Good Learning and Implicit Model Enumeration

MathSBML is an open-source, freely-downloadable Mathematica package that facilitates working with Systems Biology Markup Language (SBML) models. SBML is a toolneutral,computer-readable format for representing models of biochemical reaction networks, applicable to metabolic networks, cell-signaling pathways, genomic regulatory networks, and other modeling problems in systems biology that is widely supported by the systems biology community. SBML is based on XML, a standard medium for representing and transporting data that is widely supported on the internet as well as in computational biology and bioinformatics. Because SBML is tool-independent, it enables model transportability, reuse, publication and survival. In addition to MathSBML, a number of other tools that support SBML model examination and manipulation are provided on the sbml.org website, including libSBML, a C/C++ library for reading SBML models; an SBML Toolbox for MatLab; file conversion programs; an SBML model validator and visualizer; and SBML specifications and schemas. MathSBML enables SBML file import to and export from Mathematica as well as providing an API for model manipulation and simulation

Faster relaxed multiplication

In previous work, we have introduced several fast algorithms for relaxed power series multiplication (also known under the name on-line multiplication) up till a given order n. The fastest currently known algorithm works over an effective base field K with sufficiently many 2^p-th roots of unity and has algebraic time complexity O(n log n exp (2 sqrt (log 2 log log n))). In this note, we will generalize this algorithm to the cases when K is replaced by an effective ring of positive characteristic or by an effective ring of characteristic zero, which is also torsion-free as a Z-module and comes with an additional algorithm for partial division by integers. We will also present an asymptotically faster algorithm for relaxed multiplication of p-adic numbers

Exploiting CAFS-ISP

In the summer of 1982, the ICLCUA CAFS Special Interest Group defined three subject areas for working party activity. These were: 1) interfaces with compilers and databases, 2) end-user language facilities and display methods, and 3) text-handling and office automation. The CAFS SIG convened one working party to address the first subject with the following terms of reference: 1) review facilities and map requirements onto them, 2) "Database or CAFS" or "Database on CAFS", 3) training needs for users to bridge to new techniques, and 4) repair specifications to cover gaps in software. The working party interpreted the topic broadly as the data processing professional's, rather than the end-user's, view of and relationship with CAFS. This report is the result of the working party's activities. The report content for good reasons exceeds the terms of reference in their strictest sense. For example, we examine QUERYMASTER, which is deemed to be an end-user tool by ICL, from both the DP and end-user perspectives. First, this is the only interface to CAFS in the current SV201. Secondly, it is necessary for the DP department to understand the end-user's interface to CAFS. Thirdly, the other subjects have not yet been addressed by other active working parties

Classifying #MeToo Hash-tagged Tweets by Semantics to Understand the Extent of Sexual Harassment

This thesis contains a program that will process tweets from Twitter that use the hashtag "#MeToo" and categorize them by their relevance to the movement, their stance on the movement, and the type of sexual harassment expressed (if applicable). Being able to work with a narrowed set of tweets belonging to a specific category creates the capacity to do more in-depth research and analysis, exploring Twitter as a special platform for discussing these sensitive topics and showing that this online space for expressing personal experiences has delivered unprecedented potential avenues of study. This thesis also contains research into additional solutions towards addressing sexual harassment online, exploring the needs of society through the results to a questionnaire that was administered to university students asking for opinions on how sexual harassment is addressed on social media as well as through a literature review of current obstacles for victims

SAGE: Profiling and Optimization in Formal Methods

The quality and the correctness of software are often the greatest concern in electronic systems. Formal methods techniques and tools provide a guarantee that a design of software system is free of specific flaws. This paper discusses an open source mathematical software system SAGE, which combines and extends program analysis, testing, verification, model checking, and automated theorem proving techniques. Then we concentrate on profiling and optimization techniques in analysis and design phase of software systems in SAGE. In today's programming world optimization plays a vital role. It is an on-going, non-functional requirement that affects all stages in the development of a system, from analysis and design through development and implementation. Profiling refers to analysis of the relative execution time spent in different parts of the program. Finally, we describe various optimization techniques

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

A Framework for Incident Detection and notification in Vehicular Ad-Hoc Networks

The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. The US-DOT also notes that in a single year, congested highways due to traffic incidents cost over $75 billion in lost worker productivity and over 8.4 billion gallons of fuel. Further, the National Highway Traffic Safety Administration (NHTSA) indicates that congested roads are one of the leading causes of traffic accidents, and in 2005 an average of 119 persons died each day in motor vehicle accidents. Recently, Vehicular Ad-hoc Networks (VANET) employing a combination of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communication have been proposed to alert drivers to traffic events including accidents, lane closures, slowdowns, and other traffic-safety issues. In this thesis, we propose a novel framework for incident detection and notification dissemination in VANETs. This framework consists of three main components: a system architecture, a traffic incident detection engine and a notification dissemination mechanism. The basic idea of our framework is to collect and aggregate traffic-related data from passing cars and to use the aggregated information to detect traffic anomalies. Finally, the suitably filtered aggregated information is disseminated to alert drivers about traffic delays and incidents. The first contribution of this thesis is an architecture for the notification of traffic incidents, NOTICE for short. In NOTICE, sensor belts are embedded in the road at regular intervals, every mile or so. Each belt consists of a collection of pressure sensors, a simple aggregation and fusion engine, and a few small transceivers. The pressure sensors in each belt allow every message to be associated with a physical vehicle passing over that belt. Thus, no one vehicle can pretend to be multiple vehicles and then, is no need for an ID to be assigned to vehicles. Vehicles in NOTICE are fitted with a tamper-resistant Event Data Recorder (EDR), very much like the well-known black-boxes onboard commercial aircraft. EDRs are responsible for storing vehicles behavior between belts such as acceleration, deceleration and lane changes. Importantly, drivers can provide input to the EDR, using a simple menu, either through a dashboard console or through verbal input. The second contribution of this thesis is to develop incident detection techniques that use the information provided by cars in detecting possible incidents and traffic anomalies using intelligent inference techniques. For this purpose, we developed deterministic and probabilistic techniques to detect both blocking incidents, accidents for examples, as well as non-blocking ones such as potholes. To the best of our knowledge, our probabilistic technique is the first VANET based automatic incident detection technique that is capable of detecting both blocking and non blocking incidents. Our third contribution is to provide an analysis for vehicular traffic proving that VANETs tend to be disconnected in many highway scenarios, consisting of a collection of disjoint clusters. We also provide an analytical way to compute the expected cluster size and we show that clusters are quite stable over time. To the best of our knowledge, we are the first in the VANET community to prove analytically that disconnection is the norm rather than the exceptions in VANETs. Our fourth contribution is to develop data dissemination techniques specifically adapted to VANETs. With VANETs disconnection in mind, we developed data dissemination approaches that efficiently propagate messages between cars and belts on the road. We proposed two data dissemination techniques, one for divided roads and another one for undivided roads. We also proposed a probabilistic technique used by belts to determine how far should an incident notification be sent to alert approaching drivers. Our fifth contribution is to propose a security technique to avoid possible attacks from malicious drivers as well as preserving driver\u27s privacy in data dissemination and notification delivery in NOTICE. We also proposed a belt clustering scheme to reduce the probability of having a black-hole in the message dissemination while reducing also the operational burden if a belt is compromised