A visual critiquing language for expressing design knowledge

Many temporal and modal logic languages can be regarded as subsets of first order logic, i.e. the semantics of a temporal logic formula is given as a first order condition on points of the underlying models (Kripke structures). Often the set of possible models is restricted to models which are trees. A temporal logic language is (first order) expressively complete, if for every first order condition for a node of a tree there exists an equivalent temporal formula which expresses the same condition. In this paper expressive completeness of the temporal logic language with the set of operators U (until), S (since), and X k (k-next) is proved, and the result is extended to various other tree-like structures.

The Datalog syntax we have been considering so far corresponds to a very restricted subset of first order logic and is often referred to as pure Datalog. Several extensions of pure Datalog have been proposed in the literature or are currently under investigation.

This paper presents a new programming language named SPARCL that has four major elements: it is a visual language, it is a logic programming language, it relies on sets to organize data, and it supports partitioning constraints on the contents of sets. It is a visual programming language in that the representation of the language depends extensively on non-textual graphics and the programming process relies on graphical manipulation of this representation. It is a logic programming language in that the underlying semantics of the language is the resolution of clauses of a Horn-like subset of first order predicate logic. It uses sets as the only method of combining terms to build complex terms. Finally, one may constrain a set's structure by specifying a partitioning into pairwise disjoint subsets.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Approximate Model Checking Using a Subset of First-order Logic

Attribute-value based representations, standard in today's data mining systems, have a limited expressiveness. Inductive Logic Programming provides an interesting alternative, particularly for learning from structured examples whose parts, each with its own attributes, are related to each other by means of first-order predicates. Several subsets of first-order logic (FOL) with different expressive power have been proposed in Inductive Logic Programming (ILP). The challenge lies in the fact that the more expressive the subset of FOL the learner works with, the more critical the dimensionality of the learning task. The Datalog language is expressive enough to represent realistic learning problems when data is given directly in a relational database, making it a suitable tool for data mining. Consequently, it is important to elaborate techniques that will dynamically decrease the dimensionality of learning tasks expressed in Datalog, just as Feature Subset Selection (FSS) techniques do it in attribute-value learning. The idea of re-using these techniques in ILP runs immediately into a problem as ILP examples have variable size and do not share the same set of literals. We propose here the first paradigm that brings Feature Subset Selection to the level of ILP, in languages at least as expressive as Datalog. The main idea is to first perform a change of representation, which approximates the original relational problem by a multi-instance problem. The representation obtained as the result is suitable for FSS techniques which we adapted from attribute-value learning by taking into account some of the characteristics of the data due to the change of representation. We present the simple FSS proposed for the task, the requisite change of representation, and the entire method combining those two algorithms. The method acts as a filter, preprocessing the relational data, prior to the model building, which outputs relational examples with empirically relevant literals. We discuss experiments in which the method was successfully applied to two real-world domains.

The Semantic Web (SW) has been envisioned to enable software tools or Web Services, respectively, to process information provided on the Web automatically. For this purpose, languages for representing the semantics of data by means of ontologies have been proposed such as RDF(S) and OWL. While the semantics of RDF(S) requires a non-standard model-theory that goes beyond first order logics, OWL is intended to model subsets of first order logics. OWL consists of three variants that are layered on each other. The less expressive variants OWL-Light and OWL-DL correspond to the Description Logics \(\mathcal{SHIF}\)(D) and \(\mathcal{SHOIN}\)(D) [1], respectively, and thus to subsets of First Order Logics [2].KeywordsLogic ProgramDescription LogicInformation IntegrationReasoning AlgorithmDistribute Information RetrievalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Visual programming language research has evolved greatly since its early days. At first, attempts at visual programming mostly took the form of flowchart-like diagrams. But in recent years, a wide number of innovative approaches have been incorporated into visual languages, including object-oriented programming, form-based programming, programming by demonstration, and dataflow programming. Unfortunately, while many of these systems represent important ideas, only a few have been successful as complete visual programming languages. This tutorial explains why this is true, and describes ways in which the problem can be addressed. This tutorial explores the issues behind the successes and failures of earlier approaches from a design perspective. It identifies characteristics of successful visual programming languages, and explains how to design an object-oriented language that maintains those characteristics. It shows solutions to a number of problems by looking at existing visual programming languages, including Prograph.

