Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling Human Computation through Text-based Conversational Agents

Human Computation (HC) has established itself to be a powerful tool for carrying out certain simple and repetitive tasks in the form of microtasks, which to this day are still difficult for a machine to automate. With the latest increase in interest in machine learning, HC has similarly gotten more attention as a popular way to acquire training data in large quantities. Traditional microtask crowdsourcing platforms, such as Amazon Mechanical Turk (AMT) or Figure Eight, are typically built using web-based interfaces. However, the speed and quality of data acquired via the crowd are naturally limited by the number of available workers and their skill set. We perceive a grand opportunity in expanding the crowd by exploring alternative means to the traditional microtask crowdsourcing platforms that are reliant on the web-based interface. More specifically, as popular messaging services such as Telegram, WhatsApp and Facebook Messenger are used on a daily basis by millions of people across the world, we propose to perform HC activities inside these services through a text-based conversational agent (or chatbot). We foresee new opportunities arising in conducting HC inside the chatbot, that could leverage the access to a potentially larger and more diverse crowd. In this thesis, we set the first step towards a new alternative to the typical web-based interface used in HC. As a result, we set out to investigate the viability of facilitating microtask crowdsourcing inside chatbots. To this end, we design and implement a chatbot that acts as a medium for the execution of microtask crowdsourcing activities, which is then used for conducting several pilot experiments. In addition, we propose a mapping from Web to Chatbot tasks for several commonly found User Interface (UI) elements inside worker interfaces. Thereafter, we conduct an elaborate experimental campaign to gauge the feasibility and interest of crowd workers to use the chatbot as a new medium for performing generic microtasks. We designed, implemented and executed six common microtask crowdsourcing types; Information Finding, human OCR (CAPTCHA), Sentiment Analysis, Object Labelling, Image Annotation, and Speech Transcription. For each task type, we implemented a microtask in both a web-based and conversational interface. By measuring the execution time, quality of answers and surveying workers’ satisfaction of a total of 316 distinct workers recruited via Figure Eight, we show that chatbots can be effectively used as an alternative to the web-based interface to perform microtask crowd work. We report that out of all workers who participated in the chatbot tasks, 98.3% of the workers indicated a positive experience and were satisfied with their interaction with the chatbot, while performance in terms of task execution time and output quality was in general comparable.
SUSTAINABLE TOURISM DEVELOPMENT OF COASTAL DESTINATION – THE ROLE AND THE SIGNIFICANCE OF LOCAL RESIDENTS

Purpose – The main paradigm of sustainable tourism development are local residents, that is, their direct and indirect support in the process of sustainable touristic planning and development of any touristic destination in the world. Their attitude needs to be taken into account during the decisionmaking processes regarding future tourism development and local residents need to find their own interest (employment, ensuring a better standard of living, the possibility of entrepreneurial activities, market for new products, improvement of infrastructure and so on.). Local selfgovernment is responsible for monitoring and recognizing the need and expectations of local residents. The aim of this paper is the examination of local residents' attitudes with regard to the development of tourism and the establishment of tourism development in observed Adriatic coastal destinations. Methodology – Research was conducted in 2016 in 9 Adriatic coastal destinations via survey questionnaire, created according to the SUS-TAS model Choi and Shirakaya (2005), using the social dimension of the model. The research consisted of 371 subjects, all local residents, older than 18. The data gathered was analyzed using appropriate statistical methods with the help of a software program for social sciences Statistica, Ver. 12. Correlations analysis and factor analysis were used from the statistical methods in order to determine the attitude of local residents in researched tourism destinations. Findings – The results of this research show that local residents do not support the current direction of tourism development in researched coastal destinations and are aware of all the negative consequences of the development of mass tourism that is not governed by the principles of sustainable tourism development. Three main components of sustainable tourism, long-term planning, full community participation and sustainability of the environment in tourism, are areas in which local residents wish to be actively represented in the decision-making process. Contribution – Research data presented by this paper can suggest critical implications to local authorities who should take them into account when considering tourism development, including the role and significance of local residents without whose active participation there is no sustainable tourism development. The initiative for tourism development based on the principles of sustainable development needs to come from the local community, from the local residents, who need to understand that, in tourism, economic benefits are brought on by ecological responsibility, that is, not exhausting the local environment as the primary tourism resource and conservation of natural and built attractions. Therefore, the necessary task of those conducive to development, primarily local authorities, developmental agencies, destination management organizations, is the improvement of channels of communication and coordination.
Skewness Preference and Asset Pricing: Evidence from the Japanese Stock Market

