waashk/dblp huggingface dataset:
strangeness-based feature weighting and classification of gene expression profiles. achieving high classification accuracy is a major challenge in the diagnosis of cancer types based on gene expression profiles. these profiles are notoriously noisy in that a large number of genes might be irrelevant to or weakly associated with disease phenotypes such as tumors. assigning different weights to genes could decrease or diminish the influences of those "noisy" signals, and thereby improve classification accuracy. we propose an intuitive and simple approach to cancer classification with feature weighting. our strangeness-based feature weighting method learns weights for different genes based on their classification performance. those genes with large weights can be used as discriminative genes. we demonstrate that our implementation of k-nn classifier achieved high classification accuracy on two benchmark cancer data sets. in the case of relatively low accuracy, the proposed method could be used as a feature filter. with combined feature weighting and adaboost, we achieved a better classification accuracy (100%) than using strangeness-based k-nn alone.
emerging cooperation in a public goods game with competition. in public goods games individuals contribute to create a benefit for a group. unfortunately they also attract free-riders who enjoy the benefits without contributing. despite this, in reality, cooperation does not collapse. several explanations exist for this phenomenon. in our work, individual behavioral rules play an important role in the emergence of a global, cooperative behavior. in our setting, the individual contribution depends on the motivational level of the individual, which depends on the wealth of neighbors. this in turn is associated with the wealth of the whole society. previous works have used global persistence to analyze that wealth. here we introduce new elements in the study of the public goods game, trying to bring the model closer to real world situations. we start by analysing the behavior of agents when they interact in a grid. further, we introduce competition in the game, i.e. agents can select to contribute to two public goods, instead of just one. results show an increase in wealth, hence in cooperation, when agents perform coordinated selections of goods, and when they imitate the neighbor with highest wealth.
semi-supervised dimensionality reduction in image feature space. image feature space is typically complex due to the high dimensionality of data. effective handling of this space has prompted many research efforts in the study of dimensionality reduction in the image domain. in this paper, we propose a semi-supervised reduction method that leverages relevance feedback information in the retrieval process to learn suitable linear and orthogonal embeddings. in the reduced space constructed by the proposed embedding, relevant images are kept close to each other, while irrelevant ones are dispersed far apart. the experimental results demonstrate the superiority of our method.
reachability analysis using multiway decision graphs in the hol theorem prover. in this paper, all the necessary infrastructure is provided to define a state exploration approach within the hol theorem prover. while related work has tackled the same problem by representing primitive binary decision diagram (bdd) operations as inference rules added to the core of the theorem prover, the presented approach is based on the multiway decision graphs (mdgs). mdg generalizes bdd to represent and manipulate a subset of first-order logic formulae. considering mdg instead of bdd will raise the abstraction level of what can be verified using states exploration within a theorem prover. a canonic mdgs is defined in hol as well-formed directed formulae. then, the basic mdg operations is formalized following a deep embedding approach and the correctness proof for each operation is derived. finally, the reachability analysis is implemented as a tactic that uses the mdg theory within hol.
evaluating uncertain location-based spatial queries. location-based spatial queries (lbsq) are becoming more and more useful in location-based services such as those provided by cell-phones, wireless lan and gps technologies. they support the access to information resources by taking into account the spatial context of the user when submitting the query, and the spatial location of the searched resources (instances). in fact, in a lbsq the key-selection condition is generally a constraint on the distance of the resources in the database (instances) from the user location. one deficiency of current approaches is the fact that they do not manage the uncertainty that often characterizes the knowledge of either the user location or the searched instances. a model for representing and evaluating uncertain location-based spatial queries is proposed in which besides location uncertainty also the spatial condition can be imprecise. a two-step evaluation procedure of lbsqs is outlined based on a filter and on a refinement phase.
a case-based reasoning approach for materializing software architectures onto object-oriented designs. software architectures enable to capture early design decisions in the software design process. architecture designs are materialized onto object-oriented elements trying to realize the functional and quality requirements they prescribe. the materialization of software architectures is a very complex task and requires developers with an important background of design knowledge and expertise. in practice, developers derive object-oriented counterparts of architectural elements by recalling previous design solutions used in the past to solve new similar problems. in this paper, we propose a new approach called same (software architecture materialization explorer) to assist and guide developers (particularly novice ones) in the materialization process. the approach relies on the case-based reasoning metaphor in which developers' experiences are codified into a knowledge repository so that they can be reused, by means of a systematic reasoning procedure, to derive new architecture materializations.
