosocial citizenship education: facilitating interconnective, deliberative practice and corrective methodology for epistemic accountability

According to Val Plumwood (1995), liberal-democracy is an authoritarian political system that protects privilege but fails to protect nature. A major obstacle, she says, is radical inequality, which has become increasingly far-reaching under liberal-democracy; an indicator of ‘the capacity of its privileged groups to distribute social goods upwards and to create rigidities which hinder the democratic correctiveness of social institutions’ (p. 134). This cautionary tale has repercussions for education, especially civics and citizenship education. To address this, we explore the potential of what Gerard Delanty calls ‘cultural citizenship’ as an alternative to the disciplinary citizenship that permeates Western liberal discourse. Cultural citizenship emphasises citizenship as communication and continual learning processes, rejecting the idea of citizenship as a fixed set of cultural ideals, norms or values defined and enforced by liberal society’s legal, political and cultural institutions, including education and ‘citizenship training’. However, we contend that a critical first step, essential to democratic correctiveness, is to clear away obstacles created by the privileging of a dominant epistemic position. We conclude that Plumwood’s philosophy alongside John Dewey’s work on democracy and education provide a theoretical framework for effective democratic inquiry aimed towards interconnective, deliberative practice and corrective methodology for epistemic accountability.
Regression-Based Causal Analysis from the Potential Outcomes Perspective

Abstract Most empirical economic research is conducted with the goal of providing scientific evidence that will be informative in assessing causal relationships of interest based on relevant counterfactuals. The implementation of regression methods in this context is ubiquitous. With this as motivation, we detail a comprehensive regression-based potential outcomes framework for causal modeling, estimation and inference. This framework facilitates rigorous specification of the effect parameter of interest and makes clear the sense in which it is causally interpretable, when appropriately defined in a potential outcomes setting. It also serves to crystallize the conditions under which the effect parameter and the underlying regression parameters are identified. The consistent sample analog estimator of the effect parameter is discussed. Juxtaposing this framework with a stylized version of a commonly implemented and routinely applied modeling and estimation protocol reveals how the latter is deficient in recognizing, and fully accounting for, conditions required for identification of the relevant effect parameter and the causal interpretability of estimation results. In the context of an example, we demonstrate the conceptual advantages of this general potential outcomes framework for regression modeling by showing how it resolves fundamental shortcomings in the conventional approach to characterizing and remedying omitted variable bias.
Exploring the Effect of School Management Functions on Student’s Academic Performance: A Dilemma from Public Senior High Schools in Ghana

In every aspect of life, management has become a crosscutting tool and the axis of performance. There is a broad and ongoing discussion in our academic institutions about the impact of effective management on academic performance. This study, therefore, sought to analyze the effects of school management functions on the likelihood to enhance the academic performance of students in Public SHSs in Ghana. The study used Teacher Professional Development, monitoring and evaluation, Provision of Infrastructure outcomes, Teachers Input, Student Input, P.T. A support and Parental Involvement as variables measuring school management functions. In this study, the descriptive research design was adopted and data of 480 respondents were collected through a questionnaire survey for analysis. With the application of logistic regression analysis as the main statistical tool, the study based on Wald test values, p-values and odds ratio values identified Teacher professional development, Monitoring and evaluation, Provision of infrastructure, Teachers input, Students effort, PTA support, and Parental involvement as school management functions that significantly contributes to the likelihood (probability) of enhancing students academic performance. The study recommended that school management functions with the exception of democratic management should be intensified in various public SHSs so as to enhance students academic performance.
Testing moderation: physical punishment, parental warmth, and aggression

