Miracle-dz/UniFAR-FTUs huggingface dataset:
Digitalization influence on shopping centers strategic management

Abstract Shopping centers are an important aspect of the extremely dynamic life of the 21st century consumer. Nowadays, worldwide, shopping centers represent significant multimillion euro investments, remarking themselves with bold architectures and structures and an individual brand image. Retail markets are currently under transformation by a confluence of new retailing technologies, dramatic shifts in the demographic profile and preferences of more affluent and educated shoppers. Part of the market share in modern retail moved to new forms of selling, which weakened the offer of some traditional retailers, while also creating opportunities for retailers to move and integrate their offerings with new digital tools (via electronic commerce, social commerce and mobile commerce). Meeting customer needs and increasing sales are the primary drivers behind retailers use of information systems and technology (IS/IT). New developments in IS/IT offer new opportunities and challenges for organizations and society. The current paper proposes to analyze the way digital media and information technology (computers and mobile devices) influences the virtual and physical shopping experiences, and shopping centers management consequently. The need for communication & sales channels integration, the impact of new information technologies (IT’s), the growing role of social / digital media, the balance between personalization and privacy – all represent challenges retail industry needs to currently adapt to face digitalization. E-commerce is transforming both consumer behavior and retailing business models, by greatly expanding shopping convenience, selection and affordability.
Subjective assessment of 3D still images using attention models

The subjective process associated with image qualit y evaluation is endorsed by human psychophysical and physiological measurements. In the human visual system (HVS), the visual attention (VA) is a crucial element, which q uickly identifies the notable regions of the images, subjectively linked to Regions of Inter est (ROI). These are represented by a binary mask, indicating whether a pixel in the corr esponding image belongs to the ROI. Subjective mechanisms dominate eye movements in the first two seconds of viewing and, due to the high relation between the eye actions an d the VA, eye-tracking tests are used to validate 2D attention models: eye movements are rec ord d and processed to generate a Fixation Density Map (FDM). In the 3D domain, an es s ntial factor for VA among the additional parameters of visual dimension is the sc ene depth. The main objective of this work was to study the im pact of a particular ROI on the subjective quality perception of 3D still images, c onsidering different types and level of noise (or distortion) in and out of the ROI. The 3D Gaze images and eye movement database, obtained from an eye tracking experiment d scribed in [1] and specifically created for performance evaluation of stereoscopic 3D attention models was used. Besides the full public availability, this database has ori ginal images in various HD sizes, all in PNG format and with natural content. Binary masks were generated for each FDM and differ ent types and intensities of noise was added to each corresponding image according to the ROIs. By using the generated binary masks and image pixels positions in the ROI, the noise was added in the image regions located inside or outside the ROI. Subjecti v esting with users observing the images and scoring their quality was done to verify the importance of these regions in the subjective quality evaluation. The images were clas sified according to whether the noise was added inside or outside the ROI, the noise type (Gaussian, Speckle), parameter values (intensity level) and the noisy image view (left or right). The results have shown that Gaussian noise has less impact on the quality than Speckle, with higher intensity level and also when the noise is added to the right view and inside the ROI. This is justified due to the fact that viewers fix their eyes over the ROI during more time, thus perceiving higher distortion. As the amo unt of different image content was small, the information about the dominant eye appea rs to be inconclusive. Changing some parameters is suggested for future works, in such a way that there is more certainty on the results without interferences between analyses. As this work was limited to data contained in the image (bottom-up visual interest), some conc epts related to the visualization context (top-down visual interest), such as rarity or surpr ise, may naturally be included in future works, as well as the characteristic of looking pri marily for human faces or humanoid things. Key-Words: Attention model, bottom-up, depth, eye tracking, FDM , ROI , still images, VA.
Data clustering and imputing using a two-level multi-objective genetic algorithm (GA): A case study of maintenance cost data for tunnel fans

Abstract This study develops a new two-level multi-objective genetic algorithm (GA) to optimise clustering to reduce and impute missing cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 uses a multi-objective GA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation (vk ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Our clustering model is validated using k-means clustering. Level 2 uses a multi-objective GA to impute the reduced missing cost data in volumeusing a valid data period. The optimal population has a low vk , 0.1%, and a high intensity, 99%. It has three cluster centres, and the highest data reduction is 27%. These three cluster centres have a suitable geometry, so the cost data can be partitioned into relevant contents to be redacted for imputing. Our model shows better clustering detection and evaluation than models using k-means. The percentage of missing data for the two cost objects is the following: labour 57%, materials 81%. The second level shows highly correlated data (R-squared 0.99) after imputing. Therefore, the study concludes multi-objective GA can cluster and impute data to derive complete data for forecasting.
Interacting With Story: Examining Transportation into Video Game Narrative

