Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging neo-localism for experience innovation: A case study of an urban park and entertainment venue

In the past decade, spending on experiences relative to other types of consumer spending has increased dramatically. Companies with an experience economy mindset focus on differentiation as opposed to standardization and rely on innovation activities for growth. This design research project examined how an urban park and entertainment venue leveraged its unique brand and core experience to design and innovate memorable guest experiences. The Quartyard study integrated key concepts of experience economy logic and memorable tourism experiences with principles of human centered design. Interviews were conducted with key stakeholders to include the architects, general manager, members of the local business association, nearby residents, and guests to identify key themes and to frame the design challenge. A series of idea generation sessions were conducted to brainstorm events and activations aligned with the core experience concept. Personas and journey maps were created, and a select number of activations were prototyped and evaluated. Qualitative results comprised views of an emerging concept called neo-localism in the literature. These included being non-corporate, environmentally responsible, empowered and self-sufficient, authentic, and community building. Findings point to the importance of shared vision to motivate innovation in a context where innovation is collective, incremental, and ongoing.
Stock Market Calendar Anomalies : The Case of Malaysia by Shiok

This study investigates the ‘day of the week’ effect and the ‘twist of the Monday’ effect for Kuala Lumpur Composite Index for the period May 2000 to June 2006. Our empirical results find support for the Monday effect in that Mondays are the only days with negative returns and represent the lowest stock returns in a week. The returns on Wednesday are the highest in a week, followed by returns on Friday. Monday returns were partitioned into positive and negative returns, and we found that the Monday effect is clearly visible in a ‘bad news’ environment, but it failed to appear in a ‘good news’ environment. This study also found evidence on twist of the Monday effect, where returns on Mondays are influenced by the previous week’s returns and the previous Friday’s returns. The evidence of negative Monday returns in this period is consistent with the relevant empirical literature.
THE INTERNATIONAL COMPETITIVENESS AND ECONOMIC INTEGRATION ∗ UDC 339 . 92 ( 4-672 EU : 497 . 11 )

The paper deals with two important issues for Serbia: first, international competitiveness, and second, the impact of competitiveness on economic integration and vice versa. The European integration process has intensified the problem of competitiveness of the national economy by the following: first, by the reduction of protection measures, and second, by the liberalization of regional trade. It has been a good way to upgrade and strengthen competitiveness of the national economy. Regional economic integration is a supporting means for a gradual increase of competitiveness of a small open economy in transition. Full membership in the European Union involves strong competitive pressure on new member countries, on one hand, and a certain degree of collective protection, on the other.
Benchmark of Analytics Technologies for Smart Meter Data

With the widely installation of smart meters in the energy industry, large amounts of meter time-series data are collected and analyzed. A diversity of technologies appears to support meter data analytics. As these technologies mature, there is a need to evaluate and compare the performance of these analytics systems. In this paper, we make a benchmark study of the technologies, including Matlab, MADlib, KDB and Spark, which cover the following categories: the traditional data analytics with data shipment, indatabase data analytics, data analytics in a distributed computing environment, in-memory and column-based data analytics. The benchmarking uses the representative analytics algorithms in the assessment, which include a 3-line model for dis-aggregating energy consumption, periodic auto-regression model for energy prediction, histogram for learning energy consumption distribution, and similarity searching. We present a data generator of smart meters, evaluate the implementation effort of the algorithms for each technology, and measure the performance running in both centralized and distributed environments using real-world and synthetic datasets. The end result shows that the data mining methods can be implemented as efficient parallel processes. Overall, we establish that parallelization of using the in-memory columnar store KDB in centralized environment, and using Spark in a cluster are the promising models for managing the smart meter data.
Effect of tear/crack on soybean (Glycine max) seed coat, physiological quality and pathology of the seed

Seed coat tear is frequently occurs in some soybean cultivars. The impact of seed coat tear on seed physiology and sanitation is uncertain. Our objective was to analyze the physiological and pathological effect of tear on soybean (Glycine max) seed coat. The cultivars NS-8338-IPRO (with high incidence of tear) and NS-6906-IPRO (with low incidence of tear) were used. A tetrazolium test was used to assess the physiological quality of the seed coat before and after storage. The sanitary quality was assessed through the "Blotter Test". For each storage period, we evaluated seeds with 0% coat tear, up to 10%, and above 10%. The experiment was performed in a completely randomized design using the methodologies proposed by RAS. The coat tear on soybean seed positively contributed to the moisture damage affecting the inner part of the seed. Incidences of Fusarium sp. and Aspergillus sp. were frequently observed in torn seeds and in seeds without tear (around 9-10%) but did not interfere with seed quality. The appearance of coat tear on soybean seed is increased by moisture damage and do not serve as a gateway for the fungi to cause damage during seed emergence.
FACTORS INFLUENCING LIVING STANDARDS IN THE EU

