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关键词： Computer science   Constructionism   Fiber crafts   Learning sciences   Posthumanist Perspectives  

摘要： The learning sciences are dedicated to addressing enduring educational challenges through advancing learning theory in situ. One such challenge is the tenacious underrepresentation of women that has plagued computer science over the past decades. Historically, tangible manipulatives present a context for advancing learning theory toward more inclusive computing education. The materials of these manipulatives are typically block-based and theorized to be mediators that serve human learning. Recently, literacy studies have taken a posthumanist turn, radically questioning humanist assumptions of materials as mediators to advance understanding of how materials and people co-develop. Departing from deeply rooted notions that underpin what can be seen and supported as learning, posthumanist perspectives promise to uncover ongoing learning processes that may otherwise remain overlooked. Surprisingly, the learning sciences have not yet systematically engaged with these perspectives, despite their commitment to addressing educational challenges, including those entangled with gender biases. A particularly compelling context for (re)examining the role of materials in learning is matrix-based fiber crafts, which occupy a pivotal role in the history and future of technology innovation. Grappling with questions at the intersection of gender equity in computing, educational technology design, and the materiality of learning, this qualitative dissertation builds on constructionist and posthuman perspectives on learning to (re)examine the role of materials in learning. First, artifact analysis with computer science experts explored computational concepts in fiber crafts. Second, a middle school intervention and performative analysis of video data, semi-structured interviews, and youth projects investigated how fiber crafts drove computational learning. Third, middle school intervention video data was analyzed to research the utility of posthumanist perspectives for capturing learni

关键词： Statistics   Computer science  

摘要： Machine learning has become one of the most exciting areas in recent years, as it has achieved state-of-the-art performance and demonstrated fundamental breakthroughs in many challenging tasks. The efficiency and scalability of a machine learning system are important and restrict the applicability of the system. We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games. In this work, we propose a new model that takes the player-interaction effects into consideration and show that our proposed models have much better prediction power on several E-sports datasets, and furthermore can be used to reveal interesting patterns that cannot be discovered by previous methods. Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many machine learning models, especially deep neural networks, are vulnerable to adversarial examples, i.e., examples that are carefully crafted to fool a well-trained machine learning model while being indistinguishable from the natural images to humans. This makes it unsafe to apply deep neural networks in security-critical areas. We present two algorithms, Embedding Regularized Classifier (ER-Classifier) and Adversarial Example Detector (ADEtector), to improve the robustness of deep neural networks against adversarial examples. Motivated by the observation that adversarial examples often lie outside the natural image data manifold and the intrinsic dimension of image data is much smaller than its pixel space dimension, we propose to embed high-dimensional input images into a low-dimensional space and apply regularization on the embedding space to push the adversarial examples back to the manifold. Another algorithm, ADEtector, is inspired by the similar ideas of ER-Classifier and the fact that detecting adversarial examples are easier than classifying them correctly, we propose a novel adversarial example detector, short for ADEtec

关键词： Information technology   Engineering   Computer science  

摘要： Users are constantly battling with staying protected in the technology world and seeking security in an insecure world is one of the most relevant ongoing issues. Computers and networks need more protection from hackers, crackers, information thieves, and cyberterrorists. Cyber security focuses on protecting computers, networks, programs, and data from unintended or unauthorized access, change, or destruction. Privacy is an important factor in one’s life and one main reason may be the consequence of not having any. The world has changed in many ways, showcasing a false narrative that we are being protected with the new advances of technology and innovations, but it can be a threat in many cases. Users’ privacy are not protected because they can be tracked through cell phones, computers, and/or identity theft.

关键词： Computer science  

摘要： The increasing ubiquity of communication-capable devices such as smartphones and smart vehicles has paved the way for a plethora of useful Participatory Cyber-Physical Systems (CPSs) in the defense, medical, and commercial sectors. Such systems promise to collect and analyze vast amounts of timestamped, location-contextualized user data, allowing various agencies to act on the results. However, given that the utility of these systems depends absolutely on the number of willing participants and the quality of their data, CPSs must be accessible to attract as large a user base as possible, while also possessing countermeasures against adversarial data intended to mislead the decision-making of the systems. We begin by defining the structure of these CPS systems and the capabilities of the adversary, showing the particular danger of fake, software-simulated Sybil nodes that provide the adversary immense deceptive power at no meaningful cost or risk. We claim that no currently existing Sybil countermeasure successfully addresses both the requirements posed by CPS systems and the deceptive techniques available to the adversary—namely the introduction of a small number of physical malicious nodes who would help disguise the Sybils—and argue that a new approach is needed. To address the Sybil threat, we develop a holistic 3-stage Sybil detection framework that may be employed on-demand to detect Sybil nodes around a target location. The first stage comprises of an inter-node communication scheme that uses local radio broadcasts as proof-of-physical-presence, and is designed specifically to address the CPS requirements and the adversarial deception techniques. The second stage consists of a detection layer that examines each node's observed pairwise RSSI readings, searching for those inconsistent with their expected distributions given their claimed locations. Due to the time-constrained nature of the communication scheme however, many pairwise connections randomly fail and