It has been over ten years since Green and Brock’s (2000) initial study of transportation theory; however, there is no research up to date on examining and comparing newly emerging, interactive video game narrative to written texts and films. Drawing upon Green and Jenkins’s (2014) newest conceptual work of interactive narrative, this study examined how interactive narrative brings difference in degree of transportation and character identification. Specifically, a quasiexperiment was conducted to compare how the 40 participants responded to either interactive or traditional narratives. The results of the experiment demonstrated that experience taking and need for cognition were significant predictor of transportation and character identification; it was also found that valence was significant predictor of character identification. No difference were found between interactive narrative and traditional narrative on transportation; however, difference between interactive narrative and traditional narrative on character identification was near significant, along with finding significant correlation between transportation and character identification. These findings suggest that character identification could be more important when audiences process interactive narrative. This study is the initial attempt to test Green and Jenkins’s (2014) conceptual work of interactive narrative and transportation especially in video game context. The
Labor Demand and Unequal Payments: Does Wage Dispersion Matter? Using German Employer-Employee Data to Analyze the Influence of Intra-Firm Wage Inequality on Labor Demand

Abstract A theoretical analysis examines the relationship between intra-firm wage dispersion and employment at establishments. The analysis relies on the absence of a theoretical consensus regarding the influence of wage dispersion on labor demand. To prove the theoretical considerations, regressions were conducted on German linked employer-employee data from the Institute for Employment Research (LIAB) for 1996 through 2008. More specifically, fractional probit models for the panel data and a fixed effects regression with a log-odds transformation of the dependent variable were used to estimate the share equations of a labor demand model, including different measures of wage dispersion. The results illustrate a negative influence of the residual wage inequality that takes into account the composition of the workforce in the establishment. In addition, an increasing wage dispersion at the lower end of the wage distribution decreases the labor demand of the establishment; however, this leads to the estimates of the overall wage dispersion becoming insignificant.
Understanding Creative Entrepreneurs’ Intention to Quit: The Role of Entrepreneurial Motivation, Creativity, and Opportunity

Although the contribution of creative industries to world economy has been widely acknowledged in the last few decades, their failure rate is still high and frustrates creative entrepreneurs. However, few empirical studies have been done to explore what causes creative entrepreneurs’ intention to quit. This paper, therefore, aims to draw upon the theories of motivation and entrepreneurship to offer a better understanding of entrepreneurs’ withdrawal intention. We incorporate the theory of motivation, creativity, and opportunity to realize their inter-play roles in reducing entrepreneurs’ intention to quit in creative industry sector. A sample of 272 creative entrepreneurs in Taiwan’s creative industries is used to test the framework we propose. Results show that entrepreneurial creativity and opportunity recognition enhance entrepreneurs’ motivation by satisfying the need for basic finance, need for work–life equilibrium, need for social reputation, and need for career achievement. Moreover, we found that entrepreneurs’ motivation in need for work–life equilibrium and need for career achievement reduces their intention to quit.
Defense Spending and Unemployment. Evidence from Southern European Countries

Abstract This paper applies the ARDL approach to cointegration in order to address the defense unemployment nexus. We use information on Portugal, Italy, Greece and Spain for the period 1960–2015. Our main results suggest that: (i) there is a stable long-run relationship between the variables under consideration for Portugal, Greece and Spain. (ii) Defense spending decreases (increases) unemployment for the case of Portugal and Greece (Spain), (iii) the impact of non-defense spending is weaker than that of defense spending and (iv) Okun’s law is validated for Portugal, Greece and Spain. (v) These results are robust to the use of heterogeneous panel cointegration and causality analysis.
Evaluating Cost Efficiency in Fuzzy Environment by Using Expected Value

Today, one of the most fundamental issues within the field of industrial and nonindustrial activities is evaluate the costs performance of the units which are associated with industrial and nonindustrial activities. Data envelopment analysis (DEA) is a nonparametric method for evaluating performance. Fuzzy sets theory is a powerful tool for mentioning ambiguous situations. Traditional DEA models cannot work with fuzzy data therefor there is a need for a method which can evaluate this type of activities. Yet, in fuzzy data envelopment analysis, there isn’t a powerful method which can evaluate cost efficiency in fuzzy environment. In this paper, a new methods for obtaining cost efficiency measurement with data set of fuzzy numbers in various conditions (variable return to scale and constant return to scale) is suggested. These consist of situations where prices are fuzzy numbers and unknown exactly at each decision making unit (DMU). All offered methods are applied in an assessment project and results are mentioned.
Adaptive registration using 2D and 3D features for indoor scene reconstruction.

