Miracle-dz/UniFAR-FTUs huggingface dataset:
TENNESSEE BEEF PRODUCERS' WILLINGNESS TO PARTICIPATE IN A STATE-BRANDED BEEF PROGRAM

Abstract Tennessee cattle producer willingness to participate in a hypothetical Tennessee Branded Beef Program (TBBP) was examined using 2016 survey data. Willingness to participate in the TBBP was modeled using a probit model. Among those willing to participate, a Tobit model was used to estimate the pounds of live-weight beef producers were willing to supply into a TBBP. Age, production practices, and risk attitudes influenced willingness to participate. Among those willing to participate, projected TBBP supply per farm averaged 32,329 pounds and was influenced by on-farm animal units, production practices, perceived barriers, risk attitudes, and consequentiality beliefs.
Semantic Feature Extraction for Narrative Analysis by Saadet

A story is defined as “an actor(s) taking action(s) that culminates in a resolution(s)”. I present novel sets of features to facilitate story detection among text via supervised classification and further reveal different forms within stories via unsupervised clustering. First, I investigate the utility of a new set of semantic features compared to standard keyword features combined with statistical features, such as density of part-of-speech (POS) tags and named entities, to develop a story classifier. The proposed semantic features are based on triplets that can be extracted using a shallow parser. Experimental results show that a model of memory-based semantic linguistic features alongside statistical features achieves better accuracy. Next, I further improve the performance of story detection with a novel algorithm which aggregates the triplets producing generalized concepts and relations. A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. The algorithm clusters triplets into generalized concepts by utilizing syntactic criteria based on common contexts and semantic corpus-based statistical criteria based on “contextual synonyms”. Generalized concepts representation of text (1) overcomes surface level differences (which arise when different keywords are used for related concepts) without drift, (2) leads to a higher-level semantic network representation of related stories, and (3) when used as features, they yield a significant (36%) boost in performance for the story detection task. Finally, I implement co-clustering based on generalized concepts/relations to automatically detect story forms. Overlapping generalized concepts and relationships correspond to archetypes/targets and actions that characterize story forms. I perform co-clustering of stories using standard unigrams/bigrams and generalized concepts.
Assessment of Factors that Determine Successful Implementation of School Improvement Projects in Rwanda: A Case of the Whole School Development (WSD) Project in UNICEF Supported Child Friendly Schools

This study sought to examine the determinants of successful project implementation of projects in the Rwanda educational sector by focusing on education quality improvement projects particularly the Whole School Development (WSD) initiative which is implemented by IEE Rwanda with support from UNICEF in selected Child Friendly Schools (CFS). The WSD initiative has implementation challenges such as limited administrative support from school leaders, poor project monitoring and evaluation by the implementer and low levels of school readiness to implement the project. This study hence, sought to assess how these influence successful implementation of the project. The objective of this study is to analyze the role of monitoring and evaluation in successful implementation of the WSD project in UNICEF supported child friendly schools in Rwanda. A likert scale questionnaire was used for data collection and the data was analyzed by way of SPSS 20 using descriptive techniques such as frequencies. The findings indicate that project monitoring and evaluation influence the successful implementation of school improvement projects in Rwanda. Specifically, project monitoring and evaluation was found to have the strongest influence from regression analysis results in terms of contribution to successful implementation of the project when other factors are controlled for. This was followed by organizational readiness to implement while project team training was found to make the least significant contribution to prediction of successful project implementation among all variables in the model. The study recommends among other things that there should also be a fully fledged M&E Staff on the WSD project, the M&E staff should regularly check on project implementation progress in order to ensure successful implementation and that there should be clear guidelines for quality M&E processes on the WSD project as this was found to positively affect successful implementation of the project.
Global Discriminative Training of a Hybrid Speech Recognizer

Hybrid speech recognizers usually involve a frame-based classification followed by a segment alignment system, trained separately. The simplicity of such systems is counterbalanced by the lack of a global optimisation scheme for the whole system. In this paper we propose a discriminative training method for MLP/HMM hybrids based on the optimization of a global cost function at the phone recognition level. The MLP weights, usually updated according to the target values, are now updated according to the misclassifications present in the output of the system. Results are presented for the TIMIT phone recognition task and show that this method compares favourable with recent published results in this task. The global discriminative training method was also applied to a Portuguese speech database leading to promising results.
New solutions for digestate solid / liquid separation to apply on small alpine farm biogas plants

