Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case Study Description of the Impact of Check & Connect with Students Returning from Alternative Educational Placements

Title of Dissertation: A CASE STUDY DESCRIPTION OF THE IMPA CT OF CHECK & CONNECT WITH STUDENTS RETURNING FROM ALTERNATIVE EDUCATIONAL PLACEMENTS Robert Joseph Bartolotta, Doctor of Philosophy, 2010 Dissertation directed by: Professor Peter E. Leone, Department of Speci al Education In this case study, the impact of Check & Connect on the first semester reintegration experiences of a cohort of high school students who had returned from a disciplinary alternative educational placement was examined. Archival dat a, surveys, and interviews were used to compare the students’ experiences, academic and behavioral performance, and attendance, to a cohort of control students who returned to schools lacking the intervention and a group of students who remained at the alternative placement. Interviews were also conducted with the interventio n implementers (monitors), a sample of the intervention participants’ teacher s, and the administrators who oversaw the intervention. These interviews allowed for a mor e thorough examination of the experiences, opinions, and perceptions of Check & Connect’s value to the educational experience. Quantitative data collected showed that while both groups of students who left the alternative school experienced declines in their academic performance , the d cline was greatest among students with longer established academic and behavior al difficulties. Students with IEPs or 504 Plans had a higher average decline than the ir nondisabled peers. Improvements in the number of absences and behavioral referral s were seen with the majority of the intervention students. Results among the control students were mixed. Interviews indicated that the intervention participants experienced a stronge r connection to their school that was frequently attributed to the use of Check & Connect. Many felt that the intervention, particularly their relationships wit h the monitors, had provided them opportunities to succeed that they might not have had otherwise. Control students, and students who remained at the alternative program, expressed an interest in developing a similar connection with an adult in their sc hools. School staff interviewed believed that the benefits of Check & Connect may not be immediately evident in the quantitative data, but that the relationships made betwe n the students and monitors were potentially beneficial. Teachers found the additiona l information provided helpful and frequently requested greater sharing of resourc . Administrators believed that the positive adult relationships formed between the monitor and students reduced the likelihood of eventual school dropout. A CASE STUDY DESCRIPTION OF THE IMPACT OF CHECK & CONNECT WITH STUDENTS RETURNING FROM ALTERNATIVE EDUCATIONAL PLACEMENTS
Delay-sensitive Task Scheduling with Deep Reinforcement Learning in Mobile-edge Computing Systems

Mobile-edge computing(MEC) is considered to be a new network architecture concept that provides cloud-computing capabilities and IT service environment for applications and services at the edge of the network, and it has the characteristics of low latency, high bandwidth and real-time access to wireless network information. In this paper, we mainly consider task scheduling and offloading problem in mobile devices, in which the computation data of tasks that are offloaded to MEC server have been determined. In order to minimize the average slowdown and average timeout period of tasks in buffer queue, we propose a deep reinforcement learning (DRL) based algorithm, which transform the optimization problem into a learning problem. We also design a new reward function to guide the algorithm to learn the offloading policy directly from the environment. Simulation results show that the proposed algorithm outperforms traditional heuristic algorithms after a period of training.
Pharmacophore Structure Based Protein Modelling for Factor VIII via the Implication of Computational Tools.

Pharmacology hypothesis development and testing have been pursued through computational tools. These methods encompasses quantitative structure-activity relationships, database management and data-mining, similarity search tools, homology models, machine learning pharmacophores, data mining, network analysis tools and data analysis tools that use a computer. By the implication of computational analysis tools such as network analysis & data analysis could be implemented in order to identify substitution perceived necessary for the malfunctioning protein. The discovery and optimization of novel molecules having affinity to a target can be dealt with the use of such computational (in-silico) methods. The pharmacodynamic & pharmacokinetic properties of drug administered could be identified based on ADME (absorption, distribution, metabolism and excretion) and toxicity attributes and also characterizing physiochemical features can be perceived through such tools. Blood coagulation process is influenced by Coagulation factor VIII, which is a protein. Its deficiency causes haemophilia A. Coagulation factor VIII substitution can serve for the treatment of haemophilia related bleeding disorders. Plasma-derived products have played an active role in finding substitutions for the deficient protein. The recent researches have been focussed upon the development of coagulation factor that has increased half-time or can efficiently overcome immunological response. The next step would be the procurement of recombinant coagulation factor VIII using cheap prokaryotic expression system. Thus lowering of production costs corroborated by curtail in the production time, influencing availability of product, and also eliminating of infection risks.
Combination of Multiple Classifiers for Classifying the Diabetic Data

