Miracle-dz/UniFAR-FTUs huggingface dataset:
Supporting intercultural collaboration: an agent coordination workflow model

Workflow management has already been worldwide accepted due to its capability to make organizations model and control work processes effectively and efficiently in a distributed environment. Currently, most of the workflow management systems (WfMSs) are lack of a coordination mechanism across different organizations. We first propose an intercultural collaboration workflow process definition and then propose an agent coordination workflow model based on the consideration of cultural factors in intercultural collaboration work process modeling.
Consequences and correction of America's polarized politics

America's polarized politics are largely disconnected from mainstream public preferences. This disconnect poses fundamental dangers for the representativeness and accountability of government, as well as the already withering public trust in it. As the 2008 presidential race kicks into gear, the political climate certainly will not become less polarized. With important issues to address --including immigration policy, health care, and the funding of the Iraq war --it is critical that essential policies not be hostage to partisan political battles. Building upon the findings of the first volume of Red and Blue Nation? (Brookings, 2006), which explored the extent of political polarization and its potential causes, this new volume delves into the consequences of the gulf between "red states" and "blue states. " The authors examine the impact of these political divisions on voter behavior, Congressional law-making, judicial selection, and foreign policy formation. They shed light on hotly debated institutional reform proposals --including changes to the electoral system and the congressional rules of engagement --and ultimately present research-supported policies and reforms for alleviating the underlying causes of political polarization. While most discussion of polarization takes place in separate spheres of journalism and academia, Red and Blue Nation? brings together a unique set of voices with a wide variety of perspectives to enrich our understanding of the issue. Written in a broad, accessible style, it is a resource for anyone interested in the future of electoral politics in America. Contributors include Marc Hetherington and John G. Geer (Vanderbilt University), Deborah Jordan Brooks (Dartmouth College), Martin P. Wattenberg (University of California, Irvine), Barbara Sinclair and Joel D. Aberbach (UCLA), Christopher H. Foreman (University of Maryland), Keith Krehbiel (Stanford University), Sarah A. Binder, Benjamin Wittes, Jonathan Rauch, and William A. Galston (Brookings), Martin Shapiro (University of California-Berkeley), Peter Beinart (Council on Foreign Relations), James Q. Wilson (Pepperdine University), John Ferejohn and Larry Diamond (Hoover Institution), Laurel Harbridge (Stanford University), Andrea L. Campbell (MIT), and Eric M. Patashnik (University of Virginia).
Environmental and Resource Management Under Myopia

Myopia is important in environmental and resource management problems because they often involve inter temporal decisions over a long time horizon. We present a parsimonious extension of a standard dynamic programming equation in a continuous time and continuous state setting, which enables rich description of myopic behavior. In our model, the process of planning future controls and choosing current controls are clearly distinguished. We illustrate the behavior of various types of myopic agents with a simple example of non-renewable resource extraction, and discuss the policy relevance of the expiry of extraction permits to resource management under myopia.
Linear and Non-linear Transceiver Processing for MIMO-FBMC Systems

Filter Bank Multicarrier (FBMC) systems has drawn interest as an alternative to orthogonal frequency division multiplexing (OFDM), as FBMC offers higher spectral efficiency and less susceptibility to synchronization errors. One drawback in FBMC systems is the presence of inter-carrier-interference (ICI) and inter-symbol-interference (ISI), which degrades the system performance when operating under fading channels. In this work we consider the bit error rate (BER) performance of a multiple input multiple output (MIMO) FBMC system. We evaluate the performance of linear and non-linear transceiver processing techniques, which attempt to mitigate the effect of ICI and ISI in MIMO FBMC systems. Simulation results are presented to evaluate and compare the transceiver processing techniques discussed. Index Terms Filter bank, OQAM, receiver processing, precoding
Congestion Control of Active Queue Management Routers Based on LQ-Servo Control

This paper proposes the LQ-Servo controller for AQM (Active Queue Management) routers. The proposed controller structure is made by taking a traditional servo mechanism based on Linear Quadratic approach and by augmenting a new state variable to the feed forward loop. Since the controller structure is consists of a standard optimal feedback regulator and a feed forward controller, it is able to enhance the usefulness of resources and to reduce unnecessary memory reservations such as RAM (Random Access Memory) or SMA (Shared Memory Area) on ordinary router systems, respectively.
Local Sparse Coding for Image Classification and Retrieval

