Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Game Aesthetics

For much of the history of game studies, questions of aesthetics have either been downplayed or are entirely absent from the discourse, and when they have been addressed there has been no consistent deployment of the term. This chapter traces the development of player satisfaction models as illustrative of both the methods available for empirical game aesthetics and the problems inherent to the approaches researchers pursued in this area. First, it compares two rival theories of game aesthetics, each of which emphasizes different aspects of the play experience. Particular attention is paid to the four specific player satisfaction models that have been researched, namely (i) Richard Bartle's models; (ii) Nicholas Yee's research; (iii) Nicole Lazzaro's four fun keys model; and (iv) International Hobo's models. Additionally, by reviewing of putative connections between neurobiology and play, a sketch of avenues for future research in this field is provided.
The Neurodynamics of Belief Propagation on Binary Markov Random Fields

We rigorously establish a close relationship between message passing algorithms and models of neurodynamics by showing that the equations of a continuous Hopfield network can be derived from the equations of belief propagation on a binary Markov random field. As Hopfield networks are equipped with a Lyapunov function, convergence is guaranteed. As a consequence, in the limit of many weak connections per neuron, Hopfield networks exactly implement a continuous-time variant of belief propagation starting from message initialisations that prevent from running into convergence problems. Our results lead to a better understanding of the role of message passing algorithms in real biological neural networks.
The impacts of SOX and SEC investigation on the corporate governance of option backdating ﬁ rms Advances in Accounting, incorporating Advances in International Accounting

This study investigates the combined impact of the Sarbanes – Oxley Act of 2002 (SOX) and the subsequent related Securities and Exchange Commission's (SEC) initiatives on the corporate governance characteristics of ﬁ rms that had historically backdated stock options. Our results show that backdating ﬁ rms had both weaker board-level and committee-level corporate governance characteristics than control ﬁ rms in the pre-SOX period. In contrast, backdating ﬁ rms dress up their board-level governance to meet regulatory requirements but still feature weaker committee-level corporate governance in the post-SOX era.
An Overview of Performance Evaluation in Content-based Image Retrieval

Promoted by the professional and diverse demands of image retrieval, the technique of content-based image retrieval (CBIR) becomes more mature. More and more commercial and scientific research systems are developed. As any technique is promoted by the performance evaluation of corresponding research area, for the development of effective image retrieval applications it is imperative to study the standard of performance evaluation in content-based image retrieval. Problems such as a common image database for performance comparison and a means of getting correlation judgement for queries are explained. This paper presents a review of the methods of performance evaluation in content-based image retrieval proposed in the literatures and tries to figure out the developing direction in the future. This paper also recommends that the content-based retrieval research community should establish a standard test-bed for evaluating image retrieval effectiveness. Further work needs to be done to better involve users in the evaluation process because the ultimate aim is to measure the usefulness of a system for a user. Interactive performance evaluations including several levels of feedback and user interaction need to be developed.
Study Title: A nurse-coordinated integrated care model to support decision-making and self-care in patients with atrial fibrillation: A randomized controlled trial

Health implications in patients with atrial fibrillation Atrial fibrillation (AF) is a common abnormal cardiac rhythm characterized by an irregular and rapid heartbeat. In this condition, uncoordinated electrical activity causes ineffective atrial contractions and variable ventricular activation. Sluggish atrial blood flow increases the risk of blood clot formation, which itself independently increases the risk of cardio-embolic stroke by five-fold in AF patients, compared to people with a normal sinus rhythm. [1] In addition, AF is associated with tachycardia-induced cardiomyopathy in patients with a poorly controlled ventricular rate. [2] AF also predisposes patients to increased risks of myocardial infarction,[3, 4] heart failure,[5] dementia,[6] and death. [7] Indeed, the healthcare burden associated with AF-related hospitalization has largely surpassed that associated with heart failure. [8] Given these detrimental health consequences, international practice guidelines[9] have highlighted stroke prevention and arrhythmia control as the major focuses of AF management, whereas the management of modifiable risk factors is equally important to improving the their health outcomes of AF patients. [10, 11]
Cloud Computing and Healthcare Services

There is a significant volume of healthcare data generated daily. The data are important and vital for decision making and delivering the best care for patients. Cloud computing is a cost effective method that facilitates real-time data collection, data storage and exchange between healthcare organizations. Cloud infrastructure is characterized with a high throughput and a high volume storage; two important factors for efficient data analysis of large patients’ population. Security and privacy are of the major concerns for using cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. In order to cope with the rapid advancements in information technology and the utilization of cloud based services, efforts should be dedicated to move healthcare data form the traditional paper based to the electronic format. Then, regional legislation and policies should be enacted to regulate and control the usage of healthcare data.
Training neural word embeddings for transfer learning and translation