The research aimed at implementing biogas-refinery from livestock effluents, by defining innovative/adapted technologies and processes for Nitrogen (N) control, which could be easy to apply in the alpine context and directly manageable by farmers. Both pilot and lab-scale experiments were carried out, in order to identify the best condition for the studied process line. An innovative system was tested: Solid-Liquid (S/L) separation by means of drainage bags, after induced flocculation with specific polyelectrolytes. The choice of materials concerned geotextile tissues in polypropylene for dehydration/dewatering applications. The polyelectrolytes used for the tests are emulsifiers, cationic polyacrylamide (CPAM). The optimal concentration of additives and the identification of the correct diameter of the pores were determined through preliminary lab tests of filtration, followed by field tests, by means of hanging drainage bags. The process achieved S/L separation efficiency from 60 to 70% in 13 days, respectively for slurry and for digestate; 60% of TKN and 85-90% of TS concentrated in 30-35% of the initial material. Norg concentrated into the SF (97%), which can be transported and used outside the farm, while 60% of N-NH4 concentrates in the LF, which can be further treated (i.e. with stripping or biological processes) or/and can be used for irrigation.
Nonlinear Pedagogy and its role in encouraging 21st Century Competencies through Physical Education: A Singapore Experience Abstract Nonlinear Pedagogy is an exploratory approach to teaching and learning Physical Education

Nonlinear Pedagogy is an exploratory approach to teaching and learning Physical Education that can be potentially effective to help children acquire relevant 21st century competencies. Underpinned by Ecological Dynamics, the focus of Nonlinear Pedagogy is on the learner and includes the provision of less prescriptive instructions, and guided discovery which serve to develop greater autonomy, competency and relatedness in the learning process. This paper describes a study that examines the factors that contributed to motivation, enjoyment and the development of 21st century competencies in Primary School Children. 133 children were taught over 7-weeks to play a modified tennis game either with a Nonlinear Pedagogy or Linear Pedagogy (i.e., more teacher-centred) approach in a Singapore school. While findings from the IMI questionnaire showed that there was no difference for the subscales measured, student and teacher interviews indicated that the Nonlinear Pedagogy approach created a learning environment that facilitated perceived competence, autonomy and relatedness, thus potentially enhancing intrinsic motivation and enjoyment during practice. Nonlinear Pedagogy encourages teachers to create representative learning designs through the inclusion of a variety of modified games, the freedom to choose, an emphasis on exploration and problem-solving and can be relevant to develop 21st century competencies.
, N and Such , JM Implicit Contextual Integrity in Online Social Networks

Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks and preventing them from exchanging inappropriate information in those contexts or disseminating sensitive information from some contexts to others. Contextual integrity is a privacy theory that conceptualises the appropriateness of information sharing based on the contexts in which this information is to be shared. Computational models of Contextual Integrity assume the existence of well-defined contexts, in which individuals enact pre-defined roles and information sharing is governed by an explicit set of norms. However, contexts in Online Social Networks are known to be implicit, unknown a priori and ever changing; users relationships are constantly evolving; and the information sharing norms are implicit. This makes current Contextual Integrity models not suitable for Online Social Networks. In this paper, we propose the first computational model of Implicit Contextual Integrity, presenting an information model for Implicit Contextual Integrity as well as a so-called Information Assistant Agent that uses the information model to learn implicit contexts, relationships and the information sharing norms in order to help users avoid inappropriate information exchanges and undesired information disseminations. Through an experimental evaluation, we validate the properties of the model proposed. In particular, Information Assistant Agents are shown to: (i) infer the information sharing norms even if a small proportion of the users follow the norms and in presence of malicious users; (ii) help reduce the exchange of inappropriate information and the dissemination of sensitive information with only a partial view of the system and the information received and sent by their users; and (iii) minimise the burden to the users in terms of raising unnecessary alerts.
of the Effect of Vitamin C Infusion and Vitamin E-Coated Membrane on Hemodialysis-Induced Oxidative Stress

