Miracle-dz/UniFAR-FTUs huggingface dataset:
DISPOSITIONAL EMPLOYABILITY IN ONLINE UNIVERSITY: UPDATING FACTORS ON RESILIENCE, MOTIVATION, AND SOCIAL CAPITAL

The main objective of this paper is to analyse the current factors related to dispositional employability in online university. Specifically, it takes into account the Fugate's five latent factors (2006): 1) openness to changes at work; 2) work and career resilience; 3) work and career proactivity; 4) career motivation; and 5) work identity. On this basis, it uses a survey data developed with 930 online students at the Open University of Catalonia in 2016. Exploratoy Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) done with 30 variables prove (p=0.000; CFI=0.92; TLI=0.91; RMSEA=0.07; SRMR=0.06) that there are eight latent factors related to dispositional employability: i) three factors are identical to Fugate's proposal: openness to changes at work; work and career proactivity; and work identity; ii) work and career resilience is divided in two new factors, which are related to self-concept, and employment security; iii) career motivation is divided in two new factors, which are related to organisational culture, and knowledgerelated jobs; and iv) there is a new factor related to social capital, which is related to collaboration skills. Results allow us to detect that dispositional employability needs to be adapted to new topics related to career development and lifelong e-learning for employability, such as the organisations' practices and knowledge workers' interests. Moreover, the analysis highlights the importance of social capital. Consequently, the research concretes dimensions that lifelong online learning has to take into account for developing the dispositional employability of its students.
Determining Surface Orientation by Uni-directional Photometric Flow Fields

In the Shape from Shading methods, an extended approach named Uni-directional Photometric Flow Fields is proposed in this paper. The reflectance property of the surface material is the uniform perfect diffuser, and the light source produces a uniform parallel beam and the illuminating direction is varied slightly only in the azimuth direction. The local surfacc orientation can be determined from the image density and its directional derivatives. It is shown that the zenith angle of the illuminating direction is also determined analytically. The errors caused by calculating the directional derivatives using the numerical differences from three input image densities are evaluated, and the effectiveness of the shape reconstruction algorithm is demonstrated by computer simulations.
PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments

The goal of this article is to introduce a collaborative clustering approach to the domain of ubiquitous knowledge discovery. This clustering approach is suitable in peer-to-peer networks where different data sites want to cluster their local data as if they consolidated their data sets, but which is prevented by privacy restrictions. Two variants exist, i.e. one for data sites with the same observations but different features and one for data sites with the same features but different observations. The technique contains two parts, i.e. a collaborative fuzzy clustering technique and a particle swarm optimization to optimize the collaboration between data sites. Empirical analysis show how and when this PSO-CFC approach outperforms local fuzzy clustering.
A Context-Aware Approach for Dynamic GUI Testing of Android Applications

In this paper, we propose an automatic GUI testing approach for Android applications. With the goal of exploring unexecuted event handlers of the application under test as quickly possible, our approach constructs and maintains a dynamic GUI model of the application at run time, which is based on extended non-deterministic labelled transition system that records the weight of transitions between GUI states. Extracting only part of the GUI features of the application under test, the model keeps itself simple enough to avoid state explosion and improves the test efficiency, but provides targeted guidance for testing event generation at the same time. A practical probabilistic search-based event selection algorithm is used to leverage information provided by the model, transform weight of transitions to priority of candidate events, and select the testing event to execute. The algorithm solves the non-deterministic problem introduced by the approximation of the model. Empirical evaluation on several real world applications shows that our approach can achieve high code coverage quickly and detect bugs efficiently.
Configuring business process models

A configurable business process model (sometimes referred to as a reference business process model) may be configured to meet the specific requirements of an organization. The configuration activity is required to automatically determine the variability of a configurable process model and ensure the correctness of a specific process model. However, few approaches solve the problem. In this paper, we propose an innovative approach for automatically separating a configurable process model into atomic and correct sub-process models (sub-process models without abnormal behavioral problems). The atomic sub-process models that fulfill specific requirements are merged into specific process models that are provided for organizations. Compared with existing approaches, since the configuration activity is incorporated into the verification process of a process model at design time and can obtain all feasible configurations, our approach avoids independently handling the configuration activity and does not suffer from computational complexity. Moreover, our approach is language-independent. We have developed a prototype tool for configuring these process models.
Application of fuzzy multi-objective decision making in spatial load forecasting

