A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

A multiagent framework for automated online bargaining

Automated online bargaining is currently limited by fixed price-issuing capabilities. The authors propose a multi-agent framework for automated bargaining that uses pattern matching and a dynamic price-issuing agent to better serve customers and the seller's bottom line.     

A knowledge-based framework for automated software synthesis

Automated software synthesis is one of the central techniques used in knowledge-based software engineering to enhance the quality and efficiency of software development. Although many software synthesis systems have been developed, automatic control of these systems remains a difficult problem. Our goal is to reduce user interaction in transformational and schema-based synthesizers by means of significant advances in control mechanisms.This paper describes an approach for synthesis control that integrates a blackboard control architecture with an existing synthesis system. We present a framework language called MetaMorphos that allows explicit representations of control knowledge for use in selecting appropriate synthesis actions. MetaMorphos represents control decisions explicitly in terms of actions, events, and states. It is task-specific and contains knowledge about programming and how to select synthesizing methods based on given features. By employing a blackboard control architecture, our synthesis controller provides adaptability for dynamic control behaviors and flexibility to handle unanticipated situations during software development.Applying MetaMorphos in the domain of software synthesis, we illustrate how we use MetaMorphos to select appropriate transformations, data structure and algorithm schemas during the synthesis. An example shows how MetaMorphos handles the difficult problem of selecting schemas for two very similar problems which, in the best case, require different solutions.     

Knowledge modelling for a generic refinement framework

Refinement tools assist with debugging the knowledge-based system (KBS), thus easing the well-known knowledge acquisition bottleneck, and the more recently recognised maintenance overhead. The existing refinement tools were developed for specific rule-based KBS environments, and have usually been applied to artificial or academic applications. Hence, there is a need for tools which are applicable to industrial applications. However, it would be wasteful to develop separate refinement tools for individual shells; instead, the KrustWorks project is developing reusable components applicable to a variety of KBS environments. This paper develops a knowledge representation that embodies a KBS's rulebase and its reasoning, and permits the implementation of core refinement procedures, which are generally applicable and can ignore KBS-specific details. Such a representation is an essential stage in the construction of a generic automated knowledge refinement framework, such as KrustWorks. Experience from applying this approach to Clips, PowerModel and Pfes KBSs indicates its feasibility for a wider variety of industrial KBSs.     

A network of sensor-based framework for automated visual surveillance

This paper presents an architecture for sensor-based, distributed, automated scene surveillance. The goal of the work is to employ wireless visual sensors, scattered in an area, for detection and tracking of objects of interest and their movements through application of agents. The architecture consists of several units known as Object Processing Units (OPUs) that are wirelessly connected in a cluster fashion. Cluster heads communicate with the Scene Processing Units which are responsible for analyzing all the information sent by the former. Object detection and tracking is performed by cooperative agents, named as Region and Object Agents. The area under surveillance is divided into several sub-areas. One camera is assigned to each sub-area. A Region Agent (RA) is responsible for monitoring a given sub-area. First, a background subtraction is performed on the scene taken by the camera. Then, a computed foreground mask is passed to the RA, which is responsible for creating Object Agents dedicated to tracking detected objects. Object detection and tracking is done automatically and is performed on the OPU. The tracking information and foreground mask are sent to a Scene Processing Unit that analyzes this information and determines if a threat pattern is present at the scene and performs appropriate action.     

A text analytics framework for automated communication pattern analysis

Human-coding reliant conversation analysis methods are ineffective when analyzing large volumes of data. In this paper, we propose a text analytics framework for automated conversation pattern analysis. This framework first extracts speech acts (i.e., activities) from conversation logs, and then analyzes their flow through frequent pattern mining algorithms to reveal insightful communication patterns. Using a real-world data set collected from a customer service center, we demonstrate the usefulness of the framework for identifying patterns that are associated with service quality outcomes. Our work has implications for the design of communication policies and systems for customer service management.     

A framework for automated distributed implementation of component-based models

Although distributed systems are widely used nowadays, their implementation and deployment are still time-consuming, error-prone, and hardly predictable tasks. In this paper, we propose a method for producing automatically efficient and correct-by-construction distributed implementations from a model of the application software in Behavior, Interaction, Priority (BIP). BIP is a well-founded component-based framework encompassing high-level multi-party interactions for synchronizing components (e.g., rendezvous and broadcast) and dynamic priorities for scheduling between interactions. Our method transforms an arbitrary BIP model into a Send/Receive BIP model that is directly implementable on distributed execution platforms. The transformation consists in (1) breaking the atomicity of actions in components by replacing synchronous multiparty interactions with asynchronous Send/Receive interactions; (2) inserting distributed controllers that coordinate the execution of interactions according to a user-defined partition of interactions, and (3) adding a distributed algorithm for handling conflicts between controllers. The obtained Send/Receive BIP model is proven observationally equivalent to its corresponding initial model. Hence, all functional properties of the initial BIP model are preserved by construction in the implementation. Moreover, the obtained Send/Receive BIP model can be used to automatically derive distributed executable code. The proposed method is fully implemented. Currently, it is possible to generate C++ implementations for (1) TCP sockets for conventional distributed communication, (2) MPI for multi-processor platforms, and (3) POSIX threads for deployment on multi-core platforms. We present four case studies and report experimental results for different design choices including partition of interactions and choice of algorithm for distributed conflict resolution.     