Previous studies have shown that investor preference for positive skewness creates a potential premium on negatively skewed assets. In this paper, we attempt to explore the connection between investors’ skewness preferences and corresponding demand for a risk premium on asset returns. Using data from the Japanese stock market, we empirically study the significance of risk aversion with skewness preference that potentially delivers a premium. Compared to studies on other stock markets, our finding suggests that Japanese investors exhibit preference for positively skewed assets, but do not display dislike for ones that are negatively skewed. This implies that investors from different countries having dissimilar attitudes toward risk may possess different preferences toward positive skewness, which would result in a different magnitude of expected risk premium on negatively skewed assets.
Photoluminescence of Ag-loaded A, X, and Y type zeolites heat-treated in atmosphere

We use in-situ X-ray absorption fine structure (XAFS) to examine the relation between Ag clusters or Ag ions and properties of photoluminescence (PL). The Ag clusters are generated in the cavity of Ag-type zeolite-A, X, and Y heating in atmosphere. The Ag clusters in the zeolite cavity break down when cooled to room temperature after heating in atmosphere. Results of this study suggest that collapsing Ag cluster plays an important role in the generation of strong PL bands and that Ag clusters might not be a direct species of PL. Results also show that the local sites of Ag ions differ slightly from the unheated species induced by the formation and destruction of Ag clusters.
Enhancement of optical penetration depth of LED-based NIRS systems by comparing different beam profiles

Near-Infrared Spectroscopy (NIRS) is a non-invasive brain imaging technique involving the quantification of oxy and deoxy-hemoglobin concentrations resolved from the measurement of Near-Infrared (NIR) light attenuation within the tissue. Previous studies have shown that NIR light is more influenced by the optical properties of the superficial layers than those of the deeper target layers such as cortex. NIR light produced by the Laser source penetrates deeper regions of the tissue rather than the LED source although Laser needs more expensive instrumentation. In this study, we investigate the effect of Uniform and Gaussian beam profiles on the enhancement of LED light penetration depth. The latter beam profiles were generated and compared using Flat and Aspherical lenses applied to the LED sources. In order to increase the signal to noise ratio, the lenses were also applied to the light detector. For performance analysis, two experiments were carried out by scanning the intra space of a liquid phantom by static and dynamic (pulsating) absorbers. Monte Carlo simulations were also carried out to be compared with the experiment. The results showed that Gaussian beam profile and in particular, Bi-Convex lenses applied to both source and detector leads to a greater light penetration depth in the liquid phantom close to that of a Laser source.
Islamic Finance and Herding Behavior Theory: A Sectoral Analysis for Gulf Islamic Stock Market

This study examines herding behavior in four sectors of the Gulf Islamic stock markets. Based on the methodology of Chiang and Zheng (2010), results showed evidence of herding among investors in major sectors for the Gulf Cooperation Council (hereinafter GCC) Islamic stock market during falling periods. In addition, we found that conventional return dispersions have a dominant influence during both falling and rising market periods. We also found evidence of herding around the conventional sectors during down market periods only in banking, hotel and restaurant sectors. There is evidence of herding around the conventional sectors during up market periods for insurance and industrial sectors.
Análise de Desempenho de Metaheurísticas Aplicadas ao Problema de Restauração de Redes de Distribuição

Uma das características das Smart Grid é a restauração da rede elétrica, para isso são utilizadas metaheurísticas para encontrar a melhor configuração temporária após ocorrer uma falta. Impulsionado pela característica probabilística das metaheurísticas, este artigo visa realizar uma análise comparativa entre quatro metaheurísticas aplicadas ao problema de restauração, utilizando a representação nó-profundidade como estrutura de dados para representar a rede elétrica.
Key Determinants of Repurchase Intention toward Organic Cosmetics

This paper aims to discover factors and their influences degree to repurchase organic cosmetic in Vietnam. In addition, this research also discloses the main segments that have high demand re-buying intention based on demographic groups of gender, age, income, educational level, job, type of products, and place of production. Twenty-eight scale was designed based on previous studies and adjusted to match the 5-point Likert scale to conduct measurement. By using survey method to test hypotheses and set up conceptual models to collect 295 Vietnamese consumers who have experience in consuming organic cosmetic by explain the results through Smart PLS software. The findings show that there is positive attitude of customers to the intention of acquiring organic cosmetics, green perceived and customer satisfaction. In addition, customer satisfaction, knowledge of products, safety values, and environmental protection consciousness also play important roles to form a positive attitude of customers for products. Moreover, the consciousness of green living of consumers accounts for a high proportion in creating customer satisfaction for organic products. The results show useful information for current premises to determine the factors that influence the decision to repurchase organic cosmetic product, that provide business strategies.
Diffusion source detection in a network using partial observations

