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关键词： Computer engineering   Information technology   Computer science  

摘要： This thesis presents the architecture, design, and evaluation of the mobile edge cloud (MEC) system aimed at supporting future low-latency applications. Mobile edge clouds have emerged as a solution for providing low latency services in future generations (5G and beyond) of mobile networks, which are expected to support a variety of real-time applications such as AR/VR (Augmented/Virtual Reality), autonomous vehicles and robotics. Conventional cloud computing implemented at distant large-scale data centers incurs irreducible propagation delays of the order of 50-100ms or more that may be acceptable for current applications but may not be able to support emerging real-time needs. Edge clouds considered here promise to meet the stringent latency requirements of emerging classes of real-time applications by bringing compute, storage, and networking resources closer to user devices. However, edge clouds are intrinsically local and have a smaller scale and are thus subject to significantly larger fluctuations in offered traffic due to factors such as correlated events and user mobility. In addition, edge computing systems by definition are distributed across multiple edge networks and hence are associated with considerable heterogeneity in bandwidth and compute resources. Considering these challenges, this thesis analyzes the requirements posed by the edge clouds and proposes specific techniques for control, network routing, data migration, and dynamic resource assignment which can be employed to support low-latency applications. The thesis starts by analyzing system-level edge cloud requirements for low-latency by deploying a set of sample AR applications, namely, annotation-based assistance, and smart navigation. A city-scale MEC system is analyzed for achievable latency when running AR applications using existing core clouds as well as the proposed distributed edge cloud infrastructure. Performance evaluation results are presented to understand the trade-offs in key s

关键词： Computer engineering   Computer science  

摘要： Runtime resource management for heterogeneous computing systems is becoming more and more complex as workloads in these platforms get increasingly more diverse and the conflicts grow between heterogeneous architectural components and their resource demands. The goal of these runtime resource management mechanisms is to achieve the overall system goal for dynamic workloads while coordinating system resources in a robust and adaptive fashion. To address the complexities in heterogeneous computing systems, state-of-the-art techniques that use heuristics or machine learning have been proposed. On the other hand, conventional control theory can be used for formal guarantees, but may face unmanageable complexity for modeling system dynamics when dealing with heterogeneous computing platforms. In this thesis, we initially analyze a variety of runtime resource management methods and introduce a classification for these methods capturing the utilized resources and metrics. We cover heuristic, machine learning and control theory methods used to manage resources such as performance, power, energy, temperature, Quality-of-Service (QoS) and reliability of the system. In addition, we explore a variety of dynamic resource management frameworks that provide significant gains in terms of self-optimization and self-adaptivity. This includes simulation infrastructures, hardware platforms enhanced with multi-layer management mechanisms and corresponding software frameworks that enable management policies for these systems in an effective and adaptive manner. Ultimately, we address the problem of optimizing energy efficiency, power consumption, performance and QoS in heterogeneous systems by proposing adaptive runtime policies. The proposed methods in this thesis, take into account the constraints and requirements defined by user, dynamic workloads and coordination between conflicting objectives. The projects presented in this dissertation show effectiveness in responding to abrupt chan

关键词： Biomedical engineering   Computer science  

摘要： Medical simulation offers the opportunity to revolutionize the training of medical professionals, from paramedics to physicians and surgeons, allowing early learning to occur in a safe, controllable, and configurable virtual environment, without putting patients at risk. However, the complexity of the problems involved in the development of medical training systems as well as the spectrum of scientific fields that need to be covered have been the major limiting factors for the achievement of realistic simulations. The current state of the field of medical simulation is characterized by scattered laboratories or commercial entities using a variety of models that are neither interoperable nor independently verifiable, resulting in a steep development curve, duplication of efforts, high cost for simulators, and slow adoption of the technology. There have been some attempts to provide open-source standardization. However, they offer a collage of specialized solvers for different substances (bones, tissues, fluids, etc.), which creates redundant work and does not provide stable and efficient two-way interactions between all object types. Lastly, most of the simulation systems do not provide a means for automated assessment that can record, visualize, and analyze trainees’ performances through quantitative measures, which is an often neglected aspect of medical simulators. This dissertation addresses the issues of medical simulation in an attempt to bridge the gap left by previous works. We propose a practical and efficient virtual reality simulation framework that converts the training of medical procedures to a completely immersive virtual environment where both visual and physical realism are achieved. Our generalizable framework embeds independent dynamics models and interaction devices in separate modules while allows them to interact with each other within the same environment, which offers a flexible solution for multi-modal medical simulation scenarios and enables

