Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic mechanical and thermal behavior of novel liquid-crystalline polybutadiene-diols with azobenzene groups in side chains

Liquid-crystalline (LC) polymers containing methoxy- or butoxy-substituted azobenzene in side chains have been prepared via radical addition of the in advance synthesized nematic thiols onto double bonds of poly(butadiene)diol [Polakova et al., Pol. Bull. 54, 315–326 (2010)]. In the present work, thermal behavior of these comblike polymers has been characterized by differential scanning calorimetry and polarizing optical microscopy. LC transitions have been also determined by rheological measurement. Time–temperature superposition of mechanical functions has been successfully applied to samples undergoing direct nematic/glassy state transition.
Design of a Secured Electronic Voting SystemUsing Multimodal Biometrics

The aim of this study is to design a secured electronic voting system using multimodal biometrics. In recent years, information technology has greatly affected all aspects of life, and to a largeextent, this includes politics. In order to elect people to various positions different methods have been set up,with researchers continually trying to find improvement to the existing methods. The most recent method tobe developed is electronic voting (e-voting). It is meant to phase out outdated paper ballot, punched cards andother mechanical voting systems with paperless electronic or online voting systems. E-voting systems endeavour to make elections simple while reducing the total cost of the election. Designing an air-tight and reliable e-voting system is therefore a great task, in that, the system that must be developed must protect the privacy of the voter, be easily understood and used by the entire voting populace - no matter who they are or where they come from. A multimodal biometric system (fingerprint and facial recognition) was used in this paper to improve the security of an E-voting system
Evaluating Morphological Resources: a Task-Based Study for French Question Answering

Morphology is a key component for many Natural Language Processing applications. In this article, we focus on one prototypical application, namely Question Answering (QA). In QA, morphological relations, especially those relying on the derivation and compounding processes, are often addressed in a superﬁcial manner. Considering that some resources are able to provide deep and precise knowledge about a large spectrum of morphological processes, the issue lies ﬁrst in determining the morphological phenomena which are most relevant for QA systems and second in evaluating the coverage of existing resources in this respect. To this aim, we describe a manual annotation and analysis of French question-answer pairs, which was performed in order to produce a unique and well-characterised reference dataset. Based on this study, we evaluate ﬁve different morphological resources for French and show that some dedicated resources are still lacking, which would cover phenomena such as denominal adjectives and agent deverbal nouns.
Generation of voltage references using Multilayer Feed Forward Neural Network

Among the dynamic voltage restorer (DVR) objectives, the protection of sensitive loads against voltage swell/sag voltage, as well as correction of unbalanced problems. The DVR acts as a voltage source, which is connected in series with a sensitive load and controlled to generate the required compensation voltages. Many researches were presented in literature to identify the voltage disturbance; one of them is Synchronous Reference Frame (SRF) method. This paper provides a DVR with identification method based on artificial neural network - the (MLFFN) Multilayer Feed Forward Neural Network. To investigate this novel method performance, it is confronted with the conventional (SRF) method. All of the studies have been carried out using the simulation with the MATLAB Simulink Power System Toolbox. The results of simulation study of new DVR detecting method presented in this paper are found quite satisfactory to eliminate the voltage disturbance from utility voltage. Streszczenie. Przedstawiono dynamiczny uklad przywracania napiecia DVR jako zabezpieczenie przeciw zapadom napiecia. Uklad dziala jako źrodlo napiecia polączone w szereg z obciązeniem. W artykule opisano metode identyfikacji wykorzystującą sztuczne sieci neuronowe. Przedstawiono tez symulacje systemu. (Sterowanie wytwarzaniem napiecia kompensującego zapady napiecia z wykorzystaniem sztucznych sieci neuronowych)
Management for Elderly Includes Adequate Exercise, Carbohydrate/Fat Dietand Self-Esteem for Significant Life

Recent medical problems in the world include the prevalence of obesity, diabetes and Metabolic syndrome (Met- S) in developed and developing countries [1]. In relation to this tendency, flailty and locomotive syndrome in the elderly have also increased, which are remarkable medical and social problems [2]. We have to develop urgent work for these matters. This article outlines and reviews the points of exercise, diet and psychology in the elderly. In the United States, more than a third of persons 65 years of age or older are obese, and this group constitutes a population vulnerable to adverse outcomes, because obesity exacerbates the age-related decline in physical function and causes frailty, resulting sarcopenia and osteopenia.
How teachers engage with Assessment for Learning: lessons from the classroom