GDP per capita is often criticized for the fact that it does not reflect all the various determinants influencing living standards of inhabitants. In the paper presented, these 99 determinants, based on the evaluation of alternative indicators of living standards, are classified into eight main groups, i.e. public life, place of residence, economic area, educational area, health care, environment, interpersonal relations, and personality. The main source of data is the results of a questionnaire survey performed in 2013 and 2014 in the Czech Republic, Finland, France, Spain, and Great Britain, focusing on people’s opinions regarding how much individual determinants influence their living standards. Using Principal Component Analysis made it possible to determine which of these 99 selected variables should be considered during a complex evaluation of living standards. It was determined that only 19 variables were of importance and that they could be merged into 8 factors. Furthermore, the material side of life is not prioritized to spiritual extension by respondents, which again brings us to the idea that GDP per capita is really not the right measurement for standard of living.
Patterns and Prevalence of Metabolic Syndrome among Persons Receiving Treatment with Antipsychotic Medications

PAGE The treatm ent of schizophrenia is heavily reliant on financial, health, and social care. Despite the low occurrence of schizophrenia in the population, the total health care costs of schizophrenia are similar to m ore prevalent mental disorders. Therefore, resources need to be directed at finding and implementing the most effective and least costly treatm ent regimes. The current study examined differences in weight, lipid and glucose levels, and metabolic irregularities between schizophrenic patients receiving first and second generation antipsychotics. Results indicated that there w as a higher prevalence of metabolic disorder among patients treated with second generation antipsychotics (SGAs) than those treated with first generation antipsychotics (FGAs). Patients receiving SGAs also displayed greater increases in weight and systolic blood pressure from first to last a sse ssm en t than those being treated with FGAs. Second generation antipsychotics were not superior on m easures of clinical efficacy than first generation antipsychotics. In combination, these findings suggest that the benefits of SGAs may not outweigh the side effects and financial cost of this c lass of antipsychotics.
Value of Commercial Real Estate investments: Sustainability perspective in Sweden and France

During the recent decades the notion of sustainability has been introduced to almost all businesses and business areas. Sustainability in real estate and particularity in construction has been discussed by many researchers and journalists. Nowadays, not only many countries have adopted the sustainable construction techniques and started using green building materials but also it has started to be important to have sustainable property management, sustainable real estate valuation techniques as well as on the investment side, there has been a major trend to sustainable real estate investments. Many real estate companies are interested in investments into “green” properties both in France and in Sweden. The main objective of this thesis was to investigate the main force that drives these investments, whether they are driven by personal consciousness of investors, by the overall market trend or by the premium that they may receive from the sustainable investment. The purpose was to better understand the reasons behind the green real estate investments as well as to investigate the difference in value of certified sustainable real estate and its impact on the investment decisions. For the following thesis the qualitative research was used the most since its purpose is to get a better understanding of the motivation of companies to invest into sustainable real estate. However, quantitative data was as well applied in from of investigation about amount of investment transactions in different real estate sectors, repartition of types of investors, number of sustainable buildings with different certifications, etc. The research showed that real estate investors both in Sweden and France are most interested in the purchase of green buildings due to their ability to produce higher income and durability of buildings itself. The main premium that they are expecting from such investments is in better marketability of the building and thus shorter vacancy periods. Both qualitative and quantitative data have confirmed this conclusion.
The Effect of Carousel Brainstorming Strategy and High Conscientiousness toward Students ’ Reading Comprehension

The aim of this study is to find out the effect of Carousel Brainstorming Strategy and students’ conscientiousness toward their reading comprehension of report text at eleventh grade of SMAN 1 Tandun, Rokan Hulu. The design of the research was quasi experimental research with factorial design 2x2. The samples were XI IPA 1 as experimental class and XI IPA 2 as control class. The samples were selected by using cluster random sampling. The instruments were reading comprehension test of report text and questionnaire of conscientiousness. These instruments were tried out first to another class which had similar capabilities to both experiment and control class. The data were analyzed by using T-test. The result of this research shows that Carousel Brainstorming Strategy produces higher result on students’ reading comprehension of report text for students with high conscientiousness compared to Small Group Discussion.
Trade Policy : Theory and Practice in the Context of the FTAA The Theory of Tariffs and Quantitative Restrictions

