<div><br></div><div><p>In many complex domains, the input data are often not suited for the typical vector representations used in deep learning models. For example, in knowledge representation, relational learning, and some computer vision tasks, the data are often better represented as graphs or sets. In these cases, a key challenge is to learn a representation function which is invariant to permutations of set or isomorphism of graphs. </p><p>In order to handle graph isomorphism, this thesis proposes a subgraph pattern neural network with invariance to graph isomorphisms and varying local neighborhood sizes. Our key insight is to incorporate the unavoidable dependencies in the training observations of induced subgraphs into both the input features and the model architecture itself via high-order dependencies, which are still able to take node/edge labels into account and facilitate inductive reasoning. </p><p>In order to learn permutation-invariant set functions, this thesis shows how the characteristics of an architecture’s computational graph impact its ability to learn in contexts with complex set dependencies, and demonstrate limitations of current methods with respect to one or more of these complexity dimensions. I also propose a new Self-Attention GRU architecture, with a computation graph that is built automatically via self-attention to minimize average interaction path lengths between set elements in the architecture’s computation graph, in order to effectively capture complex dependencies between set elements.</p><p>Besides the typical set problem, a new problem of representing sets-of-sets (SoS) is proposed. In this problem, multi-level dependence and multi-level permutation invariance need to be handled jointly. To address this, I propose a hierarchical sequence attention framework (HATS) for inductive set-of-sets embeddings, and develop the stochastic optimization and inference methods required for efficient learning.</p></div>

<div>Anonymous communication networks (ACNs) are critical to communication privacy over the internet as they enable</div><div>individuals to maintain their privacy from untrusted intermediaries and endpoints.</div><div>Typically, ACNs involve messages traveling through some intermediaries before arriving at their destinations, and therefore they introduce network latency and bandwidth overheads. </div><div><br></div><div>The goal of this work is to investigate the fundamental constraints of anonymous communication (AC) protocols.</div><div>We analyze the relationship between bandwidth overhead, latency overhead, and sender anonymity or recipient anonymity against a global passive (network-level) adversary. </div><div>We confirm the widely believed trilemma </div><div>that an AC protocol can only achieve two out of the following three properties: </div><div>strong anonymity (i.e., anonymity up to a negligible chance),</div><div>low bandwidth overhead, and low latency overhead. </div><div><br></div><div>We further study anonymity against a stronger global passive adversary that can additionally passively compromise some of the AC protocol nodes.</div><div>For a given number of compromised nodes, </div><div>we derive as a necessary constraint a relationship between bandwidth and latency overhead whose violation make it impossible for an AC protocol to achieve strong anonymity. </div><div>We analyze prominent AC protocols from the literature and depict to which extent those satisfy our necessary constraints. </div><div>Our fundamental necessary constraints offer a guideline not only for improving existing AC systems but also for designing novel AC protocols with non-traditional bandwidth and latency overhead choices.</div><div><br></div><div>Using the guidelines indicated by our fundamental necessary constraints we provide two efficient protocol constructions.</div><div>First, we design a mixnet-based AC protocol Streams that provides provable mixing guarantees with the expense of latency overhead. Streams realizes a trusted third party stop-and-go mix as long as each message stays in the system for $\omega(\log \eta)$ rounds.</div><div>Second, we offer a DC-net based design OrgAn that can provide strong sender anonymity with constant latency at the expense of bandwidth overhead. OrgAn solves the problem of regular requirements of key and slot agreement present in typical DC-net based protocols, by utilizing a client/relay/server architecture.</div>

Climate change has emerged as one of the most critical issues of the 21st century. It stands to impact communities across the globe, forcing individuals and governments alike to adapt to a new environment. While it is critical for governments and organizations to make strides to change business as usual, individuals also have the ability to make an impact. The goal of this thesis is to study the beliefs that shape climate-related attitudes and the factors that drive the adoption of sustainable practices and technologies using a foundation in statistical learning. Previous research has studied the factors that influence both climate-related attitude and adoption, but comparatively little has been done to leverage recent advances in statistical learning and computing ability to advance our understanding of these topics. As increasingly large amounts of relevant data become available, it will be pivotal not only to use these emerging sources to derive novel insights on climate change, but to develop and improve statistical frameworks designed with climate change in mind. This thesis presents two novel applications of statistical learning to climate change, one of which includes a more general framework that can easily be extended beyond the field of climate change. Specifically, the work consists of two studies: (1) a robust integration of social media activity with climate survey data to relate climate-talk to climate-thought and (2) the development and validation of a statistical learning model to predict renewable energy installations using social, environmental, and economic predictors. The analysis presented in this thesis supports decision makers by providing new insights on the factors that drive climate attitude and adoption.