Using video recordings of lessons and interviews with teachers, this article explores the way in which teachers enact Assessment for Learning (AfL) practices in their classrooms. Starting with the hypothesis that AfL is built on an underlying pedagogic principle that foregrounds the promotion of pupil autonomy, we analyse the ways in which teachers instantiate this principle in practice. A distinction is drawn between lessons that embody the ‘spirit’ of AfL and those that conform only to the ‘letter’. The nature and sequence of tasks and especially ‘high organization based on ideas’ appears crucial to the former. This adds a dimension to more familiar formulations of AFL practices. We also ask whether the teachers’ beliefs about learning contribute to the different ways in which they interpret the procedures of AfL. Interviews with teachers indicated that those whose lessons captured the spirit of AfL were more likely to take responsibility for success and failure in the promotion of pupil autonomy. Thus they had a sense of their own agency and sought to use it to overcome barriers to learning.
Report Series Model-Driven Software Evolution : A Research Agenda

Software systems need to evolve, and systems built using model-driven approaches are no exception. What complicates model-driven engineering is that it requires multiple dimensions of evolution. In regular evolution, the modeling language is used to make the changes. In meta-model evolution, changes are required to the modeling notation. In platform evolution, the code generators and application framework change to reflect new requirements on the target platform. Finally, in abstraction evolution, new modeling languages are added to the set of (modeling) languages to reflect increased understanding of a technical or business domain. While MDE has been optimized for regular evolution, presently little or no support exists for metamodel, platform and abstraction evolution. In this paper, we analyze the problems raised by the evolution of model-based software systems and identify challenges to be addressed by research in this area.
Perceptions of electricity-use communications: effects of information, format, and individual differences

Electricity bills could be an effective strategy for improving communications about consumers’ electricity use and promoting electricity savings. However, quantitative communications about electricity use may be difficult to understand, especially for consumers with low energy literacy. Here, we build on the health communication and graph comprehension literature to inform electricity bill design, with the goal of improving understanding, preferences for the presented communication, and intentions to save electricity. In a survey-based experiment, each participant saw a hypothetical electricity bill for a family with relatively high electricity use, covering information about (a) historical use, (b) comparisons to neighbors, and (c) historical use with appliance breakdown. Participants saw all information types in one of three formats including (a) tables, (b) bar graphs, and (c) icon graphs. We report on three main findings. First, consumers understood each type of electricity-use information the most when it was presented in a table, perhaps because tables facilitate simple point reading. Second, preferences and intentions to save electricity were the strongest for the historical use information, independent of format. Third, individuals with lower energy literacy understood all information less. We discuss implications for designing utility bills that are understandable, perceived as useful, and motivate consumers to save energy.
A New Android Malware Detection Method Using Bayesian Classification

— Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.
On the Subject of Objects: Four Views on Object Perception and Tool Use

This paper addresses the relation between an agent and its environment, and more specifically, how subjects perceive object/artefacts/tools and their (possible) use. Four different conceptions of the relation between subject and object are compared here: functional tone (von Uexkull), equipment (Heidegger), affordance (Gibson), and entry point (Kirsh). even as these concepts have developed within different disciplines (theoretical biology, philosophy, psychology, and cognitive science) and in very different historical contexts, they are used more or less interchangeably in much of the literature, and typically conflated under the label of ‘affordance’. However, at closer inspection, they turn out to have not only similarities, but also substantial differences, which are identified and discussed here. Given that the relation between subjects and their objects is crucial to understanding human cognition and interaction with tools and technology, as well as robots’ interaction with their environment, we argue that these differences deserve some more attention than they have received so far.
CAHIER 2002-06 SOME IMPLICATIONS OF THE ZERO LOWER BOUND ON INTEREST RATES FOR THE TERM STRUCTURE AND MONETARY POLICY