The term “visual language” means different things to different people. To some, it means the objects handled by the language are visual. To others, it means the language itself is visual. To the first group of people, “visual language” means “language for processing visual information,” or “visual information processing language.” To the second group of people, “visual language” means “language for programming with visual expressions,” or “visual programming language.”

Parsing the semantic structure of a web page is a key component of web information extraction. Successful extraction algorithms usually require large-scale training and evaluation datasets, which are difficult to acquire. Recently, crowdsourcing has proven to be an effective method of collecting large-scale training data in domains that do not require much domain knowledge. For more complex domains, researchers have proposed sophisticated quality control mechanisms to replicate tasks in parallel or sequential ways and then aggregate responses from multiple workers. Conventional annotation integration methods often put more trust in the workers with high historical performance; thus, they are called performance-based methods. Recently, Rzeszotarski and Kittur have demonstrated that behavioral features are also highly correlated with annotation quality in several crowdsourcing applications. In this article, we present a new crowdsourcing system, called Wernicke, to provide annotations for web information extraction. Wernicke collects a wide set of behavioral features and, based on these features, predicts annotation quality for a challenging task domain: annotating web page structure. We evaluate the effectiveness of quality control using behavioral features through a case study where 32 workers annotate 200 Q&amp;A web pages from five popular websites. In doing so, we discover several things: (1) Many behavioral features are significant predictors for crowdsourcing quality. (2) The behavioral-feature-based method outperforms performance-based methods in recall prediction, while performing equally with precision prediction. In addition, using behavioral features is less vulnerable to the cold-start problem, and the corresponding prediction model is more generalizable for predicting recall than precision for cross-website quality analysis. (3) One can effectively combine workers’ behavioral information and historical performance information to further reduce prediction errors.

We present a new next generation domain of meta search engine. A Medical Meta Search Engine (MMSE) was designed in this research for the users with no medical expertise. It is enhanced with the domain knowledge obtained from Unified Medical Language System (UMLS) to increase the effectiveness of the searches. The power of the system is based on the ability to understand the semantics of web pages and the user queries. This MMSE transforms a keyword search into a conceptual search. This medical meta search engine aims to generate maximum output with semantic value using minimum input from the user. Since this system is designed to help people seeking information about health on the web, our target users are not medical specialists who can effectively use the special jargon of medicine and access medical databases. Medical experts have the advantage of shrinking the answer set by expressing several terms using medical terminology. Medical meta search engine provides the same advantage to its users through the automated use of the medical domain knowledge in the background. The results of our experiments indicate that, expanding the queries with domain knowledge and increase dramatically the relevance of an answer set and the number of retrieved web pages that are relevant to the user request.

