Miracle-dz/UniFAR-FTUs huggingface dataset:
Intra-Muslim Conflicts : A Linguistic Representation

The global Muslim community is multi-racial, multi-lingual, and multi-cultural. Over the centuries, religious groups emerged due to historical circumstances, political allegiances, interpretation of texts, cultural influences, and varied theological denominations. In some cases, the resulting intra-group rivalry has led to intraMuslim conflicts characterized by various levels of violence, conflict, rhetoric, and verbal abuse. This article investigates the linguistic trends related to representing intra-Muslim conflicts, the factors and strategies of utilizing linguistic representation, and the classification of common terms within the context of such conflicts. It also analyzes the implications of certain vocabularies, structures, and discourse styles that represent the positions of opposing groups, perceptions of self and others, and how linguistic representation can help resolve intra-Muslim conflict. I use a pragmatic analysis to search for cultural and religious connotations in samples of common terms employed in the conflicts in question. Although the global Muslim community is multi-lingual, Arabic terms are commonly used in intra-Muslim conflicts. Given this reality, I focus on Arabic terms and present only a few non-Arabic loan words that have been adopted. Ahmad Shehu Abdussalam is a professor in the Department of Arabic Language and Literature, Kulliyyah of Islamic Revealed Knowledge and Human Sciences, International Islamic University Malaysia, Kuala Lumpur.
Diagnosis in Designs with Block Compactors

While drastically reducing the test volume and the test application time, test response compactors lose the location of errors in the scan chains, which is necessary for diagnosis. This paper proposes a method to retrieve that information from the signatures produced by block compactors. The error diagnosis efficiently searches for the errors by making some assumptions about the shape of error vectors and learning from previous results. Experiments show that over 95% diagnostic coverage can be reached in practice.
Review on the accident-forecasting research methods

The present review is conducted in hoping to assist the researchers in the field to understand the general principles and methods of accident-forecasting and prevention in a systematic and comprehensive manner as well as provide hints to identify the research status and trends. As is known,accident-forecasting is increasingly becoming the hot topic in the whole society,which is likely to be of great significance for system safety and accident prevention in advance. Keeping these goals in mind,this paper intends to generalize the main commonly recognized methods into six major categories,including scenario analysis,regression method,time series method,Markov chain method,grey model as well as the nonlinear model. At the same time,it has also made a comparative analysis and generalization on them,as well as a study on the model selection and accuracy measure. Thus,it can be concluded that: 1) Most forecasting theories and methods are designated for certain particular application trades or fields. As the model complexity,accuracy and data requirement may differ from each other,a generally adopted method should be suitable and accurate to the specific modes and characteristic features of a certain accident; 2) In case a randomly chosen city and contingency were eliminated in order to gain more useful guide of modeling,stress should be put on analyzing the outlier in the given sample space and the heterogeneity of factors; 3) The system safety proves to be non-linear,dynamic and stochastic in nature,and,accordingly,it is better for realistic application to combine the quantitative and qualitative analysis,the methods both linear and nonlinear in nature,and static and dynamic methods in to an integrated entity; 4) The idea of prevention should be made the focus of the accident-forecasting research based on the state forecasting tasks in the future.
A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration

Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.
Market Orientation: how to asses it? An empirical investigation within italian medium-sized firms

In the last decades significant attention has been given to market orientation (MO) as the operationalization of the marketing concept, essential to gain a competitive advantage in a continuously changing scenario. Despite the great amount of contributions aimed at investigating MO along with its antecedents and consequences, a certain ambiguity still persists with reference to smalland medium-sized enterprises. As a matter of fact, empirical evidence highlighted some difficulties in assessing MO within these firms using the dimensions commonly considered in literature and mainly developed in the context of large companies. Building on these arguments, the present paper aims, on one hand, to contribute to the understanding of MO concept and identify its manifestations in the context of mediumsized firms, where marketing processes are generally quite unstructured and partly unplanned; on the other hand, to verify whether specific factors are able to influence the intensity of marketing activities realized by these firms and, if so, how do these factors differ from those identified by literature as antecedents of MO within larger organizations. To these aims we used a mixed-method approach, collecting data from 59 medium-sized manufacturing firms operating in Italy and combining qualitative and quantitative analyses.
CULTURE, SELF, AND SUBJECTIVE WELL-BEING: CULTURAL PSYCHIKIGICAL AND SOCIAL CHANGE PERSPECTIVES

