Miracle-dz/UniFAR-FTUs huggingface dataset:
Scenario : Should We Stop Covering Opportunistic Screening for Prostate Cancer in Asymptomatic Men ?

Objectives: Coverage decisions are decisions by third party payers about whether and how much to pay for technologies or services, and under what conditions. Given their complexity, a systematic and transparent approach is needed. The DECIDE (Developing and Evaluating Communication Strategies to Support Informed Decisions and Practice Based on Evidence) Project, a GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group initiative funded by the European Union, has developed GRADE Evidence to Decision (EtD) framework for different types of decisions, including coverage ones. Methods: We used an iterative approach, including brainstorming to generate ideas, consultation with stakeholders, user testing, and pilot testing of the framework. Results: The general structure of the EtD includes formulation of the question, an assessment using twelve criteria, and conclusions. Criteria that are relevant for coverage decisions are similar to those for clinical recommendations from a population perspective. Important differences between the two include the decision-making processes, accountability, and the nature of the judgments that need to be made for some criteria. Although cost-effectiveness is a key consideration when making coverage decisions, it may not be the determining factor. Strength of recommendation is not directly linked to the type of coverage decisions, but when there are important uncertainties, it may be possible to cover an intervention for a subgroup, in the context of research, with price negotiation, or with restrictions. Conclusions: The EtD provides a systematic and transparent approach for making coverage decisions. It helps ensure consideration of key criteria that determine whether a technology or service should be covered and that judgments are informed by the best available evidence.
Screening for colorectal cancer : CTC vs . OC

Colorectal carcinoma ranks as the third most commonly diagnosed cancer and the second leading cause of death from cancer in the United States [1]. It is estimated that more than 150,000 new cases are diagnosed with more than 50,000 dying from the disease yearly in the U.S. alone [1]. Similar to other cancers, it is often diagnosed at advanced stage, after the patient has developed symptoms. Many colon cancer deaths can be avoided because mostly they arise from adenomatous polyps, which may be detectable years before malig‐ nant transformation.
Using effective dimension to analyze feature transformations in deep neural networks

In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations. In this paper, we investigate how a class of input images is compressed over the course of these transformations. In particular, we use singular value decomposition to analyze the relevant variations in feature space. These variations are formalized as the effective dimension of the embedding. We consider how the effective dimension varies across layers within class. We show that across datasets and architectures, the effective dimension of a class increases before decreasing further into the network, suggesting an initial whitening transformation. Further, the decrease rate of the effective dimension deeper in the network directly correlates with training performance of the model.
FORMS – Forensic Marks Search∗

The goal of the project FORMS is to support the search and comparison of toolmarks by forensic experts with a semi-automatic system in order to identify and solve connected criminal cases. The proposed methodology uses a neural network with triplet architecture to compute similarities between toolmark images. Further, to allow an accurate evaluation under real-world conditions a dataset consisting of more than 3000 images of cylinder locks with toolmarks from real criminal cases is created as part of the project.
On the Area of Hypercube Layouts 1

This paper precisely analyzes the wire density and required area in standard layout styles for the hypercube. It shows that the most natural, regular layout of a hypercube of N nodes in the plane, in a N N grid arrangement, uses b2N=3c+ 1 horizontal wiring tracks for each row of nodes. (In the process, we see that the number of tracks per row can be reduced by 1 with a less regular design, as can also be seen from an independent argument of Bezrukov et al.) This paper also gives a simple formula for the wire density at any cut position and a full characterization of all places where the wire density is maximized (which does not occur at the bisection).
Management Series EMS-2012-04 Growth and Non-Regular Employment