Pairwise alignment between point clouds is an important task in building 3D maps of indoor environments with partial information. The combination of 2D local features with depth information provided by RGB-D cameras are often used to improve such alignment. However, under varying lighting or low visual texture, indoor pairwise frame registration with sparse 2D local features is not a particularly robust method. In these conditions, features are hard to detect, thus leading to misalignment between consecutive pairs of frames. The use of 3D local features can be a solution as such features come from the 3D points themselves and are resistant to variations in visual texture and illumination. Because varying conditions in real indoor scenes are unavoidable, we propose a new framework to improve the pairwise frame alignment using an adaptive combination of sparse 2D and 3D features based on both the levels of geometric structure and visual texture contained in each scene. Experiments with datasets including unrestricted RGB-D camera motion and natural changes in illumination show that the proposed framework convincingly outperforms methods using 2D or 3D features separately, as reflected in better level of alignment accuracy.
Large-range active load for dynamic motor testing

“Large-range active load for dynamic motor testing” Before electro motors can be used in robots, the parameters of the motors should be known. This can be done by using the data sheet of the manufacture, but this is not always sufficient enough. So the best way to get to know the parameters is by testing the motor. The test setup should be designed and build in a proper way, so that the useful parameters can determined. Like, maximal speed, maximal torque, maximal temperature and a torque-speed curve. The setup is split in three parts, the electro motor, sensors and a load. The sensors and load will be my BSc assignment. The load will an electromotor, which can easily be changed for a different one, so many electro motors can be tested, without and with a gearbox, high and low RPM. The whole load motor will be kept in place by bearings and will be attached to a load cell, thereby the torque of the MUT (motor under test) can be calculated. The active load and sensors have to work in this range.
A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot

In order to improve the working efficiency of robot promptly picking ripe apples, the harvesting robot must have the ability of continuous recognition and operation at night. Nighttime apple image has many dark spaces and shadows with low resolution, and therefore, a Retinex algorithm based on guided filter is presented to enhance nighttime image in this article. According to color feature of image, the illumination component is estimated by using guided filter which can be applied as an edge-preserving smoothing operator. And the reflection component with image itself characteristics is obtained by employing single-scale Retinex algorithm. After gamma correction, these two components of image are synthesized into a new enhanced nighttime apple image. Fifty nighttime images acquired under fluorescent lighting are selected to make experiment. Experimental results show that the image enhancement performance indexes processed by the proposed algorithm, such as average gray value, standard deviation, information entropy, average gradient, and segmentation error are superior to those of histogram equalization algorithms and Retinex algorithm based on bilateral filter. In addition, compared with the Retinex algorithm based on bilateral filter, the proposed algorithm has an average reduction of 74.56% in running time with better real-time and higher efficiency. So it can realize the continuous operation of apple harvesting robot at night.
Consumer Privacy and the Incentives to Price-Discriminate in Online Markets

Abstract This paper studies how product customization and consumer privacy affect a monopolist’s incentives to engage in perfect price discrimination. We consider a monopolist that faces an ex ante choice to commit to price discrimination or to a uniform price. We introduce a simple model in which a monopolist can use analytics to access consumer data to both price-discriminate and offer customized products. In turn, consumers can protect their privacy to avoid price discrimination at a cost. By committing not to price-discriminate, the firm induces consumers to not protect their data, which allows it to customize the product. It can then extract the extra value through an increased uniform price. This strategy is profitable when the value added through customization is sufficiently high. An intermediate quality of analytics gives the monopolist more incentives to set a uniform price.
Successful Antecedents of Women Entrepreneurs: A Case of Underdeveloped Nation

