Automating iterative tasks with programming by demonstration

Programming by demonstration is an end-user programming technique that allows people to create programs by showing the computer examples of what they want to do. Users do not need specialised programming skills. Instead, they instruct the computer by demonstrating examples, much as they might show another person how to do the task. Programming by demonstration empowers users to create programs that perform tedious and time-consuming computer chores. However, it is not in widespread use, and is instead confined to research applications that end users never see. This makes it difficult to evaluate programming by demonstration tools and techniques. This thesis claims that domain-independent programming by demonstration can be made available in existing applications and used to automate iterative tasks by end users. It is supported by Familiar, a domain-independent, AppleScript-based programming-by-demonstration tool embodying standard machine learning algorithms. Familiar is designed for end users, so works in the existing applications that they regularly use. The assertion that programming by demonstration can be made available in existing applications is validated by identifying the relevant platform requirements and a range of platforms that meet them. A detailed scrutiny of AppleScript highlights problems with the architecture and with many implementations, and yields a set of guidelines for designing applications that support programming-by-demonstration. An evaluation shows that end users are capable of using programming by demonstration to automate iterative tasks. However, the subjects tended to prefer other tools, choosing Familiar only when the alternatives were unsuitable or unavailable. Familiar's inferencing is evaluated on an extensive set of examples, highlighting the tasks it can perform and the functionality it requires

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

Topics in Programming Languages, a Philosophical Analysis through the case of Prolog

[EN]Programming languages seldom find proper anchorage in philosophy of logic, language and science. is more, philosophy of language seems to be restricted to natural languages and linguistics, and even philosophy of logic is rarely framed into programming languages topics. The logic programming paradigm and Prolog are, thus, the most adequate paradigm and programming language to work on this subject, combining natural language processing and linguistics, logic programming and constriction methodology on both algorithms and procedures, on an overall philosophizing declarative status. Not only this, but the dimension of the Fifth Generation Computer system related to strong Al wherein Prolog took a major role. and its historical frame in the very crucial dialectic between procedural and declarative paradigms, structuralist and empiricist biases, serves, in exemplar form, to treat straight ahead philosophy of logic, language and science in the contemporaneous age as well. In recounting Prolog's philosophical, mechanical and algorithmic harbingers, the opportunity is open to various routes. We herein shall exemplify some: - the mechanical-computational background explored by Pascal, Leibniz, Boole, Jacquard, Babbage, Konrad Zuse, until reaching to the ACE (Alan Turing) and EDVAC (von Neumann), offering the backbone in computer architecture, and the work of Turing, Church, Gödel, Kleene, von Neumann, Shannon, and others on computability, in parallel lines, throughly studied in detail, permit us to interpret ahead the evolving realm of programming languages. The proper line from lambda-calculus, to the Algol-family, the declarative and procedural split with the C language and Prolog, and the ensuing branching and programming languages explosion and further delimitation, are thereupon inspected as to relate them with the proper syntax, semantics and philosophical élan of logic programming and Prolog

A software based mentor system

This thesis describes the architecture, implementation issues and evaluation of Mentor - an educational support system designed to mentor students in their university studies. Students can ask (by typing) natural language questions and Mentor will use several educational paradigms to present information from its Knowledge Base or from data-mined online Web sites to respond. Typically the questions focus on the student’s assignments or in their preparation for their examinations. Mentor is also pro-active in that it prompts the student with questions such as "Have you started your assignment yet?". If the student responds and enters into a dialogue with Mentor, then, based upon the student’s questions and answers, it guides them through a Directed Learning Path planned by the lecturer, specific to that assessment. The objectives of the research were to determine if such a system could be designed, developed and applied in a large-scale, real-world environment and to determine if the resulting system was beneficial to students using it. The study was significant in that it provided an analysis of the design and implementation of the system as well as a detailed evaluation of its use. This research integrated the Computer Science disciplines of network communication, natural language parsing, user interface design and software agents, together with pedagogies from the Computer Aided Instruction and Intelligent Tutoring System fields of Education. Collectively, these disciplines provide the foundation for the two main thesis research areas of Dialogue Management and Tutorial Dialogue Systems. The development and analysis of the Mentor System required the design and implementation of an easy to use text based interface as well as a hyper- and multi-media graphical user interface, a client-server system, and a dialogue management system based on an extensible kernel. The multi-user Java-based client-server system used Perl-5 Regular Expression pattern matching for Natural Language Parsing along with a state-based Dialogue Manager and a Knowledge Base marked up using the XML-based Virtual Human Markup Language. The kernel was also used in other Dialogue Management applications such as with computer generated Talking Heads. The system also enabled a user to easily program their own knowledge into the Knowledge Base as well as to program new information retrieval or management tasks so that the system could grow with the user. The overall framework to integrate and manage the above components into a usable system employed suitable educational pedagogies that helped in the student’s learning process. The thesis outlines the learning paradigms used in, and summarises the evaluation of, three course-based Case Studies of university students’ perception of the system to see how effective and useful it was, and whether students benefited from using it. This thesis will demonstrate that Mentor met its objectives and was very successful in helping students with their university studies. As one participant indicated: ‘I couldn’t have done without it.