The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Object recognition has been effectively performed by aggregating sparse codes of local features in an image at multiple spatial scales. Though sparse coding guarantees a highfidelity representation, it does not exploit the dependence between the local features. By incorporating suitable locality constraints, sparse coding can be regularized to obtain similar codes for similar features. In this paper, we develop an algorithm to design dictionaries for local sparse coding of image descriptors and perform object recognition using the learned dictionaries. Furthermore, we propose to perform kernel local sparse coding in order to exploit the non-linear similarity of features and describe an algorithm to learn dictionaries when the Radial Basis Function (RBF) kernel is used. In addition, we develop a supervised local sparse coding approach for image retrieval using sub-image heterogeneous features. Simulation results for object recognition demonstrate that the two proposed algorithms achieve higher classification accuracies in comparison to other sparse coding based approaches. By performing image retrieval on the Microsoft Research CamPreprint submitted to Pattern Recognition Letters December 31, 2011 bridge image dataset, we show that incorporating supervised information into local sparse coding results in improved precision-recall rates.
See no Spanish: language, local context, and attitudes toward immigration

Explanations of Americans' attitudes toward immigration emphasize threats to national identity and culture. However, we do not know the specific sources of cultural threat or whether they operate locally. Native-born residents commonly voice concerns about the prevalence of Spanish, suggesting that foreign languages might be one such source of threat. This article uses survey experiments to provide one of the first causal tests of the impact of written Spanish on Americans' immigration attitudes. One experiment (N = 351) was conducted online with a nationally representative sample, while a second was embedded in an exit poll (N = 902). The experiments show that Spanish has differential impacts depending on Americans' prior contact with it. Among those who hear Spanish frequently in day-to-day life, seeing written Spanish induces anti-immigration attitudes. These findings suggest that language can foster cultural threat, and they highlight a mechanism through which local encounters can be threatening.
Source Controlled Modulation Scheme for Sources with Memory

Given an AWGN channel, we look at the problem of designing a source controlled binary antipodal signaling system for transmitting blocks of binary symbols generated either by a Markov chain source (MC) or by a hidden Markov model source (HMM). The goal is to minimize the average SNR required for any given block error rate. The particular case where the binary source is memoryless with nonuniform symbol probabilities has been studied in I. Korn et al. (2003) by optimally allocating the signal energies. In this paper we extend the previous work to include the important case of sources with memory. The proposed system integrates the block sorting burrows wheeler transform (BWT) with an optimal energy allocation scheme based on the first order probabilities of the transformed symbols. This method is compared with standard communication techniques for the transmission of sources with memory.
Carriers Selection for Content Dissemination in Social Opportunistic Networks

Content dissemination in social opportunistic networks has attracted great interests in the field. Most of the researches in literature focused on the data forwarding and routing mechanisms in such network scenarios. There were few studies on the assignment of content originators. This paper is concerned about how to identify a small subset of users as content carriers through which the content can be disseminated more efficiently. The problem is modeled as a dominating set problem in bipartite graph, in which a minimum number of carriers are selected to cover all the venues with required probability, by using available location affinity information. The NP-complete property of the problem is pointed out, along with an exact exponential exhaustive algorithm. Then three polynomial heuristic algorithms are presented to solve the venue coverage problem. Among them, the adaptive metric-based algorithm chooses carriers based on dynamically calculated metric values. Several user-centric and expected contribution based metrics are discussed and examined. The submodular algorithm selects carriers greedily based on the estimation of marginal coverage gain from each user. The reduced-union algorithm asks each venue to select a candidate set separately based on its own individual affinity information. Then the candidate union set is reduced gradually by reasonable greedy method. Synthetized data and real trace experiment results show that the submodular algorithm can be applied in scenarios where coverage threshold is low and small number of carriers is required. In most cases we can rely on metric-based algorithm to solve the carriers selection problem. Among all the metrics, total affinity and proportional contribution perform more valuable than other metrics. Meanwhile, the adaptive operation in metric-based algorithm plays more important role than the metrics in seeking the most qualified carriers. When complete affinity information is not available, the reduced-union algorithm could provide a feasible and acceptable solution to find the coverage set in a scalable way. It is also found that in practical network scenarios, it is realistic to have a small fraction of users act as content carriers, so that all the venues could be covered by them with required probability. These findings would be essentially helpful to content dissemination in affinity context-aware social opportunistic networks.
"Collaborative Production" and the Irish Boom: Work Organisation, Partnership and Direct Involvement in Irish Workplaces*