Abstract Women as the emergent economic power influence the shape of the global economy. For the prosperity of the nation, it is important that women should be involved in the job creation activities rather than providing with the job opportunities, as businesses are more time flexible than being an employee especially for women; where they are playing multiple roles at a time. In order to work on the efficiency and efficacy of the entire women, this paper studied the relationship between successful antecedent such as motivation, personality trait, creativity, access to finance, family factors and success of women entrepreneurial endeavors was being assessed. For this purpose data were collected from women entrepreneurs through a purposive sampling technique by using five likert scale questionnaire. Analysis of the study revealed that antecedents plays a positive role in the success of women entrepreneurial endeavors, however family support also appears to have a strong mediation effect on the success of women entrepreneurial endeavors but a negative value of beta indicates that women in under-developed society generally are not warmly encouraged by the family to opt for entrepreneurial endeavors. So, there is a need to work on this aspect in order to encourage other female participation in the economic growth.
Upconversion nanoparticles for bioimaging

Abstract Fluorescent labeling is a widely used indispensable tool in biology. Conventional downconversion fluorescence labels with ultraviolet or short-wavelength excitation suffer from autofluorescence, low signal-to-noise ratio, and incident photo damage to living organisms. However, upconverting fluorescent nanoparticles emit detectable photons of higher energy in the near-infrared (NIR) or visible range upon irradiation with a NIR light in a process termed upconversion. They overcome some of the disadvantages faced by conventional downconversion labels with the advantages including very low autofluorescence, absence of photo damage to living organisms, high detection sensitivity, and high depth of light penetration, thus making them an ideal fluorescent label for bioimaging. The present review focuses on the features of upconversion nanoparticles, the applications of upconversion nanoparticles in bioimaging, and the bioimaging equipments and methods and discusses the obstacles and development trend of upconversion nanoparticles in bioimaging; we hope this work will provide insights into the study of relevant fields. Upconversion nanoparticles have special photoluminescent properties. Substantial advancements have been made in the field of upconversion nanoparticles for bioimaging. A large number of modifications of upconversion nanoparticles are studied to make them more hydrophilic and biocompatible. At the same time, the safety and toxicity of nanoparticles have caused wide public concern.
Oral production outcomes in CLIL: an attempt to manage amount of exposure

Abstract This study aims at testing the benefits of CLIL (Content and Language Integrated Learning) on oral skills in English by comparing a group of secondary school CLIL learners to two Non-CLIL groups matched for amount of instruction – a two-year-ahead group and a peer group. This sampling design is an attempt to tease the effects of exposure, age and CLIL variables apart, something which has not been addressed in most previous CLIL research. The analyses (holistic evaluation, amount and density of production, and use of compensatory strategies) of participants’ story tellings indicate that CLIL learners’ oral abilities are superior to those of Non-CLIL groups, especially to those of their exposure-matched peer counterparts. Overall, CLIL learners produce denser and richer oral narrations characterized by better content, vocabulary, grammar and fluency, and a marginal use of the first language. These results could be read as indicative of the beneficial effect of CLIL instruction itself on oral production when intervening variables such as amount of exposure and age are managed. In addition, particular attention is given to the lack of positive effect of CLIL on pronunciation.
Place Classification using Dempster-Shafer Theory

Abstract The paper presents a novel place labeling method. It is assumed that an indoor mobile robot equipped with a camera or RGB-D sensor ambulates an indoor environment. The places visited by the robot are classified based on objects which have been recognized. Each object (or set of objects) votes for a set of room classes. Data aggregation is performed using Dempster-Shafer theory (DST), which can be regarded as a generalization of the Bayesian theory. The possibility of taking into account the uncertainty of data is the main advantage of the described method. The classic Dempster’s rule of data aggregation has been criticized because it can lead to non-intuitive results. Many alternative methods have been proposed and several were tested during our experiments. Most place classification methods assume a closed world model, i.e. a test sample is assigned to the most probable class even if its corresponding probability is very small. An advantage of our system is the intrinsic capability of giving unknown class as an answer in such situations, which can be used by the robot to take appropriate actions.
Color holographic system without undesirable light based on area sampling of digital lens

In this paper, we demonstrate a color holographic system without undesirable light based on area sampling of a digital lens. This system, which improves the quality of the reconstructed color image, is fundamentally realized by using three spatial light modulators and a rectangle aperture. By altering the sampling area of the digital lens on the spatial light modulator panels, we can control the offset position of the reconstructed image. Thus, the zero‐order diffraction light as the undesirable light and the reconstructed images are separated, and the chromatic aberration can be corrected. In addition, a rectangle aperture is used to eliminate the zero‐order diffraction light. The final reconstructed image with improved quality can be observed, which verifies the effectiveness of our proposed system.