Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation of the Impact of Student Government Involvement at one Public Historically Black University on the Career Choice of African American Student Participants

The purpose of this study was to investigate student participation in collegiate student governance, the impact of that involvement and its influence on career choice for African-American participants, and to enlighten educators about role and value of collegiate student government participation. If participation in student government and involvement in leadership activities is important in the overall development of a student, then the benefits and characteristics of that development may appear after the student graduates and enters society and the workforce (Cress, Astin, Zimmerman- Oster, & Burkhardt, 2001). Studies conducted over the last twenty years have attempted to measure the impact of involvement and leadership development on college students after graduation (Cress, et. all, 2001; Sommers, 1991) but not much has been written about the subject specifically tied to student government (Downey, Bosco and Silver, 1984; Kuh and Lund, 1994; Schuh and Laverty, 1983; Sermersheim, 1996). A qualitative case study/cross case analysis of multiple participants was utilized for this project. Following the collection of data through the use of a written participant profile and oral interviews with each of the survey participants, case studies were constructed and presented in a narrative form to allow the individual personalities of the participants to emerge. The use of cross case analysis allowed the researcher to group the data into themes and highlight patterns that cut across each case, more narrowly defining what related factors were significant to the impact of student government participation and the selection of a particular career choice. x The findings from this study indicate that while the impact of student government participation has an overall positive effect on students who participate, including greater career competency and self confidence, students attribute their choice of career to their chosen undergraduate major or other factors. Study findings revealed evidence that the impact of student government involvement was limited in its direct influence on career choice.
Characterization of Protein Interactions

Available information on molecular interactions between proteins is currently incomplete with regard to detail and comprehensiveness. Although a number of repositories are already devoted to capture interaction data, only a small subset of the currently known interactions can be obtained that way. Besides further experiments, knowledge on interactions can only be complemented by applying text extraction methods to the literature. Currently, information to further characterize individual interactions can not be provided by interaction extraction approaches and is virtually nonexistent in repositories. We present an approach to not only confirm extracted interactions but also to characterize interactions with regard to four attributes such as activation vs. inhibition and protein-protein vs. protein-gene interactions. Here, training corpora with positional annotation of interacting proteins are required. As suitable corpora are rare, we propose an extensible curation protocol to conveniently characterize interactions by manual annotation of sentences so that machine learning approaches can be applied subsequently. We derived a training set by manually reading and annotating 269 sentences for 1090 candidate interactions; 439 of these are valid interactions, predicted via support vector machines at a precision of 83% and a recall of 87%. The prediction of interaction attributes from individual sentences on average yielded a precision of about 85% and a recall of 73%.
Polynomial transform based DCT implementation Domagoj Babić

Discrete Cosine Transform (DCT) is an important transform of particular interest in still image compression and compression of individual video frames, while multidimensional DCT is mostly used for compression of video streams and volume spaces. An FPGA implementation of a Polynomial Transform DCT (PTDCT) algorithm, recently proposed by Zeng et al. [10], is presented. The regularity of Zeng’s algorithm and careful operation scheduling have resulted in a very efficient implementation of a two-dimensional DCT in Xilinx Virtex-II FPGA in the terms of logic requirements.
Cost-Effective Stream Join Algorithm on Cloud System

Matrix-based scheme (Join-Matrix) can prefectly support distributed stream joins, especially for arbitrary join predicates, because it guarantees any tuples from two streams to meet with each other. However,the dynamics and unpredictability features of stream require quick actions on scheme changing. Otherwise, they may lead to degradation of system throughputs and increament of processing latency with the waste of system resources, such as CPUs and Memories. Since Join-Matrix model has the fixed processing architecture with replicated data, these kinds of adverseness will be magnified. Therefore, it is urgent to find a solution that preserves advantages of Join-Matrix model and promises a good usage to computation resources when it meets scheme changing. In this paper, we propose a cost-effective stream join algorithm, which ensures the adaptability of Join-Matrix but with lower resources consumption. Specifically, a varietal matrix generation algorithm is proposed to generate an irregular matrix scheme for assigning the minimal number of tasks; a lightweight migration algorithm is designed to ensure state migration at a low cost; a complete load balance process framework is described to guarantee the correctness during the scheme changing. We conduct extensive experiments to compare our method with baseline systems on both benchmarks and real-workloads, and explain the results in detail.
Reduction of Computations in Physics-Based Animation Using Level of Detail

A new approach based on the reduction of computatio ns during animation of physics-based models is presented in this paper. The first part describes an approach called here DLOD Dynamic Level Of Detail. Problems related to the DLOD are discussed in the second part and the last part de ls with an experimental object oriented library using a DLOD mechanism. keywords: physics-based animation, level of detail
Polarised photoelectrons from silver atoms

