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Title: APPLICATION SOFTWARE DESIGN WITH THE FEATURE LANGUAGE EXTENTION
Creator: Maruyama, Shuichi
Date: 2013-04-23, 2013-05
Description: When implemented with existing mainstream programming languages, the code of interacting features will inevitably entangle in the same...
Show moreWhen implemented with existing mainstream programming languages, the code of interacting features will inevitably entangle in the same reusable program unit of the programming language such as a method. Interacting features are very common in software applications. Program entanglement destroys separation of concern, making the software difficult to develop, maintain and reuse. The Feature Language Extensions (FLX) is a set of programming language constructs that allow the programmer to develop interacting features as independently reusable program modules. This thesis addresses two questions: how to design software with FLX and whether programs that can be written in a procedural language such as Java can also be written in FLX. We illustrate our results with examples from a computer blackjack game that is implemented using FLX. For the first question, we introduce a set of seven design guidelines. Some of these guidelines are introduced for good programming practices: so that there is better separation of concern and so that FLX is complementary to object oriented design. Some of them are developed so that features written following them will be reusable, and when the features are integrated with other features, they do not need to be changed. A procedural programming language such as Java has constructs that allows a programmer to specify program units to be executed in sequence, conditionally, iteratively and recursively. Previous papers had given examples on how to implement the first two types of execution flows with FLX. In this thesis, we show how to implement the other two types of execution flows.
M.S. in Computer Science, May 2013
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Title: Feasibility and Properness in Linear Interference Alignment: Flow Tests, Sufficient Conditions, and Approximation Algorithms.
Creator: Al-Dhelaan, Fahad Abdullah
Date: 2019
Description: Interference forms a major challenge in our understanding of the capacity of wireless networks and our ability to achieve this capacity....
Show moreInterference forms a major challenge in our understanding of the capacity of wireless networks and our ability to achieve this capacity. Rather than scheduling transmissions to avoid interference, recent techniques allow for interference to be neutralized and for simultaneous transmission of messages.Linear interference alignment in MIMO networks is the technique of aligning messages, by the transmitters through the use of precoding matrices, so that the undesired messages occupy some minimal sub-space upon their arrival at an unintended receiver. The overlapping of the sub-spaces where these interfering messages fall allows the receiver to neutralize them with minimal dedication of its resources through the application of a decoding matrix.The linear interference alignment problem is to design these precoding and decoding matrices. It has been shown to be NP-hard in the literature.A network is called feasible if such a solution exists. Even deciding whether some network instance is feasible, is non-trivial. The problem of deciding feasibility was shown to be NP-hard in the literature, for constant channel coefficients.We focus on finding efficient and robust feasibility tests in the case of generic channels, where the computational complexity is unknown. We provide efficient and robust tests for the necessary condition of properness, which had previously been identified in the literature but given no efficient tests in the general case.We identify several conditions, each being sufficient for feasibility. We study their relationships and the computational complexity of testing for them. We provide polynomial-time maximum flow test for one sufficient condition in the case of uniform demands. In the case of uniform demands which divide the number of antennas at all receivers or all transmitters, we show that these sufficient and necessary conditions are equivalent with feasibility, thereby admitting efficient maximum-flow tests.We identify a subset of feasible instances where the decoding and precoding matrices can be designed in polynomial-time. Furthermore, we show that any proper instance is within a constant factor of a one of these instances. Then, we provide efficient constant approximation algorithms for the problems of maximizing demand and minimizing antennas such that an instance is feasible.
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Title: PROVENANCE FOR TRANSACTIONAL UPDATES
Creator: Arab, Bahareh Sadat
Date: 2019
Description: Database provenance explains how results are derived by queries. However, many use cases such as auditing and debugging of transactions...
Show moreDatabase provenance explains how results are derived by queries. However, many use cases such as auditing and debugging of transactions require understanding of how the current state of a database was derived by a transactional history. We introduce an approach for capturing the provenance of transactions. Our approach does not just work for serializable concurrency control protocols but also for non-serializable protocols including snapshot isolation. The main drivers of our approach are a provenance model for queries, updates, and transactions and reenactment, a novel technique for retroactively capturing the provenance of tuple versions. We introduce the MV-semirings provenance model for updates and transactions as an extension of the existing semiring provenance model for queries. Our reenactment technique exploits the time travel and audit logging capabilities of modern DBMS to replay parts of a transactional history using queries. Importantly, our technique requires no changes to the transactional workload or underlying DBMS and results in only moderate runtime overhead for transactions. We discuss how our MV-semirings model and reenactment approach can be used to serve a wide variety of applications and use cases including answering of historical what-if queries which determine the effect of hypothetical changes to past operations of a business, post-mortem debugging of transactions, and to create private data workspaces for exploration. We have implemented our approach on top of a commercial DBMS and our experiments confirm that by applying novel optimizations we can efficiently capture provenance for complex transactions over large data sets.
