Miracle-dz/UniFAR-FTUs huggingface dataset:
Branching of Banks and Union Decline Working Paper No

This paper proposes a novel explanation for the decline in unions in the United States since the late 1970s: state-by-state removal of geographical restrictions on branching of banks. Bank branch deregulation reduces union membership in the non-banking sectors by intensifying entry of new …rms, especially in sectors with high dependence on external …nance. New …rm entry, in turn, is associated with a reduction in union wage premium, and subsequently leads to adverse union voting. I provide empirical evidence for these channels using repeated cross-sectional and panel data of U.S. workers and union representation election outcomes. am especially thankful to Ross Levine who encouraged me to explore the link between bank deregulation and union membership. The views expressed herein are mine and not necessarily of the Federal Reserve Bank of Boston or the Federal Reserve System. Please address correspondence to <Alexey.Levkov@bos.frb. org>.
Tensors, Monads And Actions

We exhibit sufficient conditions for a monoidal monad T on a monoidal category C to induce a monoidal structure on the Eilenberg-Moore category C^T that represents bimorphisms. The category of actions in C^T is then shown to be monadic over the base category C.
Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques

Summary In order to overcome the shortcomings of traditional clustering algorithms such as local optima and sensitivity to initialization, a new Optimization technique, Particle Swarm Optimization is used in association with Unsupervised Clustering techniques in this paper. This new algorithm uses the capacity of global search in PSO algorithm and solves the problems associated with traditional clustering techniques. This merge avoids the local optima problem and increases the convergence speed. Parameters, time, distance and mean, are used to compare PSO based Fuzzy C-Means, PSO based Gustafson’s-Kessel, PSO based Fuzzy K-Means with extragrades and PSO based K-Means are suitably plotted. Thus, Performance evaluation of Particle Swarm Optimization based Clustering techniques is achieved. Results of this PSO based clustering algorithm is used for remote image classification. Finally, accuracy of this image is computed along with its Kappa Coefficient.
Sensing of Intelligent Robots Based on Applications of Tactile Slip Displacement Sensors

This paper discusses the design of modern tactile and slip displacement sensors for informationcontrol systems of intelligent and adaptive robots. It provides information on three approaches for using slip displacement signals (for correction of claiming force, for identification of manipulated object mass and for correction of robot control algorithm). The study presents the analysis of different methods for slip displacement signals detection, as well as new sensors schemes, mathematical models and correction methods. Special attention is paid to investigations of sensors developed by authors with the capacity, magnetic sensitive elements and with automatic adjustment of clamping force. The proposed structure of multi-functional computerized information-control system for intelligent robot is presented. Copyright © 2015 IFSA Publishing, S. L.
PERFORMANCE ESTIMATION FOR HARDWARE / SOFTWARE CODESIGN USING HIERARCHICAL COLORED PETRI NETS

This paper presents an approach for abstract modeling of the functional behavior of hardware architectures using Hierarchical Colored Petri Nets (HCPNs). Using HCPNs as architectural models has several advantages such as higher estimation accuracy, higher flexibility, and the need for only one estimation tool. This makes the approach very useful for designing component models used for performance estimation in Hardware/Software Codesign frameworks such as the LYCOS system [14]. The paper presents the methodology and rules for designing component models using HCPNs. Two examples of architectural models are presented, namely a model of the DLX pipeline processor [9], and a model of a fixed point unit used in the AMICAL system [13]. The methodology presented in this paper is a pragmatic example of the usefulness of HCPNs. The focus is not on the theoretical aspects of Petri Nets but on the application of the Petri Net methodology in an existing Codesign environment.
Multi-objective optimization of aircraft networks for weight, performance and survivability