MICHAEL TODD MCRAE: Testing moderation: Physical punishment, parental warmth, and aggression (Under the direction of Vonnie McLoyd, PhD) Physical punishment is a disciplinary technique that many parents utilize and report as being effective. Nevertheless, researchers continue to debate its usefulness and impact on child adjustment. Many have argued that physical punishment is detrimental to children’s socio-emotional functioning, and in particular, that it increases aggression. However, some have argued that whether parents utilize physical punishment is less important than how they use it, citing strong evidence that not all children who are spanked display abnormal levels of aggression. This has lead to the theory that various parenting characteristics and techniques may moderate the relationship between physical punishment and aggression. The current study examines parental warmth as a moderator of the relationship between physical punishment and aggression 30 months later. Results of the study yielded no evidence that this relationship exists. Several limitations are believed to have contributed to the null findings. Directions for future research are discussed.
Children and Young People Living Through a Serious Family Illness: Structural, Interpersonal and Personal Perspectives

This study explores the experiences of children and young people in Britain living through a serious family illness. The study considers the interplay between social structures, social relationships and individual agency. We draw on data from the Millennium Cohort Study to estimate the number of children and young people affected nationally and on seven in-depth interviews to understand young people’s experiences and the effects on their daily lives. Living through a serious family illness impacts on young people’s educational achievements, mental health and social relationships over long periods. Policy and service responses are suggested.
A Qualitative Study of the Experience of Miscarriage from Patients and Providers in the Rural U.S.

This qualitative study examines how female patients in rural communities in the Midwest, who experienced at least one miscarriage, perceived their experience of pregnancy loss. Qualitative data were collected from 10 women who did experience at least one loss in addition to 10 participants who were providers or partners of the women were interviewed. The Socio-Ecological model informed the methodology and thematic analysis. Open-ended qualitative interviews provided the basis for the analysis. Transcribed narrative data were analyzed using standard coding procedures and MAXQDA software. Women who experienced miscarriage discussed how they made meaning of the experience within the context of care and their perceptions of risk given certain socio-environmental factors. Providers, public health professionals and family studies professionals can use the study results to better inform best practices in maternity care particularly in under-served, resource-poor locations. Citation: Upton RL (2019) A Qualitative Study of the Experience of Miscarriage from Patients and Providers in the Rural U.S. J Comm Pub Health Nursing 5: 224. doi:10.4172/2471-9846.1000224
Who Shared It?: Deciding What News to Trust on Social Media

Abstract Social media platforms are becoming increasingly popular news sources. They differ from traditional media as people are exposed to stories from a variety of people and outlets, including potential fake news stories. This raises a key question: What leads people to trust news on social media? Research indicates two cues that could impact opinions of news on social media: (1) the trustworthiness of the person who shares a story; (2) the credibility of the news outlet reporting the story. This study tests those factors simultaneously with a recent survey experiment of American adults that simulates social media posts by either a trusted or untrusted public figure and directs respondents to an article manipulated to come from either a reputable news source or a fake news source. The findings highlight the impact elites sharing a story has on views toward an article on social media compared with the effects of the news outlet reporting the story. The study has significant implications for researchers, citizens, and publishers trying to understand how people evaluate the trustworthiness of news on social media and the potential impact of fake news.
Personalized recommendation algorithm based on multi-attribute model of attitude of user salient belief

Personalized recommendation technology has been widely applied in e-commerce.For the deficiency of the existing algorithms of product features can not reflect the different understanding of the same product between users,this paper presents a personalized recommendation algorithm based on multi-attribute model of attitude of user salient belief,user salient belief and multi-attribute model of attitude-based data weight are proposed.The algorithm describes user's psychology from different view,for this reason,the recommendation result is more satisfied user's needs.Experimental results show that,the proposed algorithm outperforms the traditional algorithm based on product features algorithm.
Low-stretch trees for network visualization

Low-stretch trees are spanning trees which provide approximate distance preservation for edges in the original graph by minimizing stretch. We explore the application of these trees to network visualization. In particular, we present a novel edge bundling technique, LSTB, that computes edge bundles explicitly and efficiently and does not rely on fixed vertex positions. This approach is in contrast to previous methods, which require the user to provide a layout of the input graph. We introduce an abstract framework for edge bundling methods, which provides a unifying formalization of bundling terminology and techniques, as well as a classification of such methods. Based on this framework, LSTB provides algorithmic support for sophisticated visual encodings, including dynamic layout adjustment and interactive bundle querying. In addition, we explore the use of the multiplicative weights update method to compute a distribution over low-stretch trees in order to achieve low stretch for all edges in expectation, rather than on average. We present the results of using this distribution in place of a single low-stretch tree as a routing graph for LSTB. While the distribution provides better stretch guarantees, we find that from a visual perspective a single low-stretch tree provides a better routing graph for the LSTB edge bundling application.
RECOGNITION OF HMM-BASED CHINESE INSTITUTION TERMS