Prussian blue (PB) nanoparticles were synthesized from FeCl3.6H2O, K4[Fe(CN)6].3H2O, and from Fe(NO3)3.9H2O and K4[Fe(CN)6].3H2O, and then characterized by Fourier transform infrared (FT-IR), Ultraviolet-visible spectroscopy, X-ray diffraction (XRD), Energy dispersive spectroscopy (EDS), Scanning electron microscopy (SEM), Raman spectroscopy and thermogravimetric analysis. Graphene oxide and carbon nanotubes were also synthesized and characterized. PB nanoparticles, carbon nanotubes (CNT), graphene oxide (GO) and cetyltrimethylammonium bromide (CTAB) were sequentially deposited onto glassy carbon electrode surface to form chemically modified electrode for the detection of hydrogen peroxide (H2O2) and dopamine. The following electrodes were fabricated, GC-PB, GC-MWCNT, GCGO, GC-CTAB, GC-MWCNT-PB, GC-GO-PB and GC-CTAB-PB. Cyclic and Square wave voltammetric techniques were used to measure the hydrogen peroxide detectability of the electrodes at pH ranges of (3 - 7.4) in 0.1M phosphate buffer solution, in the absence or presence of 25 μL of H2O2. The GC-CNT-PB, GC-GO-PB,GC-CTAB-PB electrodes showed a good response for the detection of hydrogen peroxide in both acidic and neutral media while the GCPB electrode only showed good response in acidic media.

<div> <div> <div> <p>Support Vector Machines (SVMs) have generally been effective in detecting instances of network intrusion. However, from a practical point of view, a standard SVM is not able to handle large-scale data efficiently due to the computation complexity of the algorithm and extensive memory requirements. To cope with the limitation, this study presents an incremental SVM method combined with a k-nearest neighbors (KNN) based candidate support vectors (CSV) selection strategy in order to speed up training and test process. The proposed incremental SVM method constructs or updates the pattern classes by incrementally incorporating new signatures without having to load and access the entire previous dataset in order to cope with evolving DoS and DDoS attacks. Performance of the proposed method is evaluated with experiments and compared with the standard SVM method and the simple incremental SVM method in terms of precision, recall, F1-score, and training and test duration.<br></p> </div> </div> </div>

With the rapid growth and maturity of Question-Answering (QA) domain, non-factoid Question-Answering tasks are in high demand. However, existing Question-Answering systems are either fact-based, or highly keyword related and hard-coded. Moreover, if QA is to become more personable, sentiment of the question and answer should be taken into account. However, there is not much research done in the field of non-factoid Question-Answering systems based on sentiment analysis, that would enable a system to retrieve answers in a more emotionally intelligent way. This study investigates to what extent could prediction of the best answer be improved by adding an extended representation of sentiment information into non-factoid Question-Answering.

<div>To leverage modern hardware platforms to their fullest, more and more database systems embrace compilation of query plans to native code. In the research community, there is an ongoing debate about the best way to architect such query compilers. This is perceived to be a difficult task, requiring techniques fundamentally different from traditional interpreted query execution. In this dissertation, we contribute to this discussion by drawing attention to an old but underappreciated idea known as Futamura projections, which fundamentally link interpreters and compilers. Guided by this idea, we demonstrate that efficient query compilation can actually be very simple, using techniques that are no more difficult than writing a query interpreter in a high-level language. We first develop LB2: a high-level query compiler implemented in this style that is competitive with the best compiled query engines both in sequential and parallel execution on the standard TPC-H benchmark. </div><div><br></div><div>Query engines process a variety of data types and structures including text, spatial, graphs, etc. Several spatial and graph engines are implemented as extensions to relational query engines to leverage optimized memory, storage, and evaluation. Still, the performance of these extensions is often stymied by the interpretive nature of the underlying data management, generic data structures, and the need to execute domain-specific external libraries. On that basis, compiling spatial and graph queries to native code is a desirable avenue to mitigate existing limitations and improve performance. To support compiling spatial queries, we extend the LB2 main-memory query compiler with spatial predicates, indexing structures, and spatial operators. To support compiling graph queries, we extend LB2 with graph data structures and operators. The spatial extension matches the performance of hand-written code and outperforms relational query engines and map-reduce extensions. Similarly, the graph extension matches, and sometimes outperforms, low-level graph engines.</div>