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Title: INFLUENCE OF TIE STRENGTH ON HOSTILITY IN SOCIAL MEDIA
Creator: Radfar, Bahar
Date: 2019
Description: Online anti-social behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has...
Show moreOnline anti-social behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities.While prior work has proposed automated methods to identify toxic situation such as hostility, they only focused on individual words. While only a bag of keywords is applied to detect hostility, this is not enough as words might have different meaning based on the relationship between participants of the discussion. In this paper, we considered the friendship between the sender and the target of a hostile conversation. First, we studied the characteristic of different types of relationship. Then, we set our goal to be more accurate hostility detection with reduced wrong red flags.Thus, we aim to detect both the presence and intensity of hostile comments based on linguistic and social features from our well-defined relationships. To evaluate our approach, we introduce a corpus of over 12K annotated Twitter tweets from over +170,000 tweets. Next, we extracted useful features such as relationship type and length of the tweet to feed into our Long Short Term Memory(LSTM) and Logistic Regression(LR) classifier. By considering the relationship type in the classifier model we improved the hostility detection AUC by close to 5 % comparing to the baseline method. Also, the F-1 score increased by 4 % as well.
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Title: CYBER PHYSICAL SYSTEM WITH COUPLED NETWORKS: SECURITY AND PRIVACY
Creator: Zhao, Jing
Date: 2019
Description: With the development of cyber physical systems, people and electronic devices are connected via various networks. In many scenarios, different...
Show moreWith the development of cyber physical systems, people and electronic devices are connected via various networks. In many scenarios, different networks are strongly coupled with each other, e.g. power grid is strongly coupled with the communication network in smart grid. On one hand, such coupling brings benefits such as improved efficiency and quick response to system service exceptions. However, the coupling of different networks also brings security and privacy problems. In this thesis we study two scenarios: the the secure coupling of visual connection with short range pairwise communication and privacy aware coupling of smart home with smart grid. For the first scenario, we propose SCsec, a secure screen-camera communication system, which achieves secure one-way communication. The throughput of SCsec is comparable to current screen communication systems. For the second scenario, we propose a novel randomized battery load hiding algorithm which ensures differential privacy for smart homes with smart meters.
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Title: ACCELERATING I/O USING DATA LABELS: A CONTENTION-AWARE, MULTI-TIERED, SCALABLE, AND DISTRIBUTED I/O PLATFORM
Creator: Kougkas, Antonios
Date: 2019
Description: Parallel file systems (PFS) have been the dominant storage solution in High-Performance Computing (HPC) for several years. However, as we move...
Show moreParallel file systems (PFS) have been the dominant storage solution in High-Performance Computing (HPC) for several years. However, as we move towards the exascale era, PFS have several limitations, such as scalability, complexity, metadata, data synchronization, and access latency, which can seriously affect storage's performance. These challenges along with the unprecedented data explosion accentuated the research conundrum known as I/O bottleneck. Moreover, the extreme computing scale, that exascale machines promise, brings forward another important limitation of the existing I/O path. Multiple large scientific applications will be accessing shared storage resources at the same time, and thus, will be competing. This phenomenon is known as cross-application I/O interference and is one of the most challenging performance degradation factors, even in today's petascale. To address some of the above issues, modern system designs have introduced a new memory and storage hierarchy, filled with novel special hardware technologies, that aims to ease, in a sense, the I/O bottleneck. However, software for management, I/O scheduling, and efficient data movement in this new complicated landscape of multi-tiered I/O infrastructure is limited at best. The added complexity of data access using buffering resources needs to be addressed and is of the utmost priority of several scientific sites and communities. This study makes steps towards I/O acceleration in HPC by proposing: a) a new subsystem for the I/O convergence between HPC and BigData storage ecosystems, b) a new subsystem equipped with several advanced I/O buffering techniques for the deep memory and storage hierarchy, and c) a new subsystem that implements several I/O scheduling algorithms to prevent the negative effects of I/O contention, and d) a new storage system that relies on a novel abstract notion of a data label that allows the I/O system to provide storage flexibility, versatility, agility, and malleability. The proposed work has been evaluated and results suggest that substantial improvements in I/O performance have been achieved.