关键词： Computer science  

摘要： Complex codebases with several layers of abstractions have abundant inefficiencies that affect the performance. These inefficiencies arise due to various causes such as developers’ inattention to performance, inappropriate choice of algorithms and inefficient code generation among others. To eliminate the redundancies, lots of work has been done during the compiling phase. However, not all redundancies can be easily detected or eliminated with compiler optimization passes due to aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis. There are also profiling tools which can reveal how resources are used. However, they can hard to distinguish whether the resource is worth fully used. More profiling tools are in needed to diagnose resource wastage and pinpoint inefficiencies. We have developed three tools to pinpoint different types of inefficiencies in different granularity. We build Runtime Value Numbering (RVN), a dynamic fine-grained profiler to pinpoint and quantify redundant computations in an execution. It is based on the classical value numbering technique but works at runtime instead of compile-time. We developed RedSpy–a fine-grained profiler to pinpoint and quantify value redundancies in program executions. Value redundancy may happen overtime at the same locations or in adjacent locations, and thus it has temporal and spatial locality. RedSpy identifies both temporal and spatial value locality. Furthermore, RedSpy is capable of identifying values that are approximately the same, enabling optimization opportunities in HPC codes that often use floating-point computations. RVN and RedSpy are both instrumentation based tools. They provide comprehensive result while introducing high space and time overhead. Our lightweight framework, Witch, samples consecutive accesses to the same memory location by exploiting two ubiquitous hardware features: the performance monitoring units

关键词： Games   Game theory   Detection algorithms   Image edge detection   Computer science   Computational modeling   Social network services   Cooperative game   Shapley value   overlapping community detection  

摘要： Community detection is of great significance and has more guiding for network analysis which has attract a lot of attention and is widely used in many areas. The process of community formation is modeled as a game in this paper, nodes in networks are taken as players, and communities are regarded as coalitions. An overlapping community detection algorithm is presented based on cooperative game theory. Furthermore, an edge weight computation is proposed for calculating Shapley value of node and coalition, and a strategy of optimal income for the individual is designed. It is for each player to choose its best coalition to stay or leave during the game process. Through choosing coalitions which can improve the value of their characteristic function, coalitions can be merged to avoid small size coalitions. It does not need any prior information about the number and the size of communities and the overlapping community structure is stable at the end of the algorithm. The experimental results both on synthetic and real networks demonstrate the good performance of the proposed method. It can detect overlapping communities more accurately.

关键词： Information technology   Computer science  

摘要： To mitigate known vulnerabilities in cybersecurity, organizations are in a desperate need for critical information, such as exploit code maturity, confidence about exploits, remediation capability, and own environmental contexts. Threats need to be contextualized so that they become visible risks for remedial actions. Extant efforts need significant improvements to prevent malicious actors from exploiting exposed risks. Rigorously founded on theories, but pragmatic research is crucial for better vulnerability management efforts. The NVD (National Vulnerability Database) Data Feeds, considered the de-facto data source in the cybersecurity domain, does not unfortunately provide a complete set of vulnerability information needed by organizations. Then, organizations need to rely on cybersecurity experts and vendors to obtain the missing pieces of information. Also, it is cumbersome and often time-consuming to collect relevant information about risks and put needed knowledge all together at one place. Using the heuristics-based rules and prioritized-risks visualization, this research takes on the challenge of transforming vulnerabilities into context-aware, visible risks that organizations can handle and mitigate. First, to derive the three constituent Metric Values of CVSS Temporal Scores which are factored into calculating Temporal and Environment Scores -- Exploit Code Maturity, Remediation Level, and Report Confidence --, this study developed heuristics-based rules data-driven by analyzing NVD datasets. Then, to guide cybersecurity analysts to analyze and prioritize threats, the most appropriate visualization tool for a context was selected based on a conceptual road-map. Subsequently, vulnerabilities of IT assets of research sites were contextualized, and their CVEs (Common Vulnerabilities and Exposures) were collected. After downloading pertinent data from NVD Data Feeds, it was data-matched with the contextualized risks. Then, based on the three Temporal Metric V

