waashk/dblp huggingface dataset:
phalanx: a graph-theoretic framework for test case prioritization. test case prioritization for regression testing can be performed using different metrics (e.g., statement coverage, path coverage) depending on the application context. employing different metrics requires different prioritization schemes (e.g., maximum coverage, dissimilar paths covered). this results in significant algorithmic and implementation complexity in the testing process associated with various metrics and prioritization schemes. in this paper, we present a novel approach to the test case prioritization problem that addresses this limitation. we devise a framework, phalanx, that identifies two distinct aspects of the problem. the first relates to metrics that define ordering relations among test cases; the second defines mechanisms that implement these metrics on test suites. we abstract the information into a test-case dissimilarity graph -- a weighted graph in which nodes specify test cases and weighted edges specify user-defined proximity measures between test cases. we argue that a declustered linearization of nodes in the graph results in a desirable prioritization of test cases, since it ensures that dissimilar test cases are applied first. we explore two mechanisms for declustering the test case dissimilarity graph -- fiedler (spectral) ordering and a greedy approach. we implement these orderings in phalanx, a highly flexible and customizable testbed, and demonstrate excellent performance for test-case prioritization. our experiments on test suites available from the subject infrastructure repository (sir) show that a variety of user-defined metrics can be easily incorporated in phalanx.
special track on autonomic computing: editorial message. with the widespread use of distributed computing in the enterprise, there have been significant advances in development paradigms for distributed computing applications. server side component models have considerably simplified development and the complexity has instead shifted to the operational side of these applications. the increase in operational complexity has reached a point where it is no longer feasible for humans to manage the applications required to run an enterprise. this coupled with the lack of skills and skyrocketing costs associated with application management make it imperative to develop techniques to allow applications to manage themselves. the initial steps to provide self-managing applications are now being taken a paradigm known as "autonomic computing" is in its infancy of evolution. it has also been identified as one of the "grand challenges" of computer applications for the next decade. in addition, the exponential growth of handheld devices (e.g. pdas, laptops, smart phones etc.) pose additional challenges since these devices themselves suffer from a number of limitations such as inadequate processing capability, restricted battery life, limited memory space, frequent line disconnection, and confined host bandwidth. lack of fixed infrastructure support and the aforementioned limitations augment the complexity of the problems in autonomic computing.
organizational engineering in public administration: the state of the art on egovernment domain modeling. society nowadays demands processes in public organizations to be reengineered so that they become more flexible, effective and efficient. public administration (pa) is a huge organization with several different activities, with stakeholders that may have conflicting interests or different priorities etc. the application of the organizational engineering approaches in the pa domain can help towards its modernization. one initial step to this direction is to create the appropriate domain models as generic and reusable representations so as to use them as blueprints for modeling the organizations of interest. in this work, we have included the state-of-the-art in egovernment domain models. we have grouped these initiatives to the following three categories depending on their modeling perspective: object, process and holistic and assess their pros and cons.
hiv drug resistance analysis tool based on process algebra. the increasing number of drugs used in hiv patient treatment and the mutations associated with drug resistance make the inference of drug resistance a complex task that demands computational systems. furthermore, the software development/update can generate an extra level of complexity in the process drug resistance analysis. an alternative to handle the complexity of drug resistance and software development is to use a formal representation of involved processes, such as process algebra. this allows mathematical reasoning about the analysis process, a precise description of system behavior, more advanced computational approaches, as concurrent/parallel execution and (semi) automatic software development. the first contribution of this research is a mapping of drug resistance algorithms rules into expressions of process algebra which facilitates the computational manipulation of theses rules. the second contribution is the hivdag (hiv drug analysis generator) system. this software supports the definition, generation and analyses of genotypic drug resistance tests based on process algebra expressions. therefore, the users can easily create/update their own drug resistance algorithms any time and independent of software development.
spatially non-homogeneous potts model parameter estimation on higher-order neighborhood systems by maximum pseudo-likelihood. this paper addresses the problem of maximum pseudo-likelihood estimation of the non-homogeneous potts image model parameters using higher-order non-causal neighborhood systems in a computationally efficient way. the motivation is the development of a new methodology for contextual classification that uses combination of sub-optimal mrf algorithms for multispectral image classification, which requires accurate parameters estimation. our objective is to make multispectral image contextual classification fully operational without human intervention. the results show that the method is consistent with real data and in the presence of random noise.
