Miracle-dz/UniFAR-FTUs huggingface dataset:
Corporate Social Responsibility in SMEs ’ Supply Chains

This research aims to conceptualise the dimensions of corporate social responsibility (CSR) in supply chains and then investigate the degree of importance and implementation of these CSR practices from SMEs perspectives. Also, it explores the drivers of SMEs’ CSR practices based on the institutional theory and the stakeholder theory. Literature review, panel discussions as well as a large-scale questionnaire survey with SMEs were conducted for this purpose. Stepwise analyses using analytic hierarchy process, performance-importance matrix and ANOVA were used for this purpose. The results showed that SMEs tend to focus on explicit CSR practices that can be easily identified by their customers. It is also shown that stakeholder and institutional pressures were valid in the performance of CSR practices, but largely biased to customers, government and regulatory pressures. This research has value in postulating and testing dimensions of CSR which fully consider supply chain contexts with empirical survey data.
EPiC Series in Computing

Ransomware is an ever-increasing threat in the world of cyber security targeting vulnerable users and companies, but what is lacking is an easier way to group, and devise practical and easy solutions which every day users can utilise. In this paper we look at the different characteristics of ransomware, and present preventative techniques to tackle these ransomware attacks. More specifically our techniques are based on ransomware behaviour as opposed to the signature based detection used by most anti-malware software. We further discuss the implementation of these techniques and their effectiveness. We have tested the techniques on four prominent ransomware strains, WannaCry, TeslaCrypt, Cerber and Petya. In this paper we discuss how our techniques dealt with these ransomware strains and the performance impact of these techniques.
Coping With the Loss of Quality of Job Future Predictors in Grid Computing Environments

Distributed processing environments, such as grids, are one of the most important platforms for meeting the user's processing needs. These environments have the potential to meet the needs of users, but they also have their own problems, including the failure of the jobs. Several attempts have been made to overcome this problem, which in general can be divided into two categories of resource side methods and job side methods. All these methods need some kind of prediction of the resources or jobs status in order to pursue a proactive approach to failures. However, due to the dynamics of these environments, the developed models quickly lose their quality and thus can not effectively help with the methods mentioned. In this paper, first, by identifying the reasons for reducing the quality of predictors in the grid environment, a solution has been proposed to deal with it, and then the proposed solution has been applied in the context of job failures. The results of experiments on the two experimental environments of AuverGrid and Grid5000 showed that the proposed method would increase the quality by 0.02 and 0.06 respectively in these two environments.
Flatness Based Control of a HVAC System ITC 4 / 46

This paper studies the flatness-based control of a nonlinear multi-input multi-output heating, ventilating and air conditioning (HVAC) system. First, a novel method for checking flatness property is used to verify that the given HVAC model is flat and that the flat outputs are the temperature and the humidity ratio of the thermal space. Since the flat outputs are also the variables, which one usually wants to control, it is relatively easy to construct a flatness-based feedforward control for the HVAC model. We define the appropriate trajectories for the flat outputs and then the controller is computed from the parametrization of system inputs in terms of flat outputs and their derivatives. The trajectories of flat outputs are chosen such that the temperature and the humidity ratio converge asymptotically from the initial values to the desired values. Simulations are made, which show the effectiveness of the approach.
Climate change impacts and market driven adaptation : The costs of inaction including market rigidities 1

This paper presents a first exercise comparing the cost of climate change stemming from integrated assessment models using reduced-form climate change damage functions with that performed by a CGE model. Furthermore, it investigates the role of market driven adaptation, which CGE models explicitly capture through their endogenous price setting mechanism, in determining these estimates. It is shown that world GDP losses computed by the CGE model are not significantly different from that used by some wellestablished hard-linked integrated assessment models when they consider the same impact categories. Specifically, the major driver of impacts is the modelling of catastrophic outcomes. Then, rigidities in market adjustments, differently said, in market-driven adaptation, are introduced. This is done restricting the elasticity of input substitution in the production function, the substitutability of domestic and imported inputs, and finally sectoral workforce mobility. We demonstrate that notwithstanding these frictions do increase the cost of climate change impacts they do not change substantively neither the qualitative nor the quantitative picture.
Mysore , Karnataka , India Mysore , Karnataka , India

Recommender system is the technique which helps the users to fetch usefull and required data according to their interests and preferences by filtering large amount of information. Over the past few years, many recommendation systems for different domains have been developed. Entertainment is one of the significant domain as it is necessary for each one of us to refresh our mood and energy. Movie recommender systems helps users to automatically choose preferred movies from a huge collection. To implement this system, collaborative filtering technique is most widely used as it is more advantageous than other techniques. But this technique also suffers from many issues such as, cold start, data sparsity, shilling attack etc. Many methods have been developed to solve these issues. Keywords— Movie recommendation system, collaborative filtering, KNN clustering, Pearson correlation, User’s psychological profile.
Résumé : On étudie d ’ abord les propriétés de résistance des matériaux nanoporeux à l ’ aide de simulations

