Miracle-dz/UniFAR-FTUs huggingface dataset:
Not So Soft Skills: The Importance of Grit to College Student Success

According to the American College Testing organization (2012), fewer than 35% of students attending public institutions graduate within five years of enrolling. This figure increases to just over fifty percent for private attendees. Clearly, the idea of a “four-year degree” is more elusive for the majority of undergraduate students than it has ever been. These facts have led researchers to consider the factors that delay, or even prevent, graduation. The concept of “grit” (Duckworth, Peterson, Matthews, & Kelly, 2007) is defined as passion and perseverance for very long-term goals and has become a popular topic in the education literature. Duckworth et al. (2007) found that grit positively associates with academic success. The purpose of the present study was to explore the relationships between grit, academic performance, and educational attainment, as measured by number of attempted credit hours at the collegiate level. We also aimed to ascertain whether academically at-risk students (those with less than a 2.0 GPA) had lower grit scores than their non-at-risk peers. We discuss our findings in the context of potential interventions and future directions for research in this area.
RAPID PROTOTYPING FOR DISTRIBUTED D-CLEAN USING C++ TEMPLATES

Earlier we have designed two coordination languages, D-Clean and D-Box for high-level process description and communication coordination of functional programs distributed over a cluster. D-Clean is the high level coordination language for functional distributed computations. The language coordinates the pure functional computational nodes required by language primitives, and it controls the dataflow in a distributed processnetwork. In order to achieve parallel features, D-Clean extends the lazy functional programming language Clean with new language primitives. Every D-Clean construct generates a D-Box expression. D-Box is an intermediate level language and describes in details the computational nodes hiding the low level implementation details and enabling direct control over the process-network. Practical experiences of the two language usage showed the difficulties of distributed program development, especially in testing and debugging. This paper aims to provide software comprehension application for a better way of understanding and utilizing the D-Clean language. Here we provide a new modeling approach of the coordination language elements and a new view of the D-Clean distributed system behaviour using C++ templates. The strong type system of C++ templates guarantees the correctness of the model. Using templates we can achieve impressive efficiency by avoiding run-time overhead.
Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM

The fault diagnosis of piezoelectric sensor patches is very important for the stability of the vibration control system and fault-tolerant control technology. In order to improve the accuracy of fault self-diagnosis of piezoelectric sensor patches, singular value decomposition (SVD) and Hilbert marginal spectrum method are proposed to extract multiple features of each IMF component and conduct feature fusion, and a support vector machine (SVM) based on particle swarm optimization (PSO) is designed for fault identification of different eigenvalues. In the experiment, the broken and degumming piezoelectric patches are simulated. Firstly, under the excitation of the square wave signal with no noise signal, when the SVD value and the maximum amplitude of Hilbert marginal spectrum are used as the fusion eigenvalue together, the diagnostic results show that the recognition accuracy can reach 95%, compared with the recognition accuracy of 70% and 80%, respectively, when the two are used as eigenvalues alone; the recognition result under fusion eigenvalue is obviously better than that of the latter. Secondly, in order to highlight the effectiveness of this method, the aforementioned experiment is conducted under the excitation of the square wave signal interfered by 0.5 dBW–1 dBW noise signal. The experimental results show that the fault recognition of the fused eigenvalue under different noise intensity signals is still superior to that of the single eigenvalue.
State transition modeling of complex monitored health data

This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.
CLASSIFYING WEB PAGES WITH VISUAL FEATURES

To automatically classify and process web pages, current systems use the textual content of those pages, including both the displayed content and the underlying (HTML) code. However, a very important feature of a web page is its visual appearance. In this paper, we show that using generic visual features we can classify the web pages for several different types of tasks. The features used in this document are simple color and edge histograms, Gabor and texture features. These were extracted using an off-the-shelf visual feature extraction method. In three experiments, we classify web pages by their aesthetic value, their recency and the type of website. Results show that these simple, global visual features already produce good classification results. We also introduce an online tool that uses the trained classifiers to assess new web pages.
Moka: Improving App Testing with Automated Mocking