The polarisation of photoelectrons ejected by circularly polarised VUV radiation from silver atoms has been measured in the wavelength range between 150 and 160 nm. Autoionisation states give rise to pronounced resonance structures in the photoionisation cross section and in the photoelectron polarisation. The measured polarisation yields direct information on the angular momentum quantum numbers J of the autoionising states and on extrema of the cross section resonances.
Detecting patterns of anomalies

An anomaly is an observation that does not conform to the expected normal behavior. With the ever increasing amount of data being collected universally, automatic surveillance systems are becoming more popular and are increasingly using data mining methods to detect patterns of anomalies. Detecting anomalies can provide useful and actionable information in a variety of real-world scenarios. For example, in disease monitoring, a timely detection of an epidemic can potentially save many lives. The diverse nature of real-world datasets, and the difficulty of obtaining labeled training data make it challenging to develop a universal framework for anomaly detection. We focus on a key feature of most real world scenarios, that multiple anomalous records are usually generated by a common anomalous process. In this thesis we develop methods that utilize the similarity between records in these groups or patterns of anomalies to perform better detection. We also investigate new methods for detection of individual record anomalies, which we then incorporate into the group detection methods. A recurring feature of our methods is combinatorial search over some space (e.g. over all subsets of attributes, or over all subsets of records). We use a variety of computational speedup tricks and approximation techniques to make these methods scalable to large datasets. Since most of our motivating problems involve datasets having categorical or symbolic values, we focus on categorical valued datasets. Apart from this, we make few assumptions about the data, and our methods are very general and applicable to a wide variety of domains. Additionally, we investigate anomaly pattern detection in data structured by space and time. Our method generalizes the popular method of spatio-temporal scan statistics to learn and detect specific, time-varying spatial patterns in the data. Finally, we show an efficient and easily interpretable technique for anomaly detection in multivariate time series data. We evaluate our methods on a variety of real world data sets including both real and synthetic anomalies.
A Multi-Experimental Examination of Analyzing Mouse Cursor Trajectories to Gauge Subject Uncertainty

Providing information online is pervasive in human-computer interactions. While providing information, people may deliberate their responses. However, organizations only receive the end-result of this deliberation and therefore have no contextual information surrounding the response. One type of contextual information includes knowing people’s response uncertainty while providing information. Knowing uncertainty allows organizations to weigh responses, ask follow-up questions, provide assistance, or identify problematic instructions or responses. This paper explores how mouse cursor movements may indicate uncertainty in a human-computer interaction context. Specifically, it hypothesizes how uncertainty on multiple-choice questions influences a mouse-movement statistic called area-under-the-curve (AUC). We report the result of two studies that suggest that AUC is higher when people have moderate uncertainty about an answer than if people have high or low uncertainty. The results suggest a methodology for measuring uncertainty to facilitate multi-method research and to assess data in a pragmatic setting.
The Analysis of the Influence of Organizational Commitment and Learning Perception Towards Employees Performance (A Case Study of PT XYZ’S Employees That Participate in the Training at Padang Learning Unit)

Since 2016, there has been a reduction trend in the amount of PT XYZ employees that participated in the training at Padang Udiklat, eventhough PT XYZ is one of the biggest State-owned Enteprise of Indonesia (BUMN). This reduction trend is directly in proportion with the performance value results of the operational unit in Padang Udiklat regional in the last four semesters added with the reduction trend of Organizational Commitment in accordance with the employees’ results of Engagement Survey held by The XYZ head office (Holding) in 2017. Based on these, this study aims to determine whether Organizational Commitment and employee’s Learning Perception are correlated with and affected Individual Performance. The subject of this study is the BUMN employees who participated in the Traning at Education and Training Unit (Udiklat) Padang in the third quarter of 2018. Although, the results showed that Perception Learning and Organizational Commitment are only capable of influencing Individual Performance as much as 32.3%. However, this study showed that both of these variables have a positive correlation and are significant towards PT XYZ employees performance. For further research, it is advised to conduct the research on a larger scope, namely, the employees of PT XYZ (Persero) nationwide and not only the employees of a certain region who participated the training in just one Training Unit. Keywords : Individual Performance ; Learning Perception ; Organizational Commitment
A Stylization Method for Edge Map Extraction