关键词： Health care management   Computer science  

摘要： As societies across the globe grow progressively older, it is important to find ways to leverage technology to benefit seniors by assisting them to care for their health. Self-tracking is a strategy that can be used for health management and augmented by using technology such as smartphones and wearable devices. Although most seniors use self-tracking for health, they most commonly rely on either paper or their own memories when tracking health data. Because their adoption of self-tracking technology is very low, seniors miss any benefits that might be gained from using self-tracking technology, such as self-knowledge, encouragement, or reduced burden. Overcoming this issue requires designing tools that appeal to seniors and meet their specific needs. Although past research has found multiple barriers for seniors’ use of self-tracking technology, such as lower accuracy when counting steps due to gait or walking speed, there is still much we do not understand about how to create self-tracking technology that better meets their needs. My research investigates self-tracking for health among older adults, with the goal of understanding their existing tracking practices, their perspectives towards self-tracking technology, and their needs as potential users. In this dissertation I discuss three kinds of health-related self-tracking among seniors: wellness, self-management of chronic conditions, and recovery from a major health event. Through a quantitative survey data reanalysis, I investigate the relationships of age and health status with tracking habits to understand the influence of each of these factors on self-tracking practices. Based on empirical evidence, I also describe seniors’ existing use of and perspectives towards self-tracking for health and the barriers for adoption of self-tracking technology among seniors. Lastly, I investigate the role of self-tracked medical recovery data among stroke survivors and healthcare providers by describing their existing us

关键词： Bioinformatics   Computer science  

摘要： Recent rapid technological advances have increased the prospects for precision medicine, allowing us to tailor medical care to individual patients or patient groups based on underlying molecular patterns. However, many existing techniques do not consider these molecular patterns in a temporal context, which may be especially important for inherently temporal functions such as those involved in development, aging, or progressive disorders. In this dissertation, we introduce three computational approaches for characterizing time-related patterns of dysregulation in such time-related processes. In previous work, our group developed the FRaC anomaly detection algorithm and used it to identify anomalous mRNA expression patterns and to characterize individual anomalies by identifying dysregulated molecular functions. However, FRaC operates by training supervised models for each feature in a data set. Scaling to substantially larger data sets, such as the massive multi-omics data sets now being produced, would require prohibitive amounts of computation time and memory. We show that by using information provided by preexisting knowledge of biologically relevant pathways, it is possible to substantially reduce usage of computational resources while preserving the same anomaly detection accuracy. However, static analysis of many expression data sets, including that provided by FRaC, is not enough to fully capture time-related patterns of dysregulation that might be present in highly time-related diseases or disorders such as autism, Huntington's disease, or Alzheimer's disease. To characterize temporal dysregulation in these and other disorders, we introduce TEMPO, a pathway-based outlier detection approach for finding pathways showing significant temporal changes in gene expression patterns in data sets with additional temporal annotation. Our experiments demonstrate that a temporal pathway approach can identify new functional, temporal, or developmental processes associated

关键词： Computer science  

摘要： In this work, we produce several prediction models for aspects of hospital emergency departments. Firstly, we demonstrate the use of a recurrent neural network to predict the rate of patient arrival at a hospital emergency department. The prediction is made on a per hour basis using date, time, calendar, and weather information. Then, we present our comparison of two prediction systems on the task of replicating the human decisions of patient admittance in a typical American emergency department. Again, a recurrent neural network (RNN) was trained to learn the task of selecting the next patient from the waiting room/queue to be admitted for treatment. Lastly, we present our attempt to produce a regression model that can predict the likelihood that a given patient will leave after waiting a specific amount of time in the emergency department’s waiting-room/queue. Such a model could be used to optimize the patient’s waiting-room/queue of an ED to minimize the likelihood of patients leaving without receiving care.