With a current utilization of around 25% of its total weight, optimization of cabling and harness plays an important role in the design of an aircraft. This is a multi-objective optimization problem as the physical properties of the harness (e.g., length and weight) as well as the logical properties of the underlying network (survivability and performance) have to be taken into account. We propose in this paper to investigate this multi-objective optimization problem and provide different algorithms to solve it. We first focus on expressing it as a mixed integer binary linear program. Due to the complexity of solving this class of linear programs, we propose various heuristics for providing a good solution to this problem.
Automatic Real-Time Facial Expression Recognition for Signed Language Translation

Automatic Real-Time Facial Expression Recognition for Signed Language Translation Jacob Richard Whitehill M.Sc. thesis, Department of Computer Science, University of the Western Cape We investigated two computer vision techniques designed to increase both the recognition accuracy and computational efficiency of automatic facial expression recognition. In particular, we compared a local segmentation of the face around the mouth, eyes, and brows to a global segmentation of the whole face. Our results indicated that, surprisingly, classifying features from the whole face yields greater accuracy despite the additional noise that the global data may contain. We attribute this in part to correlation effects within the Cohn-Kanade database. We also developed a system for detecting FACS action units based on Haar features and the Adaboost boosting algorithm. This method achieves equally high recognition accuracy for certain AUs but operates two orders of magnitude more quickly than the Gabor+SVM approach. Finally, we developed a software prototype of a real-time, automatic signed language recognition system using FACS as an intermediary framework.
An Analysis of URLs Generated from JavaScript Code

Search engines use a crawling system to recursively download web pages, analyze HTML pages, and generate a new list of URLs to crawl. As web pages are becoming more dynamic than before, JavaScript is heavily used, which poses a great challenge for the crawling system, because now many URLs are embedded in the JavaScript code and are invisible to the crawler. Worse, there is no study on the usage patterns of these URLs and the impact of JavaScript-generated URLs is unknown. We propose a browser emulation method to study the usage of URLs from JavaScript code. In order to find these URLs, we instrument a browser core to output all URLs inside a web page, including those generated from JavaScript. Then we classify these URLs into a number of types and study reasons that web developers put them in JavaScript. We analyze top Internet sites and popular web pages. The results show that more than half of them contain URLs generated from JavaScript, which accounts for about 6-19% of total URLs. Among them, 26-41% refer to potential important contents that should be indexed by search engine crawlers, and advertising URLs are about 26-35%.
Information structures of capacity achieving distribution for channels with memory and feedback

The information structures of the optimal channel input distributions P<sub>[0,n]</sub>=<sup>Δ</sup>{P<sub>Ai|Ai-1, Bi-1</sub> : i = 0,1,. , n}, which correspond to the extremum problem of feedback capacity C<sub>An→Bn</sub><sup>FB</sup> =<sup>Δ</sup> sup P<sub>[0,n]</sub> Σ<sub>i=0</sub><sup>n</sup> I(A<sub>i</sub>;B<sub>i</sub>|B<sup>i-1</sup>) are identified, for any class of channel distributions {P<sub>Bi|Bi-1,Ai</sub> : i = 0,1,. ,n} and {P<sub>Bi|B</sub><sub>i-M</sub><sup>i-1</sup><sub>,Ai</sub> : i = 0,1,. ,n}, where B<sub>n</sub> =<sup>Δ</sup> {B<sub>j</sub> : j = 0,1,. ,n} are the channel output RVs, A<sup>n</sup>=<sup>Δ</sup> {A<sub>j</sub> : j = 0,1,. ,n} are the channel inputs RVs, and M is a finite nonnegative integer. The methodology utilizes stochastic optimal control theory, to identify the control process, the controlled process, and a variational equality of directed information, to derive upper bounds on I(A<sup>n</sup> → B<sup>n</sup>)=<sup>Δ</sup> Σ<sub>i=0</sub><sup>n</sup> I(A<sup>i</sup>;B<sub>i</sub>|B<sup>i-1</sup>), which are achievable over specific subsets of P<sub>[0,n]</sub>, which satisfy conditional independence. The main theorem states, that for any channel with memory M, the optimal channel input conditional distribution occur in the subset P<sub>[0,n]</sub> =<sup>Δ</sup> {P<sub>Ai|B</sub><sub>i-M</sub><sup>i-1</sup> : i = 1,. ,n} ⊂ P<sub>[0,n]</sub>, and the corresponding extremum problem simplifies to the following characterization C<sub>An→Bn</sub><sup>FB,M</sup> =<sup>Δ</sup> sup P<sub>[0,n]</sub> Σ<sub>i=0</sub><sup>n</sup> I(A<sub>i</sub>;B<sub>i</sub>|B<sub>i-M</sub><sup>i-1</sup>).
Learning the nature of generalisation errors in a 3D morphable model