<div> <div> <div> <p>This paper introduced a near real-time acoustic unmanned aerial vehicle detection system with multiple listening nodes using machine learning models. An audio dataset was collected in person by recording the sound of an unmanned aerial vehicle flying around as well as the sound of background noises. After the data collection phase, support vector machines and convolutional neural networks were built with two features, Mel-frequency cepstral coefficients and short-time Fourier transform. Considering the near real-time environment, the features were calculated after cutting the audio stream into chunks of two, one or half seconds. There are four combinations of features and models as well as three versions per combination based on the chunk size, returning twelve models in total. To train support vector machines, the exhaustive search method was used to find the best parameter while convolutional neural networks were built by selecting the parameters manually. Four node configurations were devised to find the best way to place six listening nodes. Twelve models were run for each configuration, generating color maps to show the paths of the unmanned aerial vehicle flying along the nodes. The model of short-time Fourier transform and support vector machines showed the path most clearly with the least false negatives with 2-second chunk size. Among the four configurations, the configuration for experiment 3 showed the best results in terms of the distance of detection results on the color maps. Web-based monitoring dashboards were provided to enable users to monitor detection results. </p> </div> </div> </div>

<div>Over the past decade, universities had a significant improvement in using online learning tools. A standard learning management system provides fundamental functionalities to satisfy the basic needs of its users. The new generation of learning management systems have introduced a novel system that provides social networking features. An unprecedented number of users use the social aspects of such platforms to create their profile, collaborate with other users, and find their desired career path. Nowadays there are many learning systems which provide learning materials, certificates, and course management systems. This allows us to utilize such information to help the students and the instructors in their academic life. </div><div><br></div><div>The presented research work's primary goal is to focus on creating an intelligent personal assistant within the social learning systems. The proposed personal assistant has a human-like persona, learns about the users, and recommends useful and meaningful materials for them. The designed system offers a set of features for both institutions and members to achieve their goal within the learning system. It recommends jobs and friends for the users based on their profile. The proposed agent also prioritizes the messages and shows the most important message to the user. </div><div><br></div><div>The developed software supports model-controller-view architecture and provides a set of RESTful APIs which allows the institutions to integrate the proposed intelligent agent with their learning system. <br></div>

The Semantic Web, or the Web of Data, promotes common data formats for representing structured data and their links over the web. RDF is the defacto standard for semantic data where it provides a flexible semi-structured model for describing concepts and relationships. RDF datasets consist of entries (i.e, triples) that range from thousands to Billions. The astronomical growth of RDF data calls for scalable RDF management and query processing strategies. This dissertation addresses efficient query processing over web-scale RDF data. The first contribution is WORQ, an online, workload-driven, RDF query processing technique. Based on the query workload, reduced sets of intermediate results (or reductions, for short) that are common for specific join pattern(s) are computed in an online fashion. Also, we introduce an efficient solution for RDF queries with unbound properties. The second contribution is SPARTI, a scalable technique for computing the reductions offline. SPARTI utilizes a partitioning schema, termed SemVP, that enables efficient management of the reductions. SPARTI uses a budgeting mechanism with a cost model to determine the worthiness of partitioning. The third contribution is KC, an efficient RDF data management system for the cloud. KC uses generalized filtering that encompasses both exact and approximate set membership structures that are used for filtering irrelevant data. KC defines a set of common operations and introduces an efficient method for managing and constructing filters. The final contribution is semantic filtering where data can be reduced based on the spatial, temporal, or ontological aspects of a query. We present a set of encoding techniques and demonstrate how to use semantic filters to reduce irrelevant data in a distributed setting.