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Epi info, version 6 : a word-processing, database, and statistics program for public health on IBM-compatible microcomputers

Manual by Andrew G. Dean. revised for Version 6.03, January 1996.Program produced through collaboration between The Division of Surveillance and Epidemiology Studies, Epidemiology Program Office, Centers for Disease Control and the Global Programme on AIDS, World Health Organization.Suggested citation: Dean AG, Dean JA, Coulombier D, Brendel KA, SmithDC, Burton AH, Dicker RC, Sullivan K, Fagan RF, Arner, TG. Epi Info, Version 6: a word processing, database, and statistics program for public health on IBM compatible microcomputers. Centers for Disease Control and Prevention, Atlanta, Georgia, U.S.A., 1996.Introduction -- 1. How To Use This Manual -- 2. What Is Epi Info, Version 6? -- 3. What's New in Version 6? -- 4. Installing Epi Info -- 5. Running Epi Info -- -- Level I: Word Processing Functions -- 6. Using EPED, the Epidemiologist\ue2\u20ac\u2122s Editor, As aGeneral Word Processor -- 7. Creating Questionnaires Using EPED -- -- Level I: Entering and Analyzing Data Without Programming -- 8. Entering Data Using the ENTER Program -- 9. ANALYSIS: Producing Lists, Frequencies, Tables, Statistics, and Graphs from Epi Info Files -- -- Level II: More Refined Data Entry and Analysis -- 10. The CHECK Program: Optional Error Checking, Coding, and Skip Patterns During Data Entry -- 11. Writing Programs and Preparing Data for ANALYSIS -- 12. Example of Epidemic Investigation : an Epidemic of Paralytic Shellfish Poisoning -- 13. More ANALYSIS: Writing New Files; Restructuring Records; Communicating with the Screen and Printer; Summary Records -- 14. CSAMPLE: Analyzing Data from Complex Survey Samples -- -- Level II: Other Epi Info Functions -- 15. STATCALC and EPITABLE: Two Statistical Calculators -- 16. EXPORT: Producing Files for Use in Other Database and Statistical Systems -- 17. IMPORTing Data Files from Other Programs -- 18. MERGE: Merging and Updating Files and Records -- 19. Duplicate Data Entry and Validation--Two Approaches -- 20. EPIGLUE/EPI6: Menuing and Executive Health Information Shell with Hypertext -- 21. EPIAID, the Tutorial and Interactive Text Function in EPED -- -- Level III: Advanced Features of Epi Info -- 22. Programming the Data Entry Process with .CHK Files -- 23. Example: Programs For Nutritional Anthropometry -- 24. Precise Control of Tables: the REPORT Command -- 25. Linking Files Together: Relational Features of ANALYSIS, MERGE, and ENTER -- 26. Example: NETSS, a Disease Surveillance System, using Relational File Structure and Hypertext Output -- 27. Generating Artificial Data Files and Random Numbers; -- 28. Writing EPIAID Programs -- -- Using Epi Info in Special Environments -- 29. Using Epi Info with Languages Other Than English -- 30. Portable Computers and Epidemiologic Field Investigation -- -- Reference Section -- 31. Limitations of Speed and Memory, Debugging, and What To Do When Problems Occur -- 32. Statistics: Understanding the Results -- 33. EPED Commands -- 34. EPIAID Commands -- 35. ENTER and CHECK Commands -- 36. ANALYSIS Commands -- 37. Epi Info File Structure -- 38. Epi Info and Local Area Networks (LANs) -- 39. The Programmer\ue2\u20ac\u2122s Toolkit and the REC2QES and MAKELIST Utilities -- -- Functional Index -- Epidemic Investigation -- Disease Surveillance -- -- Alphabetic Index

Polarities & Focussing: a journey from Realisability to Automated Reasoning

This dissertation explores the roles of polarities and focussing in various aspects of Computational Logic.These concepts play a key role in the the interpretation of proofs as programs, a.k.a. the Curry-Howard correspondence, in the context of classical logic. Arising from linear logic, they allow the construction of meaningful semantics for cut-elimination in classical logic, some of which relate to the Call-by-Name and Call-by-Value disciplines of functional programming. The first part of this dissertation provides an introduction to these interpretations, highlighting the roles of polarities and focussing. For instance: proofs of positive formulae provide structured data, while proofs of negative formulae consume such data; focussing allows the description of the interaction between the two kinds of proofs as pure pattern-matching. This idea is pushed further in the second part of this dissertation, and connected to realisability semantics, where the structured data is interpreted algebraically, and the consumption of such data is modelled with the use of an orthogonality relation. Most of this part has been proved in the Coq proof assistant.Polarities and focussing were also introduced with applications to logic programming in mind, where computation is proof-search. In the third part of this dissertation, we push this idea further by exploring the roles that these concepts can play in other applications of proof-search, such as theorem proving and more particularly automated reasoning. We use these concepts to describe the main algorithm of SAT-solvers and SMT-solvers: DPLL. We then describe the implementation of a proof-search engine called Psyche. Its architecture, based on the concept of focussing, offers a platform where smart techniques from automated reasoning (or a user interface) can safely and trustworthily be implemented via the use of an API