In contrast to only a decade ago, it is now easy to collect large text corpora from the Web on any topic imaginable. However, in order for information processing systems to perform a useful task, such as answer a user’s queries on the content of the text, the raw text first needs to be parsed into the appropriate linguistic structures, like parts of speech, named-entities or semantic entities. Contemporary natural language processing systems rely predominantly on supervised machine learning techniques for performing this task. However, the supervision required to train these models are expensive to come by, since human annotators need to mark up relevant pieces of text with the required labels of interest. Furthermore, machine learning practitioners need to manually engineer a set of task-specific features which represents a wasteful duplication of efforts for each new related task. An alternative approach is to attempt to automatically learn representations from raw text that are useful for predicting a wide variety of linguistic structures. In this dissertation, we hypothesise that neural word embeddings, i.e. representations that use continuous values to represent words in a learned vector space of meaning, are a suitable and efficient approach for learning representations of natural languages that are useful for predicting various aspects related to their meaning. We show experimental results which support this hypothesis, and present several contributions which make inducing word representations faster and applicable for monolingual and various cross-lingual prediction tasks. The first contribution to this end is SimTree, an efficient algorithm for jointly clustering words into semantic classes while training a neural network language model with the hierarchical softmax output layer. The second is an efficient subsampling training technique for speeding up learning while increasing accuracy of word embeddings induced using the hierarchical softmax. The third is BilBOWA, a bilingual word embedding model that can efficiently learn to embed words across multiple languages using only a limited sample of parallel raw text, and unlimited amounts of monolingual raw text. The fourth is Barista, a bilingual word embedding model that efficiently uses additional semantic information about how words map into equivalence classes, such as parts of speech or word translations, and includes this information during the embedding process. In addition, this dissertation provides an in-depth overview of the different neural language model architectures, and a detailed, tutorial-style overview of the available popular techniques for training these models.
Vhdl Implementation and Comparison of Complex Multiplier Using Booth ’ s and Vedic Algorithm

For designing of complex number mult iplier basic idea is adopted from designing of mult iplier. An ancient Indian mathematics " Vedas" is used for designing the multiplier unit. There are 16 sutra in Vedas, from that the Urdhva Tiryakbhyam sutra (method) was selected for implementation complex multip licat ion and basically Urdhva Tiryakbhyam sutra applicable to all cases of multip licat ion. Any multi-bit multip licat ion can be reduced down to single bit mult iplication and addition by using Urdhva Tiryakbhyam sutra is performed by vertically and crosswis e. The partial products and sums are generated in single step which reduces the carry propagation from LSB to MSB by using these formulas. In th is paper simulation result for 4bit complex no. mult iplication using Booth‟s algorithm and using Vedic sutra are illustrated. The implementation of the Vedic mathematics and their application to the complex mult iplier was checked parameter like propagation delay.
Experimental Evaluation of Code Smell Detection Tools

Code smells are code fragments that can hinder the evolution and maintenance of software systems. Their detection is a challenge for developers and their informal definition leads to the implementation of multiple detection techniques by tools. This paper aims to evaluate and compare three code smell detection tools, namely inFusion, JDeodorant and PMD. These tools were applied to different versions of a same system. The results were analyzed to answer four research questions related to the evolution of the smells, the relevance of the smells detected, and the agreement among tools. This study allowed us to understand the evolution of code smells in the target system and to evaluate the accuracy of each tool in the detection of three code smells: God Class, God Method, and Feature Envy.
Effect of Financial Intermediation on Economic Development of Nigeria

This study examined the effect of financial intermediation on the development of the economy of Nigeria using data spanning 1986 to 2017. The data were obtained from Central Bank of Nigeria Statistical Bulletin, World Bank (World Development Indicators) and International Monetary Fund (World Economic Outlook). The study considered credit to private sector, lending rate and money supply as independent variables, while real GDP growth rate and unemployment rate were used as dependent variables.Autoregressive distributed Lag (ARDL) technique was employed andEviews 9 was used for the analyses. In other to achieve the objective of the study, series of tests were conducted, including normality test, stationarity test, cointegration test, ARDL estimation and error correction. The tests provided the basis for the conclusion that credit to private sector do not really contributes positively to the development of the economy. This could be due to high lending rate (interest rate). High lending rate is detrimental to the development of the economy. Thus, the study recommends, among others, that the monetary authority should make policies to compel banks to lower their lending rates to encourage the productive sectors of the economy to perform better.
Telecommunications Infrastructure and Economic Growth : A Cross-Country Analysis