restoring images with a multiscale neural network based technique. this paper describes a neural network based multiscale image restoration approach in which multilayer perceptrons are trained with artificial images of degraded gray level cocentered circles. the main objective of this approach is to make the neural network learn inherent space relations of the degraded pixels in the restoration of the image. in the conducted experiment, the degradation is simulated by submitting the image to a low pass gaussian filter and the addition of noise to the pixels at pre-established rates. the degraded image pixels make the input and the non-degraded image pixels make the output for the supervised learning process. the neural network performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. the main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. the approach is an attempt to develop a simple method that may lead to a good restored version of the image, without the need of a priori knowledge of the possible degradation cause. considering different window sizes around a pixel simulates the multiscale operation. in the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image. the neural network restoration results show the proposed approach performs similarly to existing methods with the advantage it does not require a priori knowledge of the degradation causes.
a new approach to control a population of mobile robots using genetic programming. this paper describes a new evolutionary control system (ecs) able to control a population of mobile robots. the system has two main modules: the first one, called emss (execution, management and supervision system), is the system responsible for managing all the evolutionary process that takes place in an embedded fashion in each robot. the second module, called dgp (distributed genetic programming), is an extension of classical genetic programming algorithm to support the control system evolution for the robots that are part of the mobile robots population. simulation experiments of the dgp algorithm are presented and their results are compared with the classical gp algorithm.
using unlabeled data to handle domain-transfer problem of semantic detection. due to highly domain-specific nature, supervised sentiment classifiers typically require a large number of new labeled training data when transferred to another domain. this is so-called domaintransfer problem. in this work, we attempt to tackle this problem by combining old-domain labeled examples with new-domain unlabeled ones. the basic idea is to use old-domain-trained classifier to label some informative unlabeled examples in new domain, and train the base classifier again. the experimental results demonstrate that proposed method dramatically boosts the accuracy of the base sentiment classifier on new domain.
ontology-based utterance interpretation for intelligent conversational interfaces. in this paper, we present an ontology-based utterance interpretation mechanism for intelligent conversational interfaces. we describe how this mechanism was embedded in a conversational interface applied to personal assistant agents. the main goal of such approach is to offer a system capable of performing tasks through an intuitive interface, allowing experienced and less experienced users to interact with it in an easy and comfortable way. in this mechanism, ontologies are used for syntactic and semantic interpretation. we present how to design an ontology for semantic interpretation and how the interpretation process use it for semantic analysis.
the utilization bound of uniprocessor preemptive slack-monotonic scheduling is 50%. consider the problem of scheduling a set of sporadically arriving implicit-deadline tasks to meet deadlines on a uniprocessor. static-priority scheduling is considered using the slack-monotonic priority-assignment scheme. we prove that its utilization bound is 50%.
efficient compilation of lua for the clr. microsoft's common language runtime offers a target environment for compiler writers that provides a managed execution environment and type system, garbage collection, access to os services, multithreading, and a just-in-time compiler. but the clr uses a statically typed intermediate language, which is a problem for efficient compilation of dynamically typed languages in general, and the lua language in particular. this paper presents a way to implement a lua compiler for the clr that generates efficient code. the code this compiler generates outperforms the same code executed by the lua interpreter and similar code generated by microsoft's ironpython compiler. it approaches the performance of lua code compiled to native code by luajit.
a generic mobile agent framework for ambient intelligence. the purpose of this paper is to introduce an innovative framework for implementation of ambient intelligence (ami) environments. compared to the existing state-of-the-art approaches, this framework creates a more decentralized and distributed ami environment. in addition, the proposed approach is not limited to one specific domain, unlike many others. the openness of the presented architecture allows it to support a variety of devices ranged from small-embedded sensors to complex computing facilities. finally, given that this approach is formulated based on multi-agent standard concepts, it can be easily implemented as add-on for existing software agent platforms to achieve rapid deployment. implications for the development of this framework and future directions are discussed.
