Data Science

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One aspect of a growing VR industry that developers have to face is the ethics behind the technology. This includes making sure that technology is readily available and accessible to as large of a population as possible. Current consumer virtual reality (VR) headsets typically utilize two controllers to navigate a virtual environment, leading to accessibility issues for potential users that cannot effectively operate a controller. We propose NaVRgate, a proof of concept idea that removes the need for controllers in which a user uses expressions to navigate a virtual environment. The system utilizes the computer webcam and computer vision face and eye position tracking to capture the nature of expression tracking, with certain positional thresholds representing different facial expressions. To test this system, We design a game environment where a user navigates with either a controller or the face position tracker, collecting a set of orbs scattered around the map as quickly as they can, comparing the efficiency between navigation through the novel computer vision and traditional controller methods. Users are also questioned on the difficulty of use and experience with each control input method. This paper details the process of the development and drafts, to the statistical experiment constructed to determine the efficiency of head gestures.

This paper introduces the concept of insurgency, which stems from planning theory, and presents how it could be adapted to the building of insurgent data. It articulates how informal communities could employ autonomous strategies to build data that counteracts the monopoly of information, which is largely held by the State. The paper presents a brief critique of data on informal settlements by presenting the case of the city of Fortaleza, Brazil. It concludes by suggesting alternative methods for planning, collecting and disseminating data that is reflective of communities’ values and can further their right to the city. Social and political movements generated by minorities are necessary and information is key in transforming grassroots action to legitimate policy. Insurgency in planning and information building provides a point of entry to the larger discussion of who produces information about various segments of society and how that influences their Right to the City.

Artificial Intelligence, as a discipline, has already been permeating countless fields, bringing means and methods to previously unresolved challenges, across industries. The advent of AI in Architecture is still in its early days but offers promising results. More than a mere opportunity, such potential represents for us a major step ahead, about to reshape the architectural discipline.

Our work proposes to evidence this promise when applied to the built environment. Specifically, we offer to apply AI to floor plans analysis and generation. Our ultimate goal is three-fold: (1) to generate floor plans i.e. optimize the generation of a large and highly diverse quantity of floor plan designs, (2) to qualify floor plans i.e. offer a proper classification methodology (3) to allow users to “browse” through generated design options.

Our methodology follows two main intuitions (1) the creation of building plans is a non-trivial technical challenge, although encompassing standard optimization technics, and (2) the design of space is a sequential process, requiring successive design steps across different scales (urban scale, building scale, unit scale). Then, in order to harness these two realities, we have chosen nested Generative Adversarial Neural Networks or GANs. Such models enable us to capture more complexity across encountered floor plans and to break down the complexity by tackling problems through successive steps. Each step corresponding to a given model, specifically trained for this particular task, the process can eventually evidence the possible back and forth between humans and machines.

Plans are indeed a high-dimensional problem, at the crossroad of quantifiable technics, and more qualitative properties. The study of architectural precedent remains too often a hazardous process, that negates the richness of the number of existing resources while lacking in analytical rigor. Our methodology, inspired by current Data Science methodologies, aims at qualifying floor plans. Through the creation of 6 metrics, we propose a framework that captures architecturally relevant parameters of floor plans. On one hand, Footprint Shape, Orientation, Thickness & Texture are three metrics capturing the essence of a given floor plan’s style. On the other hand, Program, Connectivity, and Circulation are meant to depict the essence of any floor plan organization.

In a nutshell, the machine, once the extension of our pencil, can today be leveraged to map architectural knowledge, and trained to assist us in creating viable design options.

Related Articles:
• Background & Framework: https://medium.com/built-horizons/the-advent-of-architectural-ai-2fb6b6d0c0a8
• Organization: https://medium.com/built-horizons/ai-architecture-4c1ec34a42b8
• Style: https://medium.com/built-horizons/architecture-style-b7301e775488

Thesis PDF Online Viewer: https://view.publitas.com/harvard-university/ai-architecture-thesis-harvard-gsd-stanislas-chaillou/page/1

Dash, pode ser facilmente comparado ao Shiny do R. Ambos possuem o mesmo objetivo a geração de páginas interativas (como Dashboards) para visualização dos dados. Criado totalmente com Python e se fossemos resumi-lo em três simples palavras diríamos: Interatividade, Incorporabilidade e Estética. Em poucas linhas de códigoé possível criar uma visão dos dados de modo que o usuário possa pensar que passamos o ano inteiro em sua montagem. E realmente isso fazia falta no Python.