Nowadays, we use mobile apps for many of our daily activities, including reading the news, streaming content, and communicating with friends and family. Because some of these apps are used daily by millions of users, it is fundamental to thoroughly assess their quality and avoid serious failures due to undetected bugs. Testing has been shown to be very powerful in identifying bugs, but its effectiveness can be hindered by the flakiness of the tests. One common cause of flakiness is the non-deterministic behavior of the code under test, which is a frequent occurrence in mobile apps, due to the numerous interactions between apps and their environment. A common strategy to mitigate this kind of flakiness during testing is to manually create test mocks for specific test execution. However, manual mock creation is time consuming. Moreover, the generated mocks can typically only be used in the context of the specific tests for which they were created. The goal of this project is to improve app testing by allowing, supporting, and partially automating the generation of smart test mocks. Specifically, we will develop and implement MOKA, a family of techniques that developers can use to collect, generalize, and use test mocks for manually and automatically generated tests. Intuitively, given an app under test (AUT), our techniques will (1) use record-and-reply techniques to collect mock data from the interactions between the AUT and the software environment, (2) generalize the collected data into smart test mocks, and (3) use these mocks to generate new tests. If successful, this research will provide unprecedented advantages to developers, who will be able to perform a more effective automated app testing while also mitigating the problem of test flakiness. To achieve this goal, we will leverage our experience on app testing, program analysis, and recordand-replay techniques. In particular, we will leverage BARISTA [2], a testing technique based on recordand-replay and developed by two of the PIs. BARISTA, which allows developers to easily create platformindependent tests for mobile apps, is currently able to record and replay user actions and assertions, but it does not account for the interactions between the AUT and its environment. Handling these interactions and leveraging them to build mock objects is the starting point of this project.
Regulatory mechanism of nitrogen metabolism and stress response in the methylotrophic yeast Candida boidinii

The methylotrophic yeast Candida boidinii is capable of growth on methanol as a sole carbon and energy source. Recently, a new method was developed to gain insight into microbial life on the plant leaf surface and revealed that C. boidinii utilizes methanol as a carbon source in the phyllosphere. However, the yeast nitrogen metabolism and its regulation are unknown. Prior to investigating the yeast nitrogen metabolism on plant leaves, I performed in vitro experiments to investigate the regulatory profile of nitrate and methylamine in C. boidinii. The transcript level of nitrate reductase (Ynr1) gene was induced by nitrate and nitrite, and was not repressed by the coexistence with other nitrogen sources. In contrast, the transcript level of amine oxidase (Amo1) gene, which was induced by methylamine, was significantly repressed by the coexistence with ammonium or glutamine. These results suggest that the sensitivity to repression by other nitrogen sources was different between Ynr1 and Amo1.
Internship Proposal: Mean Field Optimal Control for Multi-Armed Bandit Problems

Mutli-armed bandits are classical models of sequential decision making problems in which a controller (or learner) needs to decide at each time step how to allocate its resources to a finite set of alternatives (called arms). They are widely used in online learning today as they provide theoretical tools to solve many practical problems: choosing which ads you see when you visit a web page, optimally routing packets in communication networks, designing efficient strategies in complex games such as the game of go, etc. When some information is available about the different arms, this falls into the class of Markovian bandits and restless multi-armed bandits. If the number of arms is small, optimal decision rules can be computed by dynamic programming. The complexity of such an approach, however, grows quickly with the number of arms which calls for approximations. Mean field methods have become a common tool to provide approximations for systems of interacting agents or entities. They are used in many domains (statistical physics, game theory [5], performance and design of distributed systems [4] and more recently multi-agent reinforcement learning [8]). The idea of the mean-field approximation is that when the size of the population n is large, a single individual has a minor influence on the rest of the individuals. The mean field approximation consists in neglecting the effect that one individual has on the others. This approximation is known to be exact as n goes to infinity, but it can very poor for finite systems. Our recent progresses [3], however, show that it is possible to correct these methods to study systems with a relatively small number of entities (n ≈ 10), which makes it a promising direction to derive efficient approximations for multi-armed bandit problems.
Socio-Analyzer: A Sentiment Analysis Using Social Media Data

