Miracle-dz/UniFAR-FTUs huggingface dataset:
CONSUMER ATTITUDES TOWARD GENETIC TESTING AND PERSONALISED NUTRITION IN HUNGARY

The aim of the research is to reveal the most important infl uencing factors that lead to consumers’ acceptance of personalised nutrition based on genetic testing. A nationwide representative questionnaire-based consumer survey was carried out involving 500 people in Hungary in 2014. The results show that consumers are divided over the new technology. Only 27.0% of the respondents have a positive attitude toward the new possibility, and they would be glad to use this new service in order to stay healthy. The rate of those who would have a genetic test because they want to follow a diet tailored to their needs is very low (16.0%). Our result suggested that the most important factors in consumer preference on personalised nutrition are positive health message and perceived risk/benefi ts, gender, and educational level. The most important factors regarding the attitude toward genetic testing are easy usage, benefi ts, the agreement among experts, and educational level.
Using a Creativity Model to Solve the Place-Value Problem in Kindergarten

A creativity model based on paired constraints was used to solve a core problem in early American math education, place-value. To create the solution, one set of constraints precluded specific elements in existing math curricula. The other promoted substitutes, including an explicit base-10 count and a single manipulative, a count-and-combine chart that visually represented the base-10 decimal system and promoted highly-focused practice in combining tens and ones in single- and double-digit addition and subtraction. The new curriculum was piloted for the entire school year. At pre-test, there were no differences between children in the pilot and control (regular curriculum) groups. At post-test, the pilot group outperformed the control not only on place-value, but also on single- and double-digit addition and subtraction, and number line estimation. The evidence suggests that creativity models can make significant contributions to solving problems in early education.
Predicting Exam Performance from Learning Styles and Online Content Usage

We investigate the relationship between learning styles, online content usage and exam performance in an undergraduate introductory Computer Information Systems class comprised of both online video tutorials and in-person classes. Our findings suggest that, across students, (1) traditional learning style classification methodologies do not predict behavioral measures of online learning, and (2) working on the online content specifically during allotted class time is positively related to exam performance. Controlling for differences across students, we find (3) accessing content on non-class days (consistency) is positively related to exam performance, while (4) working substantially ahead of the scheduled content pace is negatively related to exam performance.
Segmentation of Prognostic Tissue Structures in Cutaneous Melanoma Using Whole Slide Images

Our work applies modern machine learning techniques to melanoma diagnostics. First, we curated a new dataset of 50 patient cases of cutaneous melanoma in whole slide images (WSIs). We applied gold standard annotations for three tissue types (tumour, epidermis, and dermis) which are important for the prognostic measurements known as Breslow thickness and Clark level. Then, we devised a novel multi-stride fully convolutional network (FCN) architecture that outperformed other networks trained and tested using the same data and evaluated on standard metrics. Three pathologists measured the Breslow thickness on the network's output. Their responses were diagnostically equivalent to the ground truth measurements, showing that it is possible to overcome the discriminative challenges of the skin and tumour anatomy for segmentation. Though more work is required to improve the network's performance on dermis segmentation, we have shown it is possible to achieve a level of accuracy required to manually perform the Breslow thickness measurement.
Object Annotations : Tell Me Something

Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and use this uncertainty reduction to rank candidate labels for describing an image. While the full estimation problem is intractable, we describe an efficient algorithm to approximate entropy reduction using a tree-structured graphical model. We evaluate our approach on the open-images dataset using a new evaluation set of 10K ground-truth ratings and find that it achieves ∼ 65% agreement with human raters, close to the upper bound of inter-rater agreement and largely outperforming other unsupervised baselines.
Title Network characterisation of the food-web of the Ross Sea , Antarctica Author ( s )

A balanced ecosystem model is used to explore the system-level characteristics of the foodweb of the Ross Sea shelf and slope before the advent of commercial fishing for Antarctic toothfish (Dissostichus mawsoni) in the region. We evaluate: (1) biomass and flow of organic matter by trophic level; (2) mixed trophic impact; and (3) ecological importance. Note that the analysis considers food-web structure and function at the spatial, temporal and ecological scale of the ecosystem model, i.e. the whole Ross Sea shelf and slope area, averaged over a typical year, and in 35 trophic groups. Effects at smaller spatial and temporal scales, and effects concerning only parts of trophic groups, are not resolved by this analysis. The Ross Sea food-web is a partially inverted biomass pyramid with a pronounced peak in biomass in the lower-middle part of the food-web, a result of high biomass of mesozooplankton and benthic invertebrates. The biomass of top predators (trophic levels>4.5) is only 0.5% of the total living biomass in the Ross Sea (bacteria excluded). The six groups with the highest ‘indices of ecological importance’ in the food-web of the Ross Sea are phytoplankton, mesozooplankton, Antarctic silverfish (Pleuragramma antarcticum), small demersal fishes, Antarctic krill (Euphausia superba) and cephalopods. Pelagic fishes and crystal krill (E. crystallorophias) are also likely to have high importance in the Ross Sea food-web. These eight groups should be the priorities for monitoring for large-scale ecosystem change in the region, for example due to climate change, trophic cascades or regime shift. The analysis presented here does not support the hypothesis that changes to the abundance of toothfish in the Ross Sea will propagate through the food-web; it appears that Antarctic toothfish only have a moderate index of ecological importance in the food-web of the Ross Sea shelf and slope. Changing the biomass of Antarctic toothfish on the Ross Sea shelf and slope is likely to have the greatest effect on the demersal fish community there. SUMMARY OF FINDINGS AS RELATED TO NOMINATED AGENDA ITEMS
Italian undergraduate students comprehending economics and business texts in English as a foreign language : a case study of language-conceptual transfer strategies used in reading domain-specific texts

