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检索条件"主题词=artificial intelligence and robotics"

共 112 条记录，以下是11-20 订阅

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Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction

Reasoning About User Feedback Under Identity Uncertainty in ...

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作者： Ariel Kobren University of Massachusetts Amherst

学位级别：博士

Intelligent, automated systems that are intertwined with everyday life---such as Google Search and virtual assistants like Amazon’s Alexa or Apple’s Siri---are often powered in part by knowledge bases (KBs), i.e., structured data repositories of entities, their attributes, and the relationships among them. Despite a wealth of research focused on automated KB construction methods, KBs are inevitably imperfect, with errors stemming from various points in the construction pipeline. Making matters more challenging, new data is created daily and must be integrated with existing KBs so that they remain up-to-date. As the primary consumers of KBs, human users have tremendous potential to aid in KB construction by contributing feedback that identifies spurious and missing entity attributes and relations. However, correctly integrating user feedback with an existing KB is complicated by the necessity to resolve , i.e., uncertainty regarding to which real-world entity a piece of data refers. Identity uncertainty abounds in the collection of raw evidence from which a KB is built. Moreover, it also gives rise to identity uncertainty , when KB entities, which were affected by user feedback, are split or merged. In this dissertation, we present a continuous reasoning framework capable of integrating user feedback with a KB, under identity certainty. To begin, we introduce Grinch, an online entity resolution (ER) algorithm---with provable correctness guarantees---capable of merging and splitting KB entities as new data arrives. We show that Grinch is efficient and achieves state-of-the-art performance in ER as well as in clustering. Next, we propose a method for using Grinch to resolve identity uncertainty in a KB's underlying data as well as in user feedback. Our approach is based on representing user feedback as , i.e., first class KB objects that participate in all parts of KB construction. Furthermore, we introduce a structured representation for feedback comprised of and

关键词： artificial intelligence and robotics

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Motion Segmentation - Segmentation of Independently Moving Objects in Video

Motion Segmentation - Segmentation of Independently Moving O...

引用

作者： Pia Katalin Bideau University of Massachusetts Amherst

学位级别：博士

The ability to recognize motion is one of the most important functions of our visual system. Motion allows us both to recognize objects and to get a better understanding of the 3D world in which we are moving. Because of its importance, motion is used to answer a wide variety of fundamental questions in computer vision such as: (1) Which objects are moving independently in the world? (2) Which objects are close and which objects are far away? (3) How is the camera moving? My work addresses the problem of moving object segmentation in unconstrained videos. I developed a probabilistic approach to segment independently moving objects in a video sequence, connecting aspects of camera motion estimation, relative depth and flow statistics. My work consists of three major parts: This work shows that a careful analysis of the motion field not only leads to a consistent segmentation of moving objects in a video sequence, but also helps us understand the scene geometry of the world we are moving in.

关键词： artificial intelligence and robotics Graphics and Human Computer Interfaces

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Higher-Order Representations for Visual Recognition

Higher-Order Representations for Visual Recognition

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作者： Tsung-Yu Lin University of Massachusetts Amherst

学位级别：博士

In this thesis, we present a simple and effective architecture called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs generalize classical orderless texture-based image models such as bag-of-visual-words and Fisher vector representations. However, unlike prior work, they can be trained in an end-to-end manner. In the experiments, we demonstrate that these representations generalize well to novel domains by fine-tuning and achieve excellent results on fine-grained, texture and scene recognition tasks. The visualization of fine-tuned convolutional filters shows that the models are able to capture highly localized attributes. We present a texture synthesis framework that allows us to visualize the pre-images of fine-grained categories and the invariances that are captured by these models. In order to enhance the discriminative power of the B-CNN representations, we investigate normalization techniques for rescaling the importance of individual features during aggregation. Spectral normalization scales the spectrum of the covariance matrix obtained after bilinear pooling and offers a significant improvement. However, the computation involves singular value decomposition, which is not computationally efficient on modern GPUs. We present an iteration-based approximation of matrix square-root along with its gradients to speed up the computation and study its effect on fine-tuning deep neural networks. Another approach is democratic aggregation, which aims to equalize the contributions of individual feature vector into the final pooled image descriptor. This achieves a comparable improvement, and can be approximated in a low-dimensional embedding unlike the spectral normalization. Therefore, this approach is friendly to aggregating higher-dimensional features. We demonstrate that the two appro

关键词： artificial intelligence and robotics Computer Sciences

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Understanding the Dynamic Visual World: From Motion to Semantics

Understanding the Dynamic Visual World: From Motion to Seman...