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Title: ACTIVE INFERENCE FOR PREDICTIVE MODELS OF SPATIO-TEMPORAL DOMAINS
Creator: Komurlu, Caner
Date: 2019
Description: Active inference is the method of selective information gathering during prediction in order to increase a predictive machine learning model's...
Show moreActive inference is the method of selective information gathering during prediction in order to increase a predictive machine learning model's prediction performance. Unlike active learning, active inference does not update the model, but rather provides the model with useful information during prediction to boost the prediction performance. To be able to work with active inference, a predictive model needs to exploit correlations among variables that need to be predicted. Then the model, while being provided with true values for some of the variables, can make more accurate predictions for the remaining variables.In this dissertation, I propose active inference methods for predictive models of spatio-temporal domains. I formulate and investigate active inference in two different domains: tissue engineering and wireless sensor networks. I develop active inference for dynamic Bayesian networks (DBNs) and feed-forward neural networks (FFNNs).First, I explore the effect of active inference in the tissue engineering domain. I design a dynamic Bayesian network (DBN) model for vascularization of a tissue development site. The DBN model predicts probabilities of blood vessel invasion in regional scale through time. Then utilizing spatio-temporal correlations between regions represented as variables in the DBN model, I develop an active inference technique to detect the optimal time to stop a wet lab experiment. The empirical study shows that the active inference is able to detect the optimal time and the results are coherent with domain simulations and lab experiments.In the second phase of my research, I develop variance-based active inference techniques for dynamic Bayesian networks for the purpose of battery saving for wireless sensor networks (WSN). I propose the expected variance reduction active inference method to detect variables that reduce the overall variance the most. I first propose a DBN model of a WSN. I then compare the prediction performance of the DBN with Gaussian processes and linear chain graphical models on three different WSN data using several baseline active inference methods. After showing that DBNs perform better than the baseline predictive models, I compare the performance of expected variance reduction active inference method with the performances of baseline methods on the DBN, and show the superiority of the expected variance reduction on the three WSN data sets.Finally, to address the inference complexity and the limitation of representing linear correlations due to Gaussian assumption, I replace the DBN representation with a feed-forward neural network (FFNN) model. I first explore techniques to integrate observed values into predictions on neural networks. I adopt the input optimization technique. Finally, I discover two problems: model error and optimization overfitting. I show that the input optimization can mitigate the model error. Lastly, I propose a validation-based regularization approach to solve the overfitting problem.
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Title: Machine Learning at the Bureau of Labor Statistics
Creator: Ellis, Robert, Kannan, Vinesh
Date: 2019-11-21
Description: Vinesh Kannan (CS '19) shares his experiences working as a...
Show moreVinesh Kannan (CS '19) shares his experiences working as a data science fellow at the Bureau of Labor Statistics (BLS). Vinesh worked on the team that produces occupation and wage data used by policymakers, hiring staff, job seekers, and researchers across the country. He helped improve machine learning systems at the BLS: automatically identifying problematic training data and classifying rare jobs. Vinesh offers advice for students who may be interested in applying for the 2020 Civic Digital Fellowship, a program that recruits university students at all levels to spend a summer working on civic technology projects with various federal agencies.
Sponsorship: College of Science, Department of Computer Science, Department of Applied Mathematics, Machine Learning at IIT
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Title: Concurrency and Locality Aware GPGPU Thread Group Scheduling
Creator: Nosek, Janusz M
Date: 2018
Description: Graphics Processing Units (GPUs) once served a limited function for rending of graphics. With technological advances, these devices gained new...
