Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of time orientation in decision-making under time pressure /

This study examined the role of time orientation on decision-making under time pressure. Time orientation, an individual difference in the attention to time and the perception that time is a scarce resource, was hypothesized to exaggerate individuals' behavioral reactions to time-pressured decisions. In addition, the study examined time orientation's influence on perceived time pressure, the presumed impetus for time-saving strategies when decision-making time is limited. Given the same objective cues, it was argued, time-oriented individuals may have a heightened perception of time pressure and may have a greater propensity to use decision heuristics designed to conserve time. Participants completed an electronic information board monitoring information search during a series of decision tasks. The study included two within-subjects variables, information and time availability, with three levels each, as well as measures of time orientation. Type A behavior, state anxiety and questions about participants' experiences of time pressure during the experiment. Several research questions were addressed in this study: First, this research replicated some of the findings in the literature regarding decision search behaviors under time pressure. The study examined the main effects of two objective conditions, the amount of information presented and the time allotted, on time-conserving strategies during decision-making. An information x time interaction was hypothesized, with time conservation becoming increasingly more pronounced as the level of time availability decreased and the level of information availability increased. Time conservation was measured in a variety of ways, including searching a smaller proportion of the available information and examining variable amounts of information per option. Interactions were significant for most of the dependent variables studied. Third, it was argued that individuals with higher levels of time orientation would perceive more time pressure than those with lower levels, and would report higher stress levels. This hypothesis received mild support. Relatedly, it was argued that high time-
Endovascular therapy in acute ischemic stroke: The way forward after results from the IMS 3, SYNTHESIS and MR Rescue trials

Abstract Endovascular therapy (EVT) has gained vogue in the management of patients with acute stroke. Newer stent-retriever devices have led to better recanalization rates. In many centers, EVT is slowly being used as an add on to or in some instances, even as an alternative to intravenous tissue plasminogen activator (IV tPA). The publication of the results of the SYNTHESIS expansion, Interventional Management of Stroke III and Mechanical Retrieval Recanalization of Stroke Clots Using Embolectomy trials in 2013 has questioned the enthusiastic use of EVT in acute stroke. They demonstrate that EVT (using a variety of devices) is no superior to IV tPA in the management of acute stroke. In the light of these controversial findings, we review the current status of EVT in the management of acute stroke.
Stability analysis and design of fuzzy control systems

The stability of a fuzzy feedback control system consisting of a fuzzy controller connected in series with a plant described by a fuzzy model is discussed. The stability analysis is based on some new theorems that guarantee sufficient conditions for asymptotical stability of the equilibrium point and total stability of the system. The stability analysis results are used to provide an approach to fuzzy controller design. The steps of the approach are specified through a design example.
Rate-distortion optimized motion compensation for video compression using fixed or variable size blocks

The authors describe a method for optimizing in a rate-distortion sense the performance of block matching motion compensation for video compression using fixed or variable size blocks. They apply recent advances in rate allocation theory and optimal tree structures to the choice of motion vector and block size for each region of the prediction image. They show that a variable block size algorithm using an optimized tree structure yields a significant improvement in rate-distortion performance over traditional motion compensation with a fixed block size. The computational complexity of such a system is not significantly higher than that of a fixed block size system. <<ETX>>
Analysis, modeling, and simulation of series-parallel resonant converter circuits

This paper presents a SPICE macromodel for a generic series-parallel resonant converter circuit. The model is derived from the averaged time-invariant state-space equations obtained from a Fourier transform. The conditions are derived under which all but the fundamental harmonic may be discarded, and the model developed based solely on the fundamental Fourier component. The single macromodel developed has a wide range of validity, and allows DC, AC, and transient analyses to be carried out in a fast, easy, and familiar manner. It also permits the converter to be incorporated alongside its control circuitry into an entire system. The simulation results from the model have been compared to results from a full simulation, and the agreement is found to be excellent, with the macromodel simulation running between 37 and 4700 times faster than the full simulation. >
An Algorithm for Automatic Name Placement Around Point Data

An algorithm for automatic name placement around point data requires both the examination of the cartographic name placement process and the utilization of spatial search techniques. The name placement process consists of selection, layout, and placement phases. Spatial search techniques are utilized to simulate the difficult layout phase. These techniques have resulted in the development of two movement methodologies, each intended to simulate a separate part of the name placement process. The automatic placement of names around point data is a feasible concept for use in map making.
Minimum-time trajectories for two robots holding the same workpiece