In an economy where cash can be stored costlessly (in nominal terms), the nominal interest rate is bounded below by zero. This paper derives the implications of this nonnegativity constraint for the term structure and shows that it induces a nonlinear and convex relation between shortand long-term interest rates. As a result, the longterm rate responds asymmetrically to changes in the short-term rate, and by less than predicted by a benchmark linear model. In particular, a decrease in the short-term rate leads to a decrease in the long-term rate that is smaller in magnitude than the increase in the long-term rate associated with an increase in the short-term rate of the same size. Up to the extent that monetary policy acts by affecting long-term rates through the term structure, its power is considerably reduced at low interest rates. The empirical predictions of the model are examined using data from Japan.
APPLYING ONLINE PEER ASSESSMENT WITH TOTAL QUALITY MANAGEMENT TO ELEVATE PROJECT-BASED LEARNING PERFORMANCE

Undergraduate science, technology, engineering, and mathematics (STEM) curriculum emphasize project-based learning (PBL) with peer assessment/on-line peer assessment (PA/OPA). Many studies have stressed that students did not improve over two rounds in PBL with OPA studies and PBL with PA have to adopt team mutual cooperation to reap effective learning. This study proposed an innovative approach that incorporate OPA with TQM as a macro-level instructional tool to guide students in teacher's directing of collaborative project development as well as seeking continuous improvement to elevate Project-Based Learning Performance in a STEM course. The effects of OPA with TQM were examined through an experiment with PBL performances hypotheses. A total of 63 junior students in an university of Taiwan voluntarily participated in this study and a quasi-experimental approach with a two-group design was adopted. The results revealed that the team members using the OPA with TQM approach tended to have higher design skill performance, better cohesive teamwork and creative problem solving attitude. Thus, the proposed approach facilitated team members to collaborate for seeking continuous improvement. However, no significant difference was reported on the influence of enhancing students’ design concept. Implication and suggestions for educators to promote the PBL with OPA and TQM were also provided in the study. Key words: collaborative learning, online peer assessment, project-based learning, STEM course, Total Quality Management.
Energy Market Liberalization and Renewable Energy Policies in OECD Countries

We analyse the impact of market liberalisation on renewable energy policies in OECD countries. To this end, we first develop an aggregated indicator of renewable energy policies using principal components analysis and then examine its determinants through panel datatechniques. Our resultsare consistent with the predictions of political-economy models of environmental policies, as brown lobbying, proxied by entry barriers in the energy sector, and citizens' preferenceshave the expected effectson policy. Brown lobbying has a negative effect on the policy indicator, evenwhen accounting for endogeneity in its effects in a dynamic panel specification and using different policy indicators. Reducing income inequality, the ratification of the Kyoto protocol and stronger green parties all positively affect the approval of more ambitious policies but with less robust results.
Detecting Sinkhole Attacks in Wireless Sensor Network using Hop Count

Nowadays, Wireless Sensor Networks (WSNs) are widely used in many areas, especially in environment applications, military applications, queue tracking, etc. WSNs are vulnerable to different types of security attacks due to various constraints such as broadcasted nature of transmission medium, deployment in open or hostile environment where they are not physically protected, less memory, and limited battery power. So, security system is the crucial requirements of these networks. One of the most notably routing attacks is the sinkhole attack where an adversary captures or insert nodes in the sensor field that advertise high quality routes to the base station. In this paper, a mechanism is proposed against sinkhole attacks which detect malicious nodes using hop counting. The main advantage of the proposed technique is that, a node can detects malicious nodes only collaborating with the neighbor nodes without requiring any negotiation with the base station. Simulation result shows that, the proposed technique successfully detects the sinkhole nodes for large sensor field.
A novel contour extraction algorithm based on dynamic programming in sausage visual inspection system

Machine vision inspection systems have been widely used in many industries to replace the traditional manual inspection. In order to improve the inspection level of sausage, a visual on-line inspection system is designed to inspect the sausage defects according to this paper. It has great significance in protecting the food safety. Extraction of smooth sausage contour is a key problem in this machine vision system. Early contour extract algorithms mainly focus on enhancing and extracting the high frequency information of images[1]. These methods are efficient, ,but they can hardly get a complete contour when the background or the foreground isn’t segmented well. Algorithms based on level set method have the advantage of stability and topology-independent[2], but they are difficult to obtain good results when extracting discrete edges. Active contour models consider the contour of target region as a continuous and closed chain structure[3],and these models obtain the optimum contour by solving the minimum energy , but the computational complexity is so high that they are not suitable in on-line inspection system.
Sources, availability and uses of knowledge in enterprises in Bogotá, Colombia