Show moreGraphics Processing Units (GPUs) once served a limited function for rending of graphics. With technological advances, these devices gained new purposes beyond graphics. Most modern GPUs have exposed their APIs to allow processing of data beyond the display, thus leading to a revolution in computing where instructions and intensive tasks can be offloaded to these now General Purpose Graphical Processing Units (GPGPUs). Many compute and memory intensive tasks have utilized GPGPUs for acceleration and these devices are especially prevalent in the financial, pharmaceutical and automotive industries. As computing resources have increased exponentially, memory resources have not and now create a limiting factor known as the memory wall. GPUs have been designed as an application specific processing unit for the streaming data access patterns found in graphical applications. They are successful at their original purpose, but when extended to general purpose problems, they meet the same memory wall data access problem as their CPU counterparts; they can be more susceptible to the effects latency due to the locality and concurrency of instructions beside data. This thesis reviews the current GPGPU landscape, including the design of current scheduling systems, GPGPU architecture, as well as a way of computing and describing the memory access penalty with Concurrent Average Memory Access Time (C-AMAT). We will also demonstrate the current GPGPU landscape, including design of schedulers, simulators as well as how Concurrent Average Memory Access Time (C-AMAT) can be computed. We have devised a solution to manipulate the number of scheduled thread groups to allow a GPGPU’s processing units to match their current memory states defined by C-AMAT. Our solution results in the increase in IPC, the reduction in C-AMAT and decrease in memory misses. The solution also has different effects on different types of computing problems, with highest improvements achieved in compute intensive memory patterns with as much as a 12% improvement in the instructions per cycle and a 14% reduction in C-AMAT.
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Title: Removing Confounds in Text Classification for Computational Social Science
Creator: Landeiro Dos Reis, Virgile
Date: 2018
Description: Nowadays, one can use social media and other online platforms to communicate with friends and family, write a review for a product, ask...
Show moreNowadays, one can use social media and other online platforms to communicate with friends and family, write a review for a product, ask questions about a topic of interest, or even share details of private life with the rest of the world. The ever-increasing amount of user-generated content has provided researchers with data that can offer insights on human behavior. Because of that, the field of computational social science - at the intersection of machine learning and social sciences - has soared in the past years, especially within the field of public health research. However, working with large amounts of user-generated data creates new issues. In this thesis, we propose solutions for two problems encountered in computational social science and related to confounding bias.First, because of the anonymity provided by online forums, social networks, or other blogging platforms through the common usage of usernames, it is hard to get accurate information about users such as gender, age, or ethnicity. Therefore, although collecting data on a specific topic is made easier, conducting an observational study with this type of data is not simple. Indeed, when one wishes to run a study to measure the effect of a variable on another variable, one needs to control for potential confounding variables. In the case of user-generated data, these potential confounding variables are at best noisily observed or inferred and at worst not observed at all. In this work, we wish to provide a way to use these inferred latent attributes in order to conduct an observational study while reducing the effect of confounding bias as much as possible. We first present a simple matching method in a large-scale observational study. Then, we propose a method to retrieve relevant and representative documents through adaptive query building in order to build the treatment and control groups of an observational study.Second, we focus on the problem of controlling for confounding variables when the influence of these variables on the target variable of a classification problem changes over time. Although identifying and controlling for confounding variables has been assiduously studied in empirical social science, it is often neglected in text classification. This can be understood by the fact that, if we assume that the impact of confounding variables does not change between the training and the testing data, then prediction accuracy should only be slightly affected. Yet, this assumption often does not hold when working with user-generated text. Because of this, computational science studies are at risk of reaching false conclusions when based on text classifiers that are not controlling for confounding variables. In this document, we propose to build a classifier that is robust to confounding bias shift, and we show that we can build such a classifier in different situations: when there are one or more observed confounding variables, when there is one noisily predicted confounding variable, or when the confounding variable is unknown but can be detected through topic modeling.
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Title: PRIVACY PRESERVING BAG PREPARATION FOR LEARNING FROM LABEL PROPORTION
Creator: Yan, Xinzhou
Date: 2018
Description: We apply Privacy-preserving data mining standards (PPDM) to the Learning from label proportion (LLP) model to create the Private-preserving...
Show moreWe apply Privacy-preserving data mining standards (PPDM) to the Learning from label proportion (LLP) model to create the Private-preserving machine learning framework. We design the data preparation step for the LLP framework to meet the PPDM standards. In the data preparation step, we develop a bag selection method to boost the accuracy of the LLP model by more than 7%. Besides that, we propose three K- anonymous aggregation methods for the datasets which have almost zero accuracy loss and very robust. After the K-anonymous step, we apply Differential privacy to the LLP model and ensure a low accuracy loss for the LLP modelBecause of the LLP model’s special loss function, not only it is possible to replace all the feature vectors with the mean feature vector within each bag, but also the accuracy loss caused by Differential privacy can be bounded by a small number. The loss function ensures low accuracy loss when training LLP model on PPDM dataset. We evaluate the PPDM LLP model on two datasets, one is the Adult dataset and the other is the Instagram comment dataset. Both of them give empirical evidence of the low accuracy loss after applying the PPDM LLP model.
