Miracle-dz/UniFAR-FTUs huggingface dataset:
International diversification benefits -a view on time-varying European market integration

National real estate markets are usually considered to be segmented one from the other. International diversification benefits in real estate markets have been shown to be larger than those in equity or bond markets. New developments in Europe (single market , monetary union, post-crisis coordination of macroprudential policies) are expected to increase integration and reduce these benefits. We study the time-varying degree of integration of European real estate markets over 1971-2018 by estimating the explanatory power of a multi-factor linear model. We find that the integration was relatively stable over time, with a temporary rise during the 2008 financial crisis. We also note that the integration dynamics within Europe has not become stronger than with non-European countries. We find that countries are individually highly integrated via the global factors excepted during short-lived events. The data do not detect stable regional clusters. The international diversification of real estate investments still matters.
English Features in Kiswahili Social Media

All languages are transformed to some extent by other languages with which they are regularly in contact. Some languages are regarded as ‘developed’ insofar as they function as the dominant mode of communication in economically developed countries (e.g. in North America and Europe). The dominant speech communities of rich economies transmit new innovations and discoveries globally, which are then translated into languages described as ‘developing’ because their use is chiefly restricted to so-called economically developing nations, such as Tanzania. In this respect, English counts among the world’s developed languages while Kiswahili is regarded as a developing language. Despite the general tendency to translate new expressions fully into a targeted developing language, there is evidence of foreign structures in Kiswahili when it is used in social media. This article analyses the English syntactic, morphological, phonological and lexical features of Kiswahili appearing in electronic platforms including WhatsApp, personal blogs (e.g. Michuziblogspot) and online social forums (e.g. Jamii Forum). This primary data is then analysed through back translation. Key Words : translation features, language, social media, markedness, borrowing
Effect of E-customization Capability on Financial Performance of Commercial Banks in Kenya

Financial performance of a commercial banks driven by a number of capabilities drawn from either the internal or the external environment. The Kenyan Government and commercial banks in particular have invested in e-commerce solution. Despite these invetments over the years, the impact is yet to be felt. The empirical literure reveals that there is a link between commercial banks e-commerce custiomization capability and financial peroformance.The study empirically analyzed the effect of e- commerce customization capability on financial performance of commercial banks in Kenya. E-commerce customization capability measured using online registration,online recommendation and realtime support while financial performance was measured using Return on Assets (ROA). The study was anchored on the Resource-Based View Theory. The study used explanatory design. A census of 43 commercial banks was taken; data for performance was extracted from audited banks statements for financial year 2016/2017. Data for e-commerce customization capability was collected from commercial banks websites. Data for financial performance was extracted from audited financial ststements of commercial banks. Data analysis was done using descriptive and inferential statistics. The study findings indicated that e-commerce customization capability had a significant effect on financial performance of commercial banks in Kenya. The study concluded that e-commerce customization capability significantly affected financial performance of commercial banks in Kneya. The study recommends that the management of commercial banks in Kenya should invest more in customization capability to improve their performance.
The Influence Of Switching Barrier Towards Customer Loyalty Of Xl Axiata Service Operator In Indonesia 2018

Abstract Recently communication has become an important part of life and it is inseparable in every daily activities of everyone in this era of information. One of the tools of communication that people nowadays are using is mobile phone. The competition in the telecommunication market is tight, as the market has really saturated. XL Axiata, as one of the players in Indonesian cellular industry is troubled with low and fluctuating customer growth, high churn rate, and low ARPU. In the condition of such a market, XL needs to focus on retaining the existing customers. Therefore, it is necessary to consider the factors that can help XL retain their customers. The objective of this research is to analyze switching barriers and also the influencing factors and their contributions toward customer loyalty. This research uses data from samples from 400 respondents who lives in Indonesia chosen by Purposive Non-Probability Sampling technique. The result revealed that there are two out of three factors of switching barrier that significantly influence customer loyalty, namely Perceived Switching Cost and Attractiveness of Alternatives. The model can predict the influence of switching barrier towards customer loyalty in Indonesia since the R² is 62.4%. Keywords: XL Axiata, switching barrier, customer loyalty, Indonesia
Developing mathematical identity and 'Understanding Mathematics in Depth': Conceptions of secondary mathematics teachers on an in-service subject kowledge enhancement course in England