This paper considers a random component-wise variant of the unnormalized power method, which is similar to the regular power iteration except that only a random subset of indices is updated in each iteration. For the case of normal matrices, it was previously shown that random component-wise updates converge in the mean-squared sense to an eigenvector of eigenvalue 1 of the underlying matrix even in the case of the matrix having spectral radius larger than unity. In addition to the enlarged convergence regions, this study shows that the eigenvalue gap does not directly affect the convergence rate of the randomized updates unlike the regular power method. In particular, it is shown that the rate of convergence is affected by the phase of the eigenvalues in the case of random component-wise updates, and the randomized updates favor negative eigenvalues over positive ones. As an application, this study considers a reformulation of the component-wise updates revealing a randomized algorithm that is proven to converge to the dominant left and right singular vectors of a normalized data matrix. The algorithm is also extended to handle large-scale distributed data when computing an arbitrary rank approximation of an arbitrary data matrix. Numerical simulations verify the convergence of the proposed algorithms under different parameter settings.
The translation and adaptation of Alice’s Adventures in Wonderland into isiNdebele

Abstract This article reflects on the translation of Lewis Carroll’s Alice’s Adventures in Wonderland into the isiNdebele language of Zimbabwe. The isiNdebele translation, Insumansumane zika-Alice, is one of the numerous translations produced towards the 150th commemorative exhibition of Carroll’s legacy following the publication of Wonderland in 1865. The translations resulted in Wonderland becoming one of the most translated English novels, thereby vindicating the ‘universal-child hypothesis’ propounded by Warren Weaver in an earlier appraisal of Wonderland translations fifty years earlier. Weaver regarded the translation of Wonderland as a measure of the popularity of the text among children, who are arguably fascinated by Alice, with whom they are assumed to identify. This article problematises this argument in the light of most translations being initiated by non-mother-tongue speakers of target languages. It is argued that Alice’s worldwide appeal should not be taken for granted as a universal one, partly because of her particular identity, which is different from the diverse target readers. Therefore, the question of whether Alice can really become African reflects on the domestication approach that was adopted by the isiNdebele translator, among others. Special attention is paid to proper nouns and other complex features characterising Carroll’s language usage.
Adaptations of Adipose Tissue Expandability in Gestation are Associated with Maternal Glucose Metabolism

During pregnancy, adipose tissue (AT) must expand to support the growing fetus and the future nutritional needs of the offspring. Limited expandability of AT is associated with insulin resistance, attributed to ectopic lipid deposition. This study aimed to investigate human AT expandability during pregnancy and its role in the pathogenesis of gestational diabetes mellitus (GDM). This cross-sectional study of omental (OM) and subcutaneous (SQ) AT collected at Caesarean delivery included fifteen pregnant women with normal glucose tolerance (NGT), six with GDM, and three with type 2 diabetes mellitus. Adipocyte size, capillary density, collagen content and capillary growth were measured. Affymetrix arrays and real-time PCR studies of gene expression were performed. Mean OM adipocyte size was greater in women with GDM than in those with NGT (p=0.004). Mean OM and SQ capillary density were lower in GDM compared with NGT (p=0.015). Capillary growth did not differ significantly between groups. The most differentially expressed AT transcript when comparing non-pregnant and pregnant women corresponded to the IGF binding protein (IGFBP)-5, the
The Effectiveness of Antecedents, Control, and Differential Reinforcement of Incompatible Behavior in Treating a Childhood Eating Disorder

Many studies have indicated that behavior modification is effective in treating eating problems in children. However, only a few of these studies have focused on the implementation of a behavior modification approach in treating selective eating and food neophobia disorder in toddlers. The purpose of this study is to evaluate the effectiveness of antecedent control procedures and differential reinforcement of incompatible behavior in treating a toddler with selective eating and food neophobia disorder. This is a single case design study with multiple baselines across three mealtime situations. The intervention consisted of 40 sessions with preand post-tests to compare and measure the child’s progress. The results showed that the child was able to increase her meat consumption 25% from 100 g to 125 g per day. This positive trend also occurred for her milk consumption, which increased from 200 ml to 350 ml per day. In addition, the child gained 1.7 kg of weight after the intervention. This study provides promising support for utilizing the antecedent control and differential reinforcement of incompatible behavior in treating selective eating and food neophobia disorder in toddlers.
The Price of Mission Complexity: A National Study of the Impact of Community College Baccalaureate Adoption on Tuition and Fees