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Title: HUMAN TRAFFICKING AND THE INTERNET: EDUCATING OUR COMMUNITY OF THE GROWING PANDEMIC
Creator: Ali, Md
Date: 2020
Description: Human trafficking is a growing pandemic with many traffickers utilizing both the surface web and dark web to recruit and sell victims of human...
Show moreHuman trafficking is a growing pandemic with many traffickers utilizing both the surface web and dark web to recruit and sell victims of human trafficking. We will explore different cases regarding human trafficking as well as how traffickers are uti- lizing technology to conduct their criminal activities. We will be going over different research studies that went over new social engineering methods that technology has introduced into the world of human trafficking and coming to a statistical analysis on preventive measures to be aware of. With increasing advancements in encryption methods and communication the ease of this crime has been committed from one’s own bedroom. This thesis will be answering the question on how to minimize or remedy this atrocious criminal activity from society by analyzing all the recommendations from each study and finding possible solutions to avoid being a victim of human trafficking.
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Title: REVEALING LINGUISTIC BIAS
Creator: Karmarkar, Sathyaveer S.
Date: 2021
Description: Readers currently face bias in articles written by writers who focus more on partiality towards any person or organization than showing the...
Show moreReaders currently face bias in articles written by writers who focus more on partiality towards any person or organization than showing the real facts. The study aims to detect and reveal such bias against them and try to portray real facts without any partiality against any person or organization. The data is fetched by selecting various articles from Google, especially those containing some bias in them. The bias was checked by measuring the subjectivity and polarity of the article using multiple libraries such as NLTK etc. We created a google form to take readers’ views showing them randomly either the biased article or the improved article after changing bias and getting their opinions.
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Title: Language, Perception, and Causal Inference in Online Communication
Creator: Wang, Zhao
Date: 2021
Description: With the proliferation of social media platforms, online communication is becoming increasingly popular. The nature of a wide audience and...
Show moreWith the proliferation of social media platforms, online communication is becoming increasingly popular. The nature of a wide audience and rapid spread of information make these platforms attractive to public entities, organizations, and individuals. Marketers use these platforms to advertise their products and collect customer feedbacks (e.g. Amazon, Airbnb, Yelp, IMDB). Politicians use these platforms to directly speak with the public and canvass for votes (e.g., Twitter, Youtube, Snapchat). Individuals use these platforms to connect with friends and share daily life (e.g., Twitter, Facebook, Instagram, Weibo). The various platforms allow users to build public image and increase reputation through a fast and cheap way. However, due to the lack of regulations and low effort of online communication, some users try to manage their public impression using vague and tricky expressions during communication, making it hard for the audience to identify the authenticity of the public messages. Studies across many disciplines have shown that words and language play an important role in effective communication but the nature and extent of this role remain murky. Prior works have investigated wording effect on audience perception, but we still need automatic methods to estimate the causal effect of lexical choice on human perception in large scale. Getting insights into the treatment effect of subtle linguistic signals is crucial for intelligent language understanding and text analysis.The causal estimation of wording effect on perception also provides us an alternative way to understand the causal relationship between word features and perception labels. Comparing with correlational associations between features and labels, which is typically learned by statistical machine learning models, we find inconsistencies between the causal and correlational associations. These inconsistencies suggest possible spurious correlations in text classification and it's significant to address this issue by applying causal inference knowledge to guide statistical classifiers.In this thesis, our first goal is to investigate wording effect in online communication and study causal inference in text. We start from a deceptive marketing task to quantify entities' word commitment from online public messaging and identify potentially inauthentic entities. We then propose several frameworks to estimate the causal effects of word choice on audience perception by adapting Individual Treatment Effect estimation from causal inference literature to our problem of Lexical Substitution Effect estimation. The findings from these projects motivate us to explore our second goal of applying causal inference knowledge to improve statistical model robustness. Specifically, we study the causal and correlational associations in text and discover possible spurious correlations in text classifiers. Then, by extending the causal discovery, we propose two frameworks to improve text classifier robustness and fairness either by directly removing bias correlations or by training a robust model with automatically generated counterfactual samples.
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Title: A Reasoning System Architecture for Spectrum Decision-making
Creator: Das, Udayan D.
