Miracle-dz/UniFAR-FTUs huggingface dataset:
Intimate Partner Physical Violence Against Women in Saudi Arabian Primary Healthcare Clinics

INTIMATE PARTNER PHYSICAL VIOLENCE AGAINST WOMEN IN SAUDI ARABIAN PRIMARY HEALTHCARE CLINICS Halah M. Eldoseri Old Dominion University, 2012 Director: Dr. Kimberly Adams Tufts Intimate partner violence against women (IPPVAW) is a serious public health concern. The Ecological Model provides a model to study several factors associated with IPPVAW. In Saudi Arabia, studies addressing IPPVAW are limited and do not cover the various aspects of the problem. The purpose of this study was to investigate the various factors associated with IPPVAW at the personal, interpersonal, community and societal levels. Methods: 200 evermarried women attending six PHC in Jeddah, Saudi Arabia were recruited via convenient sampling method. Women were interviewed on factors related to IPPVAW using an adapted version of WHO survey for violence against women. Results: 45% of women were subjected to IPPVAW and 18.5% reported IPPVAW-related injuries. Alcohol and Drug use by Husbands were significant personal factors associated with IPPVAW (p<0.001). Marital conflict and male dominance were significantly associated with IPPVAW at the interpersonal level factors (p<0.001). Husband's employment and involvement in physical fights with other men were significant community-related factors associated with IPPVAW (p<0.05). Most women did not disclose the real cause of IPPVAW-related injuries to healthcare professionals. Conclusion: factors related to husband's gender attitude require further elucidation. PHC services may benefit from screening women for IPPVAW for better management of cases.
THE STUDY OF KNOWLEDGE, ATTITUDE AND, PERFORMANCE OF MALE BARBERSHOPS OF SHIRAZ IN RELATION TO INFECTION TRANSMISSION

Introduction : AIDS, a new viral disease, and viral hepatitis, one of the 5 infectious causes of premature death in humans, incur huge costs on the society and individuals. Various studies have found barbershop activities to be effective in the transmission of HIV, Hepatitis B Virus and Hepatitis C Virus. , that is, if they do not observe the principles of infection control. The present study was therefore conducted to examine knowledge, attitude and performance in male barbershops of Shiraz concerning compliance with the principles of infection control. Materials In the present cross-sectional study, 200 male barbers selected through the cluster sampling method were interviewed on the principles of infection control. Data obtained were analyzed in SPSS-18 and Excel 2007 . Results: The findings showed that 82% of the barbers had a primary school degree or above and only 8% were illiterate. 4%, 71% and 25% of the barbers had good, average and weak knowledge, respectively. 96% of them had a positive attitude and 4% had a negative attitude. 51.5% and 48.5% of them had good and average performances, respectively. The knowledge of 1.5%, 14%, 10.5% and 74% of the barbers on symptoms of HIV was excellent, good, average and weak, respectively. Conclusion: The results of the study showed that improving knowledge, attitude and performance in male barbers of Shiraz requires holding training courses, implementing health programs and increasing control.
Health it policy in the UK: the case of electronic health records

The challenge to provide a nation-wide integrated health service is part of a UK government policy to transform healthcare using information and communications technology. The National Programme for Information Technology launched in 2002 is the largest, non-military, non-scientific government-funded IT programme, with an estimated total cost exceeding £20 bn. This chapter is a longitudinal study of the National Care Record Service, which is the largest part of the programme, aimed to provide 50 million UK (English) citizens with an electronic health record. Using episodic interviewing techniques over ten years and secondary source material, the findings reveal a serious mismatch between health IT policy and the failure to rollout electronic health records on time and within budget. Key stakeholders, notably clinicians and patient groups, continuously question the policy and merits of the programme, not merely as a “technical challenge” but as a means of transforming working practices in healthcare. This study suggests that government agencies therefore need to extend their health technology assessment criteria to include cultural, social, and economic issues, as imposing centrist, top-down health IT policy needs to secure the “buy-in” from diverse and influential groups and individuals to ensure the successful adoption and implementation of health technologies.
The Influence of Tape Diagrams and Bar Models on Middle School Students' Proportional Reasoning

vi LIST OF TABLES . xii LIST OF FIGURES . xiii LIST OF ABBREVIATIONS . xv CHAPTER ONE: INTRODUCTION . 1 Background . 1 Problem Statement . 3 Purpose Statement . 4 Research Questions . 4 Definitions of Terms . 4 Significance of the Study . 10 CHAPTER TWO: LITERATURE REVIEW . 12 Theoretical Framework . 12 Conceptual Frameworks . 16 Using Mathematical Models . 16 Modes of Representation . 17 Overview of Proportional Reasoning.18
InverSynth: Deep Estimation of Synthesizer Parameter Configurations From Audio Signals