An automatic identification system of Chinese institution terms is designed and implemented. This system uses Hidden Markov Model and probability estimation equations to evaluate the ability of forming Chinese institution terms in real text. The experiments on 500000 Chinese characters from the frontpages of People′s Daily from Nov. to Dec. 2000 involving society, economy, law and other contents, show its recall rate 94.5% and precision rate 89%.
Project-based learning through edmodo: improving critical thinking and histology concepts

The results of the observation showed that there were still many students in histology subjects who did not yet have critical thinking competencies and maximum histology concept. The purpose of this study was to find out how project-based learning through blended learning can develop critical thinking skills and student histology concept. This type of research is classroom action research. The sample of this study was students of class C histology class 2018/2019 as many as 35 students of Malang Muhammadiyah University. The research method is in the form of observation, interviews, and tests. The learning management system used in this research blended learning uses Edmodo. The results showed that there was a change in the improvement of critical thinking skills and student histology concept from cycle I to cycle II. Critical thinking skills in the cycle I showed that 23% were complete and 77% were non-complete, while the cycle II was 80% complete and 20% non-compete. Critical thinking skills from cycle I to cycle II increased by 57%. Histology concept in the cycle I found 69% complete and 31% non-complete, while the cycle II was 100% complete and 0% non-complete. Histology concept from cycle I to cycle II increased by 31%. Based on this, project-based learning through blended learning can develop critical thinking skills and student histology concept.
Threatened but Involved: Key Conditions for Stimulating Employee Helping Behavior

This article examines the relationship between employees’ job involvement and helping behavior directed toward coworkers, as well as how this relationship might be augmented when employees encounter adversity, whether due to malicious leadership (abusive supervision) or threats to their physical integrity (workplace hazards, fear of terrorism). Drawing on a two-wave survey research design that collected data from employees and their supervisors in Pakistan, the results reveal that job involvement increases the likelihood that employees go out of their way to help their coworkers, and this relationship is strongest when they have to deal with the hardships of malicious leadership or threats to their physical safety. For organizations, these findings indicate that employees perceive their own allocation of positive work energy, derived from their job involvement, to helping behaviors that assist other members as particularly useful when they also experience significant adversity, inside or outside the workplace.
A Mixed Methods Evaluation of a Pressure Training Intervention to Develop Resilience in Female Basketball Players

The present study aimed to evaluate the effectiveness of a resilience development intervention, set up around regular exposure to increased pressure. This intervention adopted a quasi-experimental design, delivered within an elite female basketball academy. The mixed methods evaluation combined individual and team resilience measures with semi-structured interviews with athletes and coaches. Quantitative results demonstrated that the intervention was effective in reducing team level vulnerabilities. Qualitative evaluations indicated that the intervention led to increased awareness, emerging leadership, stronger communication channels, and the development and execution of collective plans. Furthermore, potential avenues for intervention improvement were also addressed Lay Summary: This study aimed to test a resilience training intervention based on pressure exposure during practice. Results within a female elite basketball academy indicated that both athletes and coaches believed the team became more resilient to in-game stressors and less susceptible to team-level vulnerabilities.
Growth to Limits of Female Labor Participation in Latin America's Unequal Care Regime

Abstract:Throughout Latin America, legal barriers enabling women to make a living equal to men are largely gone. However, women continue to lag behind men in gaining access to economic resources, and women's labor participation differs significantly across class lines. Drawing on a policy-based explanation, we argue that a primary reason why the removal of legal barriers is insufficient for women's economic empowerment due to the presence of a socially unequal care regime, one that sits at the intersection of high-income inequality and the absence of universal care services.