Tight Approximation Ratio of Anonymous Pricing

We consider two canonical Bayesian mechanism design settings. In the single-item setting, we prove tight approximation ratio for anonymous pricing: compared with Myerson Auction, it extracts at least $\frac{1}{2.62}$-fraction of revenue; there is a matching lower-bound example. In the unit-demand single-buyer setting, we prove tight approximation ratio between the simplest and optimal deterministic mechanisms: in terms of revenue, uniform pricing admits a $2.62$-approximation of item pricing; we further validate the tightness of this ratio. These results settle two open problems asked in~\cite{H13,CD15,AHNPY15,L17,JLTX18}. As an implication, in the single-item setting: we improve the approximation ratio of the second-price auction with anonymous reserve to $2.62$, which breaks the state-of-the-art upper bound of $e \approx 2.72$

Automated Repair of Layout Cross Browser Issues Using Search-Based Techniques

A consistent cross-browser user experience is crucial for the success of a website. Layout Cross Browser Issues (XBIs) can severely undermine a website’s success by causing web pages to render incorrectly in certain browsers, thereby negatively impacting users’ impression of the quality and services that the web page delivers. Existing Cross Browser Testing (XBT) techniques can only detect XBIs in websites. Repairing them is, hitherto, a manual task that is labor intensive and requires significant expertise. Addressing this concern, our paper proposes a technique for automatically repairing layout XBIs in websites using guided search-based techniques. Our empirical evaluation showed that our approach was able to successfully fix 86% of layout XBIs reported for 15 different web pages studied, thereby improving their cross-browser consistency

CLAN : a tool for contract analysis and conflict discovery

As Service-Oriented Architectures are more widely adopted, it becomes more important to adopt measures for ensuring that the services satisfy functional and non-functional requirements. One approach is the use of contracts based on deontic logics, expressing obligations, permissions and prohibitions of the different actors. A challenging aspect is that of service composition, in which the contracts composed together may result in conflicting situations, so there is a need to analyse contracts and ensure their soundness. In this paper, we present CLAN, a tool for automatic analysis of conflicting clauses of contracts written in the contract language . We present a small case study of an airline check-in desk illustrating the use of the tool.peer-reviewe

Concurrent Program Verification with Invariant-Guided Underapproximation

Automatic verification of concurrent programs written in low-level languages like ANSI-C is an important task as multi-core architectures are gaining widespread adoption. Formal verification, although very valuable for this domain, rapidly runs into the state-explosion problem due to multiple thread interleavings. Recently, Bounded Model Checking (BMC) has been used for this purpose, which does not scale in practice. In this work, we develop a method to further constrain the search space for BMC techniques using underapproximations of data flow of shared memory and lazy demand-driven refinement of the approximation. A novel contribution of our method is that our underapproximation is guided by likely data-flow invariants mined from dynamic analysis and our refinement is based on proof-based learning. We have implemented our method in a prototype tool. Initial experiments on benchmark examples show potential performance benefit

A rubric based approach towards Automated Essay Grading : focusing on high level content issues and ideas

Assessment of a student’s work is by no means an easy task. Even if the student response is in the form of multiple choice answers, manually marking those answer sheets is a task that most teachers regard as rather tedious. The development of an automated method to grade these essays was thus an inevitable step.This thesis proposes a novel approach towards Automated Essay Grading through the use of various concepts found within the field of Narratology. Through a review of the literature, several methods in which essays are graded were identified together with some of the problems. Mainly, the issues and challenges that plague AEG systems were that those following the statistical approach needed a way to deal with more implicit features of free text, while other systems which did manage that were highly dependent on the type of student response, the systems having pre-knowledge pertaining to the subject domain in addition to requiring more computational power. It was also found that while narrative essays are one of the main methods in which a student might be able to showcase his/her mastery over the English language, no system thus far has attempted to incorporate narrative concepts into analysing these type of free text responses.It was decided that the proposed solution would be centred on the detection of Events, which was in turn used to determine the score an essay receives under the criteria of Audience, Ideas, Character and Setting and Cohesion, as defined by the NAPLAN rubric. From the results gathered from experiments conducted on the four criteria mentioned above, it was concluded that the concept of detecting Events as they were within a narrative type story when applied to essay grading, does have a relation towards the score the essay receives. All experiments achieved an average F-measure score of 0.65 and above while exact agreement rates were no lower than 70%. Chi-squared and paired T-test values all indicated that there was insufficient evidence to show that there was any significant difference between the scores generated by the computer and those of the human markers

Adapting Land Use and Infrastructure for Automated Driving: Part A

69A3551747105This project is concerned with adapting land use and transportation infrastructure for automated driving. Autonomous vehicles will likely yield a transformation of urban form, its land use and mobility system. We propose to establish quantitative modeling frameworks to analyze these impacts and implications. The frameworks will provide a quantifiable understanding of the tradeoffs, and reveal the underlying mechanism and identify key parameters that could shape the future of mobility systems and urban land use. Moreover, the proposed modeling frameworks will aid planning agencies with infrastructure adaptation planning and optimize a roadmap for shaping highway infrastructure towards automated mobility