Multiple classifier combination is a technique that combines the decisions of different classifiers. Combination can reduce the execution time of classification, variance of estimation errors, thereby improv- ing the overall classification accuracy. This paper introduces a genetic algorithm able to combine three different classifiers, fuzzy, MLP, K-NN. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of vote strategy. The method has been applied to classifica- tion of The Pima Indians Diabetes database, results show a significant improvement of recognition accura- cy using the genetic algorithm combination strategy compared with the recognition accuracy of each single
Measuring Formative Learning Behaviors of Introductory Statistical Programming in R via Content Clustering

Understanding student learning is an open problem in the teaching of introductory statistical programming. Formative learning is observed when analyzing theinteraction between the learning environment and the student. This can be operationalized by viewing how students respond to the error messages they receivewhile programming. Current California Common Core State Standards: Mathematics(CA CCSSM) highlight perseverance as an overarching habit of a productivemathematical thinker. Perseverance can be measured as a type of formativelearning by measuring the time and attempts that students use to correct errors.The MOBILIZE project promotes statistical programming at the high school levelin the Los Angeles Unied School District while following the CA CCSSM. Logsof high school student statistical programming during the 2013-2014 school yearwere collected along with the errors that occurred. Using these logs, error blockswere formed that follow a student's interaction with an error message. With theerror blocks we were able to observe perseverance by students on multiple daysof curriculum. Our findings suggest that there was an increase in perseverance,because students increased in time spent and attempts made to correct an erroras the number of days of programming curriculum increased. Additionally, students who showed more perseverance were more likely to eventually fix an error.Descriptive variables were explored to provide background for the variation instudent programming errors.
Heuristic Breadth-First Search Algorithm for Informative Gene Selection Based on Gene Expression Profiles

The tumor diagnosis method based on gene expression profiles will be developed into the fast and effective method in clinical domain in the near future. Although DNA microarray experiments provide us with huge amount of gene expression data, only a few of genes are related to tumor in gene expression profiles. Moreover, it is difficult to select informative genes related to tumor from gene expression profiles because of its characteristics such as high dimensionality, small sample set and many noises in gene expression profiles. According to its characteristic, a novel heuristic breadth-first search algorithm based on support vector machines is proposed, which can simultaneously find as many informative gene subsets as possible in which the number of informative genes is almost least but its classification performance is almost highest in spite of its time-consuming characteristic. Three tumor sample sets are examined by the novel approach and experiments show that the novel approach is feasible and effective in tumor classification. Experiment results show that 100% of 4-fold cross-validation accuracy has been achieved by only two, four and four genes for leukemia, colon tumor and SRBCT (Small Round Blue Cells Tumor) datasets, respectively, which is superior to the results of other tumor classification methods. To avoid the affect of different partition of sample set, the full-fold cross-validated method that can more objectively evaluate the classification performance of informative gene subset is proposed.
Pion-Pion Elastic Scattering, Dynamical Generation of the f0(500)Resonance, Nite-temperature e ects and Chiral Restoration: ALarge-N Approach

In this work, we discuss and summarize three large N approaches given to analyze how a set of massless pions behave at nite temperature. The first one focuses in the problem of obtaining thermal phase shifts, where the others aim to study chiral symmetry restoration phenomena. All the results are achieved by considering an O(N + 1)/O(N) nonlinear sigma model where N is the number of pions (or Nambu-Goldstone bosons) involved, in which thermal e ects are introduced via the imaginary time formalism.
DÖ FÖR DANMARK BERÄTTELSEN OM DEN PATRIOTISKA OFFERDÖDEN I DANSK OFFENTLIG DEBATT OCH POPULÄRKULTUR, 1940-2012

DIE FOR DENMARK THE NARRATIVE OF PATRIOTIC SACRIFICE IN DANISH PUBLIC DISCOURSE, 1940-2012 The narrative of patriotic sacrifice is a significant element in the formation of national community and identity. Citizens who have perished in wars and conflicts have been utilized as role-models and national martyrs in state propaganda, popular culture and modern mass media. The martyrs of the modern Danish nationstate have chiefly been delivered by the 19th century Schleswig wars and the resistance struggle in 1940-45. These fallen patriots are integrated features of the Danish national repertoire, invoked in memorials and other kinds of state ceremonial, but also in songs, films and national newspapers. The public interest in fallen patriots started to dwindle by the 1960s, yet the narrative of patriotic sacrifice has experienced a renaissance in present day Denmark, as the country’s engagement in Afghanistan and other conflict-zones has resulted in a new generation of martyrs. The narrative of patriotic sacrifice fulfills a double function in society. Firstly, individual deaths are given a higher meaning through references to the greater good of the nation. At the same time, however, the narratives about death serve to strengthen the nation, by infusing it with a higher, transcendental meaning.
Sentiment translation for low resourcedlanguages: experiments on Irish general electionTweets