On étudie d’abord les propriétés de résistance des matériaux nanoporeux à l’aide de simulations de Dynamique Moléculaire. Ces simulations numériques sont conduites sur une cellule de monocristal d’aluminium contenant un nanopore sphérique, sous des conditions de chargements multiaxiaux en vitesse de déformation. Afin de rendre compte des traits spécifiques issus des résultats de ces simulations, une approche par analyse limite cinématique a été développée en considérant une sphère creuse avec une matrice ductile sensible aux trois invariants isotropes des contraintes. Le critère macroscopique qui en résulte dépend de manière significative des trois invariants du tenseur des contraintes macroscopique ainsi que de la taille du nanopore.
Documento de Trabajo Working Paper N ° 101 N ° 101 ONE DECADE OF INFLATION TARGETING IN THE WORLD : WHAT DO WE KNOW AND WHAT DO WE NEED TO KNOW ?

One decade of inflation targeting in the world offers lessons on the design and implementation of inflation targeting, the conduct of monetary policy, and country performance under inflation targeting. This paper reviews briefly the main design features of 18 inflation targeting experiences, analyzes statistically if countries under inflation targeting are structurally different from noninflation targeting industrial countries, and reviews existing evidence about the success of inflation targeting. The interaction of inflation targeting design features and the conduct of monetary policy during transition to low inflation are tackled next. The paper ends by focusing on unresolved issues on design and implementation of inflation targeting and their relation to the conduct of monetary policy – open issues that have to be addressed in the next decade of inflation targeting. _____________________ We thank Mark Stone for excellent comments, as well as Ben Bernanke, Bennett McCallum, and other participants at the Central Bank of Chile Conference on “Ten Years of Inflation Targeting: Design, Performance, Challenges” (November 2000) for insightful discussion. We are also grateful to Verónica Mies for outstanding research assistance. The views and conclusions presented do not necessarily reflect the position of Columbia University, the National Bureau of Economic Research or the Central Bank of Chile. Emails: fsm3@columbia.edu, kschmidt@bcentral.cl.
HER 2-expressing breast tumors are associated with breast cancer stem-cell phenotype CD 44 + / CD 24-Tumores

Introduction: According to the cancer stem-cell theory, tumors originate from a small population of cancer stem cells, which lose the mechanism of self-regulation and begin to differentiate and proliferate indefinitely. The CD44/CD24 phenotype may be considered a stem-cell marker in breast cancer. Objective: To evaluate the correlation between CD44/CD24 phenotype and different molecular subtypes of breast cancer in invasive ductal carcinoma samples. Methods: The expression of CD44, CD44v6, and CD24 markers was investigated in 133 cases of invasive mammary carcinoma with immunohistochemistry. CD44/CD24 phenotype was identified and correlated with the molecular subtypes and classical prognostic factors such as age, histological grade, tumor size, and lymph node status. Results: Eighteen (14%) cases were positive for CD44/CD24 (CD44/CD24 or CD44v6+/CD24-) phenotype; among these, 11.1%, 27.8%, 38.9%, and 22.2% were luminal, luminal B-human epidermal growth factor receptor 2 (HER2), HER2, and triple-negative subtype, respectively. CD44/ CD24 phenotype was more common in HER2 subgroup (p = 0.0197). Conclusion: CD44/CD24 phenotype was correlated with molecular subtypes of breast cancer. The highest expression of CD44/CD24 phenotype was reported in patients with HER2+ disease, a molecular subtype associated with more aggressive behavior and worse prognosis.
LEARNING TOOL DEVELOPMENT TO TRAIN THINKING SKILL OF BIOLOGY STUDENTS USING THE PRIMA LEARNING MODEL

The purpose of the research development is to produce the learning tool to train thinking skill of biology student in genetic subject by using the PRIMA learning model. The research was designed by using Dick and Carey (2005) learning developement design, and it was conducted through 3 learning steps which is adapted from Carin (1993), they are: 1) guided discovery, 2) less structured guided discovery, 3) free discovery. The data was qualitative and quantitative data consist of learning tools validity, the result of the learning process by giving the formative test to the students, observation result to learning process and students thinking skill, and feedback information from the students, it was analysis after the learning process. The test result was 70% students and it was indicated that students complete in learning, they know about the concept in genetic subject after given the test. At the other said, average percentage of the learning process and students thinking skill was minimal 75%. The research was result the valid learning tool, practical and effective which can be used in learning process to train students thinking skill. The result of validation show that learning tool can be used (in valid category). The result of limited trial show that the learning tool was developed is practical and generaly can be said effective if looking from the test result and student thinking skill. Keywords: Learning Tool, Prima Learning Model, Thinking Skill
University of Dundee Finding Golgi Stacks in Electron Micrographs