Italian Undergraduate Students Comprehending Economics and Business Texts in English as a Foreign Language. A Case Study of Language-Conceptual Transfer and Strategies used in Reading Domain-Specific Texts. Christina Muriel Samson (February 2009) This study, within the Vygotskyian constructivist socio-cultural developmental framework, investigates the interdependence of general and domain-specific conceptual knowledge, cognitive and metacognitive strategies, attitude, motivation and context in the process of bidirectional Italian↔English transfer in Italian undergraduates comprehending domain-specific texts in the Faculty of Economics, University of Florence, Italy. The method adopted is primarily qualitative, with quantitative support. From October 2004 to April 2005, subjects wrote pilot multiple choice question tests on basic and advanced Italian economics texts. The same subjects underwent tests in Italian and English, containing translation equivalent or similar concepts in English economics and business texts. Descriptive and non-parametric statistics, sensitive to monotonic relationships between performance and proficiency test scores as variables, measured the differences between tests taken under matched circumstances. Using an item difficulty analysis, the scores were interpreted qualitatively by textual analysis, using Geeian and Hallidayian approaches. Their purpose was to investigate construal and derivation of meaning in different genres and texts types. In April 2005, the subjects answered a cognitive and metacognitive reading strategies questionnaire; in May 2005, they completed a attitude and motivation questionnaire. The data were triangulated with information from interviews of Faculty members in May 2005. The findings show undergraduate level transfer is not an automatic process. It is influenced by several variables operating synchronically and synergistically, calling for a multi-perspective view of transfer which I have defined as contextual syncretism. Within syncretism, variation in any of the variables impacts significantly on reading performance and proficiency. The data show transfer is affected by the undergraduates‘ capacity to reframe domain-specific concepts when applying them to novel domains in Italian and in English. Hence transfer is interrelated with learning strategies and pedagogy, as well as the Italian and English curricula. All these issues are linked to contextual factors regarding Faculty management and planning decisions impacting on the Faculty of Economics‘ EFL course. Due to its small size, this study cannot provide generalisations. It provides, however, opportunities for further research. Un ive rsi ty of Ca pe To wn
Incompatibility of the capulín ( Prunus serotina ssp . capuli ( Cav . ) McVaugh ) as rootstock of the sweet cherry tree ( Prunus avium L . )

The capulín (Prunus serotina ssp. capuli (Cav.) McVough 1951) is a subspecies of P. serotina Ehnr. of Mesoamerican origin used mainly as fruit. Although from the 80’s there were reports of a collection of capulín from central Mexico, called San Martín, which showed compatibility with the cherry tree (Prunus avium L.) possibly because it was a diploid capulín, this possible quality had not been the subject of a study that the cherry tree is little cultivated in the country. This situation is changing due to the release of new cherry tree varieties with low winter cold requirements. It returned to the idea of testing the materials of capulines that had been described as possible patterns of the cherry tree given the quality of the first one to adapt to the soils in Mexico and the susceptibility of the traditional rootstocks of the cherry tree to cancer (Dibotryon sp.) or blight of fire (Phytophtora sp.) mainly. In this investigation, remarkable compatibility of the Chapingo capulín with the cherry tree was not found and by looking for the causes analyzing both species by means of flow cytometry we found that the San Martin capulín is tetraploid and not diploid as it was supposed. The above is added to the possible production of phenolic compounds that is common in Prunus, producing a translocalized incompatibility that we observe in most of the grafted plants.
A Fundamental Approach for Providing Service-Level Guarantees for Wide-Area Networks by