The usage of social media is rapidly increasing day by day. The impact of societal changes is bending towards the peoples’ opinions shared on social media. Twitter has received much attention because of its real-time nature. We investigate recent social changes in MeToo movement by developing Socio-Analyzer. We used our four-phase approach to implement Socio-Analyzer. A total of 393,869 static and stream data is collected from the data world website and analyzed using a classifier. The classifier identify and categorize the data into three categories (positive, neutral, and negative). Our results showed that the maximum peoples’ opinion is neutral. The next higher number of peoples’ opinion is contrary and compared the results with TextBlob. We validate the 765 tweets of weather data and generalize the results to MeToo data. The precision values of Socio-Analyzer and TextBlob are 70.74% and 72.92%, respectively, when considered neutral tweets as positive.
Do Male CEOs Really Run Firms Better than Female Counterparts? New Evidence from Vietnam

Using firm fixed-effects regression and data from Vietnamʼs Enterprise Censuses, this study aims to test the female underperformance hypothesis. Our findings decisively undermine these hypotheses. Female-managed firms are smaller than male-managed ones. However, once observed variables and firm fixed-effects are controlled for, female-managed firms have higher revenues and return on assets and capital than male-managed firms. Female-managed firms are more likely to employ female workers and provide more jobs with social insurance. Compared with male-managed firms, moreover, female-managed firms have a higher tax payment rate as well as a higher tax payment amounts.
Multi-class classification to track students' academic outcome

Despite various measured taken by the universities and colleges, the number of college and university dropouts is still on the rise. About 19.5% of students in the U.K drop out of their colleges every year. Thus, the identification of tentative dropouts and failure-prone students can act as an early warning system while also assisting teachers in analysing the need to streamline their course according to the weaker students' need. Learners' academic performance majorly depend upon their demographic factors as well as some other learning-based factors. Objective: This paper focuses on classifying students as Pass, Fail or Dropout, based on their demographic and learning features. Learner's demographic factors like their like Age, Gender, Highest Qualification, Index of Multiple Deficiency, Physical Disability and learning factors like student's past assessment grades, their interaction with Virtual Learning Environment have been considered for this study. Dataset: A publicly available anonymised student data from Open University named as Open University Learning Analytics Dataset OULAD has been used for this study. Methodology: To carry out this multi-class classification, five different machine learning and deep learning algorithms, Artificial Neural Networks (ANN), Random Forest, Decision Trees, XGBoost, Support Vector Machine (SVM), have been implemented. Results: Accuracy has been considered as a metric of evaluation for the models. ANN performed the best with 78.08% accuracy.
Research on the influence of Service quality, passenger satisfaction and passenger loyalty on Fuzhou Metro Line 1

This paper takes Fuzhou Metro Line 1 as the research object, and concludes that the six dimensions of service quality have a significant positive impact on passenger satisfaction and passenger loyalty, except comfort. It is proposed to strengthen the visual construction of the subway and optimize the guiding signs, improve the efficiency of the access gates and optimize the surrounding environment information, strengthen personnel training and optimize the security inspection process, respond to passenger inquiries and improve passenger complaints, rationally plan vehicle connections and try to meet passenger demand. And other suggestions enhance passenger satisfaction and loyalty to Fuzhou Metro Line 1.
HIV-Infected Homosexual Men: NIAID Multicenter AIDS

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USING AGENTS TO REORGANIZE THE DYNAMIC INFORMATION RESOURCES IN A SELF ORGANIZED WEB SYSTEM

