AI Dining Suggestion App

Trying to decide what to eat can sometimes be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as Application Program Interfaces (APIs) for using them. This restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an app that eases the process of finding a restaurant to eat. This app has a Tinder-like user friendly User Interface (UI) design to change the common way that lists of restaurants are presented to users on mobile apps. It also uses the help of Artificial Intelligence (AI) with neural networks to train both supervised and unsupervised learning models that can learn from one\u27s dining pattern over time to make better suggestions at any time

A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Parts of Speech Tagging: Rule-Based

Parts of speech (POS) tagging is the process of assigning a word in a text as corresponding to a part of speech based on its definition and its relationship with adjacent and related words in a phrase, sentence, or paragraph. POS tagging falls into two distinctive groups: rule-based and stochastic. In this paper, a rule-based POS tagger is developed for the English language using Lex and Yacc. The tagger utilizes a small set of simple rules along with a small dictionary to generate sequences of tokens

Algorithms Related to Triangle Groups

Given a finite index subgroup of \PSL_2(\Z), one can talk about the different properties of this subgroup. These properties have been studied extensively in an attempt to classify these subgroups. Tim Hsu created an algorithm to determine whether a subgroup is a congruence subgroup by using permutations \cite{hsu}. Lang, Lim, and Tan also created an algorithm to determine if a subgroup is a congruence subgroup by using Farey Symbols \cite{llt}. Sebbar classified torsion-free congruence subgroups of genus 0 \cite{sebbar}. Pauli and Cummins computed and tabulated all congruence subgroups of genus less than 24 \cite{ps}. However, there are still some problems left to be solved. In the first part of this thesis, we will use the concept of Farey Symbols and bipartite cuboid graphs to determine when two subgroups of \PSL_2(\Z) are in the same conjugacy class in \PSL_2(\Z). We implemented this algorithm, and other related algorithms, with SageMath \cite{baowebsite}. In the second part of the thesis, we will extend these ideas to general triangle groups. Specifically, we will classify some small index conjugacy classes of subgroups of the triangle group $\overline{\triangle}(2,4,6)$

Precision-cut tissue slices: a novel ex vivo model for fibrosis research

Pathological scar formation, i.e. fibrosis, is characterized by a disproportionate production and deposition of extracellular matrix proteins in tissues resulting in loss of organ function. Fibrotic diseases account for up to 45% of worldwide mortality, yet there are no effective antifibrotic therapies currently available. To improve and accelerate antifibrotic drug discovery, there is an urgent need for reliable and reproducible (human) in vitro methods that reflect the cellular diversity that epitomize specific organs. This thesis delineates the successful development of a novel ex vivo/in vitro model for intestinal and renal fibrosis, namely precision-cut intestinal slices (PCIS) and precision-cut kidney slices (PCKS) prepared from murine, rat and human tissue. Our results demonstrated that the slices remain viable during culture and maintain their organ-specific phenotype. Moreover, in both PCIS and PCKS we observed that fibrosis could be induced by either culture activation or treatment with a profibrotic stimulus. Based on these findings, both models were subsequently used to evaluate the efficacy of various putative antifibrotic drugs. Using PCIS, we demonstrated that pirfenidone, LY2109761 and sunitinib could mitigate fibrogenesis on a gene level, warranting further evaluation of these compounds for the treatment of intestinal fibrosis. In addition, we demonstrated that IFNγ could be used to halt renal fibrogenesis. Furthermore, studies with precision-cut liver and intestinal slices revealed that rosmarinic acid elicited organ- and species-specific effects, illustrating the pressing need for good translational models for drug discovery. Taken together, this thesis delineates that precision-cut tissue slices can be used to unravel fibrosis and evaluate the antifibrotic potential of therapeutics

Automated software security activities in a continuous delivery pipeline

Due to the rise of cyberattacks in IT companies, software security has become a topic for debate. Currently, to secure their products, companies often use manual methods, which makes development stalled and inefficient. To speed up a software development lifecycle, security work needs to be integrated and automated into the development process. This thesis will provide an initial solution for automating the security phase into a continuous software delivery process. This solution involves integrating security tools into a Github repository by using Github Actions to create automated vulnerability scanning workflows for a software project. The solution will then be tested and evaluated with three open-source projects and one project from our sponsor, Volue

Isothiouronium Organocatalysts Through Hydrogen Bonding

The field of small‐molecule organocatalysis via noncovalent interactions has attracted the attention of an increasing number of research groups from the academic as well as industrial sectors. Isothiouronium salts have been explored quite recently as a new class of hydrogen‐bonding subunit for the purpose of molecular recognition of anions in supramolecular chemistry. The chemical modification of isothiouroniums is readily varied using synthetic methods to make several types of functional molecular systems. This chapter, for the first time, describes the research on hydrogen‐bonding isothiouronium organocatalysts considering their designed concepts and synthetic applications in both nonstereoselective and stereoselective reactions