Miracle-dz/UniFAR-FTUs huggingface dataset:
FACTORS INFLUENCING USER INVOLVEMENT IN BUSINESS PROCESS MODELLING : A FRAMEWORK

The process models are one of the most common approaches employed during the early phases of Information System Development (ISD). Process Modelling is an approach that visually describes how a business operates by defining the business processes, entities, activities, business opportunities and threats. Most ISD projects fail due to factors which include user and stakeholder involvement issues. Although various perspectives and criteria have been employed by researchers to study the factors that influence user involvement during ISD, reported evidence on involvement factors for Business Process Modelling is still limited and requires further study. The current framework is lacking in factors that are related to the process modelling domain. The objective of this paper is to discuss factors affecting user involvement in business process modelling mainly in ISD projects. We proposed a framework for user involvement in business process modelling based on previous models. The framework combines five elements; user, project, process, modeller and interaction. A case study was conducted to validate the proposed framework. The findings show that the framework is applicable in establishing user involvement before and during initial business process modelling implementation to ensure its success. The identified influencing factors includes user intention towards participation, attitude, interest, commitment, experience, perceived ease of user participation, project importance, benefits, outcome, visibility, complexity and recognition, process efficiency, process learning and innovation, process clarity, modeller satisfaction and expertise, interaction quality and nature. This framework offers guidelines for user involvement prior to the implementation of Business Process Modelling in the future.
Streaming Media over the Internet — an overview of delivery technologies

This article reviews the basic concepts of media streaming over the Internet, particularly those associated with Internet Protocols (IP) and server technologies. The article concentrates on the delivery aspects of audio and video over the Internet but does not cover audio and video compression schemes nor media capturing/authoring/ publishing tools in any detail. These aspects will be covered in future editions of EBU Technical Review.
Using Eclipse in building model-driven e-Learning supports

In this paper we discuss the role of modelling and of EMF (Eclipse Modeling Framework) and GMF (Eclipse Graphical Modeling Framework) in the design and development of supports for eLearning applications. A model driven approach to eLearning applications is presented, rooted on the explicit representation of the model of the didactic content, based on a metamodel language expressed in Ecore. Besides exploiting the integration of Java, XML and UML provided by EMF, we introduce a Prolog-based representation of models as a more convenient way to overcome the gap between the user level and the technology level and as a mean to reduce the cost of design and implementation of learning strategies. Working in a MDA perspective, the tool exploits GMF and Jet (Java Emitter Templates) to produce code for a knowledge based, SCORM® compliant Platform Specific Model. The content model provides the Platform Independent Model that allows to achieve the intended behavioural semantics.
Quark Propagator and Meson Correlators in the QCD Vacuum

Equal time, point to point correlation functions for spatially separated meson currents are calculated with respect to a variational construct for the ground state of QCD. Given such an ansatz we make no further approximations in the evaluation of the correlators. Our calculations for the vector, axial vector and scalar channels show qualitative agreement with the phenomenological predictions, whereas the pseudoscalar channel does not. However, the pseudoscalar correlator, when approximated by saturating with intermediate one pion states agrees with results obtained from spectral density functions parameterised by pion decay constant and $ $ value obtained from chiral perturbation theory. We discuss this departure in the pseudoscalar channel, in context of the quark propagation in the vacuum.
Obstacle Detection in Unstructured Environment with a Single Camera and Linear Method

Obstacle detection is very crucial for mobile robots. The algorithm presented in this paper makes use of motion cues in the video streams. Firstly, we calculate optical flow at feature points. Then rotation of the camera and FOE (focal of expansion) are evaluated separately. Rotation and FOE value are refined according to an iterative linear method. Finally, we get inverse TTC (time to contact) with rotation and FOE and detect the obstacles in the scene. The algorithm doesn't need any assumption that the ground is flat or partially flat as the conventional methods. So it is suitable for outdoor unstructured environment. Experiment results show that our algorithm works well on different kinds of terrains
Contextual query classification in web search

There has been an increasing interest in exploiting multiple sources of evidence for improving the quality of a search engine’s results. User context elements like interests, preferences and intents are the main sources exploited in information retrieval approaches to better fit the user information needs. Using the user intent to improve the query specific retrieval search relies on classifying web queries into three types: informational, navigational and transactional according to the user intent. However, query type classification strategies involved are based solely on query features where the query type decision is made out of the user context represented by his search history. In this paper, we present a contextual query classification method making use of both query features and the user context defined by quality indicators of the previous query session type called the query profile. We define a query session as a sequence of queries of the same type. Preliminary experimental results carried out using TREC data show that our approach is promising.
A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems

User simulation is essential for generating enough data to train a statistical spoken dialogue system. Previous models for user simulation suffer from several drawbacks, such as the inability to take dialogue history into account, the need of rigid structure to ensure coherent user behaviour, heavy dependence on a specific domain, the inability to output several user intentions during one dialogue turn, or the requirement of a summarized action space for tractability. This paper introduces a data-driven user simulator based on an encoder-decoder recurrent neural network. The model takes as input a sequence of dialogue contexts and outputs a sequence of dialogue acts corresponding to user intentions. The dialogue contexts include information about the machine acts and the status of the user goal. We show on the Dialogue State Tracking Challenge 2 (DSTC2) dataset that the sequence-to-sequence model outperforms an agenda-based simulator and an n-gram simulator, according to F-score. Furthermore, we show how this model can be used on the original action space and thereby models user behaviour with finer granularity.
A Model of Consumer Engagement, Brand Awareness and Brand Loyalty: Analysis of Mediation and Feedback Loop Effect

The purpose of this study is to develop a conceptual model that links the concepts of Online Consumer Engagement (OCE) to Brand Relationship Quality (BRQ). Primarily, it aims to develop understanding of the significance of this relationship with and without a mediating variable i.e. Brand Awareness. It further seeks to broaden knowledge of a strong BRQ’s positive outcomes by incorporating Word of Mouth and Brand Loyalty to the model and to analyze the iterative quality of Brand Loyalty. This study makes use of Structural Equation Modeling technique in order to carry out Confirmatory Factor Analysis (CFA), Path Analysis and Mediation Analysis. The findings revealed that all the relationships in the model were significant and were, therefore, accepted. The mediation analysis indicated that Brand Awareness partially mediates and strengthens the relationship between OCE and BRQ. Brand Loyalty’s iterative quality revealed existence of feedback loop from Brand Loyalty to Online Consumer Engagement.
From B[e] to A[e]. On the peculiar variations of the SMC supergiant LHA 115-S 23 (AzV 172)

Context. Optical observations from 1989 of the Small Magellanic Cloud (SMC) B[e] supergiant star LHA 115–S 23 (in short: S 23) revealed the presence of photospheric Hei absorption lines, classifying S 23 as a B8 supergiant. In our high-resolution optical spectra from 2000, however, we could not identify any Hei line. Instead, the spectral appearance of S 23 is more consistent with the classification as an A1 supergiant, maintaining the so-called B[e] phenomenon. Aims. The observed changes in spectral behaviour of S 23 lead to different spectral classifications at different observing epochs. The aim of this research is, therefore, to find and discuss possible scenarios that might cause a disappearance of the photospheric Hei absorption lines within a period of only 11 years. Methods. From our high-resolution optical spectra, we perform a detailed investigation of the different spectral appearances of S 23 based on modern and revised classification schemes. In particular, we derive the contributions caused by the interstellar as well as the circumstellar extinction self-consistently. The latter is due to a partly optically thick wind. We further determine the projected rotational velocities of S 23 in the two epochs of spectroscopic observations. Results. Based on its spectral appearance in 2000, we classify S 23 as A1 Ib star with an effective temperature of about 9000 K. This classification is supported by the additional analysis of the photometric UBV data. An interstellar extinction value of $E(B-V) \simeq 0.03$ is derived. This is considerably lower than the previously published value, which means that, if the circumstellar extinction due to the stellar wind is neglected, the interstellar extinction, and hence the luminosity of the star, are overestimated. We further derive a rotation velocity of $\nu \sin i \simeq 150$ km s -1 , which means that S 23 is rotating with about 75% of its critical speed. The object S 23 is thus the fourth B[e] supergiant with confirmed high projected rotational velocity. The most striking result is the apparent cooling of S 23 by more than 1500 K with a simultaneous increase of its rotation speed by about 35% within only 11 years. Since such a behaviour is excluded by stellar evolution theories, we discuss possible scenarios for the observed peculiar variations in S 23.
Motion Segmentation by Multi-stage Aane Classiication

We present a multi-stage aane motion segmentation method which combines the beneets of the dominant motion and block-based aane modeling approaches. In particular, we propose two key modiications to a recent motion segmentation algorithm developed by Wang and Adelson 1]: i) the adaptive k-means clustering step is replaced by a merging step, whereby the aane parameters of a block which has the smallest representation error, rather than the respective cluster center, is used to represent each layer, and ii) we implement it in multiple stages, where pixels belonging to a single motion model are labeled at each stage. Performance improvement due to the proposed modiications is demonstrated on real video frames.
Modeling trichromatic color appearance with only two spectrally distinct photopigments