Conceptions of 'understanding mathematics in depth', and its importance for mathematics teachers, are issues of current debate with implications for teachers' in-service training courses. This paper focuses on a group of nine mathematics teachers who had completed a government funded in-service programme which aimed to strengthen teachers' understanding of fundamental mathematics. They were non-specialists, teaching mathematics 'out-of-field'. Qualitative data was drawn from semi-structured interviews with the teachers, and an in-depth case study of one of the sample. Analysis of data showed that for these teachers, growth in knowledge was located mostly within subject matter knowledge (SMK). 'Understanding mathematics in depth (UMID) was articulated as 'knowing why' and 'being able to communicate'. When analysing themes highlighted by the teachers, two new key themes emerged relating to shifts in identity and reconstruction of subject knowledge. The findings offer improtant messages for teacher education policy and practice, with respect to the value of substantial in-service training programmes.
Online Tracking Technologies and Web Privacy

This thesis is a study of the mechanisms that enable privacy-intrusive online tracking practices. The tracking mechanisms we focus on are resilient to removal, less transparent and hard to control. We first give a brief historical background of online tracking ecosystem and frame it as whack-a-mole game which led to the rise of browser fingerprinting and evercookies, two prominent examples of advanced tracking mechanisms we study. To contribute to the understanding of real-world practices that involve browser fingerprinting, we report on the design, implementation and deployment of FPDetective, a framework for the detection and analysis of fingerprinting with a focus on font-based fingerprinting. Using FPDetective, we conduct a large scale analysis of the million most popular websites, and discover that the adoption of fingerprinting is much higher than previous studies had estimated. We study the countermeasures against font-based fingerprinting and find a vulnerability in Tor Browser’s defenses, which we help to fix. Turning to more advanced forms of fingerprinting, we present the results of previously unreported canvas fingerprinting scripts as found on the top 100,000 Alexa sites. Our results show that 5% of the top 100,000 websites employ canvas fingerprinting making it the most common fingerprinting method ever studied. Analyzing the real-world canvas fingerprinting scripts we find them to be more advanced than the original method presented by the academic research community. We report on the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. We found that 10 of the 200 globally most popular websites use Flash Local Shared Objects to respawn HTTP cookies deleted by the user. Our investigation also indicates that cookie syncing, the practice of sharing
Multi-dimensional nature of the phenomenon of fraud in sales organizations. Undiscovered land of research opportunity

Ethics as well as moral values are a powerful force which can regulate the behavior of salespeople, both inside and outside of an organization. While reasonably specific and easy to understand, ethics do not exhaust the list of determining factors which determine a person’s propensity for fraud. There is a range of fraudulent activities which take place in companies which are not perceived as immoral or at least ethically doubtful. Authors of this text argue that fraud of organizational resources by salespeople is one of the most unrecognized and one of the most devastating phenomena in the modern sales force management practice. Fraud is a largely unrecognized phenomenon, and yet it deeply affects the results generated by companies, often significantly increasing the cost of doing business, as most sales executives indicate.
Do external environmental factors affect on strategic choice decisions of innovation?: The case of service organizations in a cross-cultural comparison

Abstract Decision making of innovation comes to the fore as sufficient justification for achieving competitiveness in a context of ever-increasing rivalry. In this formulation, decision makers are likely to form mental frames and sense-making processes in order to access complex interactions of external and organizational ingredients and make strategic decisions of innovation alternatives (service innovation, process innovation, ancillary innovation). This paper examines environmental factors that may explain managers’ strategic choice of a specific type of innovation in service sector organizations located in Qatar and Greece from the viewpoint of the managers’ distinct national cultures resulting in different attitudes and values. This research work examines several main-effect propositions regarding managers’ perspective chosen innovation alternatives, depending on the external forces (service need, diversity of need, increase in population, and influence of context) are dealing with. We illustrate the main effect by looking at surface manifestations of managerial decisions of innovation as outward expressions of distinct characteristics lying deeper in each national culture of origin: Qatari managers prefer service innovations more than Greek managers do. Furthermore, this article reports on the findings of an exploratory study comparing national differences in innovation selection decision between Qatari and Greek top managers that develop a number of interaction effects. To confirm our propositions we gathered experimental data from 118 Greek top managers and 61 Qatari top managers. A multilevel regression analysis was used to examine and assess the relative impacts of the environmental forces on innovation strategic decision-making.
Adapting deep neural networks to a low-power environment