关键词： Industrial engineering   Artificial intelligence   Computer science  

摘要： The complexity of the radiotherapy planning demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid irreversible errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional QA process. However, one important gap in relying on the treatment planning QA classifiers is the lack of understanding behind a specific classifier prediction. We examine the topic of interpretable machine learning, and apply appropriate post-hoc model-agnostic local explanation methods to understand the decisions of a regions of interest (ROIs) segmentation/labeling classifier as well as a plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a team-based Shapley values framework are constructed. LIME framework approximates the predictions of the underlying classifier around the locality of a specified instance with an interpretable linear model, so the explanations are obtained from inspecting the feature weights. Team-based Shapley values framework explains the classifier decisions by calculating the average marginal contribution of each feature group across all coalitions of feature groups for a specified instance. The results demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches, and show that both QA classifiers are moderately trustworthy and can be used to confirm the expert decisions. The results also suggest that the current version of the QA classifiers should not be viewed as a replacement for human QA process.

关键词： Robotics   Computer science  

摘要： In recent times, object detection and pose estimation have gained significant attention in the context of robotic vision applications. Both the identification of objects of interest as well as the estimation of their pose remain important capabilities in order for robots to provide effective assistance for numerous robotic applications ranging from household tasks to industrial manipulation. This problem is particularly challenging because of the heterogeneity of objects having different and potentially complex shapes, and the difficulties arising due to background clutter and partial occlusions between objects. As the main contribution of this work, we propose a system that performs real-time object detection and pose estimation, for the purpose of dynamic robot grasping. The robot has been pre-trained to perform a small set of canonical grasps from a few fixed poses for each object. When presented with an unknown object in an arbitrary pose, the proposed approach allows the robot to detect the object identity and its actual pose, and then adapt a canonical grasp in order to be used with the new pose. For training, the system defines a canonical grasp by capturing the relative pose of an object with respect to the gripper attached to the robot’s wrist. During testing, once a new pose is detected, a canonical grasp for the object is identified and then dynamically adapted by adjusting the robot arm's joint angles, so that the gripper can grasp the object in its new pose. We conducted experiments using a humanoid PR2 robot and showed that the proposed framework can detect well-textured objects, and provide accurate pose estimation in the presence of tolerable amounts of out-of-plane rotation. The performance is also illustrated by the robot successfully grasping objects from a wide range of arbitrary poses.

关键词： criminalistic computer science   criminalistic techniques   Information technologies   specialty   especialidad   informática criminalística   técnica criminalística   tecnologías de la información   especialidade   tecnologias da informação   Language of Keywords: English   Spanish   Portuguese  

摘要： Los avances obtenidos en las nuevas tecnologías de la información y las comunicaciones han cambiado las metodologías para aplicar las especialidades tradicionales que conforman la técnica criminalística, además de que han proporcionado el surgimiento de otras en el enfrentamiento a la actividad delictiva1. Este trabajo actualiza y se acerca más a la aplicación de la especialidad de referencia, partiendo de la experiencia de su empleo en la provincia de Guantánamo. Se utilizaron varias técnicas de la investigación científica como la entrevista a profundidad, el análisis de documentos, el análisis secundario y el estudio de casos. [ABSTRACT FROM AUTHOR]

关键词： Task analysis   Writing   Machine learning   Forensics   Electronic mail   Computer science   Natural language processing   Authorship discrimination   deep learning   machine learning   natural language processing   neural networks  

摘要： Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an 'Agree' and a 'Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book.

关键词： Computer science  

摘要： Approximate computing frameworks configure applications so they can operate at a range of points in an accuracy-performance tradeoff space. Approximations at layers of system stack provide computation and energy efficiency via lower resource usage by allowing tolerable errors. Instead of overprovisioning the resources at each layer, higher efficiency is achievable if we consider the system stack holistically. This thesis evaluates cross-layer approximate computing. We explore three problems: 1) How to compare approximate computing frameworks in terms of accuracy-efficiency tradeoffs? 2) How to combine them without negating each other’s optimizations? 3) How to achieve significant end-to-end optimizations when the entire system stack is considered? We address the first problem by providing an improved visualization algorithm to represent tradeoff spaces. For the second problem, we analyze the approximation framework in layers of system stack one by one to evaluate end-to-end approximation opportunities. First, we introduce a family of exploration techniques to combine software approximation frameworks. Next, at the programming language layer, an automated precision tuning framework is presented that helps users explore different levels of floating-point approximations in the application. At the architecture level, an unreliable memory framework is implemented and examined. At the hardware and circuit layer, precision tuning information is used to build a reduced-precision FPU. For the third problem, we form the first holistic cross-layer approximate computing framework which integrates approximations at various layers and provides performance and energy improvements in return for minimal accuracy loss.

关键词： System science   Artificial intelligence   Computer science  

摘要： A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive. The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats the deep learning architecture design problem as a systems architecting problem. The framework provides the ability to utilize novel and intuitive search spaces to find efficient architectures using evolutionary methodologies. Secondly, it uses a parameter sharing approach to speed up the search process and explores its limitations with search space. Lastly, it introduces a multi-objective approach to facilitate architecture design based on hardware constraints that are often associated with real-world deployment. From the modeling perspective, instead of designing and staging explicit algorithms to process images/sentences, the contribution lies in the design of hybrid architectures that use the deep learning literature developed so far. This approach enjoys the benefit of a single problem formulation to perform end-to-end training and architecture design with limited computational resources.