A significant strand of recent social-scientific writing on Ireland has assigned great importance to various forms of “collaborative production”: new forms of work organisation, partnership and direct employee involvement — even suggesting that their growing diffusion might have played a major role in Ireland’s exceptional economic performance during the 1990s. This paper draws on the University College Dublin national workplace survey of employee relations to present an assessment of the degree to which new modes of collaborative production have gained ground in Ireland during the 1990s. While collaborative production is undoubtedly significant in many Irish workplaces, “exclusionary” forms of decision-making are shown to dominate the postures of establishments towards the handling of change. Arguments pointing to the “transformation”, actual or imminent, of work practices and employment relations in Ireland are rejected. Change in Ireland is shown to have much in common with developments in other economies, particularly those characterised by “Anglo-American” institutional systems, which are not readily permeable to collaborative production in its various modes.
An Efficient Hierarchical Method for Image Shadow Detection

Shadow image edge detection by using an adaptive background model is a critical component for many vision-based applications. Most background models were maintained in pixel-based forms, while some approaches began to study block-based representations which are more robust to non-stationary backgrounds. In this paper, a novel method that combines edge growing and granular computing approaches into a single framework is proposed. Efficient hierarchies can be built with these two approaches complementary to each other. In addition, a novel model is proposed for shadow edge using edge growing from the edge nodes in the coarse level of the hierarchy. As can be seen from the experimental analysis, the method we proposed has better performance than existing single-level approaches in edge detection and image segmentation.
On Some Welsh Unbounded Dependency Constructions

Essex Research Reports in Linguistics present ongoing research activities of the members of the Department of Language and Linguistics. The main purpose of these reports is to provide a quick publication outlet. They have 'pre-publication status', and most will subsequently appear in revised form as research articles in professional journals or in edited books. Copyright remains with the author(s) of the reports. Comments are welcome: please communicate directly with the authors. If you have technical problems downloading a paper, or for further information about these reports, please contact the editor:
Centre for Labour Economics

For an economic system not to exhibit debt neutrality it must be true that changes in the time profile of lump-sum taxes redistributes resources between heterogeneous consumers. OLD models have age heterogeneity because of a positive birth rate. Unless a bequest motive or child-to-parent gift motive is operative, a positive birth rate is sufficient for absence if debt neutrality. Uncertain lifetimes are neither necessary nor sufficient for absence of debt neutrality, with or without efficient life insurance markets. Heterogeneous survival probabilities are a sufficient condition. Heterogeneous time preference rates or elasticities of marginal utility does not destroy debt neutrality, since with common survival rates, changes in the pattern over time of lump-sum taxes do not redistribute resources. Any representative agent model, regardless of the scope and severity of capital market imperfections, will exhibit debt neutrality.
Opportunities for Play-Based Experiences in Post "No Child Left Behind" Kindergarten Classrooms: The Role of Training, Resources, and Accountability Pressures in Meeting Best Practices

OPPORTUNITIES FOR PLAY-BASED EXPERIENCES IN POST “NO CHILD LEFT BEHIND” KINDERGARTEN CLASSROOMS: THE ROLE OF TRAINING, RESOURCES, AND ACCOUNTABILITY PRESSURES IN MEETING BEST PRACTICE by Cristina Medellin Adviser: Professor Joseph Glick In response to No Child Left Behind (NCLB), opportunities for play-based experiences in classrooms have been challenged over the past decade. Despite research demonstrating the educational benefits of child driven play, teachers and schools have been pressured to focus on improving children’s success on standardized assessments which may not relate to the developmental achievements expected from activity based experiences. To explore teachers’ response to the tension between assessment driven mandates and best early childhood practices, this study investigated which factors influence teacher practices and values. Specifically, how do teacher training and classroom resources influence teachers’ values about the appropriateness of using child driven learning materials as well as creating opportunities in the classroom schedule for play-based experiences? Additionally, how do teachers’ perceived accountability pressures to their classroom practice influence their values and opportunities for play-based experiences? Regression analyses were performed to explore 142 NYC kindergarten teachers’ responses on the Early Childhood Time Use Scale-Profile. Results suggest that when teachers are trained in developmental theory and adequate resources, play-based experiences in the classroom remain a priority in the classroom. In addition, teacher’s value of child driven materials remains intact when training and resources are available. By contrast, when accountability pressures influence
Variations in returns/volatility and persistence in variance. An application to the Spanish stock market