Purpose: The present article aims to analyse the main sources, availability, and uses of knowledge relating to enterprise performance in Bogota, Colombia. Design/methodology/approach: This exploratory descriptive study used a survey of 919 executives and non-executives of 59 enterprises from different economic sectors in Bogota. Information was gathered via a questionnaire elaborated by the researchers consisting of 11 closed multiple-choice questions. Findings: Results indicate that in the participant enterprises knowledge management activities do not have any emphasis either on the use of technologies or on activities relating to individuals’ behaviour as sources of competitive advantage, as occurs in North American enterprises (emphasising the intensive and effective use of technologies in their different activities), and in Japanese enterprises (emphasising the development of individuals’ potential as their competitive advantage). Research limitations/implications: It is an exploratory and descriptive study from which it is not pertinent to make generalisations of the previously described results about the whole group of enterprises in Bogota motivating the development of new studies that allow the in-depth identification of the true role of knowledge management activities in enterprise performance. Practical implications: Contributes to the empirical knowledge of business reality and to the need for designing strategies allowing a better use of knowledge management to improve the competitive capacities of enterprises. Social implications: Contributes to the reflection on the importance of the essential differences providing sustainable competitive advantages to nations, organisations, and individuals over prolonged periods of time understanding knowledge as the strategic resource of contemporary society. Developing countries require best practices in the identification, transfer, spread, and use of knowledge management. Originality/value: Compares the theory on knowledge management with the empirical evidence of what really happens internally in enterprises in developing countries with respect to knowledge management activities as a competitive strategy.
Towards accurate structured output learning and prediction

Structured output prediction is the task of learning and predicting with models where the hidden state of several random variables is described jointly. Recently these models became very popular as a growing number of applications have been identified to exhibit strong dependencies between the random variables describing the predicted output. In computer vision, which is the core application focus in this thesis, a structured model can for example incorporate prior assumptions about the smoothness of annotations of neighboring pixels. This thesis introduces novel approximate methods for the two core problems of structured models: Parameter estimation or learning and prediction or inference. Both tasks are intractable in the general case where the underlying (hyper)graph characterizing the dependencies between random variables contains a non-negligible number of cycles. Our learning and inference algorithms are based on two relatively simple, yet powerful ideas. The first idea leverages on the fact that learning and prediction can be efficiently solved and are thus “easy”, in models having an underlying tree or forest graph representation. We exploit this fact to devise approximate learning and inference algorithms in general graphs by decomposing the loopy graph into subsets of either nodes or edges with forest-like connectivity. Intelligent combinations of the computations carried out on the tree graphs, lead to accurate approximations of the original problem. In this thesis we demonstrate the efficacy of this approach when applied to both model parameter estimation, and obtaining the values of the variables minimizing the energy given by the model. The second observation which sets the foundation for this work is that some optimization problems can be simplified by the introduction of a smoothing parameter, also referred to in the literature as a temperature. Using the smoothing approach we introduce a unified model for the two most popular discriminative structured models in use today, the Conditional Random Field and the structured Support Vector Machine. The unified model can lead to more accurate predictions. Furthermore, we demonstrate the value of the smoothing parameter in the context of
Urban Growth and Rural Poverty in India

Analysis of time series data on poverty in India has revealed a clearly discernable link between urban poverty decline and rural poverty decline. Previous analysis, focusing on the pre-reform period, had failed to identify an impact of urban development on rural poverty. This paper examines one possible route through which urban development might have come to exercise an influence on rural livelihoods: the impact of urban development on rural non-farm diversification. The paper shows that the non-farm sector in rural India has grown steadily during the past 25 years, with some acceleration during the late 1990s and first half of the present decade. This process has contributed to declining rural poverty both directly, through employment generation, and indirectly through an impact on agricultural wages. The paper illustrates that urban consumption growth contributes to growth in the rural non-farm economy, and thereby to rural poverty reduction. It speculates that the link from urban development to rural poverty reduction might have been stronger if urban poverty reduction had been centered in India‘s smaller towns and cities. It is in such small towns and cities that the bulk of the urban poor are concentrated, and these same towns and cities are also more tightly connected to surrounding rural areas. The paper ends by asking whether the patterns described here are unique to India, and presents evidence for Brazil that suggest that a similar process may be underway there.