These days, working with deep neural networks goes hand in hand with the use of GPUs. Once a deep neural network has been trained for hours, days, or even weeks on a desktop GPU, it is deployed in the field where it runs inference computations, which are far less expensive than training. This fact, in conjunction with the mobile nature of deep learning applications, makes very interesting the possibility of running inference locally on a mobile device. There already exist mobile applications that can perform tasks involving deep neural networks, but they rely on remote servers to run the most expensive computations. This is not ideal because the user’s privacy may be compromised or the algorithm performance may be damaged due to latency issues on a poor network connection. In this project, the possibility of running inference natively on a mobile GPU is explored. One of the main applications of deep learning is object recognition, which encompasses different problems such as classification, identification and detection. In this project, we select a very deep neural network called Faster R-CNN, which solves a detection problem, and optimize it to run natively on a mobile platform. This innovation provides to a mobile device, such as a smartphone or a tablet, the capability of identifying objects and its location in images, potentially improving the performance of applications that currently employ the device camera combined with deep neural networks. However, mobile devices have limitations in power, memory, and compute capability. This makes power and memory hungry applications such as deep neural networks hard to deploy, requiring smart software design. As a result, mobile presents both an opportunity and challenge for machine learning systems. A preliminary profiling of the network on the Nvidia Jetson TX1 module, a stateof-the-art platform used in modern smartphones and handheld consoles, shows that the convolutional and fully-connected layers take most of the forward pass execution time, up to 88.16% of the total. The network parameters take 548.3 MBytes of space, an 87.2% of which belong to fully-connected layers. Hence, the main performance and energy bottlenecks are on the convolutional and fully-connected layers. In order to overcome these bottlenecks, two optimizations are proposed. In first place, we use half-precision floating points instead of single-precision; this reduces by half the memory bandwidth, improving performance and providing energy savings. In second place, we implement a neuron-pruning technique to remove up to 80% of the neurons in the fully-connected layers; pruning reduces the memory footprint of the network model and the amount of FP operations, reducing both energy consumption and execution time. To evaluate the aforementioned optimizations, thorough experimentation is carried out on a Nvidia Jetson TX1 module. Results show that, combining all the optimizations, we obtain, on average, a speedup of 1.55x, an energy reduction of 31.3%, an improvement in energy-delay of 2.26x and a memory footprint reduction of 86%.
Teaching Mixed Reality Using Video Tutorials

: What are the learning potentials of using online video tutorials as educational tools in game programming of Mixed Reality (MR)? The paper reports on the first experiences of teaching third-semester engineering students design of MR using online step-by-step programming video tutorials. MR covers in this case both Augmented and Virtual Reality. Until recently, most of the instructional support for the software and game development came from paper tutorials. YouTube’s rapid growth in popularity and easy to use programs for video production makes video tutorials a promising alternative to paper tutorials. It is often hard to find up-to-date thoroughly–worked-through textbooks on new and emerging topics such as MR. This motivated me to use existing video tutorials as teaching materials in the course titled Mixed Reality. The learning approach was inspired by the concepts of communities of practice and constructionist learning ideas. The imitating and copying of step-by-step programming video tutorials was a part of becoming a member of virtual community around game programming. In addition, the coding and problem-solving experiences were discussed in the classroom. The constructionist part was where the students made revisions and experiments by adding, combining and testing new coding elements to what they have already done in the tutorials. The students developed applications using the game engine Unity and tested the applications using android smartphones. Extending the applications based on the tutorials facilitated deeper learning for the students. For example, when they applied or recycled code components in new program contexts they developed a better and deeper understanding of the code. Surprisingly, comprehensive, polished and worked-through tutorials promoted minor changes in the developed applications. Whereas, less polished and less professional tutorials made the students become more creative and diverse in development of extended applications. The use of video tutorials in teaching of emerging technologies such as programming of MR prototypes is increasing. It is important to reflect on the learning potentials of both MR and video tutorials and to develop new appropriate teaching strategies to fit new types of learning materials.
OPTIMAL EDGE DETECTION FILTER USING GENETIC ALGORITHM