Purpose: To determine the effect of caffeine on blink rate of adults Method: A total of 100 subjects aged 18-50 years participated in the study. The subjects were allowed to rest for 5 minutes and then conversed for 1 minute during which the subjects talked with the researcher on general issues to make for relaxation. The number of blinks per minute was noted and recorded. After which the subjects were given 1.5 grams of Nescafe coffee powder, mixed with 50 millilitres of water, under room temperature. Each subject drank the coffee drink comfortably and the exact time of coffee intake noted. Number of blinks at 15 minutes was taken after each subject consumed Nescafe coffee. Blink rate was taken for each subject at 30, 45 and 60, minutes after coffee consumption. Results: Mean baseline blink rate was 16.17 blink/min. 15 minutes post consumption of coffee, mean blink rate decreased to 14.00 blink/min. 30 minutes post consumption of coffee, mean blink rate decreased to 12.89 blink/min. 45 minutes post consumption of coffee, mean blink rate further decreased to 11.03 blink/min. Mean blink rate at 60 minutes post consumption of coffee was 14.84 blink/min. Conclusion: This study showed that caffeine significantly decreased blink rate during conversation. Blink rate at 60 minutes was noted to be gradually returning to baseline blink rate. This suggests that the effects if caffeine on blink rate in adults may last for an hour before it gradually diminishes. This study also compared the effect of caffeine based on gender and found no significant difference between blink rate in both sexes. However, there was an effect in respect to age. Older people had a significantly decreased blink rate after consumption of coffee with respect to time.

Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but also very challenging. In recent years, several drugs have been withdrawn from the market due to interaction related Adverse Events (AEs). Currently, the US Food and Drug Administration (FDA) and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.

We describe a predictive model, applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. We constructed a 352 drug DDI network from a 2011 snapshot of a widely-used drug safety database, which contains 3 700 established DDIs, and used it to develop the proposed model for predicting future DDIs. The target similarity for all selected pairs of drugs in DrugBank was computed to identify DDI candidates. The proposed model mainly follows two distinct approaches: the first one is ‘Which forces the preservation of existing (known) DDIs’ and the other one is ‘without forced to preserve existing DDIs.’ Predictions were made under each of these approaches using three different techniques: target similarity score, side effect similarity (P-score) and resulting score.

The methodology was evaluated using Drugbank 2014 snapshot as a gold standard for the same set of drugs. The proposed model generates novel DDIs with an average accuracy of 95% for force to preserve existing (known) DDIs. Average accuracy for without forced to preserve existing DDIs is 92%. These two approaches also give average AUC (Area Under the Curve) of 0.9834 and 0.8651 respectively.

The results presented in this study demonstrate the usefulness of the proposed network based drug-drug interaction methodology as a promising approach. The method described in this article is very simple, efficient, and biologically sound.

Keywords— Drug-drug Interactions, Adverse Events, Target similarity score, P-score.

This paper presents an application of statistical arbitrage to foreign exchange markets and specifically on instruments that obey a triangle rule/law. These instruments have a long-term cointegration relationship which leads to the mean-reversion behaviour evident in their data. Metatrader 4 is used to develop an indicator which in turn is used to gather and process price data and inform an investor on when and how to invest in a particular combination of three instruments obeying the triangle rule. That is, a simple strategy is outlined and its long-term profitability is demonstrated using expectation value under some mild assumption of stability of cointegration model parameters.

Obtaining the desired dataset is still a prime challenge faced by researchers while analysing Online Social Network (OSN) sites. Application Programming Interfaces (APIs) provided by OSN service providers for retrieving data impose several unavoidable restrictions which make it difficult to get a desirable dataset. In this paper, we present an iMacros technology-based data crawler called IMcrawler,capable of collecting every piece of information which is accessible through a browser from the Facebook website within the legal framework reauthorized by Facebook.The proposed crawler addresses most of the challenges allied with web data extraction approaches and most of the APIs provided by OSN service providers. Two broad sections have been extracted from Facebook user profiles, namely, Personal Information and Wall Activities. The collected data is pre-processed into two datasets and each data set is statistically analysed to draw semantic knowledge and understand the several behavior...

Presently, great accomplishment on speechrecognition, computer-vision and natural-language processing has been achieved by deep-neural networks. To tackle the major trouble in synergetic or collaborative-filtering on the idea of hidden feedback; in this task we concentrated intensively on the techniques based on neural networks. Although a few latest researches have employed deep learning, they mostly used it to sculpt auxiliary facts, along with textual metaphors of objects and acoustic capabilities of music's. When it involves the major aspect in synergetic filtering; the communication between customer and object capabilities, still resorted to matrix factorization and implemented a core product on the hidden capabilities of customers and objects. We present a popular framework named Artificial Neural Synergetic Filtering (ANSF) to substitute the core makeup with a neural design which could be very efficient to analyze a data with a random feature. ANSF is ordinary and might specific; popularize matrix-factorization beneath its frame work. To improvise ANSF modeling with non-linearity's we propose to leverage a multi-layer perceptron to investigate customer-object communication function. In-depth experiments on actual-global databases display big improvisation of our proposed ANSF over the latest techniques. Investigational results manifest that the application of core layers of artificial neural networks gives improvised overall performance.

