Towards Avatars with Artificial Minds: Role of Semantic Memory

he first step towards creating avatars with human-like artificial minds is to give them human-like memory structures with an access to general knowledge about the world. This type of knowledge is stored in semantic memory. Although many approaches to modeling of semantic memories have been proposed they are not very useful in real life applications because they lack knowledge comparable to the common sense that humans have, and they cannot be implemented in a computationally efficient way. The most drastic simplification of semantic memory leading to the simplest knowledge representation that is sufficient for many applications is based on the Concept Description Vectors (CDVs) that store, for each concept, an information whether a given property is applicable to this concept or not. Unfortunately even such simple information about real objects or concepts is not available. Experiments with automatic creation of concept description vectors from various sources, including ontologies, dictionaries, encyclopedias and unstructured text sources are described. Haptek-based talking head that has an access to this memory has been created as an example of a humanized interface (HIT) that can interact with web pages and exchange information in a natural way. A few examples of applications of an avatar with semantic memory are given, including the twenty questions game and automatic creation of word puzzles

Semantics and Security Issues in JavaScript

There is a plethora of research articles describing the deep semantics of JavaScript. Nevertheless, such articles are often difficult to grasp for readers not familiar with formal semantics. In this report, we propose a digest of the semantics of JavaScript centered around security concerns. This document proposes an overview of the JavaScript language and the misleading semantic points in its design. The first part of the document describes the main characteristics of the language itself. The second part presents how those characteristics can lead to problems. It finishes by showing some coding patterns to avoid certain traps and presents some ECMAScript 5 new features.Comment: Deliverable Resilience FUI 12: 7.3.2.1 Failles de s\'ecurit\'e en JavaScript / JavaScript security issue

Correcting Knowledge Base Assertions

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB

ADsafety: Type-Based Verification of JavaScript Sandboxing

Web sites routinely incorporate JavaScript programs from several sources into a single page. These sources must be protected from one another, which requires robust sandboxing. The many entry-points of sandboxes and the subtleties of JavaScript demand robust verification of the actual sandbox source. We use a novel type system for JavaScript to encode and verify sandboxing properties. The resulting verifier is lightweight and efficient, and operates on actual source. We demonstrate the effectiveness of our technique by applying it to ADsafe, which revealed several bugs and other weaknesses.Comment: in Proceedings of the USENIX Security Symposium (2011

Network Structure Mining and Evolution Analysis - Based on BA Scale-Free Network Model

The massive adoption of the Internet facilitates growth of online social networks, in which information can be exchanged in a more efficient way. Such as products, user accounts, web pages, there may be a variety of objects suitable to structurize this kind of networks. As a result, this gives the networks complexity and dynamics. The work in this paper is aiming to studying the topological property of online social network structure from the aspect of dynamics, and make clear the evolution processes of the networks. This is done by a Mean-Field analysis of network growth based on BA Scale-Free network model. Data resources come from the Chinese online e-commerce platform you.163.com and graphs are modeled through commentator and mutual comments by calculating degree distribution of the networks. We build a growing random model for forecasting dynamics of degree evolution. Finally, we use data set on Sina Weibo to test the model and the results are satisfying