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- All Subjects: Computer Science
- Creators: Yang, Yezhou
Description
Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the widespread deployment of Internet-of-Things (IoT) devices in smart homes. However, the spate of cyber attacks exploiting the vulnerabilities and weak security management of smart home IoT devices have highlighted the urgency and challenges of designing efficient mechanisms for detecting, analyzing, and mitigating security threats towards them. In this dissertation, I seek to address the security and privacy issues of smart home IoT devices from the perspectives of traffic measurement, pattern recognition, and security applications. I first propose an efficient multidimensional smart home network traffic measurement framework, which enables me to deeply understand the smart home IoT ecosystem and detect various vulnerabilities and flaws. I further design intelligent schemes to efficiently extract security-related IoT device event and user activity patterns from the encrypted smart home network traffic. Based on the knowledge of how smart home operates, different systems for securing smart home networks are proposed and implemented, including abnormal network traffic detection across multiple IoT networking protocol layers, smart home safety monitoring with extracted spatial information about IoT device events, and system-level IoT vulnerability analysis and network hardening.
ContributorsWan, Yinxin (Author) / Xue, Guoliang (Thesis advisor) / Xu, Kuai (Thesis advisor) / Yang, Yezhou (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2023
Description
In natural language processing, language models have achieved remarkable success over the last few years. The Transformers are at the core of most of these models. Their success can be mainly attributed to an enormous amount of curated data they are trained on. Even though such language models are trained on massive curated data, they often need specific extracted knowledge to understand better and reason. This is because often relevant knowledge may be implicit or missing, which hampers machine reasoning. Apart from that, manual knowledge curation is time-consuming and erroneous. Hence, finding fast and effective methods to extract such knowledge from data is important for improving language models. This leads to finding ideal ways to utilize such knowledge by incorporating them into language models. Successful knowledge extraction and integration lead to an important question of knowledge evaluation of such models by developing tools or introducing challenging test suites to learn about their limitations and improve them further. So to improve the transformer-based models, understanding the role of knowledge becomes important. In the pursuit to improve language models with knowledge, in this dissertation I study three broad research directions spanning across the natural language, biomedical and cybersecurity domains: (1) Knowledge Extraction (KX) - How can transformer-based language models be leveraged to extract knowledge from data? (2) Knowledge Integration (KI) - How can such specific knowledge be used to improve such models? (3) Knowledge Evaluation (KE) - How can language models be evaluated for specific skills and understand their limitations?
I propose methods to extract explicit textual, implicit structural, missing textual, and missing structural knowledge from natural language and binary programs using transformer-based language models. I develop ways to improve the language model’s multi-step and commonsense reasoning abilities using external knowledge. Finally, I develop challenging datasets which assess their numerical reasoning skills in both in-domain and out-of-domain settings.
ContributorsPal, Kuntal Kumar (Author) / Baral, Chitta (Thesis advisor) / Wang, Ruoyu (Committee member) / Blanco, Eduardo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
I present my work on a scalable and programmable I/O controller for region-based computing, which will be used in a rhythmic pixel-based camera pipeline. I provide a breakdown of the development and design of the I/O controller and how it fits in to rhythmic pixel regions, along with a studyon memory traffic of rhythmic pixel regions and how this translates to energy efficiency. This rhythmic pixel region-based camera pipeline has been jointly developed through Dr. Robert LiKamWa’s research lab. High spatiotemporal resolutions allow high precision for vision applications, such as for detecting features for augmented reality or face detection. High spatiotemporal resolution also comes with high memory throughput, leading to higher energy usage. This creates a tradeoff between high precision and energy efficiency, which becomes more important in mobile systems. In addition, not all pixels in a frame are necessary for the vision application, such as pixels that make up the background. Rhythmic pixel regions aim to reduce the tradeoff by creating a pipeline that allows an application developer to specify regions to capture at a non-uniform spatiotemporal resolution. This is accomplished by encoding the incoming image, and only sending the pixels within these specified regions. Later these encoded representations will be decoded to a standard frame representation usable by traditional vision applications. My contribution to this effort has been the design, testing and evaluation of the I/O controller.
ContributorsNguyen, Van (Author) / LiKamWa, Robert (Thesis advisor) / Jayasuriya, Suren (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Graph-structured data, ranging from social networks to financial transaction networks, from citation networks to gene regulatory networks, have been widely used for modeling a myriad of real-world systems. As a prevailing model architecture to model graph-structured data, graph neural networks (GNNs) has drawn much attention in both academic and industrial communities in the past decades. Despite their success in different graph learning tasks, existing methods usually rely on learning from ``big'' data, requiring a large amount of labeled data for model training. However, it is common that real-world graphs are associated with ``small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph machine learning (Graph ML) with low-cost human supervision for low-resource settings where limited or even no labeled data is available. This dissertation investigates a new research field -- Data-Efficient Graph Learning, which aims to push forward the performance boundary of graph machine learning (Graph ML) models with different kinds of low-cost supervision signals. To achieve this goal, a series of studies are conducted for solving different data-efficient graph learning problems, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning.