A simulation modeling framework for multiple-aisle automated storage and retrieval systems

This paper presents a simulation model framework for unit-load automated storage and retrieval systems (AS/RS) able to cope with most of the features found in industrial settings like, for example, multiple aisles, non aisle-captive cranes or double-deep unit-load AS/RS. Although these features characterize most of the industrial applications, they have been simply neglected by most of the previously published research oriented papers, which explains at least partially the low impact of the research output in practice. To help mitigating this drawback, this paper first presents an up-to-date chronological literature survey, which includes works that use any form of simulation in the modeling process. It then proposes an Object-Oriented Simulation Model for unit-load AS/RS developed with a purely generic mindset. In this model, the physical design decisions are separated from the operational decisions, which makes it possible, first, to obtain a better understanding of AS/RS dynamics and, second, to increase the model’s flexibility and generality, thus allowing many interesting scenarios to be represented. In particular, the simulation modeling framework has been used to develop a simulator and to conduct a preliminary study on a multi-aisle AS/RS.     

Conceptual framework for automated process planning

This paper presents an improved process planning model and the modular details needed for developing an automated process planning system. The proposed model takes a topdown structuring and bottomup filling approach. The validity of this model arises from the difficulty of making a process plan by the pure bottomup approach.

In the topdown structuring stage, the system generates structures suitable for the setup level, feature level, and process selection in the topdown direction. In the bottomup filling stage, sequences for the operation level, the feature level, and the setup level are successively filled into the structure in the bottomup direction. The proposed CAPP system is under development interfaced with an object-oriented data base (UniSQL).     

A framework for the automated drawing of data structure diagrams

Data structure diagrams are two-dimensional figures made up of lines that aim to pictorially indicate the interrelationships of the elements of a data structure. The various rules and factors of aesthetics that go into the way data structure diagrams are drawn are collected together. The various subjective factors are formulated into computable objectives and numeric parameters. These are distilled from a large number of data structure drawings found in various textbooks. The rules used have not reached a level of acceptance comparable to that of the relevant rules in engineering graphics. The internal architecture of a (sub)system that helps draw data structure diagrams is outlined     

A refinement framework to support validation and maintenance of knowledge-based systems

In this article, we argue that a refinement tool capable of assisting the maintainer of a Knowledge-Based System (KBS) with updating and upgrading of its knowledge base can substantially expand the scope of his activities and prolong the life of the system. We show that refinement tasks involved in KBS maintenance are similar to those taking place at the development stage, and thus a uniform refinement framework can be designed to support both activities. After defining refinement tasks expected to take place at different stages of a KBS's life cycle, we present a refinement framework capable of supporting them. It utilizes a small number of domain-independent heuristics to search for possible rule revisions which are expected to eliminate errors detected during KBS validation. An extended example is given to illustrate how different refinement tasks are carried out.     

An automated framework for software test oracle

Context

One of the important issues of software testing is to provide an automated test oracle. Test oracles are reliable sources of how the software under test must operate. In particular, they are used to evaluate the actual results that produced by the software. However, in order to generate an automated test oracle, oracle challenges need to be addressed. These challenges are output-domain generation, input domain to output domain mapping, and a comparator to decide on the accuracy of the actual outputs.

Objective

This paper proposes an automated test oracle framework to address all of these challenges.

Method

I/O Relationship Analysis is used to generate the output domain automatically and Multi-Networks Oracles based on artificial neural networks are introduced to handle the second challenge. The last challenge is addressed using an automated comparator that adjusts the oracle precision by defining the comparison tolerance. The proposed approach was evaluated using an industry strength case study, which was injected with some faults. The quality of the proposed oracle was measured by assessing its accuracy, precision, misclassification error and practicality. Mutation testing was considered to provide the evaluation framework by implementing two different versions of the case study: a Golden Version and a Mutated Version. Furthermore, a comparative study between the existing automated oracles and the proposed one is provided based on which challenges they can automate.

Results

Results indicate that the proposed approach automated the oracle generation process 97% in this experiment. Accuracy of the proposed oracle was up to 98.26%, and the oracle detected up to 97.7% of the injected faults.

Conclusion

Consequently, the results of the study highlight the practicality of the proposed oracle in addition to the automation it offers.     

Mate Marote: A flexible automated framework for large-scale educational interventions

There is big consensus that computer games may be an effective way of learning and many initiatives are being developed where aspects from cognitive sciences are being applied in the development of these games. In this article, we present Mate Marote, a flexible framework for large-scale educational interventions. Based on the delivery programs of computers to each student in Argentinian schools, we developed an environment that provides activities/games and registers usage statistics. This framework keeps installation up-to-date connecting with a central server as Internet connection is detected, synchronizing new activities, version updates and usage history. As a first testbed intervention, we deployed three games in La Rioja province (Argentina), where OLPC is the official program. These games were focused on training inhibitory control, working memory and planning skills. We found that usage statistics of games replicate previous results found at the laboratory, showing that this platform works as an intervention framework despite its unsupervised nature.     

