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At the same time, the work is of very high practical relevance for the manufacturing industry and thus has a strong economic and thus also social impact. To attack this problem, we propose a novel table cell search framework, in which we develop chain representations for both table cells and questions, and further employ deep neural networks to semantically match them. Hoax articles are long pieces of plain text that are less coherent and created by more recent editors, compared to nonhoax articles. We develop a graph-based decluttering algorithm that iteratively removes suspicious edges that malicious users use to masquerade as benign users, which outperforms existing graph algorithms to. Driven by these two observations, this dissertation systematically mines disparate sources including knowledge bases, texts, tables and human networks for question answering, by developing novel techniques on text mining, network analysis and human behavior understanding. As is known to all, the big data age contributes large-scale diversified information sources, such as structured knowledge bases (KBs), unstructured texts, and semi-structured tables. Second, what current automated QA systems can achieve is still limited in many situations, e.g., when questions have complex language structures and when finding answers involves a lot of reasoning, which necessitates the exploitation of human intelligence for question answering and problem solving. Analysis of large scale time series data collected from diverse applications has created new multifaceted challenges and opportunities. Our table cell search framework is either comparable to or significantly outperforms state-of-the-art QA systems by at least 56.7%. Moreover, we empirically verify that tables supply rich knowledge that might not exist or is difficult to be identified in existing KBs. This aspect will become immensely important in view of the advancing digitalization and networking and the increasing importance of software in the industrial, governmental and private sectors. For estimation of the subspaces, we propose an efficient greedy variable selection algorithm. Interactive training leads to reduced effort while still providing accurate results. They contribute to Fraunhofer's leading position in research in Europe. Wells Outstanding Dissertation Award went to Ritu Bohat, Biology Ph.D. graduate from the Department of Biology and Biochemistry. Through collaborations and partnerships with companies such as Texas EnteroSorbents, U.S. Silica, Halliburton and Great Plains Processing, the multicomponent sorbents Wang has developed already have a project-to-field pathway. His research has fundamentally changed the landscape of energy storage and computing,” Banerjee said. Bohat’s dissertation was “Targeting PI3K Isoforms to Improve Effectiveness of T cells Mediated Immunotherapy.” . Additionally, thanks to DDF's presentation grant, I will get chances to present my work at influential conferences in my field. We find that sockpuppets are created that vary in their deceptiveness (i.e., whether they pretend to be different users) and their supportiveness (i.e., if they support arguments of other sockpuppets controlled by the same user). Interesting or important facts and statements often appear repeatedly in copious texts including news articles and Wikipedia-like pages. We find several striking characteristics of malicious behavior that are very distinct from those of benign behavior. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. The intelligence possessed by current machines is still limited in many aspects. The Dissertation in Practice must have been successfully defended prior to the submission deadline and must have been defended within the past year. To address this challenge, we propose two solutions: (i) alleviating the impact of major latent confounders using sparse plus low-rank decomposition and (ii) eliminating the impact of all latent confounders using the prior information about the delays of the confounding paths Andrews’s research focuses on the design, discovery and utilization of novel compounds to improve computing efficiency and, thus, reduce energy consumption. Maastricht University is proud to be recognized as a top marketing department by the global research cooperations on state-of-the-art service research and by the different research awards that Maastricht cherishes. The recipient(s) will be encouraged to submit an article to IE for publication based on the DiP. We show how to effectively model the noisy distant supervision for relationship extraction, and how to avoid the error propagation usually happened in incremental extraction pipeline by integrating typing of entities and relationships in a principled framework. We
also claim that similarity is a subproblem in many applications with multiple graphs, and contribute methods for network alignment and similarity.