The potential role that may be played by information and communication technologies in promoting economic growth, especially in low-income countries is currently attracting considerable attention. Despite this interest, empirical work that measures the impact of such technologies on growth is limited. To measure the growth generating potential of these technologies, in this paper we use data from 113 countries over a 20-year period to examine the impact of terrestrial telecommunications infrastructure, by far the most widely prevalent communication technology in developing countries, on economic output. Our empirical strategy allows for the endogeneity between telecommunications infrastructure and income and follows the approach developed by Roller and Waverman (2001). Our estimates display a positive casual link between telecommunications infrastructure and GDP. The effect appears to be non-linear and is particularly pronounced for countries with a telecom penetration rate of between 5-15 percent. a GRADE and Center for Development Research (ZEF), University of Bonn, Walter-Flex Strasse 3, D53113, Bonn, Germany; em: torero@issr.ucla. edu. b Center for Development Research, em: s.chowdhury@uni-bonn.de; c Center for Development Research and Institute of Social Studies, em: bedi@iss.nl
Altered intrinsic brain activities in patients with transient ischemic attack using amplitude of low-frequency fluctuation : a resting-state fMRI study

Objective: Various neuroimaging studies reported that transient ischemic attack (TIA) was associated with brain morphology changes, whereas the intrinsic brain activity changes in TIA patients remain unknown. The purpose of the study was to investigate alterations of spontaneous brain activity in TIA patients using the amplitude of low-frequency fluctuation (ALFF) method and its correlation with clinical features. Methods: A total of 25 patients with TIA (15 males and 10 females) and 25 healthy controls (HCs) (15 males and 5 females) closely matched in age, sex, and education underwent resting-state functional magnetic resonance imaging scans. The ALFF method was used to assess spontaneous brain activities. Patients with TIA were distinguished from HCs by receiver operating characteristic (ROC) curve. The relationships between the mean ALFF signal values in many brain regions and clinical features in TIA patients were calculated by Pearson correlation analysis. Results: TIA patients had significantly lower ALFF values in the left cerebellum posterior lobe and right precentral/postcentral gyrus relative to the HCs. In contrast, the higher ALFF values in TIA patients were observed in the right prefrontal gyrus. However, no relationships were found between the mean ALFF signal values of the different areas and clinical manifestations in TIA patients. Conclusion: Our results demonstrated that TIA patients showed abnormal intrinsic brain activities in many brain regions related to motor control and cognition function, which might offer some useful information to explore neural mechanisms of TIA patients.
IWA information source on drinking water issues

The transmission of water-borne diseases is still a matter of major concern, despite worldwide efforts and modern technologies utilised for the production of safe drinking water. This problem is not confined to the developing world where treatment may be non-existent or inadequate. There may also be contamination during storage, a lack of regulations and limited understanding and awareness among the population (AAM, 1996). It may also assume serious proportions in industrial countries (Kramer et al., 1996). Mechanical failure, human error or deterioration in the quality of the source water can lead to the failure of even the best treatment systems and disinfection processes (MacKenzie et al., 1994; Roefer et al., 1996). Rapid and reliable routine monitoring of the microbiological quality of source and treated drinking water will therefore remain of fundamental importance in the control of water-borne diseases (Sobsey et al., 1993). The purpose of this report is to create a broader understanding among IWA members and other interested parties of developments in the field of rapid methods for microbiological monitoring of water. Microbiological monitoring of source and drinking water is also associated with several other issues of concern that have never been fully resolved. These include the type and range of microorganisms to be tested for, the required frequency of sampling and the ease of analysis (Pipes, 1990). Although all these issues are of great importance, this report will focus on the need for rapid microbiological analyses and the current state of the art. The document consists of two parts. First, the report itself gives an overview of the requirements for and developments in rapid microbiological methods. Only the broad issues of concern are addressed since the report is not meant to be an in-depth study. Secondly, attached to the report is a separate bibliography of literature related to the rapid detection of microbes in source and drinking water. The purpose of this bibliography is
ESR 2 polymorphism associated with the incidence and prognosis of hepatocellular carcinoma