This paper presents a non-photorealistic rendering technique that automatically extracts stylized edge map from 2D images, aiming to facilitate effective visual communication. We introduce an anisotropic DoG (Difference-of-Gaussians) filter that can extract a set of coherent, smooth, and stylized lines from 2D images. These lines effectively capture and convey important shapes and details in the image. In contrast to conventional edge detector canny method, the edge map generated by proposed algorithm has more hand-painting characteristic. Experimental results demonstrate that this technique produces stylized lines at the same time preserves significant geometric features.
Tricyclic GyrB/ParE (TriBE) Inhibitors: A New Class of Broad-Spectrum Dual-Targeting Antibacterial Agents

Increasing resistance to every major class of antibiotics and a dearth of novel classes of antibacterial agents in development pipelines has created a dwindling reservoir of treatment options for serious bacterial infections. The bacterial type IIA topoisomerases, DNA gyrase and topoisomerase IV, are validated antibacterial drug targets with multiple prospective drug binding sites, including the catalytic site targeted by the fluoroquinolone antibiotics. However, growing resistance to fluoroquinolones, frequently mediated by mutations in the drug-binding site, is increasingly limiting the utility of this antibiotic class, prompting the search for other inhibitor classes that target different sites on the topoisomerase complexes. The highly conserved ATP-binding subunits of DNA gyrase (GyrB) and topoisomerase IV (ParE) have long been recognized as excellent candidates for the development of dual-targeting antibacterial agents with broad-spectrum potential. However, to date, no natural product or small molecule inhibitors targeting these sites have succeeded in the clinic, and no inhibitors of these enzymes have yet been reported with broad-spectrum antibacterial activity encompassing the majority of Gram-negative pathogens. Using structure-based drug design (SBDD), we have created a novel dual-targeting pyrimidoindole inhibitor series with exquisite potency against GyrB and ParE enzymes from a broad range of clinically important pathogens. Inhibitors from this series demonstrate potent, broad-spectrum antibacterial activity against Gram-positive and Gram-negative pathogens of clinical importance, including fluoroquinolone resistant and multidrug resistant strains. Lead compounds have been discovered with clinical potential; they are well tolerated in animals, and efficacious in Gram-negative infection models.
Three essays on real estate finance

This thesis outlines several issues related to real estate research. The first chapter relaxes the functional form restriction in the empirical application of the hedonic pricing model, and studies its implication on the house price index construction. The second chapter takes into account both the spatial and temporal correlation among housing transactions, and shows the benefit of doing so contributing to better prediction of future house prices. The third chapter tackles the composition of proxy for market portfolio and its impact on the estimation of REITs risk premium. We show that REITs risk premium estimation is sensitive to both the structural break in the REITs market and the market proxy composition. The inclusion of real estate asset class into the market proxy accounts for a significant portion of the bias in the REITs risk premium estimation arising from using the restrictive market proxy, such as the CRSP equity index.
A Personalized Whitelist Approach for Phishing Webpage Detection

The number of phishing attacks against web services has seen a steady increase causing, for example, a negative effect on the ability of banking and financial institutions to deliver reliable services on the Internet. This paper presents an automatic approach detecting phishing attacks. Our approach combines a personalized whitelisting approach with machine learning techniques. The whitelist is used as filter that blocks phish web pages used to imitate innocuous user behavior. The phishing pages that are not blocked by the whitelist pass are further filtered using a Support Vector Machine classifier designed and optimized to classify these threats. Our experimental results show that the proposed approach improves over the current state-of-the-art methods.
The Research of Trusted Technology under Cloud Environment

In the current cloud environments, there are some problems of information in security and reliability. In order to establish more flexible and adaptable security mechanism, combining the cloud and credible is a major research direction in the field of security today. In this paper we studied some credible technology in cloud computing environment and a new reverse cloud generation algorithms is proposed. The expected value and hyper entropy of subjective cloud is used to evaluate the reputation of trust objects, and provide a basis for trust decisions online trading.
NoC Research and Practice: Design and Implementation of 2×4 2D-Torus Topology

Design and Implementation of network on chip interconnection architecture for eight compute-intensive processors are mainly presented in this paper. Firstly, it introduces the basic concept and architecture of the NoC, through analysis and comparison of three common NoC topologies, 2×4 2D Turos is chosen as the final topology, and the single routing node architecture is designed, including packet format, routing and arbitration. Secondly, routing nodes coding, routing algorithm and node degree routing direction are designed. Thirdly, the programming and simulation of 2×4 NoC interconnection architecture are designed, and it achieves uninterrupted operation. The result shows the correctness of the interconnection architecture design. Finally, it chooses XC4VSX55-12ff1148 of vertext 4 to synthesize, the maximum frequency can up to 268 MHz, which provides foundation of subsequent research and application.
Solutions for quality pre-control of crimp contacts used in electric power systems and electrical machines