Sound synthesis is a complex field that requires domain expertise. Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task, even for experienced sound engineers. In this paper, we introduce InverSynth - an automatic method for synthesizer parameters tuning to match a given input sound. InverSynth is based on strided convolutional neural networks and is capable of inferring the synthesizer parameters configuration from the input spectrogram and even from the raw audio. The effectiveness InverSynth is demonstrated on a subtractive synthesizer with four frequency modulated oscillators, envelope generator and a gater effect. We present extensive quantitative and qualitative results that showcase the superiority InverSynth over several baselines. Furthermore, we show that the network depth is an important factor that contributes to the prediction accuracy.
Fine-Grained Topic Models Using Anchor Words

Fine-Grained Topic Models Using Anchor Words Jeffrey A. Lund Department of Computer Science, BYU Doctor of Philosophy Topic modeling is an effective tool for analyzing the thematic content of large collections of text. However, traditional probabilistic topic modeling is limited to a small number of topics (typically no more than hundreds). We introduce fine-grained topic models, which have large numbers of nuanced and specific topics. We demonstrate that fine-grained topic models enable use cases not currently possible with current topic modeling techniques, including an automatic cross-referencing task in which short passages of text are linked to other topically related passages. We do so by leveraging anchor methods, a recent class of topic model based on non-negative matrix factorization in which each topic is anchored by a single word. We explore extensions of the anchor algorithm, including tandem anchors, which relaxes the restriction that anchors be formed of single words. By doing so, we are able to produce anchor-based topic models with thousands of fine-grained topics. We also develop metrics for evaluating token level topic assignments and use those metrics to improve the accuracy of fine-grained topic models.
Log Differencing using State Machines for Anomaly Detection

Huge amounts of log data are generated every day by software. These data contain valuable information about the behavior and the health of the system, which is rarely exploited, because of their volume and unstructured nature. Manually going through log files is a time-consuming and labor-intensive procedure for developers. Nonetheless logging information can expose the problematic execution of the software, even though the final outcome seem to be normal. Nowadays the automatic analysis of the log files is crucial for detecting problems, but mainly for understanding how the software behaves, which would be beneficial for the prevention of failures and improvement of the software itself. Towards that direction, this project aims the identifications of unexpected executions of the software and the determination of the root cause behind them. In more details, the expected behavior of the software can be approximated using model inference techniques and the newly incoming observed data can be analyzed to verify if they are conformed by the expected behavior. The conformance checking method that will be used is called replay. The incoming traces will be replayed in the graph, at the point they are not validated, the alignment algorithm will take over. The sequence alignment is performed in three different ways. Two of the methods are looking for the best alignment at a specific radius around the problematic node. Additionally a global alignment technique is implemented, which is based on the famous algorithm by Needleman and Wunsch for DNA sequences. Our goal required the modification of the aforementioned algorithm to not only align two sequences, but a sequence with a tree structured model. Finally the implemented tool provides the visualization of the differences in a way that makes it intuitive for the developers to understand what went wrong. Some additional information are also provided to make the investigation of the "anomaly" easier.
Which strategy best suits biology teaching

The purpose of this study was to compare the achievement of students taught with concept mapping, cooperative learning, 5E learning cycle and lecture methods with the intention of identifying which one among them could be most suitable for teaching Biology. To guide this study, four research questions were raised and tested at 0.05level of significance. The design of the study was pre-test, post-test, delayed post-test, quasi experimental repeated measures design. The samples of the study consisted of four mixed secondary schools, 259 students and eight Biology teachers. The major findings of the study include: significant effect of the four instructional methods on achievement and retention; students in the 5E learning cycle and cooperative leaning groups significantly outscored those in the concept mapping and lecture groups on achievement and retention tests; students in concept mapping outscored those in lecture group both on immediate achievement and retention tests; students in 5E learning cycle and cooperative learning groups did not significantly differ on achievement and retention tests; males and females in all the four groups did not significantly differ on the achievement tests; and a non-significant interaction effect between sex and method of instruction on achievement. It was concluded that the adoption of either 5E learning cycle or cooperative learning strategies may be appropriate for the teaching and learning of Biology. Correspondence concerning this manuscript should be addressed to O. Patrick Ajaja, Delta State University Abraka, Nigeria, osawaruajaja@yahoo.com
Traffic Monitoring and MAC-Layer Design for Future IoT Systems