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作者： Huaizu Jiang University of Massachusetts Amherst

学位级别：博士

We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in some cases, manual annotations are completely infeasible, such as the motion vector of each pixel (i.e., optical flow) since humans cannot reliably produce these types of labeling. In fact, living in a dynamic world, when we move around, motion information, as a result of moving camera, independently moving objects, and scene geometry, consists of abundant information, revealing the structure and complexity of our dynamic visual world. As the famous psychologist James J. Gibson suggested, “we must perceive in order to move, but we also must move in order to perceive”. In this thesis, we investigate how to use the motion information contained in unlabeled or partially labeled videos to better understand and synthesize the dynamic visual world. This thesis consists of three parts. In the first part, we focus on the “move to perceive” aspect. When moving through the world, it is natural for an intelligent agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, far away mountains don’t move much; nearby trees move a lot. This natural relationship between the appearance of objects and their apparent motion is a rich source of information about the relationship between the distance of objects and their appearance in images. We present a pretext task of estimati

关键词： artificial intelligence and robotics Graphics and Human Computer Interfaces

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Improving Visual Recognition With Unlabeled Data

Improving Visual Recognition With Unlabeled Data

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作者： Aruni Roy Chowdhury University of Massachusetts Amherst

学位级别：博士

The success of deep neural networks has resulted in computer vision systems that obtain high accuracy on a wide variety of tasks such as image classification, object detection, semantic segmentation, etc. However, most state-of-the-art vision systems are dependent upon large amounts of labeled training data, which is not a scalable solution in the long run. This work focuses on improving existing models for visual object recognition and detection without being dependent on such large-scale human-annotated data. We first show how large numbers of hard examples (cases where an existing model makes a mistake) can be obtained automatically from unlabeled video sequences by exploiting temporal consistency cues in the output of a pre-trained object detector. These examples can strongly influence a model's parameters when the network is re-trained to correct them, resulting in improved performance on several object detection tasks. Further, such hard examples from unlabeled videos can be used to address the problem of unsupervised domain adaptation. We focus on the automatic adaptation of an existing object detector to a new domain with no labeled data, assuming that a large number of unlabeled videos are readily available. Our approach is evaluated on challenging face and pedestrian detection tasks involving large domain shifts, showing improved performance with minimal dependence on hyper-parameters. Finally, we address the problem of face recognition, which has achieved high accuracy by employing deep neural networks trained on massive labeled datasets. Further improvements through supervised learning require significantly larger datasets and hence massive annotation efforts. We improve upon the performance of face recognition models trained on large-scale labeled datasets by using unlabeled faces as additional training data. We present insights and recipes for training deep face recognition models with labeled and unlabeled data at scale, addressing real-world challeng

关键词： artificial intelligence and robotics Computer Sciences

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Improving Face Clustering in Videos

Improving Face Clustering in Videos

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作者： SouYoung Jin University of Massachusetts Amherst

学位级别：博士

Human faces represent not only a challenging recognition problem for computer vision, but are also an important source of information about identity, intent, and state of mind. These properties make the analysis of faces important not just as algorithmic challenges, but as a gateway to developing computer vision methods that can better follow the intent and goals of human beings. In this thesis, we are interested in face clustering in videos. Given a raw video, with no caption or annotation, we want to group all detected faces by their identity. We address three problems in the area of face clustering and propose approaches to tackle them. The existing link-based face-clustering system is sensitive to a false connection between two different people. We introduce a new similarity measure that helps the verification system to provide very few false connections at moderate recall. Further, we also introduce a novel clustering method called Erdos and Renyi clustering, which is based on the observations from a random graph model theory, that large clusters can be fully connected by joining just a small fraction of their node pairs. Our results present state-of-the-art results on multiple video data sets and also on standard face databases. What happens if faces are not sufficiently clear for direct recognition, due to the small scale, occlusion, or extreme pose? We observe that, when humans are uncertain about the identity of two faces, we use clothes or other contextual cues, e.g. specific objects or textures, to infer identity. With this observation, we propose the Face-Background Network (FB-Net), which takes as input not only the faces but also the entire scene to enhance the performance of face clustering. In order for the network to learn background features that are informative about the identity, we introduce a new dataset that contains face identities in the context of consistent scenes. We show that FB-Net outperforms the state-of-the-art method which uses face

关键词： artificial intelligence and robotics Computer Sciences

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INTELLIGENT TUTORING SYSTEMS, PEDAGOGICAL AGENT DESIGN, AND HISPANIC ENGLISH LANGUAGE LEARNERS

INTELLIGENT TUTORING SYSTEMS, PEDAGOGICAL AGENT DESIGN, AND ...