Protanomaly is a common abnormality of color vision in which the long wavelength sensitive cone is lacking. Protanomalous observers are trichromatic with reduced ability to discriminate colors in the red-green part of the spectrum because the photopigments that mediate discrimination in this range are abnormally similar differing in one of two ways; first, by only a few nanometers in peak sensitivity, or second, differing only in optical density. We present a functional model that estimates the color appearance and spectral color boundaries for these two types of protanomaly. The model predicts rather similar appearance of Munsell color chips but radically different spectral color characteristics for the protanomalous observers compared to normal color observers.
A testing framework for background subtraction algorithms comparison in intrusion detection context

Identifying objects from a video stream is a fundamental and critical task in many computer-vision applications. A popular approach is the background subtraction, which consists in separating foreground (moving objects) from background. Many methodologies have been developed for automatic background segmentation but this fundamental task is still challenging. We focus here on a particular application of computer vision: intrusion detection in video surveillance. We propose in this paper a multi-level methodology for evaluating and comparing background subtraction algorithms. Three levels are studied: first, pixel level to evaluate the accuracy of the segmentation algorithm to attribute the right class to each pixel. Second, image level, measuring the rate of right decision on each frame (intrusion vs no intrusion) and finally sequence level, measuring the accordance with the time span where objects appear. Moreover, we also propose a new similarity measure, called D-Score, adapted to the context of intrusion detection.
Solar power supply for ZigBee wireles sensor network

Zigbee wireless sensor network is widely used in telemonitoring systems, one of which is telemonitoring system for environments. The sensor nodes are placed in areas in which power source are not available. Hence, a system that can supply the energy use of energy resources available in the environment is required. This paper shows the solar power supply design for a node in ZigBee wireless sensor network. The power supply system uses two 10 watt solar cells in parallel circuit with a maximum voltage of 22 volts DC and 1400 mA maximum current and two batteries with a voltage of 12 Vdc and a maximum capacity of 7.2 Ah. An optimal charging process requires ± 14 Vdc voltage and battery charging time with the input current of ± 1 ampere is 9–10 hours. Taking around from 16 to 18 hours, the process of discharging the battery with a maximum current consumption of the device uses 300 mA Zigbee.
Epoch extraction from emotional speech

This work proposes a modified zero frequency filtering (ZFF) method for epoch extraction from emotional speech. Epochs refers the instants of maximum excitation of the vocal tract. In the conventional ZFF method, the epochs are estimated by trend removing the output of the zero frequency resonator (ZFR) using the window length equal to the average pitch period of the utterance. Use of this fixed window length for the epoch estimation causes spurious or missed estimation from the speech signals having rapid pitch variations like in emotional speech. This work therefore proposes a refined ZFF method for epoch estimation by trend removing the output of ZFR using the variable windows obtained by finding the average pitch periods for every fixed blocks of speech and low pass filtering the resulting trend removed signal segments using the estimated pitch as the cutoff frequency. The epoch estimation performance is evaluated for five different emotions in the German emotional speech corpus having simultaneous electro-glotto graph (EGG) recordings. The improved epoch estimation performance indicates the robustness of the proposed method against rapid pitch variations in emotional speech signals. The effectiveness of the proposed method is also confirmed by the improved epoch estimation performance on the Hindi emotional speech database.
Correlated MIMO Rayleigh Fading Channels: Capacity, Optimal Signaling, and Asymptotics 1

The capacity of the MIMO channel is investigated under the assumption that the elements of the channel matrix are zero mean proper complex Gaussian random variables with a general correlation structure. It is assumed that the receiver knows the channel perfectly but that the transmitter knows only the channel statistics. The analysis is carried out using an equivalent virtual representation of the channel that is obtained via a spatial discrete Fourier transform. It is shown that in the virtual domain, the capacity achieving input vector consists of independent zero-mean proper complex entries, whose variances can be computed numerically. Furthermore, in the asymptotic regime of low signal-to-noise ratio (SNR), it is shown that beamforming along one virtual transmit angle is asymptotically optimal. Necessary and sucien t conditions for the optimality of beamforming are also derived. Finally, the capacity is investigated in the asymptotic regime where the number of receive and transmit antennas go to innit y, with their ratio kept constant. Using a result of Girko, an expression for the asymptotic capacity scaling with the number of antennas is obtained in terms of the two-dimensional spatial scattering function of the channel.
Creative workplace behavior