To keep up with the continuous growth in demand, cloud providers spend millions of dollars augmenting the capacity of their wide-area backbones and devote significant effort to efficiently utilizing WAN capacity. A key challenge is striking a good balance between network utilization and availability, as these are inherently at odds; a highly utilized network might not be able to withstand unexpected traffic shifts resulting from link/node failures. I motivate this problem using real data from a large service provider and propose a solution called TeaVaR (Traffic Engineering Applying Value at Risk), which draws on financial risk theory to realize a risk management approach to traffic engineering (TE). I leverage empirical data to generate a probabilistic model of network failures, and formulate a Linear Program (LP) that maximizes bandwidth allocation to network users subject to a service level agreement (SLA). I prove TeaVaR’s correctness, and then compare it to state-of-the-art TE solutions with extensive simulations across many network topologies, failure scenarios, and real-world traffic patterns. The results show that with TeaVaR, operators can support up to twice as much throughput as other TE schemes, at the same level of availability. I also construct a simulation tool that builds on my implementation of TeaVaR and simulates its usage in the data plane. This tool can be useful not only for testing TE schemes but also for capacity planning, as it allows network operators to see how their network is performing, where the bottlenecks are, and what kind of demand loads it can handle. Thesis Supervisor: Manya Ghobadi Title: Assistant Professor
GROWTH AND YIELD OF BELL PEPPER ( CAPSICUM ANNUUM VAR . GROSSUM ) IN SOILLESS MEDIA UNDER SHADE

Capsicum (Capsicum annuum L. var. grossum Sendt) is also called as bell pepper or sweet pepper and is one of the most popular and highly remunerative annual herbaceous vegetable crops. Capsicum is cultivated in most parts of the world, especially in temperate regions of Central and South America and European countries, tropical and subtropical regions of Asian continent mainly in India and China. India contributes one fourth of world production of capsicum with an average annual production of 0.9 million tonne from an area of 0.885 million hectare with a productivity of 1266 kg per hectare.
NURSES ' STUDENTS ' ATTITUDES TOWARD DEATH AND CARING FOR DYING CANCER PATIENTS DURING THEIR PLACEMENT

Caring of cancer patients requires special skills and knowledge that facilitates the professionals’ care during the patients' illness and palliative phases. This study was conducted to explore the nursing students' attitudes toward death and caring for dying cancer patients during their Placement. A descriptive study was conducted using the Formmelt Attitude toward Care of the Dying (FATCOD) scale and Death Attitude Profile-Revised (DAP-R) scale. Nursing students from ALZaytoonah University of Jordan. A sample of one hundred nursing students was recruited. The current study showed statistically significant difference among age group in relation to total score of death scale (p-value: 0.000) and fear of death, neutral acceptance, approach acceptance, and escapeacceptance subscales. The results indicated that younger students have more negative thoughts, attitudes, and emotions toward caring for dying cancer patients. In addition the results indicated that students with higher academic levels have a more positive attitude and are more eligible to provide nursing care for dying cancer patients as compared to students with less experience. In addition, the results showed that students with less experience had a greater fear of death than students with a higher academic level. According to the study results, Nurses' attitudes toward caring for dying and dead cancer patients can be considered an important predictor of quality of life among cancer patients. © EuroMediterranean Biomedical Journal 2017
Energieoptimale Längsführung von Kraftfahrzeugen durch Einsatz vorausschauender Fahrstrategien

Die vorliegende Arbeit beschaftigt sich mit der energieoptimalen Langsfuhrung von Kraftfahrzeugen, die vorausschauend bekannte Streckendaten nutzt, um eine maximal energieeffiziente Fahrstrategie nach Fahrerwunsch zu realisieren. Zur Losung dieses Optimierungsproblems wird ein ressourceneffizienter Algorithmus entwickelt und in einem eingebetteten Fahrerassistenzsystem zur automatisierten Langsfuhrung prototypisch zum Einsatz gebracht. Das System erzielt eine Kraftstoffeinsparung von etwa 10%.
The Cognitive Plausibility of Statistical Classification Models : Comparing Textual and Behavioral Evidence

100-200 words) Usage-based linguistics abounds with studies that use statistical classification models to analyse either textual corpus data or behavioral experimental data. Yet, before we can draw conclusions from statistical models of empirical data that we can feed back into cognitive linguistic theory, we need to assess whether the text-based models are cognitively plausible and whether the behavior-based models are linguistically accurate. In this paper, we review four case studies that evaluate statistical classification models of richly annotated linguistic data by explicitly comparing the performance of a corpus-based model to the behavior of native speakers. The data come from four different languages (Arabic, English, Estonian, and Russian) and pertain to both lexical as well as syntactic near-synonymy. We show that behavioral evidence is needed in order to fine-tune and improve statistical models built on data from a corpus. We argue that methodological pluralism and triangulation are the keys for a cognitively realistic linguistic theory.
Identification of Puccinia striiformis f . sp . tritici . Characterization of Wheat Cultivars for Resistance , and Inheritance of Resistance to Stripe Rust in Kazakhstan Wheat Cultivars