Since 2015, the Fraunhofer ICT Dissertation Award has been presented annually at Group level. To address this challenge, we propose two solutions: (i) alleviating the impact of major latent confounders using sparse plus low-rank decomposition and (ii) eliminating the impact of all latent confounders using the prior information about the delays of the confounding paths. This is present in the form of malicious users, such as trolls, sockpuppets and vandals, and misinformation, such as hoaxes and fraudulent reviews. The intelligence possessed by current machines is still limited in many aspects. We are inundated with vast amounts of text data, written in different genres (from grammatical news articles and scientific papers to noisy social media posts), covering topics in various domains (e.g., medical records, corporate reports, and legal acts). We cast the problem as a low-rank tensor learning problem with side information incorporated via a graph Laplacian regularization. Apart from texts, we observe that informative tabular data are also pervasive and valuable for QA. We will collect the submissions on behalf of the academic selection committee. How to utilize knowledge dispersed in these disparate sources to answer questions becomes a challenge. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information. In this thesis, we have studied the key challenges in large scale multivariate time series analysis and proposed novel and scalable solutions. However, the safety, usability and reliability of these platforms is compromised by the prevalence of online malicious behavior---for example 40% of users have experienced online harassment. Hoax articles are long pieces of plain text that are less coherent and created by more recent editors, compared to non-hoax articles The department will be responsible for forwarding the nominee’s application package to the Associate Dean for Graduate Studies Bohat’s dissertation was “Targeting PI3K Isoforms to Improve Effectiveness of T cells Mediated Immunotherapy ” One main weakness of existing text-based QA systems is the lack of capability in making sense of strings. Interesting or important facts and statements often appear repeatedly in copious texts including news articles and Wikipedia-like pages. Next, based on the properties of Wikipedia articles, we develop a supervised machine learning classifier to predict whether an article is a hoax, and another that predicts whether a pair of accounts belongs to the same user, both with very high accuracy. Second, what current automated QA systems can achieve is still limited in many situations, e.g., when questions have complex language structures and when finding answers involves a lot of reasoning, which necessitates the exploitation of human intelligence for question answering and problem solving. We propose two solutions to address this challenge: (i) a state space model based on generalized extreme value distribution to model the important case of extreme value time series and (ii) a semi-parametric approach using copulas for the general setting. Additionally, thanks to DDF's presentation grant, I will get chances to present my work at influential conferences in my field. Also, the fellowship will allow me to be fully dedicated to my dissertation research, which is a valuable opportunity for me. Wells Outstanding Dissertation Award is done at the same time you submit your committeeapproved dissertation to the College. We first create the first vandal early warning system that accurately predicts vandals using very few edits. Driven by these two observations, this dissertation systematically mines disparate sources including knowledge bases, texts, tables and human networks for question answering, by developing novel techniques on text mining, network analysis and human behavior understanding. Please note: Nominators should provide enough detail for the selection committee to be able to conduct an appropriate evaluation of the work. Wells Outstanding Dissertation Award went to Ritu Bohat, Biology Ph.D. graduate from the Department of Biology and Biochemistry. A panel on Best Practices of KDD in Asia will be organized with the Summit. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities.
Additionally, thanks to DDF's presentation grant, I will get chances to present my work at influential conferences in my field. Our method significantly outperforms all the alternatives with more than 75% accuracy gain under different quality measures. Can computational systems capture and represent different relations between biomedical entities from massive and rapidly emerging life science literature. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. This year, dissertations in Biological and Life Sciences and Humanities and Fine Arts are eligible for national honors. For this purpose, results from probability theory are translated into algorithms for testing software. However, the safety, usability and reliability of these platforms is compromised by the prevalence of online malicious behavior---for example 40% of users have experienced online harassment. We also claim that similarity is a subproblem in many applications with multiple graphs, and contribute methods for network alignment and similarity. As demonstrated by exemplar intelligent systems including IBM Watson, Google Now, Apple Siri, Microsoft Cortana, and Amazon Echo, QA techniques enjoy tremendous potential in revolutionizing the way we interact with devices and data, e.g., by allowing voice commands to automate complicated tasks like planning a trip, and directly answering questions to understand domain- specific data such as medical forum posts, product reviews and online programming tutorials. This thesis contributes (i) scalable, principled algorithms that combine globality with locality to understand graphs, and (ii) applications in two areas. Can computational systems automatically identify various real-world entities mentioned in a new corpus and use them to summarize recent news events reliably. The principle of self-organization through distributed planning is applied. We cast the problem as a lowrank tensor learning problem with side information incorporated via a graph Laplacian regularization. Interactive training leads to reduced effort while still providing accurate results. Therefore, I am very grateful for the support of DDF. For students who are graduating in the summer, the same deadline applies for submission of the committee-approved dissertation to NSM and for application to the dissertation award. We find that sockpuppets are created that vary in their deceptiveness (i.e., whether they pretend to be different users) and their supportiveness (i.e., if they support arguments of other sockpuppets controlled by the same user). Since potentially vulnerable software is used to control critical infrastructures, industrial plants, production processes, medical devices and automated driving vehicles, practical approaches to solutions for increasing software and IT security have an enormous economic and social impact. The committee-approved dissertation is the dissertation which includes the corrections required by your committee and committee chair after your defense, but before it is reviewed by the college reader. This thesis presents research spanning two aspects of malicious behavior: characterization of their behavioral properties, and development of algorithms and models for detecting them The department will be responsible for forwarding the nominee’s application package to the Associate Dean for Graduate Studies. To address this challenge, we propose two solutions: (i) alleviating the impact of major latent confounders using sparse plus lowrank decomposition and (ii) eliminating the impact of all latent confounders using the prior information about the delays of the confounding paths. Firstly, it is encouraging because it means that my work is recognized by the UMN community, by people outside of my area. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to combine them. This fellowship encourages me to delve into the fundamental study and provides me an opportunity to effectively wrap up thesis research. This is present in the form of malicious users, such as trolls, sockpuppets and vandals, and misinformation, such as hoaxes and fraudulent reviews Andrews’s research focuses on the design, discovery and utilization of novel compounds to improve computing efficiency and, thus, reduce energy consumption. We show how to effectively model the noisy distant supervision for relationship extraction, and how to avoid the error propagation usually happened in incremental extraction pipeline by integrating typing of entities and relationships in a principled framework. Question answering (QA) systems that can precisely answer user questions are becoming more and more desired, in contrast to traditional search engines only retrieving lengthy web pages.