Victims of road traffic accidents face severe health problems on-site or after the event when they arrive at hospital lately in their emergency cycle. Road traffic accident has negative effect on the physical, social and emotional security of human lives which often lead to mortality, illness, pain, grief and even disability. This paper proposes a scheme that reduces the severity of road traffic accidents given its inevitable occurrence. The rational is to search for nearest hospitals to the accident location using Dijkstra algorithm and Fuzzy logic to recommend suitable hospitals out of list of nearest hospitals to timely attend to the emergency situation considering factors such as distance, severity of the accident, available facilities in the hospitals and other factors. The obtained results showed the practicability of the system to recommendation of quick solution to accident emergencies.

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented ...

Secure information distribution is a strategic capability as significant as weapons systems and tactics to military operations. The Department of Defense has recognized the importance of establishing and maintaining secure distributed file systems and explores the practicality and applicability of one such system, Trusted Ficus File System (TRUFFLES), in the DoD infrastructure. Integrated into this research are discussions of Privacy Enhanced Mail (PEM), which is currently an integral part of the TRUFFLES implementation. This thesis concludes with a discussion of the actual installation of a PEM reference implementation, and future requirements for the TRUFFLES installation at the Naval Postgraduate School.

Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. At the core is data. Data science is ultimately about using this data in creative ways to generate business value.
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Urban trees play a key role in reducing greenhouse gas emissions, cleaning air, promoting physical activity, and improving mental health. However, it is still largely unknown how the density and species of urban street trees may impact local public health. This study demonstrates how open data mining and large-scale spatial data integration can contribute to deeper insights into the effects of urban forestry. We analyze the impact of the spatial distribution of street trees by species in New York City by combining crowd-sourced tree census data-which includes geolocation, species, size, and condition for each of 652,169 street trees-with pollen activity, allergen severity, land use, housing conditions, and neighborhood demographic data. We further integrate neighborhood asthma hospitalization and emergency department visit rates and air quality data (PM2.5) to investigate how street trees impact local air quality and the prevalence of acute respiratory illness. Using a geographically weighted regression model, the results indicate that although a greater concentration of trees contributes to better local air quality, species with severe allergenicity can increase local asthma hospitalization rates in vulnerable populations, controlling for other covariates.

The term “behavioral” has become a hot topic in recent years in various disciplines; however, there is yet limited understanding of what theories can be considered behavioral theories and what fields of research they can be applied to. Through a cross-disciplinary literature review, this article identifies sixty-two behavioral theories from 963 search results, mapping them in a diagram of four groups (factors, strategies, learning and conditioning, and modeling), and points to five discussion points: understanding of terms, classification, guidance on the use of appropriate theories, inclusion in data-driven research and agent-based modeling, and dialogue between theory-driven and data-driven approaches.

The classic conceptual modeling around business processes followed by the 'bus matrix' methodology of designing the data cubes of data warehouses (Kimball & Ross 2013). For a serious system, such a quantity of management questions and dimensions, the bus matrix results a difficult-to-understand conceptual data model. The subject of automation and conceptual design-to which many individual methods already have been developed-are relevant topics in today's literature also. In the 2010s data warehouse projects were realized in Hungarian higher education to inform the decision makers of the universities about their own institutions. As we participated in this project in 2009-2010, we faced that our bus matrix at the end contained about 80-120 indicators with nearly 200 dimensions (dimensional attributes), therefore we worked on the early stenography to formalize the management question. We provide a kind of 'business intelligence problem solving thinking' and a kind ...

We performed eye color classification with machine learning on sequenced genomes from the Harvard Personal Genome Project using support vector classifiers on 78 genomes that were tiled by Curoverse. The classifier consistently predicted eye color to 95% accuracy. From the coefficients of the classifier, we were then able to locate rs12913832, a SNP in the OCA2/HERC2 region responsible for producing the blue eye color, and we proved this located gene is consistent with previous literature.

International Conference on Data Science and Applications (DSA 2020) will act as a major
forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Data Science and Applications. It will also serve to facilitate the
exchange of information between researchers and industry professionals to discuss the latest
issues and advancement in the area of Data Science & Applications

Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.

The information age is characterized by the huge amount of data available to people. The exact amount of
data in the globe was estimated at about 4.4 zettabytes as at 2013 and this is assumed to rise to about 44
zettabytes by 2020. Arguably, a zettabyte is equivalent to 44 trillion gigabytes. The Internet is regarded
as the largest source of data, an enormous source of information with a great stock of various web pages &
hyperlinks. The information on the internet can be queried with the aim of enabling users to find
whatever they want. In the same vein, the number and diversity of internet users continue to increase.
Millions of users all over the world search the Web on a daily basis with the aim of finding the right
answer or solution to problems. The Web has revolutionized access to digitally available data; as such,
web information search and retrieval have become key aspects of human interactions. Information
retrieval is a complex process, the study of which has attracted a lot of research efforts. This paper carried
out a systematic review of information retrieval, search, andranking algorithms. The objective is to
provide a theoreicalk framework and identify research gaps that can serve as a basis for the development
of an enhanced recursive model for individualized search