Culture serves as a major force shaping the way people conceptualize the self, think about happiness, and cope with difficulties and upheavals in life. The present paper starts with a conceptual analysis of traditional Chinese and modern Western views of the self, to contrast the social-oriented versus individual-oriented nature of the self embedded in the two cultural traditions. These culture-specific modes of self construction can then shed light on the diverse meanings people hold for happiness and well-being in different societies. Building on the social-oriented view of the self, which emphasizes relatedness, fluidity, morality, self-constraint, harmony, and sensitivity to others, subjective well-being for the Chinese is construed around fulfilling one’s obligations and maintaining homeostasis (dialectical balance). In contrast, emanating from an individual-oriented view of the self, which emphasizes independence, self-determination, consistency, and personal striving, happiness is a prize to be fought over, and entirely one’s responsibility to accomplish this ultimate goal of life. Similarly, culture-specific preferences in individual-society and/or interpersonal alignment mirror the two contrasting views of the self, and link up with the collaborative project of pursuing happiness.
Does Might Make Right?: An Experimental Investigation on the Impact of Who Owns the Property Rights

This paper investigates the role of property rights in environmental decisions and choices regarding the distribution of income in a downstream water pollution problem. The results confirm that who owns the property rights is a significant determinant of these decisions. More specifically, under certain conditions a property rights owner who suffers the consequences of negative externalities acts, on average, more environmentally friendly than a property rights owner who causes such negative externality. Similarly, when it comes to the distribution of income, the property rights owners who cause negative externalities allocate on average a larger share of the income to themselves.
Automatic chord recognition based on the probabilistic modeling of diatonic modal harmony

One of the distinctive traits of our society in the last decade is the availability and consequent fruition of multimedia content in digital format. The Internet, the growing density of storage systems and the increasing quality of compressed file formats, played the main roles in this revolution. Nowadays, audio and video contents are easily created, stored and shared by million of people. This huge amount of data has to be efficiently organized and archived, to ease the fruition of large databases as online shops (as Amazon, iTunes) and content sharing website (Youtube, Soundcloud). The task of extraction of meaningful descriptors from digital audio and classification of musical content are addressed by a new research field named Music Information Retrieval (MIR). Among the descriptors that MIR aims at extracting from audio are rhythm, harmony, melody. These descriptors are meaningful for a musician and can find many applications as computer aided music learning, and automated transcription. The high demand for reliable automated transcriptions comes from the hobby musicians too. Official transcriptions are not always published and often the information about chords is enough to reproduce the song. This thesis propose a system that performs the two tasks of beat tracking and chord recognition. The beat-tracking subsystem exploits a novel technique in finding the beat instants, based on the simultaneous tracking of many possible paths. This algorithm provide a useful self-evaluation property that can be exploited to achieve better accuracy. The downbeat is extracted by the same algorithm, proving the validity of the same approach at higher metrical level. The chord-recognition system proposed contemplates all the four most used key modes in western pop music (previously only major and minor modes are considered). Two novel parametric probabilistic models of keys and chords are proposed, where each parameter has a musical meaning. The performances of the two parts of the system exceed those taken as state-of-art reference. Finally the information gathered by our
HIGH PLASMA MITOCHONDRIAL DNA EARLY IN PREGNANCY IS A RISK FACTOR FOR DEVELOPMENT OF PREECLAMPSIA

Hany A. El-Kallaf and Adel F. Al-Kholy. Department of Obstetrics & Gynecology and Medical Biochemistry, Faculty of Medicine, Benha University, Egypt. Manuscript Info Abstract . Manuscript History Received: 19 January 2019 Final Accepted: 21 February 2019 Published: March 2019
An Intensive Field Survey of Physical Environments in a Mangrove Forest