The process of finding the limits of the image (edge detection) means finding the boundary between the neighboring regions which differ from each other in gray levels value. The extraction of features such as edges and curves from an image is useful for many purposes. Features, such as edges and curves are useful in i) texture analysis ii) 3-D surface restructuring iii) segmentation iv) image matching. Edges are important features in an image since they represent significant local intensity changes. They provide important clues to illumination. Due to the absence of specific suitable method to detect all types of edges, the present work try to find an optimal filter to define image edges and compare it with traditional filters, the GA algorithm used to do so.
Social Support, Source Credibility, Social Influence, and Impulsive Purchase Behavior in Social Commerce

ABSTRACT Social commerce (s-commerce)—the use of social media to support electronic commerce—has become pervasive. This paper aims to investigate an important type of consumer behaviour that could generate considerable economic value: impulsive purchase behaviour. Specifically, we focus on the role of peer influence. Social influence theory posits that the process via which peers change a consumer’s behaviour can be interpreted along two dimensions: informational and normative. Furthermore, drawing from literature, source credibility and social support are proposed as the antecedent factors of the influencing processes in this context. We surveyed 303 s-commerce participants in Sina Weibo to empirically test the research model. The results indicate that peers’ expertise and trustworthiness are significantly related to both types of social influence that could exert an influence on a consumer. Further, consumers’ exchange of informational and emotional social support significantly facilitates social influence among them. This study contributes to both the s-commerce and the impulsive purchase literature by revealing the role of peer influence in consumers’ impulsive consumption behaviour in the s-commerce setting. The practical implications are also illustrated in the paper.
Efficient Sampling Algorithm for Optimization Under Uncertainty

Uncertainty is a part of a real world optimization problem. Computational speed is critical in optimizing large scale stochastic problems. The major bottleneck in solving large scale stochastic optimization problems is the computational intensity of scenarios or samples. This research proposes a novel sampling technique which takes above mentioned problem. This thesis analyzes existing and novel sampling techniques by conducting large scale experiments with different functions. The sampling techniques which were analyzed are Monte Carlo Sampling (MCS), Latin Hypercube Sampling (LHS), Hammersley Sequence Sampling (HSS), Latin Hypercube-Hammersley Sequence Sampling (LHS-HSS), Sobol Sampling, and the proposed novel technique which is Latin Hypercube-Sobol Sampling (LHS-SOBOL). It was found that HSS performs better up to 40 uncertain variables, Sobol up to 100 variables, LHS-HSS up to 250 variables, and LHS-SOBOL for large scale uncertainties which was tested for 800 variables. Thus, by analyzing the results of this work we can conclude that LHS-HSS can be used for uncertainties from 2 to 100 variables, and LHS-SOBOL for larger than 100 variables.
Innovative sport and leisure approaches to quality of life in the smart city

ABSTRACT Innovation in sport and leisure activities have recently become a popular subject of scholarly attention. The present article contributes to the academic exploration of the diversity of innovation in leisure by addressing how innovation, leisure and sport can be integrated in urban planning. We do so by using a Norwegian urban development project triggered by a critical juncture as an empirical case. In 2012, the Norwegian parliament made a resolution to relocate the national air force base located in Bodø. A result of this resolution was the withdrawal of the military from the city, implying extensive loss of jobs connected to the operation of the air force base. The resolution therefore led to a crisis state that became the catalyst for a substantive urban development project – The “Smart Bodø” initiative. In this paper we examine five examples from the “Smart Bodø” initiative that can be viewed as socially innovative approaches to sport and leisure in the smart city. Separately and collectively, the empirical examples demonstrate how technology, leisure, sport and innovation can be interlinked in urban planning and contribute to quality of life for citizens.