Date: 2021
Description: Spectrum is a public resource; yet understanding how spectrum is allocated and used is a daunting task. Usable spectrum is already fully...
Show moreSpectrum is a public resource; yet understanding how spectrum is allocated and used is a daunting task. Usable spectrum is already fully allocated, but the demand for spectrum continues to grow and there are opportunities for utilizing spectrum in more efficient ways. Understanding how spectrum is allocated and its utilization in time and space is necessary to take advantage of these emerging opportunities. A combination of fragmented information from varied information sources, a complex regulatory environment, variability of regulations and physics by band, real-time spectrum usage dynamics, and a status quo with knowledge concentration among a few, makes understanding spectrum a considerable challenge for all stakeholders including researchers, students, policymakers, and new telecom operators. After considerable study of spectrum, its allocation, regulation, and usage, we have developed a system architecture that is a significant step towards easing the burden of understanding spectrum information. Our system architecture connects information from disparate sources and leads to a richer understanding of spectrum usage, how it is governed, and its potential for future use. Classes of information are modeled as knowledge graphs, and the interplay of knowledge graphs produces a richer set of insight and can lead to more informed decision-making. Further, we show mechanisms for connecting spectrum information with real-time observations to get a comprehensive view of spectrum usage dynamics. While focused on the United States, this work should be applicable to other spectrum contexts worldwide. This work, of considerable technical value, also has democratic value in making complex information accessible and allowing the public to determine whether spectrum, a natural resource, is being used for the public good.
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Title: Towards a Self-Programmable Storage Solution in Extreme-Scale Environments
Creator: Devarajan, Hariharan
Date: 2021
Description: Traditional compute-centric scientific discovery has led to a growing gap between computation power and storage capabilities. However, in the...
Show moreTraditional compute-centric scientific discovery has led to a growing gap between computation power and storage capabilities. However, in the data explosion era, where data analysis is essential for scientific discovery, slow storage systems led to the research conundrum known as the I/O bottleneck. Scientists have proposed several optimizations to address the I/O bottleneck. However, selecting and applying the appropriate optimization is a complex task, often left to the users. Additionally, the explosion of data has led to the proliferation of applications as well as storage technologies. This has created a complex matching problem between diverse application requirements and heterogeneous storage resources for the users. We need to move towards a Self-Programmable storage system that can automatically understand the I/O requirements of applications, transparently leverage the heterogeneity of storage, and reconfigures itself dynamically by utilizing application and storage information. In this work, we present the Jal System for building Self-Programmable storage. The Jal System consists of three layers: the application layer, the transfer layer, and the storage layer. The application layer uses automatic extraction of I/O requirements from applications using a source-code-based profiler. The storage layer defines a data abstraction, using a shared log store, to efficiently unify heterogeneous storage resources under a single platform. Finally, the transfer layer defines data management algorithms that consider multi-application and multi-storage information to optimize data operations. Additionally, we illustrate the benefits of utilizing the technologies within the Jal System on modern scientific AI applications. Our evaluations have demonstrated that each technology within the Jal System can accelerate I/O for modern scientific workflows. We have implemented software, tools, and system libraries for modern HPC systems. In the future, we envision building a fully integrated system that efficiently utilizes all the Jal System technologies. Additionally, we plan to extend the strategies and techniques in Jal System to other scientific domains such as AI and IoT.
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Title: Towards Assisting Human-Human Conversations
Creator: Nanaware, Tejas Suryakant
Date: 2021
Description: The idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in...
Show moreThe idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in initiating conversations and overcome social anxiety so as to be able to talk and have successful conversations. By providing humans with assistive conversational support, we can augment the conversation that can be carried out. The AdvisorBot can also help to reduce the time taken to type and convey the message if the AdvisorBot is context aware and capable of providing good responses.There has been a significant research for creating conversational chatbots in open-domain conversations that have claimed to have passed the Turing Test and can converse with humans while not seeming like a bot. However, if these chatbots can converse like humans, can they provide actual assistance in human conversations? This research study observes and improves the advanced open-domain conversational chatbots that are put in practice for providing conversational assistance.While performing this thesis research, the chatbots were deployed to provide conversational assistance and a human study was performed to identify and improve the ways to tackle social anxiety by connecting strangers to perform conversations that would be aided by AdvisorBot. Through the questionnaires that the research subjects filled during their participation, and by performing linguistic analysis, the quality of the AdvisorBot can be improved so that humans can achieve better conversational skills and are able to clearly convey their message while conversing. The results were further enhanced by using transfer learning techniques and quickly improve the quality of the AdvisorBot.