xbird/d: distributed and parallel xquery processing using remote proxy. in this paper, we focus on an aspect of distributed xquery processing that involves data exchanges between processor elements. we first address problems of distributed xml query processing and explain how the problems differ from traditional database problems. then, in order to achieve efficient and transparent data exchange, we adopt the use of remote proxy, in which each shipped data is wrapped in a proxy sequence, and the proxy sequence is returned to the remote peer. when accessing the proxy sequence, actual results (possibly partial results) are fetched from a data provider, and then the data provider evaluates its entity sequence in a call-by-need fashion. our scheme allows parallel query execution and reduces network traffic and redundant buffer utilization by exchanging required data directly between a consumer and a provider.
special track on real-time systems: editorial message. real-time systems are gradually turning into a public "utility"' in people's daily life. the advances in micro electro mechanical systems (mems), wireless networking, and the growth in semiconductor technology and the miniaturization of energy capacity have served as a catalyst in this process. with more and more emerging applications and architectures it is essential to revitalize and enrich the real-time systems arena. the focus of this track is on the application of both novel and well-known techniques in real-time systems design and development.
the virtual reality challenges in the health care area: a panoramic view. this paper presents a general view of computational applications in the health area, highlighting the use of virtual reality technology resources in brazil and other countries. from this initial survey, suggestions for improvements in this area were presented along with the relevant research needed to make these improvements and overcome the current obstacles.
sql-ids: a specification-based approach for sql-injection detection. vulnerabilities in web applications allow malicious users to obtain unrestricted access to private and confidential information. sql injection attacks rank at the top of the list of threats directed at any database-driven application written for the web. an attacker can take advantages of web application programming security flaws and pass unexpected malicious sql statements through a web application for execution by the back-end database. this paper proposes a novel specification-based methodology for the detection of exploitations of sql injection vulnerabilities. the new approach on the one hand utilizes specifications that define the intended syntactic structure of sql queries that are produced and executed by the web application and on the other hand monitors the application for executing queries that are in violation of the specification. the three most important advantages of the new approach against existing analogous mechanisms are that, first, it prevents all forms of sql injection attacks; second, its effectiveness is independent of any particular target system, application environment, or dbms; and, third, there is no need to modify the source code of existing web applications to apply the new protection scheme to them. we developed a prototype sql injection detection system (sql-ids) that implements the proposed algorithm. the system monitors java-based applications and detects sql injection attacks in real time. we report some preliminary experimental results over several sql injection attacks that show that the proposed query-specific detection allows the system to perform focused analysis at negligible computational overhead without producing false positives or false negatives. therefore, the new approach is very efficient in practice.
icer: a tool for finding errors in a uml model. detecting errors in an early phase of software development can help to reduce the cost of software systems. many research attempts presented a fixed set of rules to help finding errors in a model. however, flexibility is one of the characteristics during software development. the correctness of a model usually depends on many factors such as development environments. the predefined rules usually fail to provide such flexibility. this paper will apply the idea from yacc/bison to allow software engineers to define the validity of their application models in a profile. central to the profile mechanism is the instance-of relation, which is established between an application model and a profile. as a result, engineers can first define the validity of an application in a profile and thus find errors in a model when the model is not a valid instance of the profile. based on the profile mechanism, we build a tool, called icer, which can automatically help developers to check the instance-of relation between a profile and an application model. as an example, we apply the reification of the factory method pattern to illustrate how the icer tool helps to support error detection.
fuzzy and neuro-fuzzy estimates of the total height of eucalyptus trees. because of its many uses, including for paper, cellulose, and energy production, the eucalyptus is an economically important tree. the estimate of eucalyptus total height is useful for forest handling and predicting production. the objective of this work is to study the relation between a eucalyptus tree's diameter at breast height (dbh) and age to its total height. based on this data, we present decision systems developed using fuzzy logic and neuro-fuzzy systems. both proposed models are compared with classical mathematical models; the neuro-fuzzy system has the highest accuracy and precision.
