Duplicate Defect Detection

Discovering and fixing faults is an unavoidable process in Software Engineering. It is always a good practice to document and organize fault reports. This facilitates the effectiveness of development and maintenance process. Bug Tracking Repositories, such as Bugzilla, are designed to provide fault reporting facilities for developers, testers and users of the system. Allowing anyone to contribute finding and reporting faults has an immediate impact on software quality. However, this benefit comes with one side-effect. Users often file reports that describe the same fault. This increases the triaging time spent by the maintainers. At the same time, important information required to fix the fault is likely to be distributed across different reports.;The objective of this thesis is twofold. First, we want to understand the dynamics of bug report filing for a large, long duration open source project, Firefox. Second, we present a new approach that can reduce the number of duplicate reports. The novel element in the proposed approach is the ability to concentrate the search for duplicates on specific portions of the bug repository. This improves the performance of Information Retrieval techniques and classification runtime of our algorithm. Our system can be deployed as a search tool to help reporters query the repository or it can be adopted to help maintainers detect duplicate reports. In both cases the performance is satisfactory. When tested as a search tool our system is able to detect up to 53% of duplicate reports. The approach adapted for maintainers has a maximum recall rate of 59%

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it - an improved and generalized version of Bayesian Blocks (Scargle 1998) - that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multi-variate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by (Arias-Castro, Donoho and Huo 2003). In the spirit of Reproducible Research (Donoho et al. 2008) all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.Comment: Added some missing script files and updated other ancillary data (code and data files). To be submitted to the Astophysical Journa

Hardware accelerated redundancy elimination in network system

With the tremendous growth in the amount of information stored on remote locations and cloud systems, many service providers are seeking ways to reduce the amount of redundant information sent across networks by using data de-duplication techniques. Data de-duplication can reduce network traffic without the loss of information, and consequently increase available network bandwidth by reducing redundant traffic. However, due to the heavy computation required for detecting and reducing redundant data transmission, de-duplication itself can become a bottleneck in high capacity links. We completed two parts of work in this research study, Hardware Accelerated Redundancy Elimination in Network Systems (HARENS) and Distributed Redundancy Elimination System Simulation (DRESS). HARENS can significantly improve the performance of redundancy elimination algorithm in a network system by leveraging General Purpose Graphic Processing Unit (GPGPU) techniques as well as other big data optimizations such as the use of a hierarchical multi-threaded pipeline, single machine Map-Reduce, and memory efficiency techniques. Our results indicate that throughput can be increased by a factor of 9 times compared to a naive implementation of the data de-duplication algorithm, providing a net transmission increase of up to 3.0 Gigabits per second (Gbps). DRESS provides further acceleration to the redundancy elimination in network system by deploying HARENS as the server\u27s side redundancy elimination module, and four cooperative distributed byte caches on the clients\u27 side. A client\u27s side distributed byte cache broadcast its cached chunks by sending hash values to other byte caches, so that they can keep a record of all the chunks in the cooperative distributed cache system. When duplications are detected, a client\u27s side byte cache can fetch a chunk directly from either its own cache or peer byte caches rather than server\u27s side redundancy elimination module. Our results indicate that bandwidth savings of the redundancy elimination system with cooperative distributed byte cache can be increased by 12% compared to the one without distributed byte cache, when transferring about 48 Gigabits of data

Spread spectrum-based video watermarking algorithms for copyright protection

Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can now benefit from hardware and software which was considered state-of-the-art several years ago. The advantages offered by the digital technologies are major but the same digital technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly possible and relatively easy, in spite of various forms of protection, but due to the analogue environment, the subsequent copies had an inherent loss in quality. This was a natural way of limiting the multiple copying of a video material. With digital technology, this barrier disappears, being possible to make as many copies as desired, without any loss in quality whatsoever. Digital watermarking is one of the best available tools for fighting this threat. The aim of the present work was to develop a digital watermarking system compliant with the recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark can be inserted in either spatial domain or transform domain, this aspect was investigated and led to the conclusion that wavelet transform is one of the best solutions available. Since watermarking is not an easy task, especially considering the robustness under various attacks several techniques were employed in order to increase the capacity/robustness of the system: spread-spectrum and modulation techniques to cast the watermark, powerful error correction to protect the mark, human visual models to insert a robust mark and to ensure its invisibility. The combination of these methods led to a major improvement, but yet the system wasn't robust to several important geometrical attacks. In order to achieve this last milestone, the system uses two distinct watermarks: a spatial domain reference watermark and the main watermark embedded in the wavelet domain. By using this reference watermark and techniques specific to image registration, the system is able to determine the parameters of the attack and revert it. Once the attack was reverted, the main watermark is recovered. The final result is a high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen

Fault Detection and Fail-Safe Operation with a Multiple-Redundancy Air-Data System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83640/1/AIAA-2010-7855-622.pd

Data Collection and Machine Learning Methods for Automated Pedestrian Facility Detection and Mensuration

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view imagery. We test data from these two viewpoints individually and with an ensemble method that we refer to as our “dual-perspective prediction model”. In order to obtain this data, we developed a data collection pipeline that combines crowdsourced pedestrian facility location data with aerial and street-view imagery from Bing Maps. In addition to the Convolutional Neural Network used to perform pedestrian facility detection using this data, we also trained a segmentation network to measure the length and width of crosswalks from aerial images. In our tests with a dual-perspective image dataset that was heavily occluded in the aerial view but relatively clear in the street view, our dual-perspective prediction model was able to increase prediction accuracy, recall, and precision by 49%, 383%, and 15%, respectively (compared to using a single perspective model based on only aerial view images). In our tests with satellite imagery provided by the Mississippi Department of Transportation, we were able to achieve accuracies as high as 99.23%, 91.26%, and 93.7% for aerial crosswalk detection, aerial sidewalk detection, and aerial crosswalk mensuration, respectively. The final system that we developed packages all of our machine learning models into an easy-to-use system that enables users to process large batches of imagery or examine individual images in a directory using a graphical interface. Our data collection and filtering guidelines can also be used to guide future research in this area by establishing standards for data quality and labelling

TCP – Random Early Detection (RED) mechanism for Congestion Control

This thesis discusses the Random Early Detection (RED) algorithm, proposed by Sally Floyd, used for congestion avoidance in computer networking, how existing algorithms compare to this approach and the configuration and implementation of the Weighted Random Early Detection (WRED) variation. RED uses a probability approach in order to calculate the probability that a packet will be dropped before periods of high congestion, relative to the minimum and maximum queue threshold, average queue length, packet size and the number of packets since the last drop. The motivation for this thesis has been the high QoS provided to current delay-sensitive applications such as Voice-over-IP (VoIP) by the incorporation of congestion avoidance algorithms derived from the original RED design [45]. The WRED variation of RED is not directly invoked on the VoIP class because congestion avoidance mechanisms are not configured for voice queues. WRED is instead used to prioritize other traffic classes in order to avoid congestion to provide and guarantee high quality of service for voice traffic [43][44]. The most notable simulations performed for the RED algorithm in comparison to the Tail Drop (TD) and Random Drop (RD) algorithms have been detailed in order to show that RED is much more advantageous in terms of congestion control in a network. The WRED, Flow RED (FRED) and Adaptive RED (ARED) variations of the RED algorithm have been detailed with emphasis on WRED. Details of the concepts of forwarding classes, output queues, traffic policies, traffic classes, class maps, schedulers, scheduler maps, and DSCP classification shows that the WRED feature is easily configurable on tier-1 vendor routers

Recognition of human interactions with vehicles using 3-D models and dynamic context

textThis dissertation describes two distinctive methods for human-vehicle interaction recognition: one for ground level videos and the other for aerial videos. For ground level videos, this dissertation presents a novel methodology which is able to estimate a detailed status of a scene involving multiple humans and vehicles. The system tracks their configuration even when they are performing complex interactions with severe occlusion such as when four persons are exiting a car together. The motivation is to identify the 3-D states of vehicles (e.g. status of doors), their relations with persons, which is necessary to analyze complex human-vehicle interactions (e.g. breaking into or stealing a vehicle), and the motion of humans and car doors to detect atomic human-vehicle interactions. A probabilistic algorithm has been designed to track humans and analyze their dynamic relationships with vehicles using a dynamic context. We have focused on two ideas. One is that many simple events can be detected based on a low-level analysis, and these detected events must contextually meet with human/vehicle status tracking results. The other is that the motion clue interferes with states in the current and future frames, and analyzing the motion is critical to detect such simple events. Our approach updates the probability of a person (or a vehicle) having a particular state based on these basic observed events. The probabilistic inference is made for the tracking process to match event-based evidence and motion-based evidence. For aerial videos, the object resolution is low, the visual cues are vague, and the detection and tracking of objects is less reliable as a consequence. Any method that requires accurate tracking of objects or the exact matching of event definition are better avoided. To address these issues, we present a temporal logic based approach which does not require training from event examples. At the low-level, we employ dynamic programming to perform fast model fitting between the tracked vehicle and the rendered 3-D vehicle models. At the semantic-level, given the localized event region of interest (ROI), we verify the time series of human-vehicle relationships with the pre-specified event definitions in a piecewise fashion. With special interest in recognizing a person getting into and out of a vehicle, we have tested our method on a subset of the VIRAT Aerial Video dataset and achieved superior results.Electrical and Computer Engineerin