We develop a graph-based decluttering algorithm that iteratively removes suspicious edges that malicious users use to masquerade as benign users, which outperforms existing graph algorithms to. At the same time, the work is of very high practical relevance for the manufacturing industry and thus has a strong economic and thus also social impact. Driven by these two observations, this dissertation systematically mines disparate sources including knowledge bases, texts, tables and human networks for question answering, by developing novel techniques on text mining, network analysis and human behavior understanding. Therefore, I am very grateful for the support of DDF. They contribute to Fraunhofer's leading position in research in Europe. To address this challenge, we propose two solutions: (i) alleviating the impact of major latent confounders using sparse plus lowrank decomposition and (ii) eliminating the impact of all latent confounders using the prior information about the delays of the confounding paths. The principle of self-organization through distributed planning is applied. Interesting or important facts and statements often appear repeatedly in copious texts including news articles and Wikipedia-like pages. Maastricht University is proud to be recognized as a top marketing department by the global research cooperations on state-of-the-art service research and by the different research awards that Maastricht cherishes. This thesis focuses on developing principled and scalable methods for extracting typed entities and relationship with light human annotation efforts, to overcome the barriers in dealing with text corpora of various domains, genres and languages. Through her analysis, Stover explores power relations between Victorian adults and children. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification Andrews’s research focuses on the design, discovery and utilization of novel compounds to improve computing efficiency and, thus, reduce energy consumption. We find that sockpuppets are created that vary in their deceptiveness (i.e., whether they pretend to be different users) and their supportiveness (i.e., if they support arguments of other sockpuppets controlled by the same user). Can computational systems automatically identify various real-world entities mentioned in a new corpus and use them to summarize recent news events reliably. Therefore, we investigate an important yet largely under-addressed problem: Given millions of tables, how to precisely mine table cells to answer a user question. In view of the fundamental and still increasing importance of IUK technologies for society, high attention should be paid to cognitive security. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to combine them. To be considered for the award, do the following no later than the above deadlines. This year, dissertations in Biological and Life Sciences and Humanities and Fine Arts are eligible for national honors. Kunze Endowed Graduate Student Award for most outstanding research by a current doctoral student. Can computational systems capture and represent different relations between biomedical entities from massive and rapidly emerging life science literature. Please note: Nominators should provide enough detail for the selection committee to be able to conduct an appropriate evaluation of the work. The first study in his dissertation found that outreach programs for cancer treatments were significantly more effective when personalized according to the patient’s socioeconomic status. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. We are inundated with vast amounts of text data, written in different genres (from grammatical news articles and scientific papers to noisy social media posts), covering topics in various domains (e.g., medical records, corporate reports, and legal acts). Wells Outstanding Dissertation Award is done at the same time you submit your committee-approved dissertation to the College. Distinguished Dissertation Awards honor current or former students whose dissertations make a significant, impactful contribution to their discipline. As is known to all, the big data age contributes large-scale diversified information sources, such as structured knowledge bases (KBs), unstructured texts, and semi-structured tables. We also claim that similarity is a subproblem in many applications with multiple graphs, and contribute methods for network alignment and similarity.