引用

作者： Danielle Allessio University of Massachusetts Amherst

学位级别：博士

According to the most recent data from the National Center of Education Statistics (NCES) there were approximately 5 million English Language Learners (ELLs) in the U.S. public schools in the Fall of 2016, representing about 10% of the student population (2019). Spanish is the primary language for most ELL students, by a large margin. As a group, ELLs have faced a deeply rooted and persistent math achievement gap (U.S. Department of Education, 2015). Despite research indicating that intelligent tutors and animated pedagogical agents enhance learning, many tutors are not designed with ELLs in mind. As a result, Hispanic ELL students may experience difficulty accessing the relevant content when using a tutor. This mixed-method research investigates how a tutor can reach Hispanic ELL students, based on the social and cultural Identity framework of the Figured Worlds Theory by Holland et al., (1998). Students will socially and culturally engage with their animated pedagogical agents constructing figured worlds of learning and connection that have the power to shape the students’ senses of themselves as learners of math. This study investigates how Hispanic ELL students perceive the utility of and relate to a learning companion (LC) design. Data was examined from 76 middle school students interacting with a math tutor, MathSpring. The findings indicate that ELL students find the MathSpring LC more useful and helpful than do non-ELL students and the ELL students designed LCs that looked more like themselves than did the non-ELL students. The findings also indicate that students formed ‘She/Me Connection’ and ‘She is Like Me’ figured worlds.

关键词： artificial intelligence and robotics Educational Technology

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Automated Change Detection in Privacy Policies

Automated Change Detection in Privacy Policies

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作者： Adhikari, Andrick

学位级别：硕士

Privacy policies notify Internet users about the privacy practices of websites, mobile apps, and other products and services. However, users rarely read them and struggle to understand their contents. Also, the entities that provide these policies are sometimes unmotivated to make them comprehensible. Due to the complicated nature of these documents, it gets even harder for users to understand and take note of any changes of interest or concern when these policies are changed or revised. With recent development of machine learning and natural language processing, tools that can automatically annotate sentences of policies have been developed. These annotations can help a user quickly identify and understand relevant parts of the policy. Similarly a tool can be developed that can help identify changes between different versions of a policy that can be informative for the user. For example, suppose according to the new policy a website will start sharing audio data as well. The proposed tool can help users to be aware of such important changes. This thesis presents a tool that takes two different versions of a privacy policy as input, matches the sentences of one version of a policy to the sentences of another version of the policy based on semantic similarity, and inform the user of key relevant changes between two matched sentences. We discuss different supervised machine learning models that are explored to develop a method to annotate the sentences of privacy policies according to expert-identified categories for organization and analysis of the contents. Different word-embedding and similarity techniques are explored and evaluated to develop a method to match the sentences of one version of the policy to another version of a policy. The annotation of the sentences are used to increase the efficiency of the matching process. Methods to detect changes between two matched sentences through analysis of the structure of sentences are then implemented. We combined th

关键词： Change detection Natural language processing Privacy policy artificial intelligence and robotics Computer Sciences

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Knot Flow Classification and its Applications in Vehicular Ad-Hoc Networks (VANET)

Knot Flow Classification and its Applications in Vehicular A...

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作者： Schmidt, David East Tennessee State University

学位级别：硕士

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its design and the manner it is constructed, KFC holds great potential for implementation across a distributed system. The purpose of this thesis was to explain and extrapolate the afore mentioned IDS, highlight its effectiveness, and discuss the conceptual design of the distributed system for use in future research.

关键词： Machine Learning Internet of Things Internet of Vehicles Intrusion Detection Knot Flow Classification Data Science artificial intelligence and robotics Information Security Numerical Analysis and Scientific Computing Other Computer Sciences

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PROBABILISTIC MODELS FOR IDENTIFYING AND EXPLAINING CONTROVERSY

PROBABILISTIC MODELS FOR IDENTIFYING AND EXPLAINING CONTROVE...

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作者： MYUNGHA JANG University of Massachusetts Amherst

学位级别：博士

Navigating controversial topics on the Web encourages social awareness, supports civil discourse, and promotes critical literacy. While search of controversial topics par- ticularly requires users to use their critical literacy skills on the content, educating people to be more critical readers is known to be a complex and long-term process. Therefore, we are in need of search engines that are equipped with techniques to help users to understand controversial topics by identifying them and explaining why they are controversial. A few approaches for identifying controversy have worked reason- ably well in practice, but they are narrow in scope and exhibit limited performance. In this thesis, we first focus on understanding the theoretical grounding of the state-of-the-art algorithm. We derive an underlying probabilistic model that explains the state-of-the-art controversy detection algorithm. We revisit the properties and as- sumptions from the derived model, and propose new methods to identify controversy on Webpages. We then point out that the current approaches for controversy detec- tion do not consider time while controversy is a dynamically changing phenomenon. This causes current methods to have delays in recognizing emerging controversial topics or exaggerated effects on outdated controversies. We address time-adaptable controversy detection by estimating the dynamically-changing controversy trend of topic by interpolating the observed level of contention and the public interest over time on the topic. Finally, we offer a method that explains controversy by generating a summary of each stance. Our method ranks social media postings using a score of how likely it is that the given post can be a representative summary of controversy.

关键词： artificial intelligence and robotics Computer Sciences Databases and Information Systems Social Influence and Political Communication Social Media

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