service sharing with trust in pervasive environment: now it's time to break the jinx. in such as a highly dynamic and open environment, it has become a challenge to deploy multiple context-sensitive services due to the unwillingness of service provider to share resources. this apprehension to share resources stems mainly from a lack of trust. sometimes infrastructure plays pivotal role to solve this issue with dynamic access control to replace traditional static policies. but when it comes to effective resource sharing in an infrastructure-less environment we face problems such as poor storage and computational capability. in this paper, we have developed a lightweight and distributed trust model based on recommendation, which will guarantee that service providers can securely share an unlimited number of resources, limited only by their hardware and bandwidth limitations. the multi-hop recommendation protocol incorporates a flexible behavioral model to handle interactions during service sharing and usage. this protocol will also assess risk using recommendations from context-sensitive services, in the trust framework, to help ensure smooth access to resources and services.
integrating java and prolog through generic methods and type inference. p@j is a framework, based on the tuprolog open-source engine, allowing prolog code to be used as possible implementation of a java method: java annotations are used for specifying all the necessary information to fill the java-prolog gap. this framework is useful to inject a declarative, logic-based paradigm into mainstream object-oriented programming, so as to easily code functionalities related to automatic reasoning, adaptivity, and conciseness in expressing algorithms. in this paper, an extension of p@j is presented which improves the invocation technique for such prolog-implemented methods. java type inference of generic method calls is intensively used to automatically infer all the necessary paradigm mismatch information: this results in an elegant and concise invocation style, which further reduces the gap between prolog goal satisfaction and java method invocation. this new approach inspires some interesting applications: we show examples related to the implementation of abstract data types and parsers for context-free grammars.
an infrastructure for developing context aware applications in pervasive environments. this paper presents the lotus, an infrastructure to develop context-aware applications, providing the mechanisms for acquiring, representing, reasoning and delivering of contextual information. this infrastructure consists of a framework, which is composed by an api and a middleware, a language to represent contextual information and a mechanism to share and delivery contextual information.
texture feature extraction and description using fuzzy set of main dominant directions of variable scales in content-based medical image retrieval. the paper presents a new method for texture feature extraction and description. gabor wavelet transform, statistical method and fuzzy logic are used together to compute the fuzzy set of main dominant directions of variable scales of medical images of the same kind and the membership degree of dominance of each element in the fuzzy set. texture feature vector of each image is computed based on the fuzzy set. moreover, the dominance of each element in the fuzzy set is introduced into the similarity measure as weight. experiments show that the method proposed in this paper reduces the dimension of texture feature vector and alleviates heavy computation while keeping and even improving the retrieval performance.
optimized dynamic semantic composition of services. this paper proposes a design for the optimized dynamic (re)composition of services that supports various user requests and accounts for changes in user's context. the composition mechanism relies on semantic descriptions of services' functionality to compose services at runtime. the design-time component of our solution is a domain ontology that is used by the composition service for deriving different composition possibilities and for finding the appropriate similarity relations between different semantic constructs representing the user request and service descriptions. the composition mechanism combines a greedy optimization for the initial selection of candidate constituent services and a global optimization for reaching the final composition.
imputation-boosted collaborative filtering using machine learning classifiers. as data sparsity remains a significant challenge for collaborative filtering (cf, we conjecture that predicted ratings based on imputed data may be more accurate than those based on the originally very sparse rating data. in this paper, we propose a framework of imputation-boosted collaborative filtering (ibcf), which first uses an imputation technique, or perhaps machine learned classifier, to fill-in the sparse user-item rating matrix, then runs a traditional pearson correlation-based cf algorithm on this matrix to predict a novel rating. empirical results show that ibcf using machine learning classifiers can improve predictive accuracy of cf tasks. in particular, ibcf using a classifier capable of dealing well with missing data, such as na&iuml;ve bayes, can outperform the content-boosted cf (a representative hybrid cf algorithm) and ibcf using pmm (predictive mean matching, a state-of-the-art imputation technique), without using external content information.