A field observation of physical environments in a mangrove forest was made in Fukido River with a surrounding mangrove swamp in Ishigaki Island, Okinawa, Japan. The characteristic flow structures are observed in creek and swamp and then found to be similar to hydraulic phenomenon in a compound meandering channel. The thermal environments in the creek and swamp are investigated by a heat budget analysis, indicating the significant importance of the heat transport by tidal current and the heat flux to swamp bottom. We also examine the sediment dynamics in detail by measuring a vertical turbulent flux of suspended sediments.
The Bucharest Academy of Economic Studies FLAMES RECOGNITION FOR OPINION MINING

The emerging world-wide e-society creates new ways of interaction between people with different cultures and backgrounds. Communication systems as forums, blogs, and comments are easily accessible to end users. In this context, user generated content management revealed to be a difficult but necessary task. Studying and interpreting user generated data/text available on the Internet is a complex and time consuming task for any human analyst. This study proposes an interdisciplinary approach to modelling the flaming phenomena (hot, aggressive discussions) in online Italian forums. The model is based on the analysis of psycho/cognitive/linguistic interaction modalities among web communities' participants, state-of-the art machine learning techniques and natural language processing technology. Virtual communities' administrators, moderators and users could benefit directly from this research. A further positive outcome of this research is the opportunity to better understand and model the dynamics of web forums as the base for developing opinion mining applications focused on commercial applications.
Predicting Healthcare Expenditure Increase for an Individual from Medicare Data

Healthcare expenditure is a growing concern in the US. In 2012, the total US annual healthcare expenditure reached $2.8 trillion. As a percentage of GDP, it is the highest among all the nations worldwide. In this study we investigate the variability of patient healthcare expenses year-onyear, depending on the different medical conditions patients get diagnosed with, the prescription drugs they consume and the demographic variables. We work with anonymized but publicly available Medicare data, which has more than 114,000 beneficiaries and more than 12,400 features. We address the problem of accurately predicting which beneficiaries’ inpatient claim amounts increased between 2008 and 2009, using an ensemble of six different classification algorithms. We achieved a sensitivity (recall) of 80%, an overall accuracy of 77.56% and a precision of 76.46% on the test data set. We demonstrate its benefits to the healthcare stakeholders: the insurance providers for projecting cost and revenue more accurately, the high-risk patients for choosing the right insurance plan, and the healthcare providers for deciding which patients need additional monitoring. Our research shows that kidney conditions, COPD (chronic obstructive pulmonary disease), hypertension, stroke/transient ischemic attack, cancer and osteoporosis are among the most influential conditions behind expenditure increase.
In whom can I trust ? Determinants of consumer trust for different service providers

This study examines how consumer trust is developed across multiple consumer interaction channels (front line employees, self service channels and marketing communications), how these are influenced by management policies and consumer previous experience, and the outcome of consumer loyalty. Two different consumer contexts are examined, insurance and mobile telecommunications, and an online survey undertaken with 657 US consumers. Structural equation modeling confirmed the overriding importance of management policies as an antecedent to trust and the significance of all three consumer interaction channels. The impact of these three interaction channels differed in the two contexts. The study contributes by incorporating multiple channels and management practices and managerial implications are proposed.
Number : WUCSE-2006-19 2006-0101 Acceleration of Profile-HMM Search for Protein Sequences in Reconfigurable Hardware