The authors present an algorithm which minimizes the time for two robots holding the same workpiece to move along a given path. The method can be applied to any constrained robot system, including the case where one robot arm moves in contact with a surface. The unique feature of these systems is that they are redundantly actuated because they have more actuators then degrees of freedom. In addition to finding the optimum torque histories, the algorithm determines the contact force between each robot and the workpiece throughout the motion. Constraints on these internal forces are easily introduced into the algorithm. Examples are given for two planar arms holding a common workpiece. It is shown that arbitrary bounds on the internal forces of interaction on the workpiece may easily be imposed; this causes a reduction in the path traversal time. The motions found using this algorithm establish a performance limit on cooperating robot systems, and can be used to guide the analysis and design of such systems. <<ETX>>
A Validation Study of the CEFR Levels of Phrasal Verbs in the English Vocabulary Profile

This article reports on a part of the development and validation project for the English Vocabulary Profile (EVP). The previous version of the EVP included 439 phrasal verbs as well as 4,666 individual word entries. Each of their meanings is ordered according to its CEFR level. The aims of the study are to identify the actual difficulty of each phrasal verb, to validate the tentative decision of the CEFR levels, and also to explore factors that explain the difficulties, by using textbook corpora. In order to carry out this research, we developed a phrasal verb test of 100 items, consisting of four A1 items, nineteen A2 items, forty B1 items and thirty-seven B2 items. Approximately 1,600 Japanese students took this test. We analysed the test data, using item response theory. The results of the test show that although the average difficulties of the phrasal verbs in each level were ordered according to the level prediction, the ranges of the difficulties in each level overlapped. The analysis of textbook corpora reveals that there is a complex relationship between the difficulty levels of phrasal verbs and their frequencies in the textbooks. We discuss its implications and possible improvements for the EVP.
Stable visual servoing with neural network compensation

We propose a stable 2D visual servoing algorithm for planar robot manipulators. We assume that gravity and friction are unknown and that there exists modeling errors in the vision system. By using a radial basis function neural network, it is shown that these uncertainties can be compensated. We prove that without or with unmodeled dynamics, the 2D visual servoing with neural networks compensation is Lyapunov stable.
Simulation and evaluation of WSN Performancesusing True Time Tool Box

Routing is a functional important aspect in wireless ad hoc that handles, discover, and maintain the paths between the nodes within a network. Ad hoc On-Demand Distance Vector (AODV) routing protocol was continues to be an active and successful research since its introduction Protocol in ad hoc wireless networks. In this paper, we have emphasized mainly on the routing protocol implementation of AODV for ZigBee network based on IEEE 802.15. 4 standard in the SIMULINK MATLAB TRUETIME database, which facilitates the task of controller co-simulation, running in real time kernels, network transmissions, thus has a comparison is made with other networks [802.11 (WLAN), NLM-WIRLESS. This study includes analysis of indicators of performance such as Packet Delivery Ratio (PDR) and the Packet loss Ratio (PDR)with the change of emission power, the study of the variation of the parameters such as transmit power, under Receiver threshold, signal reach. Keywords—ZigBee, true time,AODV, transmitting, PDR,PLR,WSNpower,MATLAB.
MEASURING CHANGES IN INFORMATION SHARING AMONG LIFE SCIENCE RESEARCHERS

AbstractIn a world that is increasingly focused on knowledge work, there is a need to understand how information and knowledge are created, retained, and shared in organizations. Research into information sharing, incorporating theories of social interaction, creates a comprehensive framework for studying information sharing. This article reports the findings from the first stage of a longitudinal case study designed to capture the information sharing practices in a new work environment for life science researchers. Data reported in this article sets a benchmark for future measurement of collaboration and information sharing in the context of an interdisciplinary life sciences center and furthers the research about the connection between information sharing and knowledge exchange/sharing.
Consensus Problem of Second-order Dynamic Agents with Heterogeneous Input and Communication Delays

Consensus problem of second-order multi-agent systems with velocity damping term in agent’s dynamics is investigated. Based on frequency-domain analysis, decentralized consensus condition, which depends on the input delays, is obtained for the system based on undirected and symmetric graph with heterogeneous input delays. For the system based on directed graph with both heterogeneous input delays and communication delays, decentralized consensus condition, which is dependent on the input delays but independent on the communication delays, is also obtained. Simulations illustrate the correctness of the results.
Computer Network Security and Precaution Evaluation based on Incremental Relevance Vector Machine Algorithm and ACO

