Sparsity-based Defense against Adversarial Attacks on Linear Classifiers

Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent work raises important questions about the robustness of such architectures, by showing that it is possible to induce classification errors through tiny, almost imperceptible, perturbations. Vulnerability to such "adversarial attacks", or "adversarial examples", has been conjectured to be due to the excessive linearity of deep networks. In this paper, we study this phenomenon in the setting of a linear classifier, and show that it is possible to exploit sparsity in natural data to combat $\ell_{\infty}$-bounded adversarial perturbations. Specifically, we demonstrate the efficacy of a sparsifying front end via an ensemble averaged analysis, and experimental results for the MNIST handwritten digit database. To the best of our knowledge, this is the first work to show that sparsity provides a theoretically rigorous framework for defense against adversarial attacks.Comment: Published in IEEE International Symposium on Information Theory (ISIT) 201

A unification-based natural language interface to a database.

An implementation of a Lexical Functional Grammar (LFG) natural language front-end to a database is presented, and its capabilities demonstrated by reference to a set of queries used in the Chat-80 system. The potential of LFG for such applications is explored. Other grammars previously used for this purpose are briefly reviewed and contrasted with LFG. The basic LFG formalism is fully described, both as to its syntax and semantics, and the deficiencies of the latter for database access application shown. Other current LFG implementations are reviewed and contrasted with the LFG implementation developed here specifically for database access. The implementation described here allows a natural language interface to a specific Prolog database to be produced from a set of grammar rule and lexical specifications in an LFG-like notation. In addition to this the interface system uses a simple database description to compile metadata about the database for later use in planning the execution of queries. Extensions to LFG's semantic component are shown to be necessary to produce a satisfactory functional analysis and semantic output for querying a database. A diverse set of natural language constructs are analysed using LFG and the derivation of Prolog queries from the F-structure output of LFG is illustrated. The functional description produced from LFG is proposed as sufficient for resolving many problems of quantification and attachment

UFRA: a UIMA-based Approach to Federated Language Resource Architecture

In this paper we address the issue of developing an interoperable infrastructure for language resources and technologies. In our approach, called UFRA, we extend the Federate Database Architecture System adding typical functionalities caming from UIMA. In this way, we capitalize the advantages of a federated architecture, such as autonomy, heterogeneity and distribution of components, monitored by a central authority responsible for checking both the integration of components and user rights on performing different tasks. We use the UIMA approach to manage and define one common front-end, enabling users and clients to query, retrieve and use language resources and technologies. The purpose of this paper is to show how UIMA leads from a Federated Database Architecture to a Federated Resource Architecture, adding to a registry of available components both static resources such as lexicons and corpora and dynamic ones such as tools and general purpose language technologies. At the end of the paper, we present a case-study that adopts this framework to integrate the SIMPLE lexicon and TIMEML annotation guidelines to tag natural language texts

An intelligent user interface for browsing satellite data catalogs

A large scale domain-independent spatial data management expert system that serves as a front-end to databases containing spatial data is described. This system is unique for two reasons. First, it uses spatial search techniques to generate a list of all the primary keys that fall within a user's spatial constraints prior to invoking the database management system, thus substantially decreasing the amount of time required to answer a user's query. Second, a domain-independent query expert system uses a domain-specific rule base to preprocess the user's English query, effectively mapping a broad class of queries into a smaller subset that can be handled by a commercial natural language processing system. The methods used by the spatial search module and the query expert system are explained, and the system architecture for the spatial data management expert system is described. The system is applied to data from the International Ultraviolet Explorer (IUE) satellite, and results are given

A portable natural language interface from Arabic to SQL.

In recent years, natural language interface systems have been built based on the Front End and the Back End architecture which gives a guarantee of modularity and portability to the system as a whole. An Arabic Front End has been built that takes an input sentence, producing syntactic and semantic representations, which it maps into First Order Logic. Expressing the meaning of the user's question in terms of high level world concepts makes the natural language interface independent of the database structure. It is then easier to port the interface Front End to a database for a different domain. The syntactic treatments are based on Generalised Phrase Structure Grammar (GPSG) whereas the semantics are expressed in formal semantics theory. The focus is mainly to provide syntactic and semantic analyses for Arabic queries based on correct Arabic linguistic principles. The proposed treatments are proved and tested by building a prototype system. The prototype is implemented using one of the existing systems called Squirrel. An Arabic morphological analyser is also proposed and implemented to distinguish between two types of morphemes: internal morphemes which are a part of the word's pattern, and external morphemes which are independent words attached to the word but which are not part of the word's pattern. So, the system focuses on the extraction of morphemes from the various inflexions or forms of any Arabic word

Disease Navigation Application

Telemedicine is in the current line of requirements of modern society. Automated symptom-based disease detection and appointment booking is a well-known research topic in the field of informatics and system design. That helps in elevating awareness and early detection of disease at the ease of home through appointment booking. The purpose of the application is the capability to articulate disease symptoms, gathering all relevant information of the patient and a recommendation system that eventually evaluates all symptoms and maps effectively to a specialty department. Using the natural language processing-based approach, the user can input text of symptoms to be mapped with Google Search API and then finally parse them and generate recommendations based on the user input. Then it enables the user to choose a doctor and book an appointment based on the availability of that doctor. The application is developed by using HTML, CSS, and JavaScript to build the front end and using microservice API exposed as REST built with the Django Python framework to build the back end. Using an SQLite database for the back end of the application helps to store the data locally and makes the application functional. In addition, appointment booking, and disease recommendation are two inevitable features from an end-user perspective