In this paper, we present a new method to statistically recover the full 3D shape of a face from a set of sparse feature points. We attribute noise in the feature point positions to generalisation error of the model. We learn the variance of these feature points empirically using out-of-sample data. This allows the shape reconstruction to probabilistically model the way in which feature points deviate from their true position. We are able to reduce the reconstruction error by as much as 12%.
NGOMSL simulation model in an Emergency Department

Natural Goals Operators Methods and Selection Rules Language (NGOMSL) model of the clinical process in an Emergency Department (ED) was developed using Micro Saint Sharp. This model advanced our understanding of a care provider's cognitive behavior in a dynamic ED environment. It also revealed the understanding of proximal workflow in the ED and the workload related to clinical processes. The benefits of this study are (a) improved understanding of the relevant form factors of the clinical process that contribute to a heavy workload in the ED and (b) prevention of potential errors caused by the workload. To understand the current ED workflow, hierarchical task analysis (HTA) charts were developed. The HTA charts were used to understand the detailed process mappings of nurses in the ED. Based on this multi-level analysis of the HTA charts, the NGOMSL simulation model was developed.
Empathic Design vs . Empathetic Design : A History of Confusion

Many different design models have been developed, and formalizing and classifying their definitions to create a coherent and unified guide to design models will help stimulate the development of both new models and new products. Classification will also help to eliminate design models whose definitions have never been formalized and as such can be a source of confusion and misinformation. “Empathic Design” [1] and “Empathetic Design” [2] are two models in need of just such classification: their names are similar enough to confuse effortlessly. In this paper, I investigate references to the two design methods in the literature, locating a formal definition for Empathic Design in the literature and failing to locate one for Empathetic Design. In doing so, I demonstrate that one of these methods is valid while the other still needs to be formalized lest its use continue to cause confusion about its implementation.
Estimation of decenteralized unknown dynamics for a 2DOF manipulator using a time varying extended state observer

In this paper, an estimation of the dynamics of a 2 degrees of freedom robot manipulator with uncertainties, is derived, using an extended state observer. Decentralizing approach in multivariable systems, is an methodolgy used in an increasing number of practical systems to simplify the controller design. On the other hand, these systems usually contain uncertainties, and also parts of the system's dynamics are unknown. Due to the robust characteristics of extended state observers, these observers would be able to estimate these unknown dynamics, which has been discussed in the literature thoroughly. In this paper, a special class of extended state observers, namely the time varying extended state observer, will be discussed to estimate the dynamics and the disturbances related to the couplings in a manipulator with 2 degrees of freedom. The proposed observer significantly reduces the estimation error's peaking phenomenon, using time variant gains. Thus, it has a better performance compared to similar observers in the literature. This specific class, has time varying parameters, which makes its design simpler and presents a better performance, compared to other linear or nonlinear observers available. As shown in the simulations, the estimation error pick phenomena reduces drastically.
AWSCS-A System to Evaluate Different Approaches for the Automatic Composition and Execution of Web Services Flows