This paper presents two main methods of Sentiment Analysis (SA) of User-Generated Content for a low-resource language: Irish. The first method, automatic sentiment translation, applies existing English SA resources to both manually- and automatically-translated tweets. We obtained an accuracy of 70% using this approach. The second method involved the manual creation of an Irish-language sentiment lexicon: SentiFocloir. This lexicon was used to build the first Irish SA system, SentiFocalTweet, which produced superior results to the first method, with an accuracy of 76%. This demonstrates that translation from Irish to English has a minor effect on the preservation of sentiment; it is also shown that the SentiFocalTweet system is a successful baseline system for Irish sentiment analysis.
Multilingual Aspects of Speech Sound Disorders in Children. Communication Disorders across Languages.

Read more and get great! That's what the book enPDFd multilingual aspects of speech sound disorders in children will give for every reader to read this book. This is an on-line book provided in this website. Even this book becomes a choice of someone to read, many in the world also loves it so much. As what we talk, when you read more every page of this multilingual aspects of speech sound disorders in children, what you will obtain is something great.
Perceptual assessment of binaural decoding of first-order ambisonics

The first-order Ambisonics microphone (e.g. Soundfield®) is a both compact and efficient set-up for spatial audio recording with the benefit of a full 3D spatialization. Another advantage is that the signals delivered by this microphone (i.e. B-Format) can be rendered over headphones by applying appropriate processing, while ensuring that the 3D spatial information is preserved. With the growing use of personal devices, it should be considered that most audio content is listened to over headphones. Thus first-order Ambisonics recording provides an attractive solution to pick-up 3D audio content compatible with headphone reproduction. Binaural decoding refers to the processing to adapt B-Format for headphone rendering (i.e. binaural format). One solution is based on binaural synthesis of virtual loudspeakers. One promising way to improve the decoding is active processing which takes information from a pre-analysis of the sound scene, particularly in terms of spatial information. This paper will compare various binaural decoders. The perceived quality is assessed by a listening test for a set of audio excerpts taken from the sound recording of classical music and which includes solo, trio, chorus, applauses and public ambience.
Integration of Zachman Enterprise Framework and Nonaka Knowledge Management Model to develop knowledge Management system

This study aims to review and synthesis the previous literature on knowledge management to present a comprehensive model that assist effective knowledge management. Review of the literature identified that the knowledge management system serves as an important factor in the performance of any organisation, where effective knowledge management system can assist knowledge disseminating and implementation. The studies conducted on knowledge management used Nonaka Knowledge management model extensively for knowledge management and Zachman Enterprise Framework used to design system for the organisations. Previous literature has identified that there is no study available that integrate Nonaka Knowledge Management model and Zachman Enterprise Framework to develop a structure of knowledge management system. The current study attempts to present a knowledge management system by integrating Nonaka Knowledge Management Model and Zechman Enterprise Framework. Moreover, Zachman architecture is composed of 36 conceptual cells (six categories and six filtering questions that coincide), these categories and questions may be applied as filtration devices upon data that is collected and understood based upon the Nonaka Model. The integration of these two can facilitate the knowledge management system that can effectively be used by employees of an organisation for their operational activities. Moreover, comprehensively gathered information can be systematically shared regarding human resources, documents, and other imperative resources pertinent to the achievement of the organizational goals and objectives.
Secured Multi-Owner Data Sharing for the Dynamic groups in the Cloud

Now-a-days the cloud computing became a leading edge for the traditional information technology by its characteristics like low maintenance and the resource sharing. Along with its advantages it also brings some challenging issues for securing the data files. Here we are proposing a new scheme MONA for the secure sharing of data files among the users in the un trusted cloud by using the group signature and the broadcast encryption scheme. But in this scheme also some limitations in terms of reliability and scalability. Hence in this paper we are further extending the basic concept of MONA by increasing the reliability and scalability by increasing the group managers dynamically.
Anti-parallel Patterns in Fine-grain Data-parallel Programs