ContributorsDing, Kaize (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Yang, Yezhou (Committee member) / Caverlee, James (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation presents novel solutions for improving the generalization capabilities of deep learning based computer vision models. Neural networks are known to suffer a large drop in performance when tested on samples from a different distribution than the one on which they were trained. The proposed solutions, based on latent space geometry and meta-learning, address this issue by improving the robustness of these models to distribution shifts. Through the use of geometrical alignment, state-of-the-art domain adaptation and source-free test-time adaptation strategies are developed. Additionally, geometrical alignment can allow classifiers to be progressively adapted to new, unseen test domains without requiring retraining of the feature extractors. The dissertation also presents algorithms for enabling in-the-wild generalization without needing access to any samples from the target domain. Other causes of poor generalization, such as data scarcity in critical applications and training data with high levels of noise and variance, are also explored. To address data scarcity in fine-grained computer vision tasks such as object detection, novel context-aware augmentations are suggested. While the first four chapters focus on general-purpose computer vision models, strategies are also developed to improve robustness in specific applications. The efficiency of training autonomous agents for visual navigation is improved by incorporating semantic knowledge, and the integration of domain experts' knowledge allows for the realization of a low-cost, minimally invasive generalizable automated rehabilitation system. Lastly, new tools for explainability and model introspection using counter-factual explainers trained through interval-based uncertainty calibration objectives are presented.
ContributorsThopalli, Kowshik (Author) / Turaga, Pavan (Thesis advisor) / Thiagarajan, Jayaraman J (Committee member) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
In the era of data explosion, massive data is generated from various sources at an unprecedented speed. The ever-growing amount of data reveals enormous opportunities for developing novel data-driven solutions to unsolved problems. In recent years, benefiting from numerous public datasets and advances in deep learning, data-driven approaches in the computer vision domain have demonstrated superior performance with high adaptability on various data and tasks. Meanwhile, signal processing has long been dominated by techniques derived from rigorous mathematical models built upon prior knowledge of signals. Due to the lack of adaptability to real data and applications, model-based methods often suffer from performance degradation and engineering difficulties. In this dissertation, multiple signal processing problems are studied from vision-inspired data representation and learning perspectives to address the major limitation on adaptability. Corresponding data-driven solutions are proposed to achieve significantly improved performance over conventional solutions. Specifically, in the compressive sensing domain, an open-source image compressive sensing toolbox and benchmark to standardize the implementation and evaluation of reconstruction methods are first proposed. Then a plug-and-play compression ratio adapter is proposed to enable the adaptability of end-to-end data-driven reconstruction methods to variable compression ratios. Lastly, the problem of transfer learning from images to bioelectric signals is experimentally studied to demonstrate the improved performance of data-driven reconstruction. In the image subsampling domain, task-adaptive data-driven image subsampling is studied to reduce data redundancy and retain information of interest simultaneously. In the semiconductor analysis domain, the data-driven automatic error detection problem is studied in the context of integrated circuit segmentation for the first time. In the light detection and ranging(LiDAR) camera calibration domain, the calibration accuracy degradation problem in low-resolution LiDAR scenarios is addressed with data-driven techniques.
ContributorsZhang, Zhikang (Author) / Ren, Fengbo (Thesis advisor) / Li, Baoxin (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
Insufficient training data poses significant challenges to training a deep convolutional neural network (CNN) to solve a target task. One common solution to this problem is to use transfer learning with pre-trained networks to apply knowledge learned from one domain with sufficient data to a new domain with limited data and avoid training a deep network from scratch. However, for such methods to work in a transfer learning setting, learned features from the source domain need to be generalizable to the target domain, which is not guaranteed since the feature space and distributions of the source and target data may be different. This thesis aims to explore and understand the use of orthogonal convolutional neural networks to improve learning of diverse, generic features that are transferable to a novel task.
In this thesis, orthogonal regularization is used to pre-train deep CNNs to investigate if and how orthogonal convolution may improve feature extraction in transfer learning. Experiments using two limited medical image datasets in this thesis suggests that orthogonal regularization improves generality and reduces redundancy of learned features more effectively in certain deep networks for transfer learning. The results on feature selection and classification demonstrate the improvement in transferred features helps select more expressive features that improves generalization performance. To understand the effectiveness of orthogonal regularization on different architectures, this work studies the effects of residual learning on orthogonal convolution. Specifically, this work examines the presence of residual connections and its effects on feature similarities and show residual learning blocks help orthogonal convolution better preserve feature diversity across convolutional layers of a network and alleviate the increase in feature similarities caused by depth, demonstrating the importance of residual learning in making orthogonal convolution more effective.