Profile Hidden Markov models are highly expressive representations of functional units, or motifs, conserved across protein sequences. Profile-HMM search is a powerful computational technique that is used to annotate new sequences by identifying occurrences of known motifs in them. With the exponential growth of protein databases, there is an increasing demand for acceleration of such techniques. We describe an accelerator for the Viterbi algorithm using a two-stage pipelined design in which the first stage is implemented in parallel reconfigurable hardware for greater speedup. To this end, we identify algorithmic modifications that expose a high level of parallelism and characterize their impact on the accuracy and performance relative to a standard software implementation. We develop a performance model to evaluate any accelerator design and propose two alternative architectures that recover the accuracy lost by a basic architecture. We compare the performance of the two architectures to show that speedups of up to 3 orders of magnitude may be achieved. We also investigate the use of the Forward algorithm in the first pipeline stage of the accelerator using floating-point arithmetic and report its accuracy and performance. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 WASHINGTON UNIVERSITY THE HENRY EDWIN SEVER GRADUATE SCHOOL DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING ACCELERATION OF PROFILE-HMM SEARCH FOR PROTEIN SEQUENCES IN RECONFIGURABLE HARDWARE by Rahul Pratap Maddimsetty Prepared under the direction of Dr. Jeremy D. Buhler A thesis presented to the Henry Edwin Sever Graduate School of Washington University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 2006 Saint Louis, Missouri WASHINGTON UNIVERSITY THE HENRY EDWIN SEVER GRADUATE SCHOOL DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
On the research and design of deep web crawler

As the web grows, more and more data has become available under dynamic forms of publication, such as legacy databases accessed by an HTML form. In this way, integration of this data relies more and more on the Web Crawler that can automatically fetch pages for further processing. As a result, there is an increasing need for tools that can help users generate such agents. The method is described for automatically generating agents to collect Deep Web pages. This method uses a pre-defined ontology repository for identifying the contents of these pages and takes the advantage of some patterns that can be found among web sites to identify the navigation paths to follow. The results of a number of experiments carried out with sites from different domains demonstrate the accuracy of the method.
Using Abstract Meaning Representation of Text for Summarization

With the advent of World Wide Web, the ever growing number of documents have been making it difficult for us to retrieve factual information. Summarizing documents gives us the potential to understand and process more information in a limited time. A summary can be used in a representative way as a pointer to some parts of the original document, or in a informative way to cover all relevant information of the text abstractively by accommodating facts and the grammatical structure. Creating a concise summary requires the information of rich language semantics. Text Summarization is a significant problem widely researched in the domain of Natural Language Processing and is broadly categorized as extractive and abstractive summarization. Extractive summarization is finding the significant words and n-grams from the original document. In contrast to the former, abstractive summarization focuses on producing a summary by understanding and rephrasing the text using words that have similar meanings. This thesis studies the problem of abstractive summarization using Abstract Meaning Representation (AMR) of text on a large scale (using CNN/DailyMail dataset). AMR is a semantic representation of a sentence as a labelled directed graph or feature structure, derived from PENMAN Sentence Plan Language. We use attention based neural Seq2Seq model for generating summaries abstractively from anonymized linearized AMRs’ of CNN/Dailymail News articles. Our model is then evaluated on a set of test articles provided in the CNN/Dailymail dataset and the ROUGE scores are reported along with human evaluation results.
Using complexity thinking to develop a new model of student retention

This paper proposes a new approach to the modeling of student retention in higher education, namely the use of Complexity Thinking, in conjunction with Exploratory Factor Analysis and Multidimensional Scaling. To illustrate our proposal we analyse a small data sample collected from undergraduate engineering students at a highly regarded traditional Swedish university. This analysis shows that issues affecting student retention should be viewed as nested, interconnected systems, in which certain components are more influential than others, rather than in linear terms.
Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

The Sentinel-2 satellite mission offers high resolution multispectral time-series image data, enabling the production of detailed land cover maps globally. When mapping large territories, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixel-wise classification methods. However, the radical shift of the computer vision field away from hand-engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In particular, convolutional neural networks learn features which take into account the context of the pixels and, therefore, a better representation of the data can be obtained. In this paper, we assess fully convolutional neural network architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time-series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time-series image data, an adaptation of the U-Net model (a fully convolutional neural network) for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps. We conclude that fully convolutional neural networks can yield improved results with respect to pixel-wise Random Forest classifiers for classes where texture and context are pertinent. However, this new approach shows higher variability in quality across different landscapes and comes with a computational cost which could be to high for operational systems.