Interactive Simplifier Tracing and Debugging in Isabelle

The Isabelle proof assistant comes equipped with a very powerful tactic for term simplification. While tremendously useful, the results of simplifying a term do not always match the user's expectation: sometimes, the resulting term is not in the form the user expected, or the simplifier fails to apply a rule. We describe a new, interactive tracing facility which offers insight into the hierarchical structure of the simplification with user-defined filtering, memoization and search. The new simplifier trace is integrated into the Isabelle/jEdit Prover IDE.Comment: Conferences on Intelligent Computer Mathematics, 201

Clear Visual Separation of Temporal Event Sequences

Extracting and visualizing informative insights from temporal event sequences becomes increasingly difficult when data volume and variety increase. Besides dealing with high event type cardinality and many distinct sequences, it can be difficult to tell whether it is appropriate to combine multiple events into one or utilize additional information about event attributes. Existing approaches often make use of frequent sequential patterns extracted from the dataset, however, these patterns are limited in terms of interpretability and utility. In addition, it is difficult to assess the role of absolute and relative time when using pattern mining techniques. In this paper, we present methods that addresses these challenges by automatically learning composite events which enables better aggregation of multiple event sequences. By leveraging event sequence outcomes, we present appropriate linked visualizations that allow domain experts to identify critical flows, to assess validity and to understand the role of time. Furthermore, we explore information gain and visual complexity metrics to identify the most relevant visual patterns. We compare composite event learning with two approaches for extracting event patterns using real world company event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data Science (VDS), 201

User Story Software Estimation:a Simplification of Software Estimation Model with Distributed Extreme Programming Estimation Technique

Software estimation is an area of software engineering concerned with the identification, classification and measurement of features of software that affect the cost of developing and sustaining computer programs [19]. Measuring the software through software estimation has purpose to know the complexity of the software, estimate the human resources, and get better visibility of execution and process model. There is a lot of software estimation that work sufficiently in certain conditions or step in software engineering for example measuring line of codes, function point, COCOMO, or use case points. This paper proposes another estimation technique called Distributed eXtreme Programming Estimation (DXP Estimation). DXP estimation provides a basic technique for the team that using eXtreme Programming method in onsite or distributed development. According to writer knowledge this is a first estimation technique that applied into agile method in eXtreme Programming

Translationese and post-editese : how comparable is comparable quality?

Whereas post-edited texts have been shown to be either of comparable quality to human translations or better, one study shows that people still seem to prefer human-translated texts. The idea of texts being inherently different despite being of high quality is not new. Translated texts, for example,are also different from original texts, a phenomenon referred to as ‘Translationese’. Research into Translationese has shown that, whereas humans cannot distinguish between translated and original text,computers have been trained to detect Translationesesuccessfully. It remains to be seen whether the same can be done for what we call Post-editese. We first establish whether humans are capable of distinguishing post-edited texts from human translations, and then establish whether it is possible to build a supervised machine-learning model that can distinguish between translated and post-edited text

A basis for the exploration of hypermedia systems : a guided path facility : a thesis presented in partial fulfillment of the requirements for the degree of Master of Arts at Massey University

This thesis examines the potential of a paths facility as an aid to navigating large hypermedia systems. The use of the navigational metaphor as applied to finding information is continued with the idea of following a path through information 'space'. This idea assumes that each node, or chunk of information, on the path can be considered a landmark that can be easily returned to when side-trips are taken off the path to explore the surrounding space. The idea of a guided path assumes the re-use of a path, and also assumes that there is extra information available about the path. This meta-information is very important for providing information to help path-followers make better sense of the path, both in terms of content and context, but also in making more effective use of the nodes on the path and in navigating the variety of interface conventions seen in the test environment - HyperCard. A small pilot study has been carried out using two groups of users performing a directed information-seeking task. One group used HyperCard's navigational facilities to find information in a group of stacks, while the other group used a guided path as a base on which to explore the same group of stacks. Both groups had a time limit, at the end of which they completed a number of questionnaires to indicate task completion, as well as providing a subjective evaluation of the facilities they used. The guided path facility appears to be most effective for inexperienced users for a number of reasons. It presents a simplified view of the complex system - the information available has already been filtered and selected, and a simple and consistent navigational interface reduces the cognitive overheads associated with learning a variety of mechanisms present in different stacks. An important feature of a path facility seems to be the provision of meta-information, especially scope information which can reduce the incidences of disorientation. Another feature is the provision of a history facility which provides a backtracking capability. It may also be used in the creation of paths using the length of visit as a criterion for node inclusion on a new path

Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201