A methodology is proposed to select the information set in ARMA-GARCH models in order to forecast the future evolution of an univariate heteroscedastic time series when it is suspected that the DGP is time changing. Using this methodology the stability of the DGP in the Spanish Stock Market is analysed. In this case it is shown that the DGP is time-varying and, in particular, the persistence in variance is over-valued using classical methods. Furthermore, the predictive intervals obtained have better coverage properties, by more adequately reflecting the uncertainty associated to the evolution of the time series being analysed.
Towards a Multi-Agent System for Medical Records Processing and Knowledge Discovery

Medical files and observation papers have always been an important source of knowledge. Unfortunately, most of the times they are still stored as physical documents either printed or handwritten, thus making it difficult to transfer this precious information from one place to another, or centralizing and extracting new knowledge from it. But with nowadays advancements in computer science this problem may be handled. In this paper we propose a patient centered Multi-agent system project that is able to extract relevant information from the patients' health records and store that knowledge on a centralized data-store, based on a predefined ontology scheme. The system's purpose is to standardize and enrich the knowledge by performing various mining tasks on the given text. The final purpose is to provide different hospital departments with a tool which they can query for useful information about the patients' medication, treatments and/or scheduled tests and exams and get suggestions regarding their treatment intentions.
Efficient contrast enhancement with truncated adaptive gamma correction

Gamma correction is an efficient tool typically used to adjust the contrast and brightness of digital images. Recently, an adaptive gamma correction method is proposed by relating gamma parameter with the cumulative distribution function (CDF) of gray level histograms. It gains satisfied contrast enhancement (CE) effects on dimmed images, but is invalid on bright regions which are universal in real scenarios, such as excessive exposure, white objects and highlight regions. In order to attenuate such a deficiency, we propose an improved adaptive gamma correction algorithm in this paper. Instead of directly applying CDF curves as the gamma parameter, we truncate them by thresholding with a reasonable limit. Accordingly, contrast of input dimmed images could be improved without losing details in bright regions. Both qualitative and quantitative experimental results demonstrate the promising performance of our proposed CE method in enhancing the contrast and brightness of dimmed images.
Hidden Markov Model Symbol Recognition for Sketch-Based Interfaces

A central challenge for sketch-based interfaces is robust symbol recognition. Artifacts such as sketching style, pixelized symbol representation and affine transformations are just a few of the problems. Temporal pattern recognition through Hidden Markov Models (HMM) can be used to recognize and distinguish symbols as pixel-driven gestures. The key challenges of such a system are the type and amount of necessary pre-processing, feature extraction, HMM parameter selection and optional post processing. In this paper, we describe a recognition strategy based on HMMs and include recognition results on twelve sketched symbols. In addition, we have successfully applied this methodology to a PDA sketch-based interface to control a team of robots. The symbol recognition component is used to identify sketched formations and issue commands that drive the behavior of the robot team.
Independence, Invariance and the Causal Markov Condition

This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation.
Introduction of Inquiry-based Science Teaching in Rwandan Lower Secondary Schools: Teachers' attitudes and perceptions

This study describes, discusses and analyses the Rwandan lower secondary school teachers’ responses to the introduction of inquiry as a teaching approach in the science curriculum as one of the changes that the curriculum in Rwanda has undergone through in the aftermath of the 1994 genocide. The study investigates the science teachers’ understanding of inquiry-based science teaching, their attitudes towards the introduction of inquiry into the science curriculum, the activities they are engaged in with regard to inquiry-based science teaching and learning, the factors influencing their current teaching practices and their perceptions about what may be done for a better implementation of inquiry-based science teaching. Guided by a pragmatic research approach, I believed that collecting diverse types of data would provide a deeper understanding of the research problem and therefore adopted a two phases’ sequential explanatory mixed methods design. During the first phase, data were collected by means of a survey questionnaire administered to a purposeful sample of 200 science teachers at lower secondary school in Rwanda. Findings from the survey informed the second phase consisting of data collection by means of semi-structured one-to-one interviews with 15 purposefully selected teachers from the sample used in the first phase then supplemented by a contextual observation in their schools. The data from the questionnaire were subject to a descriptive statistical analysis while data from interviews were subject to analysis involving transcribing and reading interview transcripts, coding and categorizing information, identifying patterns, and interpreting. The data analysis produced five main assertions providing answers to the research questions. Participant teachers displayed varying understanding of what inquiry-based science teaching is, associating it with a number of its characteristics such as a learner centred teaching approach mostly based on experiments and practical work. There were a few teachers who did not have accepted understandings of inquiry-teaching. Furthermore, teachers had a positive attitude towards the introduction of inquiry and favoured the change even though they indicated a number of factors preventing them from adequately implementing the new teaching approach. As for their practices, traditional classroom