the effect of correlation coefficients on communities of recommenders. recommendation systems, based on collaborative filtering, offer a means of sifting through the enourmous amounts of content on the web by composing user ratings in order to generate predicted ratings for other users. these kinds of systems can be viewed as a network of interacting peers, where each user is a node and the links to all other nodes are weighted according to how similar the corresponding users are. predicted ratings are generated for a user for unknown items by requesting and aggregating rating information from the surrounding neighbors. however, the different methods of computing user similarity, or weighting the network links, very often do not agree with each other, and, as a result, the structure of the network of recommenders changes completely. in this work we perform an analysis of a range of similarity measures, comparing their performance in terms of prediction accuracy and coverage. this allows us to understand the effect that similarity measures have on predicted ratings. based on the obtained results, we argue that user-similarity may not sufficiently capture the relationships that recommenders could otherwise share in order to maximise the utility of these communities.
the minimization of hardware size in reconfigurable embedded platforms. to minimize the hardware fabrications in reconfigurable devices, this paper explores hardware synthesis to derive reconfiguration plans during the design time based on schedules derived by cad tools, where a schedule includes the starting time, the execution time, and the intertask data transmissions for each task. we propose scheduling algorithms to derive optimal solutions for hardware descriptions with the same reconfiguration latency based on backward configuration and backward ordering strategies. for general cases, where a hardware description might be shared by tasks, we develop an algorithm based on a duplication merging strategy with performance evaluations. our proposed algorithms could be applied after the hardware/software co-design procedures of task partitioning and scheduling to optimize the hardware requirements during the design time.
web page genre classification. in this paper we present an automatic genre-based web page classification system. unlike subject or topic based classifications, genre-based classifications focus on functional purposes and classify web pages into categories such as online shopping, technical paper, or discussion forum. until now, the genre classifications are not well developed due to the subjectivities and difficulties to define the genre, the features, and even the categories. in this paper, we define five top-level genre categories, each of which has several subcategories, and develop new methods to extract 31 features from web pages to identify the categories. we analyze not only the contents of the web pages, but also the urls, html tags, java scripts, and vb scripts. we developed a genre classification system that achieved average accuracy of 93%. in addition, we combined this genre classification with our subject-based classification to produce a comprehensive web page classification system.
on the conceptual tag refinement. social tagging is a well known approach of assigning keywords to (web) documents in order to share the personal meaning of users about the content. however, the ambiguity of the assigned keywords hinders the knowledge sharing process. in this paper we present an approach for supporting tagging process by interpreting the keywords (tags) using a conceptual tag model, which leads to a semantic tagging process. moreover, the approach introduces the tag refinement process that proposes extensions of given tags in order to help the user to better express his/her "tagging" need. we present several evaluation studies in order to demonstrate the efficiency of the approach.
towards trace semantics for ws-cdl with alignments. ws-cdl (web service choreography description language) provides a global view to describe the interactive behaviors between multiple participants of web services. although the alignment property plays an important role in the ws-cdl specification, little attention has been paid on the research of alignments. in this paper, we aim at exploring the essence of alignment property and providing formal semantics for ws-cdl with alignments. to achieve this goal, we first define a formal language cdla, which includes the basic components in ws-cdl. we then discuss the influence of alignment property to the semantics of these components and propose five possible local action based trace semantics for cdla.
scheduling grid tasks under uncertain demands. the uncertainty of the demands of grid applications can cause unpredicted performance and, consequently, can make ineffective schedules derived for target demand values. to produce effective results, schedulers need to take into account the difficulty in estimating the demands of applications. in this paper, a scheduler based on fuzzy optimization is proposed to deal with such uncertainties. it is shown, via numerical results, that the proposed scheduler presents advantages when compared to classical schedulers.
author identification using writer-dependent and writer-independent strategies. in this work we discuss author identification for documents written in portuguese. two different approaches were compared. the first is the writer-independent model which reduces the pattern recognition problem to a single model and two classes, hence, makes it possible to build robust system even when few genuine samples per writer are available. the second is the personal model, which very often performs better but needs a bigger number of samples per writer. we also introduce a stylometric feature set based on the conjunctions and adverbs of the portuguese language. experiments on a database composed of short articles from 30 different authors and support vector machine (svm) as classifier demonstrate that the proposed strategy can produced results comparable to the literature.