Parallel systems and parallel programming are becoming increasingly more important. The developer in want of raw speed can no longer expect sequential processors to become faster and needs to turn to parallel platforms and parallel programs to satisfy his need for speed. But writing a parallel program is difficult and writing one with a decent performance even more so. This thesis introduces the umbrella concept of “anti-parallel patterns” parts of parallel programs that cause its performance to be less than expected. These patterns aim at helping developers understand, estimate and improve the performance of their parallel programs. To achieve these goals we model the effect of a given pattern on the performance and we supply solutions that can be applied in the face of an anti-parallel pattern. To help us model the behaviour of a pattern, we will define benchmark programs; programs that contain only one anti-parallel pattern. We will also discuss and test remedies that can be applied to decrease the performance loss the pattern causes. An additional advantage of the benchmark programs is that they can be used to compare the effect of a pattern on different parallel platforms. This work defines four anti-parallel patterns that are commonly found in fine-grain data-parallel programs such as those running on NVIDIA GPUs. For each anti-parallel pattern we give a definition and a thorough discussion of its behaviour on these GPUs. We present a number of benchmark programs written using NVIDIA’s CUDA technology in order to model the behaviour of the pattern. We also present remedies and examples of how to apply them. Finally, we demonstrate the usefulness of anti-parallel patterns by considering a prefix sum implementation in CUDA. We show how we can use the patterns to understand, estimate and improve the program’s performance.
Motion artifact reduction in PPG signals

The aim of this thesis was to investigate methods for artifact removal in PPG signals and to implement and evaluate a few existing algorithms claiming that the amplitude information is recovered when removing motion artifacts from photoplethysmographic signals (PPG) captured from pulse oximeters. We developed a new proposed method that uses a two-stage based approach with singular value decomposition and fixed fast ICA algorithm in order to generate a PPG-correlated reference signal that is used in adaptive noise cancellation. The results were promising and our proposed method is easy to implement and converges quickly with good extraction performance. It has a few design parameters and only needs the estimated period of the PPG signal. Our method could be used in a clinical routine for prediction of intradialytic hypotension. However it should be mentioned that although our method has great potential the simulations were only conducted on two healthy males. Further studies on a larger dataset might be needed in order to establish a full value of the efficacy of our method.
Personal motivations of Iranian men and women in making decision to do face cosmetic surgery: A qualitative study

Background:Face cosmetic surgery is one of the most common surgeries in Iran; currently, it is popular among men and women. Considering the risk of complications in this type of operations, this question arises that what is personal motives for decision to do face cosmetic surgery? The purpose of this study is to explore personal motivations of decision to do face cosmetic surgery.Methods:This study was conducted using qualitative research and content analysis approach. The studied population included 21 men and women aged 22-52 who had undergone face cosmetic surgery 1-5 years ago. Sampling started with purposive sampling and ended with data saturation. Interviews were used to collect data. All interviews were recorded and transcribed immediately. Data was analyzed using conventional content analysis.Results:Data analysis revealed main theme “personal motivations” that including three main category, feelings of inferiority, Escape from loneliness, and Fear of the unseen. The results of the study showed that feelings of inferiority, Escape from loneliness and Fear of the unseen by relatives and society are the greatest and most important motivations for undergoing cosmetic surgery and accepting the risk of surgeries.Conclusion:Considering the negative physical and mental health implications of cosmetic surgeries, health care providers particularly nurses by community based views can make efforts to change positive attitudes of people and improve psychological insecurities through cooperation of families and educational interventions by understanding the reasons for which people decide to undergo cosmetic surgeries
American Indian University Students' Knowledge, Beliefs, and Behaviors Associated with HIV/AIDS

ABSTRACT Manuscript considers results of a research study that assesses American Indian university students' factual knowledge, understanding, and perceptions of susceptibility to HIV/AIDS, and relationships between their attitudes and decisions to engage in HIV-risk behaviors. Participants responded to a 57-item scaled survey and several demographic items. Survey items were grouped into five categories: factual knowledge about HIV/AIDS, perceptions about sexual intercourse and condom use, perceptions about susceptibility to HIV/AIDS, self-efficacy and HIV/AIDS, and personal beliefs about HIV/AIDS. Survey results indicate unique approaches should be employed when conducting HIV/AIDS prevention education programs for the heterogeneous American Indian/Alaska Native tribes in the United States.
Debtor level collection operations using Bayesian dynamic programming

Abstract After a borrower defaults on their repayment obligations, collectors of unsecured consumer credit debt have a number of actions (e.g., telephone calls, formal letters,) they can take to secure some repayment of the debt. If these actions fail, collectors could seek legal proceedings. The operations management challenge in this setting is to decide which of these actions to take, how long to take them, and in what sequence to take them. Ideally, this collection policy should depend on how the defaulter has been performing during the collection process so far. In particular, it should take into account how many payments the defaulter has made under the current action, compared with how long that action has been tried. Other potential considerations aside, the objective of a collections policy typically is to maximize the recovery rate, i.e., the percentage of the defaulted debt that is recovered in the collections process. In this paper, we use a Bayesian Markov Decision Process (MDP) model to find an optimal policy of what action to undertake in the next period given the current information on the individual debtor’s repayment performance thus far. The proposed model will be empirically validated with data provided by a European bank’s in-house collections department. The model will be able to use by banks to decide their debt collection strategy.