Electric distribution system planning is to provide an economic expansion plan to meet the future demands in its territory. A forecast of the future electric demand and its geographic distribution is a prerequisite for distribution planning. The quality and accuracy of this forecast have a large influence on the quality of the electrical distribution system planning. Spatial load forecasting emerges to provide a more accurate prediction of both the magnitudes and locations of future electric loads. Since the load growth pattern is dominated by its land-use (residential, commercial, or industrial), the land usage study of small area is important to capture the future loads accurately. There are many factors which will affect the customer land-use decision, for example, distance to highway, distance to urban pole, and the costs. The customer's preferences can be estimated based on these objective factors. Then the land utilization and the electricity consumption can be estimated. Since the objectives sometimes are conflicting with each other, it can be cumbersome to use conventional cost function approach to determine the land usage decision. This paper applies a fuzzy multi-objective decision making scheme to the urban redevelopment and spatial load forecasting, which is more naturally and straight forwardly used to handle the spatial load forecasting problem. An example is used to illustrate the proposed methodology.
Felix: Implementing Traffic Measurement on End Hosts Using Program Analysis

Network measurement is an essential component of many SDN applications, but most existing controller platforms force programmers to implement measurement tasks by installing fine-grained forwarding rules on switches---an approach that significantly increases configuration and management complexity. This paper proposes a radically different approach: rather than implementing measurement tasks directly on network switches, we argue for pushing measurement to the edge and utilizing the abundant resources available on end hosts. At a technical level, our approach is based on two key ideas: (i) we express measurement tasks using programs in a high-level, declarative query language, and (ii) we use program analysis to calculate predicates that can be used to answer queries at the edge of the network. We present an implementation of our approach on top of the NetKAT framework, we develop case studies illustrating the benefits of our approach, and we conduct experiments that quantify performance on realistic benchmarks.
Prefrontal Set Activity Predicts Rule-Specific Neural Processing during Subsequent Cognitive Performance

Prefrontal neurons have been shown to represent task rules. Here we show the mechanisms by which the rule-selective activity in the prefrontal cortex influences subsequent cognitive performance based on that rule. Using functional magnetic resonance imaging, we found that the frontopolar cortex interacted with posterior areas differently depending on whether subjects were going to perform a phonological or semantic task. Moreover, we found that the sustained “set” activity in this region predicted the activity that could be recorded in the posterior areas during the performance, as well as the speed of that performance. We argue that the prefrontal set activity does not reflect simple maintenance of the task rules but the process of implementing the rule for subsequent cognitive performance and that this is done through rule-selective interactions with areas involved in execution of the tasks.
Evaluating Teacher Preparation Programs With Teacher Evaluation Ratings: Implications for Program Accountability and Improvement

States are incorporating evaluation ratings into new, multioutcome teacher preparation program (TPP) evaluation systems, yet little is known about the relationships between TPPs and the evaluation ratings of program graduates. To address this gap, we use teachers’ ratings on the North Carolina Educator Evaluation System to determine whether TPPs are associated with the evaluation ratings of their initially prepared teachers. We find that (a) teachers from certain TPPs have significantly different evaluation ratings than graduates of other programs; (b) it is important to adjust for elements of school context when analyzing the evaluation ratings of program graduates; (c) evaluation ratings are a function of preparation quality, not just selection into programs; and (d) evaluation ratings provide evidence on the performance of TPPs that is distinct from value-added. Our results indicate that evaluation ratings can be a valuable component of TPP evaluation systems and provide guidance for incorporating ratings into such systems.
Characterization of affective states by pupillary dynamics and autonomic correlates

With the recent advent of new recording devices and an easier access to signal processing tools, researchers are increasingly exploring and studying the Pupil Dilation (PD) signal. Recently, numerous studies pointed out the relations between PD dynamics and psychophysiological states. Although it is well known that PD is controlled by the Autonomic Nervous System (ANS), and ANS responses are related to emotional events/stimuli, the relationship between emotional states and PD is still an open issue. The aim of this study is to define the statistical properties of the PD signal, to understand its relation with ANS correlates such as Heart Rate Variability (HRV) and respiration (RESP), and to explore if PD could provide information for the evaluation of the psychophysiological response of ANS to affective triggering events. ECG, RESP, and PD data from 13 normal subjects were recorded during a memory recall paradigm, and processed with spectral and cross-spectral analysis. Our results demonstrate that variability indices extracted from fast PD oscillations, not observable through standard cardiorespiratory identification in the frequency domain, would be able to discern psychophysiological responses elicited by basic emotional stimuli. A strong linear coupling was found between the variables, due to the influence of RESP on both PD and HRV within the High Frequency (HF) band, from 0.15 to 0.45 Hz. Most importantly, our results point at PD features as possible candidates for characterizing basic emotional stimuli.
Exploiting Online Discussions in Collaborative Distributed Requirements Engineering