This study conducted a questionnaire survey (N = 2938) examining the relationship between office behavior and creativity. The analysis showed a positive correlation between creativity and three behavioral characteristics (collaboration, flexibility, and demonstration) in an office environment. However, these correlations vary depending on individuals’ personalities. In particular, those with creative personalities cannot augment their creativity if the office does not possess extraordinarily high levels of the three behavioral characteristics as creativity cannot be enhanced very much in an office with only a medium level of these characteristics. At the same time, those who do not have a creative personality can increase their creativity even when they are in an office with a medium level of the three behavioral characteristics. However, turning an office into a place with a) Graduate School of Economics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, inamizu@e.u-tokyo.ac. jp A version of this paper was presented at the ABAS Conference 2018 Winter (Inamizu, 2018). © 2018 Nobuyuki Inamizu. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Annals of Business Administrative Science 17 (2018) 69–82 http://doi.org/10.7880/abas.0180309a Received: March 9, 2018; accepted: April 4, 2018 Published in advance on J-STAGE: April 11, 2018
Expression invariant face recognition using contourlet transform

The performance of many state-of-the-art expression invariant face recognition systems hampers when fewer faces are available in the gallery. This paper addresses the issue of expression invariant face recognition with small gallery set. The contourlet transform is an established tool for capturing contour-like edges. The contourlet transform generates prominent features as its local and directional properties have strong resemblance with human visual cortex. We have proposed a novel approach that fuses the features from spatial domain and contourlet transform domain. Feature extraction is performed by employing the LBP and WLD descriptors. The experiments are performed on the JAFFE face database and the Yale face database. The results indicate that the proposed feature level fusion approach yields a robust feature vector and exemplary recognition rates.
Probabilistic Aspect Mining Model for Drug Reviews

Recent findings show that online reviews, blogs, and discussion forums on chronic diseases and drugs are becoming important supporting resources for patients. Extracting information from these substantial bodies of texts is useful and challenging. We developed a generative probabilistic aspect mining model (PAMM) for identifying the aspects/topics relating to class labels or categorical meta-information of a corpus. Unlike many other unsupervised approaches or supervised approaches, PAMM has a unique feature in that it focuses on finding aspects relating to one class only rather than finding aspects for all classes simultaneously in each execution. This reduces the chance of having aspects formed from mixing concepts of different classes; hence the identified aspects are easier to be interpreted by people. The aspects found also have the property that they are class distinguishing: They can be used to distinguish a class from other classes. An efficient EM-algorithm is developed for parameter estimation. Experimental results on reviews of four different drugs show that PAMM is able to find better aspects than other common approaches, when measured with mean pointwise mutual information and classification accuracy. In addition, the derived aspects were also assessed by humans based on different specified perspectives, and PAMM was found to be rated highest.
Compiling actions by partial evaluation

Compiler generation based on Mosses’ action semantics has been studied by Brown, Moura, and Watt, and also by the second author. The core of each of their systems is a handwritten action compiler, producing either C or machine code. We have obtained an action compiler in a much simpler way: by partial evaluation of an action interpreter. Even though our compiler produces Scheme code, the code runs as fast as that produced by the previous action compilers.
Program evolution and its impact on software engineering

Large scale, widely used programs such as operating systems are never complete. They undergo a continuing evolutionary cycle of maintenance, augmentation and restructuring to keep pace with evolving usage and implementation technologies. The paper provides quantitative evidence, from widely different environments, of the existence and nature of this evolutionary process. Interpretations and possible significance of some of the observed phenomena are discussed. Some implications for software engineering and for project planners and managers are noted.
Clinical applications of optical coherence tomography in the posterior pole: the 2011 José Manuel Espino Lecture - Part I

Optical coherence tomography (OCT) is now a standard of care in ophthalmology and is considered essential for the diagnosis and monitoring of many retinal diseases. One of the major advances obtained with OCT was the understanding of the pathophysiology of macular holes. Non-full-thickness macular holes have been revisited because high-resolution OCT images can detect a lamellar macular defect that is not always visible clinically, and surgery has been advocated by some authors. OCT can be valuable in determining the need for and/or timing of surgical intervention on epiretinal membranes or vitreomacular traction syndrome. In addition, we can use this technology as a predictive factor in the prognosis and follow-up of the most common posterior pole pathologies.