Crimping is the most widely used method of pressure connection for permanent electrical contact between a wire conductor and a wire conductor terminating device. In this paper we present two solutions for connections quality pre-control of crimp contacts used in electric power systems and electrical machines and for early identification of the improperly executed connections, before final assembly. Thus can be avoid any damage that may occur during operation. The first solution supposes an experimental determination of specific losses by calculating the initial rate of temperature rise. This can be made drawing the tangent at the origin at heating curve. The method can be used to identify bad connections by passing a current through the winding at ambient temperature and simultaneously recording connections temperatures in the first few minutes since the current is setting. The second proposed solution is to apply to each element crimping an one level thermal indicator and making a test heating with a heating current corresponding to indicator critical temperature.
Brief report: development of the inflammatory bowel disease family responsibility questionnaire.

OBJECTIVE To present psychometric data on youth and parent versions of the Inflammatory Bowel Disease-Family Responsibility Questionnaire (IBD-FRQ), a measure of family involvement in IBD management. METHODS Fifty-eight adolescents with inflammatory bowel disease (IBD), along with 55 mothers and 26 fathers completed the IBD-FRQ, a demographics questionnaire, and a measure of family involvement in decision making in non-IBD domains. Medical information was obtained via chart review. RESULTS Support for the internal consistency of the IBD-FRQ was obtained. Evidence of validity was documented via moderate to high intercorrelations among reporters. Youth involvement increased with youth age, while maternal and paternal involvement decreased with youth age. Across all reporters, maternal involvement was higher than paternal involvement. CONCLUSIONS Preliminary analyses offer support for the measure's reliability and validity. The measure shows promise as a means of assessing family involvement in IBD condition management; however, further validation studies are needed.
Inferring Smartphone Positions Based on Collecting the Environment's Response to Vibration Motor Actuation

Smartphones have become ubiquitous in recent years and offer many useful services to their users, such as notifications about incoming calls and messages, or news updates in real-time. These notifications however do not consider the current user's and phone's context. As a result, they can disturb users in important meetings or remain unnoticed in noisy environment. In this paper, we therefore propose an approach to infer the phone's context based on its vibration motor. To this end, we trigger the phone's vibration motor for short time periods and measure the response of its environments using the built-in microphone and/or accelerometers.Our evaluation shows that leveraging accelerometers allows to recognize the current phone's context with an accuracy of more than 99%. As a result, our proposed solution outperforms our previous work based on played and recorded ringtones in terms of classification performance, user annoyance, as well as potential privacy threats.
Tackling Knowledge Gaps in Digital Service Delivery

Today, in more and more enterprises digital services are offered to employees. An inherent characteristic of digital service is the self-service concept. Based on findings from a case study and academic literature, it is argued for the importance of employee knowledge when designing digital services. It is revealed that existing service blueprinting approach are not capable of adequately reflecting knowledge gaps. To fill this research gap, the paper proposes an agenda for future research.
Data domain description using support vectors

This paper introduces a new method for data domain description, inspired by the Support Vector Machine by V.Vapnik, called the Support Vector Domain Description (SVDD). This method computes a sphere shaped decision boundary with minimal volume around a set of objects. This data description can be used for novelty or outlier detection. It contains support vectors describing the sphere boundary and it has the possibility of obtaining higher order boundary descriptions without much extra computational cost. By using the di(cid:11)erent kernels this SVDD can obtain more (cid:13)exible and more accurate data descriptions. The error of the (cid:12)rst kind, the fraction of the training objects which will be rejected, can be estimated immediately from the description.
Impact of artificial "gummy" fingers on fingerprint systems

Potential threats caused by something like real fingers, which are called fake or artificial fingers, should be crucial for authentication based on fingerprint systems. Security evaluation against attacks using such artificial fingers has been rarely disclosed. Only in patent literature, measures, such as live and well detection, against fake fingers have been proposed. However, the providers of fingerprint systems usually do not mention whether or not these measures are actually implemented in emerging fingerprint systems for PCs or smart cards or portable terminals, which are expected to enhance the grade of personal authentication necessary for digital transactions. As researchers who are pursuing secure systems, we would like to discuss attacks using artificial fingers and conduct experimental research to clarify the reality. This paper reports that gummy fingers, namely artificial fingers that are easily made of cheap and readily available gelatin, were accepted by extremely high rates by 11 particular fingerprint devices with optical or capacitive sensors. We have used the molds, which we made by pressing our live fingers against them or by processing fingerprint images from prints on glass surfaces, etc. We describe how to make the molds, and then show that the gummy fingers, which are made with these molds, can fool the fingerprint devices.