Using SVG and XSLT for graphic representation

Using SVG and XSLT for graphic representation In this paper we will present an XML based framework that can be used to produce graphical visualisation of scientific data. The approach rather than producing ordinary histogram and function diagaram graphs, tries to represent the information in a more graphical appealing and easy to understand way. For examples the approach will give the ability to represent the temperature as the level of coulored fluid in a thermometer. The proposed framework is able to maintain the value of the datas strictly separated from the visual form of its representation (positions of element, colours, visual representation etc.). By defining appropriate data structures and expressing them using XML, the framework gives the user the ability to create graphic representations using standard SVG and XSLT. Since XML can be used for describing complex data information, we represent every level of the graphic representation with an XML structure. To describe our architecture we defined the following XML dialects, each one with different markup tags, reflecting the semantical values of the elements. Data definition level. Used to define the value of the datas that can be used in the graphic representation Data representation level. Used to define the graphic representation, it defines how the values expressed by the data definition level are represented. Both data representation and data definition files are based on a DTD to impose the constraints. Data representation level is the core of the system, and defines a powerful language for representation. Source primitives. Used to define for the source of the graphic elements, for example static file or SVG code. Modification primitives. Used to define the modifications that can affect a graphic element, for example rotation, scaling or repetition. Disposition primitives. Used to define the possible dispositions along x, y and z axes, for example to impose a order in the representation of elements. Action primitives. Used to define the possible actions that canbe activated by graphic elements for different user behaviours. For example a mouse action can activate a link to a different resource, or can change the value of any of the other primitives of the data structure, as image source or disposition, or can show a tooltip . XSLT is used to output a SVG file derived from the two files describing the graphic representation. Our aim is to provide an abstract language to be used to represent in different ways the same concept. In fact, we can link a data definition file with different data representation levels, providing different kinds and levels of complexity for the same concept. An example use could be the representation of the temperature described before, where the temperature itself could be represented either as the level of mercury in the termomether, or as the rotation of an arrow in a gauge. The transformation process is made from an XML source tree into an XML result tree, using XPath to define patterns. XSLT transformation process is based on templates, that define some actions (like adding or removing elements, or sorting them) to be performed when a part of the document matches a template. To implement some of the complex graphics operations we are using XSLT extensions that allow to perform mathematical operations. These XSLT extensions are not yet standard and require specific compliant parser, as Apache Xalan, that allows the developer to interface with Java classes in order to increase XSLT areas of application, from simple node transformations to quite complex operations

Robot Mindreading and the Problem of Trust

This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot

Explainable NLP for Human-AI Collaboration

With more data and computing resources available these days, we have seen many novel Natural Language Processing (NLP) models breaking one performance record after another. Some of them even outperform human performance in some specific tasks. Meanwhile, many researchers have revealed weaknesses and irrationality of such models, e.g., having biases against some sub-populations, producing inconsistent predictions, and failing to work effectively in the wild due to overfitting. Therefore, in real applications, especially in high-stakes domains, humans cannot rely carelessly on predictions of NLP models, but they need to work closely with the models to ensure that every final decision made is accurate and benevolent. In this thesis, we devise and utilize explainable NLP techniques to support human-AI collaboration using text classification as a target task. Overall, our contributions can be divided into three main parts. First, we study how useful explanations are for humans according to three different purposes: revealing model behavior, justifying model predictions, and helping humans investigate uncertain predictions. Second, we propose a framework that enables humans to debug simple deep text classifiers informed by model explanations. Third, leveraging on computational argumentation, we develop a novel local explanation method for pattern-based logistic regression models that align better with human judgements and effectively assist humans to perform an unfamiliar task in real-time. Altogether, our contributions are paving the way towards the synergy of profound knowledge of human users and the tireless power of AI machines.Open Acces

Combating Fake News: A Gravity Well Simulation to Model Echo Chamber Formation In Social Media

Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required. One solution is to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We initially propose a method to automatically recognize suspected fake news, and to provide news consumers with more information as to its veracity. We suggest that fake news is comprised of two components: premises and misleading content. Fake news can be condensed down to a collection of premises, which may or may not be true, and to various forms of misleading material, including biased arguments and language, misdirection, and manipulation. Misleading content can then be exposed. While valuable, this framework’s utility may be limited by artificial intelligence, which can be used to alter fake news strategies at a rate exceeding the ability to update the framework. Therefore, we propose a model for identifying echo chambers, which are widely reported to be havens for fake news producers and consumers. We simulate a social media interest group as a gravity well, through which we identify the online groups postured to become echo chambers, and thus a source for fake news consumption and replication. This echo chamber model is supported by three pillars related to the social media group: technology employed, topic explored, and confirmation bias of group members. The model is validated by modeling and analyzing 19 subreddits on the Reddit social media platform. Contributions include a working definition for fake news, a framework for recognizing fake news, a generic model for social media echo chambers including three pillars central to echo chamber formation, and a gravity well simulation for social media groups, implemented for 19 subreddits

A Configurable Transport Layer for CAF

The message-driven nature of actors lays a foundation for developing scalable and distributed software. While the actor itself has been thoroughly modeled, the message passing layer lacks a common definition. Properties and guarantees of message exchange often shift with implementations and contexts. This adds complexity to the development process, limits portability, and removes transparency from distributed actor systems. In this work, we examine actor communication, focusing on the implementation and runtime costs of reliable and ordered delivery. Both guarantees are often based on TCP for remote messaging, which mixes network transport with the semantics of messaging. However, the choice of transport may follow different constraints and is often governed by deployment. As a first step towards re-architecting actor-to-actor communication, we decouple the messaging guarantees from the transport protocol. We validate our approach by redesigning the network stack of the C++ Actor Framework (CAF) so that it allows to combine an arbitrary transport protocol with additional functions for remote messaging. An evaluation quantifies the cost of composability and the impact of individual layers on the entire stack

Preventing false temporal implicatures: interactive defaults for text generation

Introduction Given the causal and temporal relations between events in a knowledge base, what are the ways they can be described in text? Elsewhere, we have argued that during interpretation, the reader-hearer H must infer certain temporal information from knowledge about the world, language use and pragmatics. It is generally agreed that processes of Gricean implicature help determine the interpretation of text in context. But without a notion of logical consequence to underwrite them, the inferences---often defeasible in nature---will appear arbitrary, and unprincipled. Hence, we have explored the requirements on a formal model of temporal implicature, and outlined one possible nonmonotonic framework for discourse interpretation (Lascarides & Asher [1991], Lascarides & Oberlander [1992a]). Here, we argue that if the writer-speaker S is to tailor text to H , then discourse generation can be informed by a similar formal model o