The share of non-regular employment has been increasing in many developed countries during the past two decades. The objective of this paper is to study a cause of the upward trend in non-regular employment by focusing on productivity growth. Data from Japan shows that productivity growth reduces both unemployment and the proportion of nonregular workers to total employed workers. In order to study the impact of long-run productivity growth on unemployment and non-regular employment, I develop a search and matching model with disembodied technological progress and two types of jobs, regular and non-regular jobs. The numerical analysis demonstrates that faster growth reduces the share of non-regular employment, but the effect of faster growth on unemployment is ambiguous.
ABRASIVE JET MACHINING-RESEARCH REVIEW 1

Abrasive jet machining is an effective machining process for processing a variety of Hard and Brittle Material. And has various distinct advantages over the other non-traditional cutting technologies, such as, high machining versatility, minimum stresses on the work piece, high flexibility no thermal distortion, and small cutting forces. This paper presents an extensive review of the current state of research and development tin the abrasive jet machining process. Further challenges and scope of future development in abrasive jet machining are also projected. This review paper will help researchers, manufacturers and policy makers widely.
Reporting Standards on the Judgments of Accountants

This pioneering study examines the impact of the provision of additional guidance on International Financial Reporting Standards (IFRS) as a “decision aid” on the accuracy of judgments of the accountants. To extend the prior research on accounting judgment and decisionmaking, we also examine the interactive effects of task complexity and additional guidance on the judgments of accountants. The results provide evidence that those accountants who are provided with decision aid in the form of additional guidance on IFRS make more accurate judgments than accountants who are not provided with such guidance. Furthermore, the study provides evidence that this additional guidance improves the judgments of accountants when they undertake tasks which they find complex. The results indicate that additional guidance on IFRS needs to be provided and suggests that accountants should exploit any guidance which is currently provided in IFRS and by the International Financial Reporting Standards Interpretations Committee. © 2014 University of Illinois. All rights reserved. JEL classification: M480
Supplementary : Analyzing Federated Learning through an Adversarial Lens

While the loss is a function of a weight vector w, we can use the chain rule to obtain the gradient of the loss with respect to the weight update δ, i.e. ∇δL = αm∇wL. Then, initializing δ to some appropriate δini, the malicious agent can directly minimize with respect to δ. However, the baseline attack using implicit boosting (Figure 1) is much less successful than the explicit boosting baseline, with the adversarial objective only being achieved in 4 of 10 iterations. Further, it is computationally more expensive, taking an average of 2000 steps to converge at each time step, which is about 4× longer than a benign agent. Since consistently delayed updates from the malicious agent might lead to it being dropped from the system in practice, we focused on explicit boosting attacks throughout.
Chapter 5 Improving Learning Style Identi fi cation by Considering Different Weights of Behavior Patterns Using Particle Swarm Optimization

Matching the course content to students’ learning style has been shown to benefit students by improving their learning outcome, increasing satisfaction, and reducing the time needed to learn. Consequently, an accurate method for identifying these learning styles is of a high importance. Up to the present, there have been proposed several such methods that use students’ behavior in online courses to automatically identify their learning style. However, the precision of existing approaches peaks at approximately 80 %, thus leaving room for improvement. This paper introduces a novel approach, which combines the advantages of artificial/computational intelligence and rule-based techniques. More specifically, a rule-based method is extended to consider the different weights of behavior patterns using a particle swarm optimization algorithm. The approach has been evaluated with 75 students, and results show improved performance over similar state-of-the-art methods. By identifying learning styles with higher precision, students can benefit from adaptive courses that are tailored more precisely to their actual learning styles and teacher can benefit by being able to provide students with more helpful interventions.
Examining Mediating Role of Self-Efficacy on Undergraduates’ Perceived Employability

The aim of the present work is to examine the relationship between perceived social supports, selfmanaged career behaviour, and perceived employability of undergraduates at both public and private Malaysian universities. The study also examines the mediating effect of self-efficacy on the perceived employability of undergraduates. The research sample was 267 undergraduates from public and private universities in Malaysia. Partial Least Squares Structural Equation Modelling (PLSSEM) is used to perform the data analysis in present study. The result showed that both self-managed career behaviour and self-efficacy correlate significantly with perceived employability among undergraduates. Additionally, the findings found that self-efficacy mediates the relationship between self-managed career behaviour and perceived employability. However, there is no significant positive correlation between perceived social supports and perceived employability. Similarly, there is no mediating effect of self-efficacy on the relationship between perceived social supports and perceived employability. This study contributes to the knowledge base that focuses on a better understanding of the perceived employability of the younger generation via the existing employability model.
Bayesian Modeling in Finance