This paper proposes a system named AWSCS (Automatic Web Service Composition System) to evaluate different approaches for automatic composition of Web services, based on QoS parameters that are measured at execution time. The AWSCS is a system to implement different approaches for automatic composition of Web services and also to execute the resulting flows from these approaches. Aiming at demonstrating the results of this paper, a scenario was developed, where empirical flows were built to demonstrate the operation of AWSCS, since algorithms for automatic composition are not readily available to test. The results allow us to study the behaviour of running composite Web services, when flows with the same functionality but different problem-solving strategies were compared. Furthermore, we observed that the influence of the load applied on the running system as the type of load submitted to the system is an important factor to define which approach for the Web service composition can achieve the best performance in production.
An Algorithm for Personalized Product Recommendation based on Preference and Intention Learning

We propose a hybrid learning approach to provide automated assistance for personalized product recommendation. The novel feature of this work is that the system learns and uses models of both user preferences and the user’s intentional context. Both learning types are based on the same user input, but elicit different aspects of the user model. User preference is learned via Support Vector Machine (SVM) with user ratings on the products, whereas the user’s intentional context is inferred using a Hidden Markov Model (HMM) from given product access sequences. We propose a product recommendation scheme based on an analysis on both the preference and intentional context model. An empirical analysis shows that the hybrid approach is able to support users with different preference structures and intentional contexts.
Cerebral Activation Associated with Sexual Arousal in Response to a Pornographic Clip: A 15O–H2O PET Study in Heterosexual Men

This study attempted to use PET and 15O-H2O to measure changes in regional cerebral blood flow (rCBF) during sexual arousal evoked in 10 young heterosexual males while they watched a pornographic video clip, featuring heterosexual intercourse. This condition was compared with other mental setups evoked by noisy, nature, and talkshow audiovisual clips. Immediately after each clip, the participants answered three questions pertaining to what extent they thought about sex, felt aroused, and sensed an erection. They scored their answers using a 1 to 10 scale. SPM was used for data analysis. Sexual arousal was mainly associated with activation of bilateral, predominantly right, inferoposterior extrastriate cortices, of the right inferolateral prefrontal cortex and of the midbrain. The significance of those findings is discussed in the light of current theories concerning selective attention, "mind reading" and mirroring, reinforcement of pleasurable stimuli, and penile erection.
Models and Methods of Analysis and Optimization for Systems with Real-time and Embedding Constraints Version 1.0 Team Members

The purpose of this Research Team proposal is to promote the use of synchronous formalisms for the high-level modeling, the full formal design, and the distributed implementation satisfying real-time constraints of (some specific classes of) embedded software. It is based on prior work by the authors on the SYNCCHARTS, ESTEREL, and SYNDEX formalisms , which included extensive algorithmic studies on dedicated modeling, compilation, analysis, and optimization techniques. In particular we gained praised expertise in the fields of formal semantics of synchronous reactive systems, or of optimized mapping (i.e. distribution and scheduling) between application algorithms and physical architectures descriptions. We intend to combine these aspects of conceptual modeling, structural model-based specification, and optimized distributed implementation into a consistent design flow, with a definite concern for UML precise (profile) notations, advanced analysis techniques on reactive program specifications, and up-to-date mapping techniques between well-defined descriptive models of both application and physical architecture, taking relevant optimization criteria into account. These advances in modeling and design will be supported by software tool production, and applicative collaborations drawn from the fields of automotive, avionic, telecommunication, or electronic appliances (mobile phones, digital Video. . .) industries. These will deal with modern technology challenges as System-on-Chip or synchronous field buses.
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation

For many real-world optimization problems, the robustness of a solution is of great importance in addition to the solution's quality. By robustness, we mean that small deviations from the original design, e.g., due to manufacturing tolerances, should be tolerated without a severe loss of quality. One way to achieve that goal is to evaluate each solution under a number of different scenarios and use the average solution quality as fitness. However, this approach is often impractical, because the cost for evaluating each individual several times is unacceptable. In this paper, we present a new and efficient approach to estimating a solution's expected quality and variance. We propose to construct local approximate models of the fitness function and then use these approximate models to estimate expected fitness and variance. Based on a variety of test functions, we demonstrate empirically that our approach significantly outperforms the implicit averaging approach, as well as the explicit averaging approaches using existing estimation techniques reported in the literature
Extended Model-Based Feedforward Compensation in ℒ1 Adaptive Control for Mechanical Manipulators: Design and Experiments