Transmission electron microscopy (EM) can acquire images in which a range of subcellular organelles are clearly resolved simultaneously. There exist mature stereology techniques for extracting quantitative specimen information from section-based EM images and such techniques have been adopted successfully for use in immuno-EM. A bottleneck preventing the application of these nanomorphomics methods to high throughput applications is the recognition of organelle structures. This papers addresses this issue for one important organelle, the Golgi apparatus. A support vector machine is trained as a local Golgi detector based on rotationally invariant features. The SVM output is used to drive a graph-cuts segmentation. The ability of the method to detect and segment Golgi stacks is evaluated on a set of 36 micrographs.
Formal Validation of Core SALT Translation to LTL in Isabelle/HOL

Temporal notations are widely accepted for formal specification of functional properties amenable to automated formal verification. The SALT temporal specification language was developed as an extension of the popular LTL notation to simplify creating temporal specifications: it provides, among others, concise operators and restricted regular expressions. SALT formulas can be translated to LTL by a freely available compiler and thereby directly used for model checking. Clearly defined semantics of the specification notation is indispensable for creating precise unambiguous descriptions of the desired behavioural properties and for making subsequent formal verification meaningful. SALT semantics has been given through translation to LTL so far, which is in parts rather sophisticated and not easily comprehensible. This report presents a clear and explicit semantics formalisation for a substantial language subset of SALT through translation to an expressive interval temporal logic with explicit time variables. The formal definition and validation is performed in the Isabelle/HOL theorem prover. In the course of the formal validation we particularly prove that the semantics resulting from translation to LTL is equivalent to the explicit semantics definition.
A Novel method for Copy Move Blind Forgery Detection technique for Digital Image using DCT

Digital images are the integral part of our daily life. Images share information through newspapers, internet, and magazines. With ease of availability of image processing and editing tools, the image forgery has become common. Copymove forgery is one of the many types of image forgery. In this type of forgery, a region of image is copied and pasted onto another region of the same image. This work proposes a novel method in which the image is divided into several overlapping blocks then the absolute value of Discrete cosine transform (DCT) of each block is calculated to extract feature of image. Finally the approximate mean value of each block is calculated and block matching method is used to identify the copied region. 1 International Journal of Pure and Applied Mathematics Volume 118 No. 24 2018 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl. eu/hub/ Special Issue http://www.acadpubl. eu/hub/
From Field Consensus to Fragmentation : How Means-ends Decoupling Hinders Progress on Grand Challenges

Grand challenges are complex problems with far reaching societal implications that lack a clear technical solution. To make progress on grand challenges, many different communities need to collaborate over an extended period of time in complex organizational fields. However, institutional scholars have primarily focused on how communities form consensus on a grand challenge’s initial goals during the earliest stages of field emergence. There is little exposition of how various communities collaborate after initial field goals have been set and substantive solutions need to be pursued. With an in-depth study of the nanotechnology field, we show how disparate communities created alignment on field level goals during the early phase of field emergence. In a later phase, however, these same communities shifted to local action to the neglect of prior shared goals. This process decoupled the means and ends pursued by the participating communities producing field fragmentation. Our research shows how early consensus around a field’s goals and the means to achieve these goals diverged over time. We contribute a more precise understanding of field dynamics and explain the mechanisms and conditions that can generate means-end decoupling. We conclude by discussing how field fragmentation and institutional complexity affects progress on grand challenges.
Coding Model via l 1 / 2 – norm Minimization based Linear Prediction and Sparse Decomposition

Sparse linear predictive analysis using the l1–norm minimization criterion has been shown to provide a valid alternative to the traditional linear predictive approach. The sparser the resulted speech residual is, the fewer pulses needed to represent it. To find a sparse residual further, we propose to use the l1/2–norm as the optimization objective of linear prediction, and use an iteratively reweighted l1 minimization approach to solve it. We also find that the procedure of determining the locations and amplitudes of the optimal pulses in the multipulse analysis is equivalent to a sparse decomposition problem, which is efficiently solved by the optimized orthogonal matching pursuit approach. The objective and informal subjective evaluations over the TIMIT database give proof of the effectiveness of this new model, performing better than the traditional approach for modeling and coding of speech.
Submission Number : EB-18-00920 Corruption and support for economic reform in sub-Saharan Africa