The traditional mission of community colleges is rooted in the provision of sub-baccalaureate education at a low price, but a total of 19 states have changed their legislative policies and currently allow community colleges to offer community college baccalaureate (CCB) degree programs. This study examines the impact of CCB adoption on the tuition and fees at CCB-adopting institutions. We leverage a novel national dataset and employ a difference-in-differences regression approach to find that CCB adoption is associated with increases in tuition and fees at public community colleges.
The Noise of Silence: How Suppression of Free Expression Threatens the Fragile Peace in Post-Genocide Rwanda

The argument is made here that Rwanda faces the serious risk of another violent conflict if the flaws of the transition are not corrected to bring about a toleration of dissent and criticism on the part of government. It also entails the removal of the country’s media instruments from the control and manipulation of the state. Correcting the flaws of the transitional process should begin with the international community shedding the guilt of its failure to prevent the genocide, demanding the regime’s commitment to greater respect for fundamental civil and political rights (including freedom of expression and press freedom) in return for economic support. It also requires an end to the rather condescending notion that Rwandan’s deserve nothing better than an authoritarian government which secures peace and keeps the economy on the right footing by use of, among other things, draconian laws against the media and free expression.
Generational War on Inflation: Optimal Inflation Rates for the Young and the Old

How does a grayer society affect the political decision making regarding inflation rates? Is deflation preferred as society ages? In order to answer these questions, we compute the optimal inflation rates for the young and the old respectively and explore how they change with demographic factors, by using a New Keynesian model with overlapping generations. According to our simulation results, there indeed exists a tension between the young and the old on the optimal inflation rates. The optimal inflation rates are different between the young and the old. Also, they can be significantly different from zero, in particular, when heterogeneous impacts from inflation via nominal asset holdings are considered. The optimal inflation rates for the old can be largely negative, reflecting their positive nominal asset holdings as well as lower effective discount factor. Societal aging may exert downward pressure on inflation rates through a politico-economic mechanism.
Multilevel Graph-Based Decision Making in Big Scholarly Data: An Approach to Identify Expert Reviewer, Finding Quality Impact Factor, Ranking Journals and Researchers

Digital libraries, such as conference papers, journal documents, books and thesis, research patents, and experiments generate a vast amount of data, named as, Scholarly Big Data. It covers scholarly related information for both researcher's perspective as well as publisher's perspective, such as academic activities, author's demography, academic social networks, etc. The relationships among Big Scholarly Data can be worthy of solving researcher as well as journal related concerns, if they are prudently treated to extract knowledge. The best approach to efficiently process these relationships is the graph. However, with the rapid growth in the number of digital articles by various libraries, the relationships raise exponentially, generating large graphs, which have become increasingly challenging to be handled in order to analyze scholarly information. On the other hand, many researchers and publishers/journals have severe concerns about the ranking control mechanisms and the consideration of quantity rather than quality. Therefore, in this paper, we proposed graph-based mechanisms to perform four critical decisions that are the need of the today's scholarly community. To improve the quality of the article, we proposed a mechanism for selecting and recommending suitable reviewers for a submitted paper based on researchers’ expertise and their popularity in that particular field while avoiding conflict of interest. Also, due to shortcomings in the existing journal ranking approaches, we also designed a journal ranking mechanism including its new impact factor and relative ranking by using a modified version of traditional page ranking algorithm and excluding self-authors citations as well as self-journal citations. Similarly, researchers ranking is also important for various motives that is calculated based on the expert's field, citation count, and a number of publications while avoiding any loophole to increase the ranking such as, self-citations and wrong citations. Also, to efficiently process big graphs generated by a massive number of scholarly related relationships, we proposed an architecture that uses the parallel processing mechanism of the Hadoop ecosystem over the real-time analysis approach of Apache Spark with GraphX. Finally, the efficiency of the proposed system is evaluated in terms of processing time and throughput while implementing the designed decision mechanisms.
Community Detection in Social Networks Considering Topic Correlations

Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To ach