As with any exercise in fashioning a free trade arrangement the intention is to remove barriers to trade in their many forms from the reduction (perhaps elimination) of tariff barriers, quantitative restrictions and the execution of any number of business facilitation measures intended to enable more robust commercial interaction across borders, change of trade practices and facilitating the easier movement of factors of production in this respect. Just as tariff barriers remain an impediment to global trade, which is currently increasing at some 8% a year, these barriers at the hemispheric level exert bottlenecks on free trade imperatives in the Americas.
Fully coupled discrete energy-averaged model for Terfenol-D

A fully coupled 3D energy-avaraged model is presented which describes the magnetomechanical behavior of Terfenol-D. Conventional energy averaging with eight easy axis orientations yields an unphysical kink in the magnetization response and fails to describe the gradual approach to saturation present in Terfenol-D magnetostriction. Superposition of an empirically weighted global anisotropy energy onto an anisotropy energy locally defined around each easy axis eliminates the unphysical kink in the response, while an implicit definition of the domain volume fraction describes the gradual approach to saturation. Anhysteretic bulk material response is described through a weighted sum of individual domains; the weights (or domain volume fractions) are calculated using Boltzmann-type energy averaging. A hysteretic extension is built from an evolution equation for the domain volume fractions. Although solution of the implicit equation for the anhysteretic domain volume fractions requires iteration, the model takes only 20% longer than its original non-iterative version because of the small size of the iteration loop. Comparison of the model with sensing and actuation measurements reveals an average modeling error below 3%. A reduced version of the model, proposed by eliminating certain easy axis orientations, has a 30% lower computational time, with an average modeling error below 6%. VC 2012 American Institute of Physics. [doi:10.1063/1.3687372]
Machine Learning to Enable AR Technology-Paper Review ( Working Title )

There has been rapid advancement of machine learning and deep neural nets (DNN) over the last decade. This represents a shift in how data is processed and applied to everything computers do. DNN have surpassed the human ability to identify images as shown by the results of the ImageNet challenge (Figure 1). This shift the ability of computers to do what humans have traditionally excelled at has enabled incredible new technologies. This rapidly expanding field of research and devices will shape the world over the next decade. In this report, I will be summarize 5 research articles which relate to the field of augmented reality (AR). These papers cover object detection, visual tracking, action detection, and hardware optimization. AR headsets such as Magic Leap and HoloLens utilize these different techniques to allow users to interact with digital displays as if they are a part of their physical world. Object detection allows AR devices to detect key objects and integrate these objects in their programs. Visual tracking helps make these objects exist in a consistent world space. Action detection allows users to use gestures to interact with the display in a natural way. Finally, hardware optimization allows for all of these processing intensive neural networks to exist within the power and real-time constraints of a wireless device. Although I will be approaching these subjects from a AR perspective, they also enable other exciting technologies. I will first explain how I decided on which papers to review and what criteria I focused on to compare them. Then I will review each of them by summarizing their content and comparing them to each other. One thing to note is that the first four papers are all fairly related and can be compared easily. The last paper on hardware optimization stands alone, but it is long and full of plenty of interesting information on it’s own. Finally, I will discuss what I have learned from this project.
Electric Power Steering

The present invention provides an electric power steering apparatus, the torque sensor can not be used to accurately detect the steering torque, the steering assist can be appropriately controlled. When the torque sensor (3) normal to (3) the detection signal of the angle calculated according to an alternative torque (T0) by the torque sensor detecting a torque value (Ti) for comparison, an alternative torque correction value calculation (Tc) and its storage. Further, the normal, the steering assist control in accordance with (3) detected by the torque sensor torque detection value (Ti) in the torque sensor (3), in the torque sensor (3) when an exception occurs, according to the use of alternative torque correction value (Tc ) replace the corrected torque corrected (T0) obtained alternative torque (T1) for steering assist control.
Optimized Storage Placement over Large Scale Sensor Networks