Aim: To investigate the role of ESR2 polymorphism in incidence and prognosis of patients with hepatocellular carcinoma (HCC). Methods: The DNA from patients with HCC and hepatitis type B virus carriers was used to observe the distribution of ESR2 variations and carry out the clinical correlation analysis. Results: Single nucleotide polymorphisms (SNPs) in ESR2 gene (rs1256049, rs4986938) were significantly correlated with HCC risk. Individuals carrying AA/GA genotype of rs1256049 were associated with lower HCC risk when compared with GG genotype (OR 0.399; 95% CI 0.256-0.621; P<0.001). The opposite result was found in individuals with the rs4986938 AA/GA genotype (OR 2.154; 95% CI 1.370-3.385; P=0.001). The LL genotype (frequency n≤20) of CA repeat allele was associated with moderate-well differentiated degree (p=0.001) and early stage of HCC (p=0.001), and mean OS time was 46.2 (95% CI: 39.3-53.1) months for patients with LL genotype and 29.4 (95% CI: 24.334.5) months for patients with SS (frequency n>20) genotype (P=0.005). Conclusions: Findings indicated SNPs in ESR2 gene (rs1256049, rs4986938) play a different role in HCC risk and SS genotype lead to poor prognosis of HCC patients underwent hepatectomy. It provided a potential reference for the diagnosis and treatment of HCC.
High Performance Large Graph Analytics by Enhancing Locality

HIGH PERFORMANCE LARGE GRAPH ANALYTICS BY ENHANCING LOCALITY Naga Shailaja Dasari Old Dominion University, 2015 Co-Directors: Dr. Desh Ranjan and Dr. Mohammad Zubair Graphs are widely used in a variety of domains for representing entities and their relationship to each other. Graph analytics helps to understand, detect, extract and visualize insightful relationships between different entities. Graph analytics has a wide range of applications in various domains including computational biology, com­ merce, intelligence, health care and transportation. The breadth of problems that require large graph analytics is growing rapidly resulting in a need for fast and effi­ cient graph processing. One of the major challenges in graph processing is poor locality of reference. Local­ ity of reference refers to the phenomenon of frequently accessing the same memory location or adjacent memory locations. Applications with poor data locality reduce the effectiveness of the cache memory. They result in large number of cache misses, requiring access to high latency main memory. Therefore, it is essential to have good locality for good performance. Most graph processing applications have highly ran­ dom memory access patterns. Coupled with the current large sizes of the graphs, they result in poor cache utilization. Additionally, the computation to data access ratio in many graph processing applications is very low, making it difficult to cover the memory latency using computation. It is also challenging to efficiently paral­ lelize most graph applications. Many graphs in real world have unbalanced degree distribution. It is difficult to achieve a balanced workload for such graphs. The parallelism in graph applications is generally fine-grained in nature. This calls for efficient synchronization and communication between the processing units. Techniques for enhancing locality have been well studied in the context of regular applications like linear algebra. Those techniques are in most cases not applicable to the graph problems. In this dissertation, we propose two techniques for enhancing locality in graph algorithms: access transformation and task-set reduction. Access transformation can be applied to algorithms to improve the spatial locality by chang­ ing the random access pattern to sequential access. It is applicable to iterative algo­ rithms that process random vertices/edges in each iteration. The task-set reduction technique can be applied to enhance the temporal locality. It is applicable to algo­ rithms which repeatedly access the same data to perform certain task. Using the two techniques, we propose novel algorithms for three graph problems: k-core decompo­ sition, maximal clique enumeration and triangle listing. We have implemented the algorithms. The results show that these algorithms provide significant improvement in performance and also scale well.
Design adaptive nonparametric function estimation: A unified approach

It is known that the kernel methods based on local constant fits are not design adaptive. That is, the bias of these estimators can have an adverse effect when the derivative of marginal density or regression function is large. The issue is examined by considering a class of kernel estimators based on local linear fits. These estimators have the ability of design-adaptation and can be used to estimate conditional quantiles and to robustify the usual mean regression. The conditional asymptotic normality of these estimators are established. Applications of such a generalized local linear method are discussed. ot Supported by an NSF postdoctoral Fellowship. t Completed while visiting the Department of Statistics, University of North Carolina, Chapel Hill. Abbreviated title. Design adaptive nonparametric regression AMS 1980 subject classification. Primary 62G20. Secondary 62G05, 62J99.
Structural Analysis and Design of STATCOM ’ s Integrator Anti Windup Based Synchronous PI Controller

STATCOM is usually installed to a power system that has a poor power factor and often poor voltage regulation. During the normal or steady state operation of the power system the STATCOM injects reactive power in order to maintain the desired voltage profile. It is customary for the STATCOM to have a safety margin in terms of reactive power, so that it responds to different faults and loading conditions. In this paper integrator anti windup based synchronous PI controller is proposed that brings the change in the dynamics of the STATCOM control loop in order to provide better voltage regulation by using less reactive power from the device safety margins while responding to different faults and loading conditions. Keywords-component; STATCOM, PI controller, integrator anti windup, voltage regulation