It is reported that oxidative stress plays an important part in the pathogenesis of cardiovascular diseases in chronic kidney disease (CKD) patients. This contributes to the increased cardiovascular morbidity and mortality in that group of patients. Clinical studies point to links between oxidative stress, inflammation, anemia and malnutrition (Panichi et al., 2011). The presence of inflammation and the length of duration of dialysis are the most important determinants of oxidative stress, which make them additional risk factors of cardiovascular disease. There are several deficiencies of antioxidant defense mechanisms which have been found in CKD patients. These include reduced levels of vitamin C, increased levels of oxidized vitamin C, reduced intracellular levels of vitamin E, reduced selenium concentrations, and deficiency in the glutathione scavenging system (Locatelli et al., 2003). The main interest of this chapter is particularly focused on the effect of vitamin C infusion and vitamin E-coated membrane on hemodialysis-induced oxidative stress and, also setting this issue in the broader context of oxidative stress mitigation and the search for appropriate markers of oxidative stress. The level of oxidative stress can be determined by several markers, because oxidants themselves have very short half-lives. Only lipids, proteins, carbohydrates, and nucleic acids modified by oxy-radicals are suitable as markers because of their long lifetime. According to the NKF KDOQI Clinical Practice Guidelines for Cardiovascular Disease in Dialysis Patients these markers include lipid peroxidation products (such as malonyldialdehyde (MDA), exhaled alkanes, oxidized low-density lipoproteins, advanced lipid oxidation products), oxidatively modified arachidonic acid derivatives (such as F2-isoprostanes and isolevuglandins); oxidatively modified carbohydrates (such as reactive aldehydes and reducing sugars); oxidatively modified aminoacids (such as cysteine/cystine, homocysteine/homocystine, 3-chlorotyrosine, 3-nitrotyrosine); oxidatively modified proteins (such as thiol oxidation, carbonyl formation, advanced oxidation protein products, 3-nitrotyrosine, advanced glycation end-products and oxidatively modified DNA (such as 8 hydroxy 2’deoxyguanine) (K/DOQI Workgroup, 2005). In the literature other oxidative stress markers are also mentioned, such as the lipid peroxidation products acrolein, 4-hydroxynonenal or thiobarbituric acid-reactive substances (TBARS); evaluation of
Understanding and supporting collaborative design using an horizontal tabletop display

Based on our research on evaluating a Virtual desk environment for architects and designers is it possible to derive requirements for supporting collaborative design activities ? We first examine questions related to internal and external interactions in architectural design, to the role of external representations and their extension as shared objects of design in collaborative horizontal tabletop. Then we describe the Virtual Desktop and the EsQUIsE software. We will synthesize the results of our former investigations on this setting, in order to derive new orientations for the design and research.
Random Matrices, Matrix Completion and Multi-Task Learning

Each project should be completed by a team (1-4 people). Projects may contain different sections e.g. theory, algorithms, data exploration. Each team will produce a short report (up to 10 pages) and deliver a 5×(number of team members) minutes presentation, with each team member presenting a 5-minutes part. Every team member should be responsible for a part of the project this should be clearly indicated at the end of the report and for the 5 minutes of presentation. The projects described here are fluid in nature, and I am happy to discuss variations, changes, as well as projects different from the ones described below.
Categorical Labels and the Citation Network

Bibliographic analysis considers the author’s research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeer. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.
Semantic Proximity Establishment in the Tasks of Knowledge Extraction and Named Entities Recognition

The paper deals with the problem of establishing text segments containing the similar semantic units for the tasks of analytical text processing within the semantic technology platform. The methods and instruments presented in the paper provide the discovery of relevant content based on users' focused interests within a certain domain. The hybrid approach comprising linguistic rules and example-based learning techniques is employed. The legal and mass media texts are considered. In this paper a brief description of the NER task history is cited, the Pullenti-based engine is specified, the two-step Semantic Expansion Algorithm is presented, the Distributional Semantics methods for domain terms extraction are discussed as well as some technical challenges and the prospective directions of further research and development.
CUSTOMER CHURN PREDICTION IN MOBILE TELECOMMUNICATIONS USING FEATURE SELECTION BASED ENSEMBLE LEARNING TECHNIQUE