Data storage has become an important issue for energy efficient data management over sensor networks. In this paper, we investigate into the optimized storage placement problem over large scale sensor networks, aiming to achieve minimized energy cost. In order to efficiently deal with the large scale deployment area with irregular shape, we propose to utilize the “hop” as the computation unit instead of the “node”, such that the computation complexity can be greatly reduced. We propose methodologies to solve the optimization problem respectively in situations for unlimited number of storages and limited number of storages. The ultimate goal of this paper is to give a fundamental guidance of optimized storage placement for large scale sensor networks. Simulation results confirm the performance of our methodologies.
The tension between anonymity and privacy

Privacy in the context of information and data is often defined in terms of anonymity, particularly in regulations such as the GDPR. Operationally, it is appealing to define privacy in terms of computable data properties as this makes it possible to verify compliance. A well known example of privacy defined as such is k-anonymity. At the same time, uncertainty regarding real-world privacy is increasing with the amount of data collected about us all. We present arguments for why focusing on anonymity or computable properties of data is not very helpful in this regard. In particular, we count exploitable failures of privacy defined in terms of computable properties of n-bit data and conclude that these counterexamples to protection cannot be rare.
Maximum Tsallis Entropy Imitation Learning : Supplementary Material

In this supplementary material, we provide proofs of theorems in the main paper and implementation details of our algorithms. We first show that the maximum causal Tsallis entropy (MCTE) problem is concave with respect to the state-action visitation ρ(s, a). Then, the optimality condition of an MCTE problem is derived from Karush–Kuhn–Tucker (KKT) conditions. We also show that the proposed framework can be interpreted as robust Bayes estimation with the Brier score. Finally, to convert our problem into the generative adversarial training framework, the interchangability of maximization and minimization is proved.
ross-correlation of instantaneous amplitudes of field potential oscillations : A traightforward method to estimate the directionality and lag between brain reas

Researchers performing multi-site recordings are often interested in identifying the directionality of functional connectivity and estimating lags between sites. Current techniques for determining directionality require spike trains or involve multivariate autoregressive modeling. However, it is often difficult to sample large numbers of spikes from multiple areas simultaneously, and modeling can be sensitive to noise. A simple, model-independent method to estimate directionality and lag using local field potentials (LFPs) would be of general interest. Here we describe such a method using the cross-correlation of the instantaneous amplitudes of filtered LFPs. The method involves four steps. First, LFPs are band-pass filtered; second, the instantaneous amplitude of the filtered signals is calculated; third, these amplitudes are cross-correlated and the lag at which the cross-correlation peak occurs is determined; fourth, the distribution of lags obtained is tested to determine if it differs from zero. This method was applied to unctional connectivity artial directed coherence LFPs recorded from the ventral hippocampus and the medial prefrontal cortex in awake behaving mice. The results demonstrate that the hippocampus leads the mPFC, in good agreement with the time lag calculated from the phase locking of mPFC spikes to vHPC LFP oscillations in the same dataset. We also compare the amplitude cross-correlation method to partial directed coherence, a commonly used multivariate autoregressive model-dependent method, and find that the former is more robust to the effects est th dire of noise. These data sugg valid method to study the
XMM-Newton Observations of NGC 507: Super-solar Metal Abundances in the Hot ISM

We present the results of XMM-Newton observations of NGC 507, a dominant elliptical galaxy in a small group of galaxies. After carefully considering various systematic effects on abundance measurements, we report ‘super-solar’ metal abundances (both Fe and aelements) present in the hot ISM: Z F ~ = 2-3 times solar with an observational limit of as high as 4 times solar inside the D25 ellipse of NGC 507. This is the highest ZFe reported so far, and fully consistent with those expected by the stellar evolution models where heavy elements are enriched by both type I1 and Ia supernovae ejecta. No unusual constraint either on the SNe rate or IMF is required. Among various factors affecting the accurate abundance measurement, we find that selecting a proper emission model is most important. As opposed to the X-ray spectral data with limited s/n and poor spatial/spectral resolution obtained in the previous missions, the spatially resolved XMM spectra provide enough statistics to untie the model-Z degeneracy and statistically require at least 3 emission components in each concentric shell (2 thermal components representing a finite range of kT in the hot ISM + 1 hard LMXB component). We show that a simpler model (such as a two-component model) produce a much lower best-fit ZFe. The abundances of a-elements (most accurately determined by Si) is also found to be supersolar and its radio to Fe is close to the solar ratio, suggesting a considerably contribution of heavy elements from Type Ia SNe. We estimate -70% of MFe in the hot ISM originate from Type Ia.