ContributorsChan, Tsz (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
Vision Transformers (ViT) achieve state-of-the-art performance on image classification tasks. However, their massive size makes them unsuitable for edge devices. Unlike CNNs, limited research has been conducted on the compression of ViTs. This thesis work proposes the ”adjoined training technique” to compress any transformer based architecture. The architecture, Adjoined Vision Transformer (AN-ViT), achieves state-of-the-art performance on the ImageNet classification task. With the base network as Swin Transformer, AN-ViT with 4.1× fewer parameters and 5.5× fewer floating point operations (FLOPs) achieves similar accuracy (within 0.15%). This work further proposes Differentiable Adjoined ViT (DAN-ViT), whichuses neural architecture search to find hyper-parameters of our model. DAN-ViT outperforms the current state-of-the-art methods including Swin-Transformers by about ∼ 0.07% and achieves 85.27% top-1 accuracy on the ImageNet dataset while using 2.2× fewer parameters and with 2.2× fewer FLOPs.
ContributorsGoel, Rajeev (Author) / Yang, Yingzhen (Thesis advisor) / Yang, Yezhou (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2023
Description
Intelligent transportation systems (ITS) are a boon to modern-day road infrastructure. It supports traffic monitoring, road safety improvement, congestion reduction, and other traffic management tasks. For an ITS, roadside perception capability with cameras, LIDAR, and RADAR sensors is the key. Among various roadside perception technologies, vehicle keypoint detection is a fundamental problem, which involves detecting and localizing specific points on a vehicle, such as the headlights, wheels, taillights, etc. These keypoints can be used to track the movement of the vehicles and their orientation. However, there are several challenges in vehicle keypoint detection, such as the variation in vehicle models and shapes, the presence of occlusion in traffic scenarios, the influence of weather and changing lighting conditions, etc. More importantly, existing traffic perception datasets for keypoint detection are mainly limited to the frontal view with sensors mounted on the ego vehicles. These datasets are not designed for traffic monitoring cameras that are mounted on roadside poles. There’s a huge advantage of capturing the data from roadside cameras as they can cover a much larger distance with a wider field of view in many different traffic scenes, but such a dataset is usually expensive to construct. In this research, I present SKOPE3D: Synthetic Keypoint Perception 3D dataset, a one-of-its-kind synthetic perception dataset generated using a simulator from the roadside perspective. It comes with 2D bounding boxes, 3D bounding boxes, tracking IDs, and 33 keypoints for each vehicle in the scene. The dataset consists of 25K frames spanning over 28 scenes with over 150K vehicles and 4.9M keypoints. A baseline keypoint RCNN model is trained on the dataset and is thoroughly evaluated on the test set. The experiments show the capability of the synthetic dataset and knowledge transferability between synthetic and real-world data.
ContributorsPahadia, Himanshu (Author) / Yang, Yezhou (Thesis advisor) / Lu, Duo (Committee member) / Farhadi Bajestani, Mohammad (Committee member) / Arizona State University (Publisher)
Created2023
Description
One of the challenges in Artificial Intelligence (AI) is to integrate fast, automatic, and intuitive System-1 thinking with slow, deliberate, and logical System-2 thinking. While deep learning approaches excel at perception tasks for System-1, their reasoning capabilities for System-2 are limited. Besides, deep learning approaches are usually data-hungry, hard to make use of explicit knowledge, and struggling with interpretability and justification. This dissertation presents three neuro-symbolic AI approaches that integrate neural networks (NNs) with symbolic AI methods to address these issues. The first approach presented in this dissertation is NeurASP, which combines NNs with Answer Set Programming (ASP), a logic programming formalism. NeurASP provides an effective way to integrate sub-symbolic and symbolic computation by treating NN outputs as probability distributions over atomic facts in ASP. The explicit knowledge encoded in ASP corrects mistakes in NN outputs and allows for better training with less data. To avoid NeurASP's bottleneck in symbolic computation, this dissertation presents a Constraint Loss via Straight-Through Estimators (CL-STE). CL-STE provides a systematic way to compile discrete logical constraints into a loss function over discretized NN outputs and scales significantly better than state-of-the-art neuro-symbolic methods. This dissertation also presents a finding when CL-STE was applied to Transformers. Transformers can be extended with recurrence to enhance its power for multi-step reasoning. Such Recurrent Transformer can straightforwardly be applied to visual constraint reasoning problems while successfully addressing the symbol grounding problem. Lastly, this dissertation addresses the limitation of pre-trained Large Language Models (LLMs) on multi-step logical reasoning problems with a dual-process neuro-symbolic reasoning system called LLM+ASP, where an LLM (e.g., GPT-3) serves as a highly effective few-shot semantic parser that turns natural language sentences into a logical form that can be used as input to ASP. LLM+ASP achieves state-of-the-art performance on several textual reasoning benchmarks and can handle robot planning tasks that an LLM alone fails to solve.
ContributorsYang, Zhun (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023