Large, distributed software development projects, like Open Source Software (OSS), adopt different collaborative working tools, including online fo- rums and mailing list discussions that are valuable source of knowledge for re- quirements engineering tasks in software evolution, such as model revision and evolution. In our research, we aim at providing tool support for retrieving infor- mation from these online resources, and for analyzing it. The solution we propose combines natural language processing techniques, machine learning, statistical and search based techniques to address two key problems, namely the so called expert finding problem, and the problem of identifying requests for changing re- quirements or soliciting new requirements, by exploiting online discussions. In this paper, we describe the solution approach set up so far with the help of an OSS scenario, discuss some preliminary evaluation, and highlight future work.
Handling nonnegative constraints in spectral estimation

We consider convex optimization problems with the constraint that the variables form a finite autocorrelation sequence, or equivalently, that the corresponding power spectral density is nonnegative. This constraint is often approximated by sampling the power spectral density, which results in a set of linear inequalities. It can also be cast as a linear matrix inequality via the positive-real lemma. The linear matrix inequality formulation is exact, and results in convex optimization problems that can be solved using interior-point methods for semidefinite programming. However, these methods require O(n/sup 6/) floating point operations per iteration, if a general-purpose implementation is used. We introduce a much more efficient method with a complexity of O(n/sup 3/) FLOPS per iteration.
Automatic Scoring of Pronunciation Quality with Hybrid Measure

In this paper, a hybrid measure for automatic scoring of Mandarin pronunciation quality is presented. Different to prevalent algorithms, mono-phone-based and tri-phone-based acoustic models are applied and two types of features are combined to get the score of “Goodness of Pronunciations” with Support Vector Machine algorithm, which are the average logarithm of the frame-based posterior probability and the normalized logarithm of the phoneme-based posterior probability. With the hybrid measure, the average correlation coefficient between machine scores from automatic system and the rater scores is improved from 0.8379 to 0.8549 which almost reach the coefficient 0.8564 between different raters.
Dynamic Clone Sharing Scheme in Mobile Cloud Computing: A Delaunay Triangulation Approach

Owing to the nature of elastic resource pooling, cloud computing offers a great opportunity to extend the potentials of Mobile Devices (MDs) for supporting the resources-hungry applications. In the mobile cloud platforms, each MD is associated with a system-level clone in the cloud infrastructure. It provides the potentials in supporting the clone sharing. The clone sharing can reduce the communication delay between an MD and its clone when the MD moves far away from its clone. It is because that clone sharing can migrate the service from the remote source clone to a nearby target clone. Considering the mobility of MDs, how to select the appropriate target clone is still a big challenge. In this paper, we first pose the clone sharing question. The clone sharing can reduce the service delay between a MD and its clone when the MD move far away from its clone. It is because that it migrate the service from the remote source clone to a local target clone. However, how to select the appropriate target clone is a big challenge. We propose a clone sharing scheme, called as Triangulation-based Clone Sharing Scheme (TCS), to solve the problem. In the TCS scheme, we firstly disclose the relationship between clone serving area and the MD. Secondly, the TCS scheme splits the clone network into Triangle topology by our Clone Triangulation Topology Construction and Reconstruction (CTTCR) algorithm. It can accurately obtain the target clone for clone migration. We also prove the correctness of CTTCR algorithm. Simulation results show that our scheme works very well.
Peirce: an Algorithm for Abductive Reasoning Operating with a Quaternary Reasoning Framework

Abductive reasoning algorithms formulate possible hypotheses to explain observed facts using a theory as the basis. These algorithms have been applied to various domains such as diagnosis, planning and interpretation. In general, algorithms for abductive reasoning based on logic present the following disadvantages: (1) they do not allow the explicit declaration of conditions that may affect the reasoning, such as intention, context and belief; (2) they allow little or no consideration for criteria required to select good hypotheses. Using Propositional Logic as its foundation, this study proposes the algorithm Peirce, which operates with a framework that allows one to explicitly include conditions to conduct abductive reasoning and uses a criterion to select good hypotheses that employs metrics to define the explanatory power and complexity of the hypotheses. Experimental results suggest that abductive reasoning performed by humans has the tendency to coincide with the solutions computed by the algorithm Peirce.
A parametrized three-dimensional model for MEMS thermal shear-stress sensors