The Bayesian statistical method provides an alternative approach to study some of the classical problems in finance. In the existing finance literature, research that uses Bayesian econometrics is primarily in the area of asset pricing. Bayesian applications in corporate finance have been rather limited, despite its great potential as a viable alternative to address some challenging problems in corporate finance that are difficult to solve with the traditional approach. Bayesian estimation techniques, the Markov Chain Monte Carlo (MCMC) methods in particular, are very conductive to estimating non-linear models with high-dimensional integrals in the likelihood or models with a hierarchical structure. In this paper, we outline the basic concepts of Bayesian modeling, describe most commonly used estimation techniques, and review its applications in the existing finance literature. 1 Dong’s email address is xdong1@scu.edu and Pan’s email is chpan@scu.edu. We are grateful to Sanjiv Das and John Heineke for their helpful comments and suggestions. 2 Introduction Most quantitative methods and statistical analysis in finance take the classical (frequentist) approach, which essentially considers the probability of an event the limit of its long-run frequency. Data are assumed to be a repeatable random sample from the underlying population that has a distribution with unknown but fixed parameters. Inference is based on unbiased estimators, hypothesis testing and confidence intervals, and often relies on large-sample approximations. In reality, however, financial data violate these assumptions most of the time. For example, rare events such as financial crises do not repeat frequently enough, so large samples of time-series data are not available. Moreover, financial variables tend to behave differently during crisis. Stock market volatility increases during a crisis period, as do the correlations cross different stock markets (see e.g., Bekaert et al. 2005). In this case, the underlying parameters might be random variables themselves. Some of these strong assumptions are not important, nor relevant in a Bayesian framework. Bayesian statistics considers data fixed and parameters uncertain. In the Bayesian framework, probability distributions are subject to change as new information (from data) becomes available. In this paper, we outline the basic concepts of Bayesian modeling, describe most commonly used estimation techniques, and review some of its applications in the existing finance literature. Bayes’ Theorem and Bayesian Model Analysis Bayes’ Theorem Bayes’ Theorem was first discovered in the 1740s by the English Reverend Thomas Bayes, when he was trying to learn how to infer causes from effects. It was eventually published after his death (Bayes 1763), but made little impact. Over a decade later, in 1774 Pierre-Simon Laplace, a French mathematician, independently rediscovered the theorem and published it (Laplace 1774). Laplace continued to use it, extended it, and made it more popularly known.
Frequency and Morphological Patterns of Recognition and Thematic Classification of Essay and Full Text Scientific Publications ∗

This article discusses the author’s classification method for academic documents, scientific materials and articles published in Russian language. The research aims to utilize a maximum possible extent of the educational environment information potential in modern conditions. The task is to develop the tools for data mining of scientific materials allowing to generate a brief description of a particular scientific article, its table of content, as well as a field of science it belongs to. For the purpose of this exercise we selected a scientific repository containing textbooks with commentaries related to ten fields of science, including History, Chemistry, Law, Biology, Medicine, Physics, Philosophy and Economics. The main feature of the proposed classification method involves a use of a minimal theoretical and linguistical set of indicators together with hierarchical algorithm of classification (based on a regression decision trees method), both aimed to identify stable statistical rules of text classification. The procedure for text data transformation into a set of relative indicators is based on a frequency-morphological analysis, which allows to retain unique stylistic features of the texts involved. There are two experiments presented. In the first experiment it was possible to successfully define some consistent patterns (logical rules and meaningful indicators) of classification allowing to differentiate between a proper publication (a textbook, monography, etc) and a short commentary/annotation, using only relative indica-
Comparison of the biomechanical function and clinical effects of plate and multi-pin fixation in the treatment of Sanders II calcaneal fractures