building secure e-business systems: technology and culture in the uae. over the past few years, internet-enabled business, or e-business, has drastically improved efficiency and revenue growth of companies. information is in the center of everything, and it is constantly under threat from many sources. without proper protection, e-business applications can be susceptible to attacks or unauthorized activity. security has been an elemental concern in e-business since its beginning in middle east region and especially in united arab emirates (uae). it is argued that e-business cannot attain its potential until the security concerns are addressed adequately. the survey results have been discussed in respect of security measures and their contribution towards consumers' decision-making processes.
an evaluation of a model-based testing method for information systems. this work presents an experimental study of a model-based testing method. we evaluate the use of the method and its tool, during the development of part of an information system. in this study we verified that the use of the method can generate benefits related to cost reduction, from the reduction of test effort and, at the same time, benefits related to test quality, from the failure detection capability improvement.
a framework for execution and 3d visualization of situated cellular agent based crowd simulations. this paper presents a framework supporting the definition and implementation of crowd simulation systems based on interacting situated agents. the framework supports the specification and execution of visually rich 3d virtual environment endowed by the presence of mobile agents acting and interacting inside it according to a multi-agent model that also supports the definition of agents' environment and its relevant elements. the paper introduces the multi-agent model underlying the framework and its basic architecture. sample applications are also described so as to show the potential of the framework in executing models comprising several hundreds of agents producing an effective visualization of the generated dynamics.
power-efficient and scalable load/store queue design via address compression. this paper proposes an address compression technique for load/store queue (lsq) to improve the scalability and power efficiency. a load/store queue (lsq) typically needs a fully-associative cam structure to search the address for collision and consequently poses scalability challenges of power consumption and area cost. using the proposed approach, the lsq can reduce the area cost ranging from 32% to 66% and power consumption ranging from 38% to 71%, depending on the compression parameter. the approach can provide 3.08% overall processor energy reduction and causes only 0.22% performance loss at an optimal configuration.
improving denial of service resistance using dynamic local adaptations. we improve the resistance of gossip-based multicast to (distributed) denial of service (dos) attacks using dynamic local adaptations at each node. each node estimates the current state of the attack on the system, and then adapts its behavior according to this local estimation. the adaptation is achieved through modeling the problem of propagating messages under a dos attack as an optimization problem, and solving it using linear programming, independently at each node. simulation results show that when the system is under attack, the local decisions each node takes bring the system to a stable point, which is the solution of the linear programming problem. the adaptation leads to propagation times that are 30% faster than those of existing dos-resistant gossip-based protocols.
spatial vagueness and imprecision in databases. the impossibility of current spatial database systems and gis to handle spatial vagueness and imprecision has been recognized as an important problem in the spatial database domain. for years, researchers have focused on identifying the appropriate concepts that are necessary to effectively and efficiently deal with the vagueness and imprecision that not seldomly appears, but is widespread amongst spatial objects (especially those that are naturally occurring such as forests, mountains, and rivers). in this paper, we analyze the most popular formalisms for dealing with spatial uncertainty in databases. the analysis in this paper is centered around the definition of our vague spatial algebra (vasa) which exemplifies a complete approach to handle vagueness in spatial databases. we compare vasa with existing concepts based on rough set theory and fuzzy set theory.
electricity market simulation: multiagent system approach. this paper suggests a multiagent system (mas) approach for market simulation. this is achieved through analysis, modeling, implementation and simulation of artificial markets populated by software agents that represent economic self interested agents. software agents are the constructs of a complex system, an artificial market that model a real existing market or an outline of a market design. the interest in simulating a market is multiple: exploiting existing market rules, searching for market design flaws and loopholes, and supporting decision making during a market mechanism design process. the main aim of the suggested approach is to analyze the behavior that emerges from the interaction of self interested agents acting in an artificial market. aemas (artificial economy multiagent system), a multiagent system architecture inspired by the market oriented programming (mop) approach is defined. in different economical sectors, e.g. energy markets, there is no consensus about which structures lead to social welfare maximization outcomes. an approach to find adequate architectures allows different market structure instances to be created and simulated, to ease the design and analysis of alternative structures. these alternatives can then be compared and potential design flaws eventually risen by simulation identified. taking the electricity market as an example, two instances of the proposed architecture are presented, corresponding to the centralized dispatch arrangement common to non restructured markets, and the auction based pool, common to restructured markets.