This paper presents an accurate and efficient model of MEMS thermal shear-stress sensors featuring a thin-film hotwire on a vacuum-isolated dielectric diaphragm. We consider three-dimensional (3-D) heat transfer in sensors operating in constant-temperature mode, and describe sensor response with a functional relationship between dimensionless forms of hotwire power and shear stress. This relationship is parametrized by the diaphragm aspect ratio and two additional dimensionless parameters that represent heat conduction in the hotwire and diaphragm. Closed-form correlations are obtained to represent this relationship, yielding a MEMS sensor model that is highly efficient while retaining the accuracy of three-dimensional heat transfer analysis. The model is compared with experimental data, and the agreement in the total and net hotwire power, the latter being a small second-order quantity induced by the applied shear stress, is respectively within 0.5% and 11% when uncertainties in sensor geometry and material properties are taken into account. The model is then used to elucidate thermal boundary layer characteristics for MEMS sensors, and in particular, quantitatively show that the relatively thick thermal boundary layer renders classical shear-stress sensor theory invalid for MEMS sensors operating in air. The model is also used to systematically study the effects of geometry and material properties on MEMS sensor behavior, yielding insights useful as practical design guidelines.
Self-aware framework for adaptive augmented reality

In the dynamic environments of UAR, software is expected to be modifiable to accommodate for new user needs, expectations, and changing environments. Reflective middleware provides, in a certain way, an open implementation and presents an efficient alternative to deal with highly dynamic environments. The present article describes an implementation of Reflective Middleware based on an object oriented metamodel and its application in the field of AR. Along the way, it introduces support services that deterministically apply to every component, even those yet to be engineered. These services have been defined as Generic Services, the process of creating generic services is demonstrated with various examples, showing their advantages as regards to issues of code reduction and flexibility.
Word Clustering Based on Un-LP Algorithm

Word clustering which generalizes specific features cluste r words in the same syntactic or semantic categories into a group. It is an effective approach to re duce feature dimensionality and feature sparseness which are clearly useful for many NLP applications. This paper proposes an unsupervised label propagation algorithm (Un-LP) for word clustering which uses multi-exemplars to represent a cluster. Experiments on a synthetic 2D dataset show the strong ability of selfcorrecting of the proposed algorithm. Besides, the experimental results on 20NG demonstrate that our algorithm outperforms the conventional cluster algorithms.
A cutting process for random mappings

In this paper we consider a cutting process for random mappings. Specifically, for 0 < m < n, we consider the initial (uniform) random mapping digraph Gn on n labeled vertices, and we delete (if possible), uniformly and at random, m noncyclic directed edges from Gn. The maximal random digraph consisting of the unicyclic components obtained after cutting the m edges is called the trimmed random mapping and is denoted by G nm . If the number of noncyclic directed edges is less than m, then G nm consists of the cycles, including loops, of the initial mapping Gn. We consider the component structure of the trimmed mapping G nm . In particular, using the exact distribution we determine the asymptotic distribution of the size of a typical random connected component of G nm as n, m → ∞. This asymptotic distribution depends on the relationship between n and m and we show that there are three distinct cases: (i) $m=o(\sqrt{n})$ , (ii) $m=\beta\sqrt{n}$ , where β > 0 is a fixed parameter, and (iii) $\sqrt{n}=o(m)$ . This allows us to study the joint distribution of the order statistics of the normalized component sizes of G nm . When $m=o(\sqrt{n})$ , we obtain the Poisson–Dirichlet(1/2) distribution in the limit, whereas when $\sqrt{n}=o(m)$ the limiting distribution is Poisson–Dirichlet(1). Convergence to the Poisson–Dirichlet(θ) distribution breaks down when $m=O(\sqrt{n})$ , and in particular, there is no smooth transition from the ${\cal P}$D(1/2) distribution to the ${\cal P}$D(1) via the Poisson–Dirichlet distribution as the number of edges cut increases relative to n, the number of vertices in Gn. © 2006 Wiley Periodicals, Inc. Random Struct. Alg. , 30, 287–306, 2007
Tentacle Transcriptome and Venom Proteome of the Pacific Sea Nettle, Chrysaora fuscescens (Cnidaria: Scyphozoa)

Jellyfish venoms are rich sources of toxins designed to capture prey or deter predators, but they can also elicit harmful effects in humans. In this study, an integrated transcriptomic and proteomic approach was used to identify putative toxins and their potential role in the venom of the scyphozoan jellyfish Chrysaora fuscescens. A de novo tentacle transcriptome, containing more than 23,000 contigs, was constructed and used in proteomic analysis of C. fuscescens venom to identify potential toxins. From a total of 163 proteins identified in the venom proteome, 27 were classified as putative toxins and grouped into six protein families: proteinases, venom allergens, C-type lectins, pore-forming toxins, glycoside hydrolases and enzyme inhibitors. Other putative toxins identified in the transcriptome, but not the proteome, included additional proteinases as well as lipases and deoxyribonucleases. Sequence analysis also revealed the presence of ShKT domains in two putative venom proteins from the proteome and an additional 15 from the transcriptome, suggesting potential ion channel blockade or modulatory activities. Comparison of these potential toxins to those from other cnidarians provided insight into their possible roles in C. fuscescens venom and an overview of the diversity of potential toxin families in cnidarian venoms.