We explore the relationship between experiences of corruption and support for economic reform in sub-Saharan Africa. We find that the relationship changes in a short period of time: results vary across three rounds of the Afrobarometer survey. In the first round, an individual’s own experience of bribery is correlated with support for reform. In the second round, the local intensity of bribery matters, while in the third round neither variable is important. While this changing relationship may reflect rapid development in the region, including economic growth, inward investment, and the diffusion of technology, our key contribution is to demonstrate that policy recommendations based on an analysis of one round of data or pooled data may be misleading. Submitted: November 17, 2018.
The impact of Comorbidity and Treatment Characteristics on Adherence to Tuberculosis Treatment in Kisumu East Subcounty , Kenya

Despite the great strides made in TB diagnosis and highly effective shorter periods of treatment, there are still poor treatment outcomes in a significant proportion of the patients. Poor adherence to TB treatment is one of the leading causes of poor outcomes of treatment. Kisumu East Subcounty in Kenya records some of the highest adverse outcomes of care. Therefore a cross sectional descriptive study was conducted in the Subcounty to establish the impact of co-morbidity and treatment characteristics on TB treatment adherence among tuberculosis patients above 18 years attending TB clinics in Kisumu East Subcounty. The data was analyzed using SPSS version 21. Descriptive statistics, bivariate analysis and binary logistic regression were used in the analysis and data presented in contingency tables and figures. Significance was assumed at P value ≤0.05. Belief that TB treatment affects other treatments; (OR: 0.03; 95% CI:5. 0-0.1 ;P<0.001), retreatment (OR: 11; 95% CI: 4.2-29.6 ;P<0.001), not completing previous treatments (OR:19; 95% CI:6-73 ;P<0.001) self discontinuation (OR:0.1; 95% CI: 0.01-1 ;P<0.049) were significant in predicting adherence. These aspects can serve as a guide for flagging some patient as having potential for non-adherence thus appropriate follow-up measures instituted in time. It is important to establish the effects of socio-demographic aspects on adherence in every TB care setting for appropriate follow-up.
jmbReview Biotransformation of Pueraria lobata Extract with Lactobacillus rhamnosus vitaP 1 Enhances Anti-Melanogenic Activity

1Department of Oriental Medicine Biotechnology, College of Life Sciences, Kyung Hee University, Yong-in 17104, Republic of Korea 2Department of Herbal Medicine Resources, Kangwon National University, Samcheok 25949, Republic of Korea 3DanjoungBio Co., Ltd., Wonju 26303, Republic of Korea 4Genencell Co., Ltd., Yong-in 16950, Republic of Korea 5Department of Medicinal and Industrial Crops, Korea National College of Agriculture and Fisheries, Jeonju 54874, Republic of Korea 6Cardiovascular and Metabolic Disease Center, College of Medicine, Inje University, Busan 47392, Republic of Korea
Analysis for Psychological Questionnaires with Semi-supervised Learning

Sentiment analysis is a more difficult task than topic classification, spam etc. The challenges include how to describe the topic of a given review and how to find the topic-specific sentiment features. This paper applies sentiment analysis to psychological questionnaires. A role-based sentiment analyzer is proposed to detect sentiment features in thousands of questionnaires coming from sandplay therapy and to determine the polarity of them automatically, which help guiding and evaluating the therapeutic process. We decompose the method into 3 steps: detecting topic terms in each review according metadata words, labeling semantic roles on all topic sentences and identifying sentiment features related to topic terms through the labeled roles. A key of our method is that it can find topic-specific sentiment words depending on semantic orientation of roles. Addressing the shortage of manually tagged questionnaires, one semi-supervised learning model -Spectral Graph Transducer (SGT) is applied to sentiment analysis based on a new feature representation on reviews, i.e. Positive-Related Vector Percentage. The experiments on sandplay questionnaires showed our sentiment analyzer performed well especially on long reviews, and when using SGT it improved rapidly in a small amount of tagged training data.
Providing End-to-End Secure Communication in Low-Power Wide Area Networks ( LPWANs )

Recent technologies for low-rate, long-range transmission in unlicensed sub-GHz frequency bands enables the realization of Long-range Wide Area Network. Despite the rapid uptake of LPWANs, security concerns arising from the open architecture and usage of the unlicensed band are also growing. While the current LPWAN deployments include basic techniques to deal with end-to-end encryption there are specific security issues that arise due to the overall architecture and protocol layer design. In this paper, a new scheme to establish end-to-end secure communication in long-range IoT deployments is introduced. The advantages over the existing approaches and architectural design are presented in the context of typical smart cities application scenarios.