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). In most IoT application scenarios, billions of things are interconnected together using standard communication protocols to provide services for different applications in the healthcare industry, smart cities, transportation, and food supply chain. Despite their advantage of connecting things anywhere, anytime, and anyplace, IoT presents many challenges due to the heterogeneity, density, the power constraints of things, and the dynamic nature of the network that things might connect and disconnect at any time. All of these increase the communication delay and the generated data, and it is thereby necessary to develop resource management solutions for the applications in IoT. One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step. To overcome the communication cost overhead in the network and to solve the problem efficiently, the nodes use distributed learning algorithms. Next, the problem is extended to include energy constraints on the transmission decisions of the nodes, where each one of them has a battery of finite capacity, which is replenished by an energy-harvesting process. This constrained problem is solved using energy-aware channel access games under different scenarios of perfect and imperfect information. 5 In the distributed sensing part, a traffic-monitoring system, integrated into a WSN, is proposed as a potential application to implement the channel access solution. This system maximizes the privacy of the sensed traffic by using low-cost and low-power sensor devices that integrate passive infrared sensors (PIR) and ultrasonic rangefinders. To estimate the parameters required to solve the real-time monitoring problem (vehicle detection, classification, and speed estimation), the measurements of these sensors are analyzed using a set of optimized machine-learning algorithms. The selection of these algorithms is due to the continuous variation of the sensed environment over time, the lack of the system state dynamic models, and the limitation in the resources.
Scalable and Accurate Memory System Simulation

Title of Dissertation: Scalable and Accurate Memory System Simulation Shang Li Doctor of Philosophy, 2014 Dissertation directed by: Professor Bruce Jacob Department of Electrical & Computer Engineering Memory systems today possess more complexity than ever. On one hand, main memory technology has a much more diverse portfolio. Other than the main stream DDR DRAMs, a variety of DRAM protocols have been proliferating in certain domains. Non-Volatile Memory(NVM) also finally has commodity main memory products, introducing more heterogeneity to the main memory media. On the other hand, the scale of computer systems, from personal computers, server computers, to high performance computing systems, has been growing in response to increasing computing demand. Memory systems have to be able to keep scaling to avoid bottlenecking the whole system. However, current memory simulation works cannot accurately or efficiently model these developments, making it hard for researchers and developers to evaluate or to optimize designs for memory systems. In this study, we attack these issues from multiple angles. First, we develop a fast and validated cycle accurate main memory simulator that can accurately model almost all existing DRAM protocols and some NVM protocols, and it can be easily extended to support upcoming protocols as well. We showcase this simulator by conducting a thorough characterization over existing DRAM protocols and provide insights on memory system designs. Secondly, to efficiently simulate the increasingly paralleled memory systems, we propose a lax synchronization model that allows efficient parallel DRAM simulation. We build the first ever practical parallel DRAM simulator that can speedup the simulation by up to a factor of three with single digit percentage loss in accuracy comparing to cycle accurate simulations. We also developed mitigation schemes to further improve the accuracy with no additional performance cost. Moreover, we discuss the limitation of cycle accurate models, and explore the possibility of alternative modeling of DRAM. We propose a novel approach that converts DRAM timing simulation into a classification problem. By doing so we can make predictions on DRAM latency for each memory request upon first sight, which makes it compatible for scalable architecture simulation frameworks. We developed prototypes based on various machine learning models and they demonstrate excellent performance and accuracy results that makes them a promising alternative to cycle accurate models. Finally, for large scale memory systems where data movement is often the performance limiting factor, we propose a set of interconnect topologies and implement them in a parallel discrete event simulation framework. We evaluate the proposed topologies through simulation and prove that their scalability and performance exceeds existing topologies with increasing system size or workloads. Scalable and Accurate Memory System Simulation
Recent Trends in Context Exploitation for Information Fusion and AI