Abstract It is well-known that the correct evaluation for computer network attack is very significant to avoid computer network attack. In order to improve the generalization performance of relevance vector machine, an incremental relevance vector machine algorithm(IRVM) based on ant colony optimization (ACO) is firstly presented and applied to computer network attack evaluation in the paper. In the experiments, we collect 85 samples of KDDCUP99 datasets as our experimental data, the detection accuracy of the hybrid algorithm of ACO and relevance vector machine is 97.65%, and the detection accuracy of the hybrid algorithm of relevance vector machine is 94.12%. It is shown that the detection accuracy of the hybrid algorithm of ACO and relevance vector machine for computer network attack is higher than that of relevance vector machine.
IMAGACT4ALL Mapping Spanish Varieties onto a Corpus-Based Ontology of Action

IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech resources, which makes use of the universal language of images to identify action types. IMAGACT4ALL is an Internet infrastructure for mapping languages onto the ontology. Because the action concepts are represented with videos, extension into new languages is done using competence-based judgments by mother-tongue informants without intense lexicographic work involving underdetermined semantic description. It has already been proved on Spanish and Chinese and it is now in the process of being extended to Hindi, Bengali, Sanskrit, Urdu, Oriya, Polish, European and Brazilian Portuguese. IMAGACT4ALL has also been successfully used to implement language varieties, as European and American (Argentinian) Spanish. The first part of this paper presents the infrastructure and the methodology for mapping languages onto the ontology. In the second part we present the results of a comparative analysis of European and American Spanish data derived from the database, that show relevant distinctions in the referential properties of the Spanish verbal lexicon in the two language varieties.
Neural tree network/vector quantization probability estimators for speaker recognition

A new classification system for text-independent speaker recognition is presented. This system combines the output probabilities of distortion-based classifiers and a discriminant-based classifier. The distortion-based classifiers are the vector quantization (VQ) classifier and Gaussian mixture model (GMM). The discriminant-based classifier is the neural tree network (NTN). The VQ and GMM classifiers provide output probabilities that represent the distortion between the observation and the model. Hence, these probabilities provide an intraclass measure. The NTN classifier is based on discriminant training and provides output probabilities that represent an interclass measure. Since, these two classifiers base their decision on different criteria, they can be effectively combined to yield improved performance. Two combining methods are evaluated for several speaker recognition tasks, including speaker verification and closed set speaker identification. The results show the both methods to yield advantages for the speaker recognition tasks. <<ETX>>
Simulations of the DECT receiver with the decision feedback equaliser

The performance of different receiver structures of the DECT (Digital European Cordless Telecommunications) system is studied. The performance of the DFE-type equaliser is studied and compared with simpler structures. The adaptive DFE equaliser uses a rather simple VSLMS (variable step least mean square) algorithm and coherent detection. This combination has not previously been used in the DECT system. Non-coherent limiter-discriminator and differential detectors and the coherent MSK receiver are also implemented in the simulation environment for performance comparisons.
Design Structure Matrices for Software Development

In this dissertation we present our research into applications of Design Structure Matrices (DSMs) and related techniques in the context of software development. We pursue two research angles. On the one hand, we investigate whether the combination of DSMs and the Net Option Value (NOV) model can be used as a quantitative methodology to assess modularity in software design. In order to evaluate this methodology we introduce a tool to facilitate its use and we apply it to conduct a comparative assessment on aspect-oriented and object-oriented design pattern implementations. Based on these experiments we formulate critiques with regard to the applicability of the NOV model as a modularity metric for software. On the other hand, we investigate whether DSM diagrams can serve as a basis for a novel kind of support tool for software developers. We formulate requirements and a concrete approach for a DSM-based source-code browser for object-oriented software development. In order to validate this approach we present a prototype implementation of such a tool and we demonstrate how it can be used in real-world situations.
A MATURITY MODEL: DOES IT PROVIDE A PATH FOR ONLINE COURSE DESIGN?