1. ABSTRACT The Web is a constantly growing dynamic environment where the components are changed in non-linear ways. These components represent the targets to researches in order to better understand the behavior of the Web, where the owners and the users in this environment exist as out factors. Web page Usage information is the term which describes ways and methods of using the Web. Various factors affect the use of the diversity of the resources in the Web, The non-linear way of its growth, and the evolution in the methods for how we build the Web pages which eventually leads to reflecting the users’ interests. Search engines are created to meet the users need for information on the Web. Generally, the researchers seek user’s satisfaction through utilizing these search engines to serve the user. One of the most efficient methods in this domain is the use of semantic measure algorithms to recognize the outputs of the information resources according to the users' needs. The Web is represented as three aspects: Content, Structure, and Usage. Three components can lead to having a semantic Web in order to reinforce the semantic value. This paper will present a model that uses new Web Usage information to see the effects on the semantic values, and how it will help us achieve a robust well organized Web. It will consider the Usage space as the field of our research as we will simulate this environment in the MAS “Multi Agents System” and CAS “Complex Adaptive System “paradigm.
Recent glacier and lake changes in High Mountain Asia and their relation to precipitation changes

Abstract. We present an updated, spatially resolved estimate of 2003–2008 glacier surface elevation changes for the entire region of High Mountain Asia (HMA) from ICESat laser altimetry data. The results reveal a diverse pattern that is caused by spatially greatly varying glacier sensitivity, in particular to precipitation availability and changes. We introduce a spatially resolved zonation where ICESat samples are grouped into units of similar glacier behaviour, glacier type and topographic settings. In several regions, our new zonation reveals local differences and anomalies that have not been described previously. Glaciers in the Eastern Pamirs, Kunlun Shan and central TP were thickening by 0.1–0.7 m a−1, and the thickening anomaly has a crisp boundary in the Eastern Pamirs that continues just north of the central Karakoram. Glaciers in the south and east of the TP were thinning, with increasing rates towards southeast. We attribute the glacier thickening signal to a stepwise increase in precipitation around ∼1997–2000 on the Tibetan Plateau (TP). The precipitation change is reflected by growth of endorheic lakes in particular in the northern and eastern TP. We estimate lake volume changes through a combination of repeat lake extents from Landsat data and shoreline elevations from ICESat and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for over 1300 lakes. The rise in water volume contained in the lakes corresponds to 4–25 mm a−1, when distributed over entire catchments, for the areas where we see glacier thickening. The precipitation increase is also visible in sparse in situ measurements and MERRA-2 climate reanalysis data but less visible in ERA-Interim reanalysis data. Taking into account evaporation loss, the difference between average annual precipitation during the 1990s and 2000s suggested by these datasets is 34–100 mm a−1, depending on region, which can fully explain both lake growth and glacier thickening (Kunlun Shan) or glacier geometry changes such as thinning tongues while upper glacier areas were thickening or stable (eastern TP). The precipitation increase reflected in these glacier changes possibly extended to the northern slopes of the Tarim Basin, where glaciers were nearly in balance in 2003–2008. Along the entire Himalaya, glaciers on the first orographic ridge, which are exposed to abundant precipitation, were thinning less than glaciers in the dryer climate of the inner ranges. Thinning rates in the Tien Shan vary spatially but are rather stronger than in other parts of HMA.
SEMI-AUTOMATED TOPOLOGY ADJUSTMENT OF PARTIAL WALL GEOMETRY

Abstract. The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract building information from point cloud data to create a set of parametric objects. The automation of this process is highly desired by the industry but is currently hindered by occlusions, clutter and the complexity of the building geometry. To create an as-built BIM, it is vital to not only accurately reconstruct the building’s structure but also to compute the topology between the objects. More specifically, we target the topology of the reconstructed partial wall geometry as this forms the basis for other objects. In this work, a novel method is presented to automatically adjust the topology of wall geometry in an as-built BIM. We present a semi-automated method that procedurally evaluates the configuration of reconstructed objects and adjusts them to create a more faithful BIM. A wall connection evaluation algorithm is proposed that takes as input the centrelines of partial wall geometry and a set of floor and ceilings mesh segments and outputs the topologically adjusted objects. The method is tested on a variety of scenes and shows promising results to reliably compute the topology of as-built models. The generated geometry is similar to the geometric modification proposed by expert modelers. A key advantage is that the algorithm operates directly in Revit and Rhino and can be used for new models as well as for updating existing models.