AI is related to information fusion (IF). Many methods in AI that use perception and reasoning align to the functionalities of high-level IF (HLIF) operations that estimate situational and impact states. To achieve HLIF sensor, user, and mission management operations, AI elements of planning, control, and knowledge representation are needed. Both AI reasoning and IF inferencing and estimation exploit context as a basis for achieving deeper levels of understanding of complex world conditions. Open challenges for AI researchers include achieving concept generalization, response adaptation, and situation assessment. This article presents a brief survey of recent and current research on the exploitation of context in IF and discusses the interplay and similarities between IF, context exploitation, and AI. In addition, it highlights the role that contextual information can provide in the next generation of adaptive intelligent systems based on explainable AI. The article describes terminology, addresses notional processing concepts, and lists references for readers to follow up and explore ideas offered herein.
EFFECT OF GNRH ANALOGUE (GONADORELIN) ON PREGNANCY RATES IN RIVER BUFFALOES

Field trials were carried out to assess the effect of gonadorelin (Fertagyl, intervet, Holland) on pegnancy rates of river buffaloes. In this study 84 lactating cyclic buffaloes were randomly allotted to three treatment groups. (1) untreated control (n=40); (2) I.M. injection of 0.25 mg gonadorelin at the time of artificial insemination (n=20) and (3) I.M. injection of 0.25 mg of gonadorelin on days 11 to 13 postinsemination (n=24). Pregnancy was determined by rectal palpation of the uterus on days 42 to 55 after insemination. Pregnancy rates to first service (AI) in groups 1, 2 and 3 were 25%, 55% and 58%, respectively. In groups 2 and 3, a greater proportion of buffaloes that received GnRH became pregnant comparing to that of those untreated (P<0.01). The results demonstrated that a single dose of the gonadorelin significantly improved pregnancy rates in buffaloes when administered at the time of AI or during the mid luteal phase after AI.
College Students’ Cognitive Learning Outcomes in Technology-Enabled Active Learning Environments: A Meta-Analysis of the Empirical Literature

Technology-enabled active learning environments (TE-ALEs) have attracted considerable research interest, particularly in higher education. However, research shows inconsistent results describing the influence of TE-ALEs toward students’ cognitive learning outcomes. This study was designed to identify high-quality empirical research examining college students’ cognitive learning outcomes and to utilize meta-analysis to determine the overall effectiveness of TE-ALEs. A systematic literature search identified 31 high-quality peer-reviewed journal articles that met the inclusion criteria. Meta-analysis showed that the calculated effect size of TE-ALEs more positively influenced students’ cognitive learning than traditional lecture-based environments. Moderator variable analysis suggested that social context, study design, and sample size were significant factors that influence the effectiveness of TE-ALE. TE-ALEs were found more effective when instructors employed individualized learning contexts as well as when bias was reduced in randomized controlled trials. TE-ALEs were also found to be more effective in small courses rather than in large courses.
Proactive identification of query failure

When a user fails to find any useful information to support the task at hand after issuing a query, the user experiences a query failure. Since users possess limited cognitive resources, query failures often lead to user frustration as no clear benefit is obtained from the associated search interactions. Therefore, to improve users' search experiences, we conducted a controlled‐lab study with 40 participants, seeking to explore the extent to which query failures can be proactively identified before users start examining the retrieved results. Specifically, based on the data collected from 693 query segments generated in 80 search sessions, we used past search behaviors and current query attributes as features to build classifiers and examined the performance in capturing query failures. We report that (1) analytics algorithms utilizing past search behavioral data have significantly better performances than the baseline model in tasks of different types, and (2) The knowledge of users' search intentions can help improve the performance of the prediction model. Results pave way for developing proactive system supports for task‐based search interactions.
Exploring the effect of video feedback from unknown peers on e-learners’ English-Chinese translation performance

Abstract This study aims to investigate the effectiveness of peer video feedback on adult e-learners’ language learning. The participants were 60 first-year e-learning students majoring in telecommunications at an e-learning college in Beijing and participating in a 19-week English course. They were divided evenly into two groups with two peer feedback methods, namely video feedback and written feedback. The feedback process was anonymous to some extent, since the feedback was provided by unknown peers with the instructor as the intermediator. A pretest, a posttest, an online questionnaire, and a semi-structured interview were employed as research instruments. The results indicated that peer video feedback was more effective in improving e-learners’ translation performance than written feedback. The questionnaire survey and the interview administered to the video group also showed that the video feedback method was generally welcomed and was advantageous in that it was easier to use than written feedback, saved time, and fostered a sense of belonging.