This paper deals with a new control scheme for Parallel Kinematic Manipulators (PKMs) based on the L1 adaptive control theory. The original L1 adaptive controller is extended by including an adaptive loop based on the dynamics of the PKM. The additional model-based term is in charge of the compensation of the modeled nonlinear dynamics in the aim of improving the tracking performance. Moreover, the proposed controller is enhanced to reduce the internal forces, which may appear in the case of Redundantly Actuated PKMs (RA-PKMs). The generated control inputs are first regulated through a projection mechanism that reduces the antagonistic internal forces, before being applied to the manipulator. To validate the proposed controller and to show its effectiveness, real-time experiments are conducted on a new four degrees-of-freedom (4-DOFs) RA-PKM developed in our laboratory.
Deflection-Aware Tracking-Principal Selection in Active Wireless Sensor Networks

This paper addresses the problem of energy efficiency balanced with tracking accuracy in wireless sensor networks (WSNs). Specifically, we focus on the issues related to selecting tracking principals, i.e., the nodes with two special tasks: 1) coordinating the activities among the sensors that are detecting the tracked object's locations in time and 2) selecting a node to which the tasks of coordination and data fusion will be handed off when the tracked object exits the sensing area of the current principal. Extending the existing results that based the respective principal selection algorithms on the assumption that the target's trajectory is approximated with straight line segments, we consider more general settings of (possibly) continuous changes of the direction of the moving target. We developed an approach based on particle filters to estimate the target's angular deflection at the time of a handoff, and we considered the tradeoffs between the expensive in-node computations incurred by the particle filters and the imprecision tolerance when selecting subsequent tracking principals. Our experiments demonstrate that the proposed approach yields significant savings in the number of handoffs and the number of unsuccessful transfers in comparison with previous approaches.
The Rhetoric and Reality of Leading the Inclusive School: Socio-Cultural Reflections on Lived Experiences

This paper details a cross-cultural study of inclusive leadership practices within a basic education context in each of the following countries: Australia, Canada, and Colombia. Each school was selected after district educational leaders identified the school as being inclusive of students with diverse learning needs over an extended period of time. The researchers were particularly interested in the norms and assumptions that were evident within conversations because these were viewed as indicators of the nature of the embedded school culture within each context. School leaders and teachers were interviewed to determine the link between rhetoric and reality, and what inclusion ‘looked like’, ‘felt like’, and ‘sounded like’ at each site, and whether any discernible differences could be attributed to societal culture. A refractive phenomenological case study approach was used to capture the messages within each context and the lived experiences of the participants as they sought to cater for the needs of students. Data were collected from semi-structured interviews with school leaders and teaching staff. Each researcher conducted environmental observations, documenting the impressions and insights gained from the more implicit messages communicated verbally, non-verbally, and experientially from school structures, visuals, and school ground interactions. Themes were collated from the various narratives that were recounted. Both similarities and distinct socio-cultural differences emerged.
Pengaruh Komitmen Organisasional dan Kepuasan Kerja terhadap Intention to Stay Relawan di Organisasi Non-Profit (Studi pada TurunTangan Malang)

The purposes of this research is to analyze the influence of organizational commitment and job satisfaction on volunteer’s intention to stay in TurunTangan Malang. The type of this research is explanative research with quantitative approach. The sampel of this research are 55 respondents with saturated sampling technique which are active volunteers. Data analysis technique uses multiple linear regression analysis by considering the classical assumption test. The results of this test indicate that organizational commitment and job satisfaction influence simultaneously on volunteer’s intention to stay. For partial influence, only organizational commitment alone has a positive and significant influence on volunteer’s intention to stay and also becomes the dominant independent variable. While job satisfaction does not significantly influence volunteer’s intention to stay. Key Word : Organizational Commitment, Job Satisfaction, Intention to Stay