Customer acquisition and customer retention are one of the most competitive factors in most of the companies. Due to ever increasing competitions of customers in companies, the company owners are unable to maintain the customer satisfaction which leads to customer churn. The customer wishes to leave the service of the company causes churn. Most of the sectors are affected by churn problems. Telecommunication is one of the main industries that are affected by churn problem. Prediction of customers who are at risk of leaving a company is known as churn prediction and it is imperative for sustainable growth of a company. Supervised classification technique suits best for solving this problem. In this research work a technique is proposed using Correlation based Symmetric uncertainty feature selection and ensemble learning for customer churn prediction in telecommunication industry. Dataset has been collected from UCI Dataset repository and various other telecommunication websites. To predict whether a customer will be a churner or non-churner, there are a number of data mining techniques applied for churn prediction, such as artificial neural networks, decision trees, and support vector machines. The proposed customer churn prediction technique for telecommunication industry is tested using various parameters like accuracy, precision, recall, fmeasure and error rate. Proposed technique is compared with support vector machine technique and Bayesian network. Experimental results demonstrate that proposed technique outperforms various algorithms in certain parameters and also gives fewer errors as compared to the previous techniques.
Gene therapy for retinal degeneration due to a defect in the retinal pigment epithelium.

The Royal College of Surgeons (RCS) rat is a well characterised model of autosomal recessive retinitis pigmentosa (RP) due to a defect in the retinal pigment epithelium (RPE). It is homozygous for a null mutation in the gene encoding Mertk, a receptor tyrosine kinase found in RPE cells, which is required for phagocytosis of shed photoreceptor outer segments. The absence of Mertk results in accumulation of outer segment debris. This subsequently leads to progressive loss of photoreceptor cells. Recently, MERTK has been established as a human retinal dystrophy gene. Retinal dystrophies are the most common cause of visual impairment in the Western World, for which no effective treatment exists. In order to evaluate the efficacy of virus mediated gene replacement therapy in the RCS rat, we produced recombinant adeno-associated viruses (AAV) and lentiviruses containing murine Mertk cDNA. Vectors were subretinally injected into the right eye of 10 day old RCS rats the left eye was left untreated as an internal control. Animals were examined at various time points by light and electron microscopy, electroretinography, and ophthalmoscopy. A detailed assessment of the duration and extent of the morphological rescue and the resulting functional benefits is presented in this thesis. AAV-2-mediated gene therapy resulted in preservation of retinal function for more than 9 weeks, when there is no activity in untreated eyes. Photoreceptors were still present at this time point and debris layer thickness was reduced. After subretinal delivery of human immunodeficiency virus type 1 (HIV-1) based lentiviral vectors carrying a functional copy of Mertk to the RCS rat eye, correction of the phagocytic defect, slowing of photoreceptor cell loss and preservation of retinal function was observed for up to 7 months, the latest time point evaluated. Whilst this was an improvement of the rescue compared to that achieved with AAV-2, lentiviral vectors raise more safety concerns regarding clinical application. Due to these biosafety issues, gene therapy vectors based on non-human lentiviruses and integration-deficient vectors have been developed. As part of this project, the potential of equine infectious anemia virus (EIAV) and non-integrating HIV-1-based vectors for the management of retinal degenerative disorders has been evaluated. The results presented in this thesis support the use of viral vectors for the treatment of retinal dystrophies. However, the development of an efficient therapy depends on the identification of patients and characterisation of pathological changes. Therefore, a panel of DNA samples from patients with autosomal recessive and sporadic forms of RP was screened for mutations in the MERTK gene. A new homozygous frame- shifting deletion was identified in four affected members of a family with RP. Clinical examination of these patients showed distinctive clinical signs that may improve the chances of identifying further patients and families in the future.
Neural and rule-based Finnish NLP models—expectations, experiments and experiences

In this article I take a critical look at some recent results in the field of neural language modeling of Finnish in terms of popular shared tasks. One novel point of view I present is comparing the neural methods’ results to traditional rule-based systems for the given tasks, since most of the shared tasks have concentrated on the supervised learning concept. The shared task results I re-evaluate, are morphological regeneration by SIGMORPHON 2016, universal dependency parsing by CONLL-2018 and a machine translation application that imitates WMT 2018 for German instead of English. The Uralic language used throughout is Finnish. I use out of the box, best performing neural systems and rule-based systems and evaluate their results