The aim of this study was to compare the biomechanical function and clinical effects of plate and multi-pin fixation in the treatment of Sanders II calcaneal fractures. A three-dimensional finite element model of the Sanders II calcaneal fracture was established by subtracting several unit structures from the normal model from a healthy volunteer. The fractured calcaneal were reduced and fixed with a steel plate or multi-pin. Loading of the fractured calcaneus via the tendo calcaneal was simulated and the resulting von Mises stresses and equivalent strain were observed at 0° (neutral position) for plate group, and at 0° (neutral position) and 30° (plantar flexed position) for multi-pin group, respectively. Finally, a retrospective clinical study was conducted, in which 59 patients received either the steel plate or the multi-pin fixation was surveyed for satisfaction as well as soft tissue complications. The von Mises stress values at 0° for plate group, and at 0° and 30° (plantar flexed position) for multi-pin group were 144.69 MPa, 167.27 MPa, and 176.92 MPa, respectively. The equivalent strain of fracture line at 0° for plate group, and at 0° and 30° (plantar flexed position) for multi-pin group were 0.44 mm/mm, 0.22 mm/mm, 0.21 mm/mm respectively. The multi-pin fixation had a slightly higher excellent and good rate compared to the plate fixation (83.4% vs 78.3%) (P > 0.05). Calcaneal plate fixation had a higher overall rate of the soft tissue complications than that of the multi-pin fixation (P < 0.05). Our findings suggested multi-pin fixation may be a better method for Sanders II calcaneal fractures as such fixation will provide a higher fracture biomechanical stability than plate internal fixation and possibly reduced soft-tissue injury.
Effect of Garlic Extract and Mineral Oil Spray on Flowering , Harvesting Time , Yield and Fruit Quality of Peach ( Prunus persica ) Trees cv . ‘ Florida prince ’

This investigation was carried out during two successive seasons on six years-old peach trees cv. ‘Florida prince’ grown in the Nubaria district, Behera Governorate, Egypt, to study the effect of garlic extract and mineral oil spray on flowering, harvesting time, yield and fruit quality attributes. Trees were sprayed once at the dormant bud stage after winter pruning (mid-November) to run-off with 1, 2 and 4% (v/v) of garlic extract and 2% mineral oil alone or in combinations. The following data was determined: flowering time (full bloom), harvesting percentage at 5-day intervals with four harvests, yield, average fruit weight, volume, and diameter, fruit firmness, percentages of seed/fruit and flesh/fruit weight, total soluble solids (TSS), titratable acidity (TA), total sugars, total chlorophyll and anthocyanin contents. 4% garlic extract advanced full bloom by 20 and 16 days, increased yield by about 57 and 37% and also advanced the beginning of harvest time by about 5 and 10 days than the control in the first and second seasons, respectively. Fruits obtained from trees sprayed with 4% garlic extract had the heaviest and largest fruits with the highest TSS and the lowest TA than other treatments in both seasons. _____________________________________________________________________________________________________________
The Use of Artificial Intelligence to Identify People at Risk of Oral Cancer : Empirical Evidence in Malaysian University