Optimization framework for DFG-based automated process discovery approaches

The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

     

A framework for automated construction of resource space based on background knowledge

Resource Space Model is a kind of data model which can effectively and flexibly manage the digital resources in cyber-physical system from multidimensional and hierarchical perspectives. This paper focuses on constructing resource space automatically. We propose a framework that organizes a set of digital resources according to different semantic dimensions combining human background knowledge in WordNet and Wikipedia. The construction process includes four steps: extracting candidate keywords, building semantic graphs, detecting semantic communities and generating resource space. An unsupervised statistical language topic model (i.e., Latent Dirichlet Allocation) is applied to extract candidate keywords of the facets. To better interpret meanings of the facets found by LDA, we map the keywords to Wikipedia concepts, calculate word relatedness using WordNet’s noun synsets and construct corresponding semantic graphs. Moreover, semantic communities are identified by GN algorithm. After extracting candidate axes based on Wikipedia concept hierarchy, the final axes of resource space are sorted and picked out through three different ranking strategies. The experimental results demonstrate that the proposed framework can organize resources automatically and effectively.     

一种关键字驱动的自动化测试框架

为了提高软件自动化测试脚本的可复用性,提出了一种基于关键字驱动的自动化测试框架（LKDT）。首先对目前现有自动化测试框架进行了归纳分析;然后提出了LKDT的系统架构,给出了其中所包含的关键技术;最后以实际的Linux桌面应用程序为例对LKDT框架与现有框架进行实际对比验证,结果表明该框架具有降低测试脚本规模、提高整体测试效率等优越性。     

A framework for verification,validation, and refinement of knowledge bases: The VVR system

Verification and validation (V&V) of Knowledge Bases (KBs) are two sides of the same coin: one is intended to assure the structural correctness of the KB, while the other is intended to assure the functional correctness of the domain model embodied in the KB. Knowledge base refinement aims to appropriately revise the KB if a structural or functional error is detected during the V&V process. This paper presents a uniform framework for verification, validation and refinement of KBs represented as sets of production rules, called the VVR system. It incorporates a contradiction-tolerant truth maintenance system (CTMS) for performing both verification and validation analyses, and some simple explanation-based learning techniques for guiding the refinement process. Verification analysis consists of detecting and correcting the main types of structural anomalies: circular rules, redundant rules, inconsistent rules, and inconsistent data, and checks the KB for completeness and violated semantic constraints. In terms of validation, given a set of test cases, the VVR system is capable of detecting and correcting functional errors caused by overgeneralization and/or overspecialization of the KB. If the set of test cases is not available, the VVR system can generate synthetic test cases intended to help the user evaluate KBS performance. © 1994 John Wiley & Sons, Inc.     

A secure framework exploiting content guided and automated algorithms for real time video searching

This paper presents an architecture that allows End Users, via the services of Search Engines, to search, in a secure and efficient way, the video content belonging to Content Providers. The search can be accomplished with any searching scheme that the Search Engines wish to provide, as long as certain security constraints are met. However we propose specific algorithms that demonstrate an efficient way to search video data without sacrificing security effectiveness of the system. The search is completed without the End Users or Search Engines needing to purchase the premium content beforehand, and without the Content Providers needing to purchase the search technology. The business motivation of this technique is to assist End Users to purchase content best suiting their requirements—they are offered search results only, not actual content. The objective is to face the problem caused by the current segregation between content ownership and video processing technology ownership. To face this segregation, we present an architecture that guarantees security of Content Provider’s data and Search Engine’s technology and we also present two innovative algorithms that make real time video searching a feasible process. Particularly these algorithms (a) organize video content into a graph based hierarchical structure and (b) perform content guided, non interactive and real time search by exploiting the graph based video structures. The proposed algorithms are incorporated in the presented architecture under the given security constraints. Experimental results and comparisons with conventional techniques are presented to demonstrate the outperformance of the proposed algorithms.

A framework for the design and automated implementation of communication aspects in multi-agent systems

This paper presents REFCON, a framework for the automated development of Agent Communication Contexts (ACCs) in multi-agent systems (MASs). ACCs are intended to capture the interaction requirements of a MAS.A formal specification framework is first presented, aimed at modelling an ACC as a set of rules for filtering and filling messages, based on their contents, and the names and roles of the exchanging agents. A XML-based specification language is then introduced, which encodes the specification formalism for the sake of its computer processing. Finally, an object-oriented software architecture capable of supporting ACC-based MAS development is presented.REFCON key characteristic is that it allows a seamless integration of ACC support (even) into an existing MAS, at run-time, independently of the agent platform used for the implementation. This is made possible by a layered software architecture based on computational reflection, a technology that allows transparent evolution and adaptation of existing systems. The REFCON framework is also dynamic, in the two-fold sense that it is capable of both adding new rules and handling multiple contexts, which it can easily switch among, at run-time. The ACC-based design of an example MAS for document sharing is briefly discussed, as a demonstration of the principles put forward.