Stripe (yellow) rust, caused by Puccinia striiformis f.sp. tritici, is one of the major factors reducing the productivity of wheat crop. The region of Central Asia is one of the most important wheat areas in the world. As there was practically no breeding work in the past on stripe rust, most varieties released for commercial production are susceptible to the disease. This problem is especially important for Kazakhstan because of changing epidemic situations and the few resistant wheat cultivars. In order to effectively combat stripe rust, it is necessary to find donors of resistance and study genetics of resistance. Seedlings of winter wheat cultivars and advanced breeding lines from Central Asia were tested for resistance to five races of P. striiformis f.sp. tritici that are currently prevalent or were prevalent in the past in the USA. More virulent US races of P. striiformis f.sp. tritici for wheat germplasm from Central Asia were represented by PST-17 and PST-100. Cultivars ‘Taza’, ‘Krasnovodopadskaya 25’, and ‘Ulugbek 600’ have all-stage (also called seedling) resistance. The most effective resistant sources against stripe rust in this region are those of genes Yr2+, Yr4+, Yr5, Yr10, and Yr15. Resistance genes Yr1, Yr6, Yr7, Yr8, Yr11, Yr12, and YrA became ineffective in Kazakhstan were postulated in many cultivars. The most virulent pathotypes in Kazakhstan were 7E159, 15 159, 47 143 and 111E158, which are virulent to 9 or 10 Yr-genes of the 16 Yr genes studied. The use of these pathotypes for evaluating wheat germplasm for resistance could help to improve breeding for stripe rust resistance. The number of genes and characters of gene interaction conferring resistance to stripe rust of the most important wheat genotypes were determined. The genetic studies identified genes conferring resistance to stripe rust in commercial varieties. Based on genes in resistance donors, we should be able to develop cultivars possessing effective gene or combination of genes regarding to known virulent races of the pathogen. _____________________________________________________________________________________________________________
UAV-Net: A Fast Aerial Vehicle Detector for Mobile Platforms

Vehicle detection in aerial imagery is a challenging task due to small object sizes, high object density and partial occlusions. While past research mostly focused on improving detection accuracy, inference speed is another important factor when using CNN object detectors in a real life scenario – especially when targeting mobile platforms like unmanned aerial vehicles (UAVs). In this work, we compare several established detection frameworks in terms of their accuracy-speed trade-off and show that the Single Shot MultiBox Detector (SSD) offers the best compromise. We subsequently undertake a thorough evaluation of several design choices to further increase detection speed while sacrificing little to no accuracy. This includes the choice of base network architecture, improved prediction layers and an automatic model pruning approach. Given our evaluation results, we finally construct UAV-Net – a novel aerial vehicle detector that has a model size of less than 0.4 MiB and is more than 16 times faster than current top performing approaches. UAV-Net is well suited for on-board processing and operates in real time on a Jetson TX2 platform. Nevertheless, its accuracy is on par with state-of-the-art approaches on the DLR 3K, VEDAI and UAVDT datasets. Code and models are available on the project website.
One Year Prevalence of Musculoskeletal Disorders during Training among Boxing Players in Haryana : A Retrospective Study

Objective: To find out overall, gender specific, site specific and type of injury specific prevalence rate of musculoskeletal injuries in boxing players. Methodology: Study Design: Cross sectional, survey study, retrospective model. Sample size: 105 boxing players (73 males and 32 females). Method: Injury data was collected from each player using modified Nordic Musculoskeletal Injury Questionnaire. Data analysis: Data was entered into MS Excel for further analysis. Results: 48 players out of 105 was injured leading to 46% overall prevalence of injury among boxing players. Males injured slightly higher than females. Upper limb injuries were the most common area followed by lower limb. Wrist & Hand, Knee & Leg, Low back the most common sites of injury. Sprain and strain accounted more than 90% of total injuries. Conclusion: Prevalence of musculoskeletal injuries is high among boxing players. Conditioning by coaches and early rehabilitation by physiotherapists are essential to reduce the injury rate in this population.
Postcolonial Network Analysis of Joseph Conrad’s Heart of Darkness

In the field of literary studies, Joseph Conrad’s masterpiece, Heart of Darkness (1899), has been extensively researched. Following the emergence of digital humanities, new methods of analysis on the novella have also been conducted; one of which focuses on Network Text Analysis (NTA) of the work (Hunter and Smith, 2014). In reaction to this, for the contribution of recent studies in literature, this essay attempts to perform an analysis on network construct between the Company and Kurtz (the two main nodes in the network) in Heart of Darkness using two web-based network analyzers: Voyant Tools and Cytoscape. The analysis refers to, to name a few, Ferguson’s (2017) observation on network in history and Jagoda’s (2016) observation on network aesthetics in novels. This essay ultimately seeks to map out the network linking the two nodes and see how postcolonial view on the visualization is conducted.