Maturity models are successfully used by organizations attempting to improve their processes, products, and delivery. As more faculty include online course design and teaching, a maturity model of online course design may serve as a tool in planning and assessing their courses for improvement based on best practices. This article presents such a maturity model. Online education has moved to the mainstream of higher education and may surpass all other course delivery methods in quality in the near future. According to a survey of Chief Academic Officers of U.S. degree-granting institutions of higher education (Allen & Seaman, 2003), • 81% of all institutions of higher education offer at least one fully online or blended course. • 34% of the institutions offer complete online degree programs. • 67% say online education is a critical long-term strategy for their institution. This survey also suggests that academic leaders believe online education may be superior to face-to-face (F2F) education. The results show that • 75% expect learning outcomes for online education to be equal to or better than F2F
Expanding Mindshift Learning

This research-in-progress builds upon the mindshift learning theory by pursing a further understanding of why individuals have difficulties transitioning to new technologies. The theory is expanded by incorporating the circumstance when multiple shifts in mindset are needed as a result of the introduction of a new technology – a necessary contribution since information technology innovations are often accompanied by complementary organizational innovations. This is achieved by bridging the mindshift learning theory with the innovation literature and proposing a technology mindshift matrix consisting of two variables; degree of newness and degree of change. By incorporating the degree of change, the extent that a new technology impacts services and/or development processes, we expand the learning component of the theory. Research hypotheses are identified and a methodology is proposed to evaluate them. There are several implications that this research has for both practitioners and researchers. A pilot study will commence in the spring of 2008.
The arts and afterschool programs: A research synthesis.

Among all the fields of study in our schools, the arts are at the forefront in the celebration of diversity, individuality, and surprise: " the possibilities for growth in and through the arts cease only when we do " (Eisner, The Arts and the Creation of Mind, 2002). The artist endeavors: to see, to know, to shape, to show. It is also true that by following in the footsteps of the artist we are led: to see, to know, to shape, to show, and thus come to recognize, humble or small, the artist in us all (Booth & Hachiya, 2004).
Backpropagation algorithm for a generalized neural network structure

The author presents a generalized neural network structure and the associated backpropagation learning algorithm. This structure is an extension of a neural net structure presented by P.J. Werbos (1988, 1990). Generalization is aimed at ease of implementation and flexibility in structure selection. It is shown how certain network structures can be accommodated using this general structural form. Networks that are investigated include feedforward, recurrent, and memory networks. <<ETX>>
Community-based assisted organization of personal photographs

In this paper, we propose a content-based method the for semi-automatic organization of photo albums based on the analysis of how different users organize their own pictures. The goal is to help the user in dividing his pictures into groups characterized by a similar semantic content. The method is semi-automatic: the user starts to assign labels to the pictures and unlabeled pictures are tagged with proposed labels. The user can accept the recommendation or made a correction. To formulate the suggestions is exploited the knowledge encoded in how other users have parti- tioned their images. The method is conceptually articulated in two parts. First, we use a suitable feature representation of the images to model the different classes that the users have collected, second, we look for correspondences between the criteria used by the different users. Boosting is used to integrate the information provided by the analysis of multiple users. A quantitative evaluation of the proposed approach is obtained by simulating the amount of user interaction needed to annotate the albums of a set of members of the flickr ® photo-sharing community.
Review on Classification Algorithms in Data Mining

— Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Classification is a data mining technique based on machine learning which is used to classify each item in a set of data into a set of predefined classes or groups. Classification methods make use of mathematical and statistical techniques such as decision trees, linear programming, neural network and support vector machines. Classification is process of generalizing the data according to different instances. Several major kinds of classification algorithms including k-nearest neighbor, naïve bays, support vector machines and neural network. This paper provides an inclusive survey of different classification algorithms and their advantages and disadvantages.
Review of Incidence Studies of Tardive Dyskinesia Associated With Typical Antipsychotics

Unless researchers make an organized effort, patients with tardive dyskinesia are difficult to study and easily lost to follow up. Because there is no treatment for tardive dyskinesia, investigators are obliged to study the natural history of the disorder and identify risk and prognostic factors to further understand pathophysiologic mechanisms and to guide prevention and treatment efforts. Abundant variability exists among incidence studies of tardive dyskinesia, depending to some extent on design issues. In this article, the design concepts of incidence and prevalence studies are described, along with results, methodological problems, and identified risk factors in various tardive dyskinesia incidence studies involving the use of typical antipsychotic medications.
Fuzzy Controled QoS for Scalable Cloud Computing Services

An important characteristic of cloud infrastructures is scalability on demand. A scalability service monitors performance load metrics and decides to scale up or down, by provision or revoke of cloud resources. This could guarantee Quality of Service (QoS) and enforce Service Level Objectives (SLOs). The approach of this paper shows that with additional imprecise information (e.g. expected daytime performance) the up and down scale mechanism of such an infrastructure can be improved and SLA violation can be avoided. Keywords—Cloud Computing; Scaling Service; Fuzzy Logic; SLA; QoS