ly, a port specifies the endpoint of a connection on a networked device. Ports are identified as numbers. Many port numbers are standardized14 and denote a specific network or application protocol (e.g. port number 80 for HTTP or 23 for Telnet). A lot of the software components serving the respective protocols contain well-known and well-documented vulnerabilities or weaknesses (for instance, a system with anonymous login enabled). Thus, it is only a matter of finding computers that make use of those vulnerable protocols in order to try and attack the affected systems. This search can be automated using a number of tools. One famous example is SATAN (Security Administrator Tool for Analyzing Networks). SATAN is downloadable at no charge and comes with an easy-to-use HTML-based user interface. The tool automatically and efficiently scans any given host (or the respective subnet) for a number of known vulnerabilities and produces a clear and readable report. This report can then be used as a basis to look for actual vulnerabilities.15 2.3. How to Write a Computer Virus Computer viruses and worms are a common way of accessing computer systems without authorization. The effects differ: some viruses merely cause annoyance and inconvenience; others affect the functionality and stability of computing environments or compromise the confidentiality or integrity of (sometimes valuable) information. Three major aspects are important if someone wants to write their own computer virus: ● Payload. What does the virus do once it has infected a system? The amount of damage to be caused by the virus is arbitrary and can be freely specified by the author; ● Delivery and Propagation. How does the virus reach the target systems? And, in case of a (selfpropagating) worm, how does the worm automatically spread to other computer systems? ● Execution. How is the virus payload executed on the target machine? Computer viruses are not dangerous as long as they are not executed. There are two generic methods of ensuring that a virus is executed: (1) The virus can be programmed to exploit one of the countless vulnerabilities that exist in many of today’s mail clients or operating system components. Consequently, these types of viruses are executed without any form of user interaction; (2) Alternatively, the virus can rely on user interaction, in which case the virus is typically delivered as a simple e-mail attachment with a subject heading and content that invites people to open the attachment. The virus is executed as soon as the attachment is opened. Short Analysis of the ILOVEYOU Worm This section gives a short overview of the infamous ILOVEYOU worm by exploring each of the three aspects identified above. The goal is to give some insights into the internals of a virus (or worm) and show how easy it is to create this digital germ. ● Payload: The ILOVEYOU worm is written in Visual Basic Script, an easy-to-learn and powerful programming language by Microsoft, which is often used in (dynamic) web pages or e-mails. The complete payload of the ILOVEYOU worm serves the purpose of selfpropagation exclusively, and no further damage is done. Among other activities,16 the worm searches for certain types of files on the target machine and modifies those files depending on the type of file. It will, for instance, replace all occurrences of JPEG files (a commonly used image format) with a copy of itself and add the .vbs extension (denoting a Visual Basic Script file). If the user later tries to open one of the modified JPEG files, he or she will not see the image but rather (re-)activate the worm. The respective excerpt from the worm’s source code is given in Figure 2:17

: A general purpose visual programming language, based on object-oriented methodology, offers substantial Benefits of reusable code, shorter creation and maintenance time, faster overall understanding of the code, and reduced staff learning time. It should be an ideal fit for sites with long-lived code or very large bodies of code. The value of visual programming languages is well understood for very specific tasks; this report reviews the plan for research and design of a general purpose visual object-oriented programming language through nine steps, which includes general specifications for the preferred language and environment.

Parsing visual languages with picture layout grammars

Managing the graphical complexity of real-life programs expressed in visual programming languages is a great challenge in each visual programming environment. Besides general methods like displaying/layouting graphs and icons on different levels of abstraction or organizing multiple window environments, built-in language mechanisms which permit the structured and modular design of visual programs are essential to overcome this problem. We address these problems by discussing a concept for visual modules in a diagrammatic parallel language as well as a method to describe massive parallel programs consisting of hundreds of processes by means of a visual replication mechanism. >

Wrappers play an important role in extracting specified information from various sources. Wrapper rules by which information is extracted are often created from the domain-specific knowledge. Domain-specific knowledge helps recognizing the meaning the text representing various entities and values and detecting their formats. However, such domain knowledge becomes powerless when value-representing data are not labeled with appropriate textual descriptions or there is nothing but a hyper link when certain text labels or values are expected. In order to alleviate these problems, we propose a probabilistic method for recognizing the entity type, i.e. generating wrapper rules, when there is no label associated with value-representing text. In addition, we have devised a method for using the information reachable by following hyperlinks when textual data are not immediately available on the target web page. Our experimental work shows that the proposed methods help increasing precision of the resulting wrapper, particularly extracting the title information, the most important entity on a web page. The proposed methods can be useful in making a more efficient and correct information extraction system for various sources of information without user intervention.