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Title: Unsupervised Learning of Visual Odometry Using Direct Motion Modeling
Creator: Andrei, Silviu Stefan
Date: 2020
Description: Data for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on...
Show moreData for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on unsupervised learning methods and achieved remarkable results which surpass in some instances the accuracy of supervised methods. Many unsupervised approaches rely on predicted monocular depth and so ignore motion information. Moreover, unsupervised methods which do incorporate motion information do so only indirectly by designing the depth prediction network as an RNN. Hence, none of the existing methods model motion directly. In this work, we show that it is possible to achieve superior pose estimation results by modeling motion explicitly. Our method uses a novel learning-based formulation for depth propagation and refinement which transforms predicted depth maps from the current frame onto the next frame where it serves as a prior for predicting the next frame's depth map. Experimental results demonstrate that the proposed approach surpasses state of the art techniques for the pose prediction task while being better or on par with other methods for the depth prediction task.
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Title: Combining Simulation and Emulation for Planning and Evaluation of Smart Grid Security, Resilience, and Operations
Creator: Hannon, Christopher
Date: 2020
Description: The modern power grid is a complex, large scale cyber-physical system comprising of generation, transmission and distribution elements....
Show moreThe modern power grid is a complex, large scale cyber-physical system comprising of generation, transmission and distribution elements. However, advancements in information technology have not yet caught up to the legacy operational technology used in the electric power system. Coupled with the proliferation of renewable energy sources, the electric power grid is in a transition to a smarter grid; operators are now being equipped with the tools to make real-time operational changes and the ability to monitor and provide situational awareness of the system. This shift in electric power grid priorities requires an expansive and reliable communication network to enhance efficiency and resilience of the Smart Grid. This trend calls for a simulation-based platform that provides sufficient flexibility and controllability for evaluating network application designs, and facilitating the transition from in-house research ideas into production systems. In this Thesis, I present techniques to efficiently combine simulation systems, emulation systems, and real hardware into testbed systems to evaluate security, resilience, and operations of the electric power grid. While simulating the dynamics of the physical components of the electric power grid, the cyber components including devices, applications, and networking functions are able to be emulated or even implemented using real hardware. In addition to novel synchronization algorithms between simulation and emulation systems, multiple test cases in applying software-defined networking, an emerging networking paradigm, to the power grid for security and resilience and phasor measurement unit analytics for grid operations are presented which motivate the need for a simulation-based testbed. The contributions of this work lay in the design of a virtual time system with tight controllability on the execution of the emulation systems, i.e., pausing and resuming any specified container processes in the perception of their own virtual clocks, and also lay in the distributed virtual time based synchronization across embedded Linux devices.
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Title: DATA PRIVACY AND DEEP LEARNING IN THE MOBILE ERA: TRACEABILITY AND PROTECTION
Creator: Chen, Linlin
Date: 2020
Description: Privacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly...
Show morePrivacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly expedite lots of data orientated applications, especially like deep learning services. With the tremendous value exhibited by the data, the privacy of data subjects who generate the data, has also raised much attention. Meanwhile more regulations and legislation have been enacted or enforced, intending to enforce the companies and organizations to strictly comply with the personal privacy protection while collecting or utilizing their data. All these moves will substantially change the ways to train the deep learning models and provide AI services, and in some ways might hinder the development of deep learning if not coming up with some sophisticated mechanisms. On the other hand, deep learning has been showing incredibly promising performance in a variety of areas like face recognition, voice recognition, recommendation & advertising, autonomous driving, medical imaging, etc.. This keeps us thinking will deep learning also in turn influence privacy and be leveraged to compromise privacy. Meanwhile we also observe that mobile devices become so ubiquitous that more shares of data are generated on mobile devices, and mostly those data are both extremely sensitive for data subjects as well as extremely valuable for developing deep learning. We shouldn’t neglect the impact of mobile devices on both privacy and deep learning.In this thesis I explore the research on the interactions between privacy and deep learning, especially with the mobile devices being involved in. Specifically I work on: 1). How does privacy change the way we use the data when building deep learning models, and present the mechanism for privacy protection towards deep learning. 2). How does deep learning in turn make privacy more vulnerable to be compromised, and demonstrate the privacy compromise by facilitating deep learning to trace the source mobile devices and link the personal identities.
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