The purpose of this study was to evaluate the ability of a fuzzy neural network model and fuzzy regression model to predict the likelihood of an individual in developing oral cancer based on knowledge of their risk habits and demographic profiles at Oral Cancer Research and Coordinating Centre, University of Malaya, Malaysia. Performances of the two artificial intelligent prediction models were compared with the prediction made by a group of oral cancer clinicians. The prediction performance was measured in terms of sensitivity and specificity. The mean accuracy, sensitivity and specificity of the models were 59.9, 45.5 and 85.3 for fuzzy neural network models; 63.1, 54.2 and 78.6 for oral cancer clinicians predictions and 67.5, 69.0 and 64.7 for fuzzy regression prediction models. Areas under the receiver operating characteristic curves reflect the prediction accuracy of the models. There were no significant differences in the prediction performance among the three models for single-input and two-input predictor sets. However, fuzzy regression and fuzzy neural network performed better than oral cancer clinicians when the size of input predictor set was increased to three and four. In short, this study is perhaps one of the first that address the use of artificial intelligence to identify oral cancer in Malaysia.
Histopathologic Study of Antiestrogenic Anticancer Nolvadex Induced Liver Damage in Rats and Vitamins Ameliorative Effect

This study was designed to evaluate the effects of antiestrogenic anticancer Nolvadex (used for breast cancer treatment) on rat liver and the possible protective effects of vitamin C and/ or E. Material and methods: A total of 140 adult female albino rats were used; divided into seven groups; each containing 20 rats: First group: as control. Second group: orally daily dosed with Nolvadex 20 mg/kg b. w. for three weeks. Third group: orally given vitamin C (0.02 g/100 g b wt), 15 min before daily Nolvadex administration. Fourth group: given vitamin E (120 mg/Kg b.w), 15 min prior to daily Nolvadex administration. The fifth group was given combination of the two vitamins C & E (0.02 g/100 g b.w.) and (120 mg/kg b.w.) respectively, 15 min before daily Nolvadex administration. Each of the remaining two groups was daily given vitamin C (0.02 g/100 g b.w.) and/or E (120 mg/kg b.w.) for two weeks. Paraffin sections were used for histopathological , quantitative image analysis DNA ploidy and histochemical studies . Electron microscopy was performed. Results: Histopathological degenerative effects in the form of vacuolar degeneration, fatty changes and hydropic degeneration were noticed in Nolvadex treated rat liver. Karyolysis and karyorrhexis were also seen.Dysplasia and chromatin clumping were observed in scattered hepatocytes together with a decrease in DNA content (hypoploidy) and marked diminution of protein and mucopolysaccharides content.Histopathological, histochemical and ultra structural changes were diminished in rats treated with vitamins C and/ or E prior to Nolvadex. Conclusion: The treatment of rats with vitamins C and/or E prior to Nolvadex resulted in amelioration of the histopathological ,histochemical and ultrastructural changes in liver. [Fatma A. Morsy, Amina Gamal el-Din, Nermeen M. Shaffie and Manal A. Badawi. Histopathologic Study of Antiestrogenic Anticancer Nolvadex Induced Liver Damage in Rats and Vitamins Ameliorative Effect. Cancer Biology 2014;4(2):1-15]. (ISSN: 2150-1041): (ISSN: 2150-105X (online). http://www.cancerbio. net. 1
Aware Word Embedding Approach for Sentiment Analysis

Nowadays, many business owners want to know the feedback of their products. If they get the feedback from customers, they can promote the quality of their products. So, Sentiment analysis has become a popular research problem to tackle in NLP field. It is the process of identifying whether the opinion or reviews expressed in a piece of work is positive, negative or neutral. We can apply sentiment analysis in brand monitoring, customer service, market research and analysis. Word embedding step is a problem in sentiment analysis of neural network models. Most existing algorithms for continuous word representation typically only model the syntactic context of words but ignore the sentiment of text. It is a problematic for sentiment analysis as they usually map words with similar syntactic context but ignore opposite sentiment polarity, such as good and bad, like and dislike. We solve this issue by proposing a method, sentimentaware word embedding (SAWE). SAWE encodes sentiment information in the continuous representation of words by using (1) prediction the model and (2) ranking model. Finally, we evaluate our proposed method on IMDB movie review and twitter datasets, after that we prove our method outperform than other word embedding methods like word2vec and GloVe. KeywordsSentiment analysis, Natural Language Processing, Word Embedding, SAWE, Recurrent Neural Networks