improving flow-insensitive solutions for non-separable dataflow problems. flow-insensitive solutions to dataflow problems have been known to be highly scalable; however also hugely imprecise. for non-separable dataflow problems this solution is further degraded due to spurious facts generated as a result of dependence among the dataflow facts. we propose an improvement to the standard flow-insensitive analysis by creating a generalized version of the dominator relation that reduces the number of spurious facts generated. in addition, the solution obtained contains extra information to facilitate the extraction of a better solution at any program point, very close to the flow-sensitive solution. to improve the solution further, we propose the use of an intra-block variable renaming scheme. we illustrate these concepts using two classic non-separable dataflow problems --- points-to analysis and constant propagation.
cluster-based novel concept detection in data streams applied to intrusion detection in computer networks. in this paper, a cluster-based novelty detection technique capable of dealing with a large amount of data is presented and evaluated in the context of intrusion detection. starting with examples of a single class that describe the normal profile, the proposed technique detects novel concepts initially as cohesive clusters of examples and later as sets of clusters in an unsupervised incremental learning fashion. experimental results with the kdd cup 1999 data set show that the technique is capable of dealing with data streams, successfully learning novel concepts that are pure in terms of the real class structure.
special track on enterprise information systems: editorial message. enterprise information systems are those intended to support business in the contemporary knowledge-based global economy. therefore, developing and deploying these systems means to deal with complex and cross-disciplinary enterprise integration issues. eis area embraces a plethora of subjects that range from enterprise resources planning (erp), enterprise content management (ecm) and customer relationship management (crm) to decision support systems and business intelligence. this track complements other sac'2008 tracks such as database theory, technology, and applications, data mining, data streams, document engineering, e-business applications, information access and retrieval, organizational engineering, requirement engineering, software engineering, and computer security, by stressing technology and business integration issues.
information systems implementation: the big picture. the ability to adapt to changes has emerged as a new paradigm for successful business operations. the paper outlines an approach for the deployment of enterprise information systems taking organizational transformation into account. a framework for enterprise architecture is introduced which links business-, application-, and infrastructure architecture. it is the core reference for the construction of the business and it landscape. blueprints give a comprehensive view on the building blocks of architecture and how the interact and provide the fundamental decision basis for the deployment of enterprise information systems. furthermore criteria and process how to analyze the adaptability of information systems are described. based on the assessment principal strategies for information system deployment are pointed out.
sscm: middleware for structure-based service collaboration. the collaboration of services scattered over the internet is often required in solving a complicated scientific problem or finishing a complex business process. centralized client/server architecture used in traditional service collaboration mechanism such as workflow has exhibited many weaknesses. we adopt the concept from peer-to-peer computing and synergetics, and introduce an innovative middleware for structured-based service collaboration, in which each service node belongs to one or more structured collaboration organizations and is allowed to join and leave (planned departure or sudden failure) freely. we also propose neighborhood-based maintenance mechanism to cope with the dynamics of collaboration organization structure in a decentralized manner.
enterprise ontology based splitting and contracting of organizations. our research program aims at finding and testing principles for deciding on organization splits and starting cooperation over the split. we tested a method to make underpinned choices on the organization split and to ensure completeness in contracting. using actors from enterprise ontology as organization building blocks of a dutch governmental agency, experts constructed their own gut feeling organization split and systematically listed (a) ownership of assets (b) quality of business and information services and (c) critical chain-dependencies. the proposed organization split is, confirming an earlier experiment, quite close to graph theory based calculated alternatives. the listing of contracting items helped to determine in a fast and shared way subsequent implementation steps, e.g., ensuring mutual information supply and formulating performance indicators.
ppepr: plug and play electronic patient records. the integration of electronic patient record (epr) systems is at the centre of many of the new regional and national initiatives to integrate clinical processes across department, region, and national levels. web service technologies offer significant solutions to provide an interoperable communication infrastructure but are unable to support precise definitions for healthcare messages, functionality, and standards, required for making meaningful integration. the lack of interoperability within healthcare standards adds complexity to the initiatives. this heterogeneity exists within two versions of same standard (e.g. hl7), and also between standards (e.g. hl7, openehr, cen tc/251 13606). we therefore introduce an integration platform ppepr (plug and play electronic patient records), which is based on the principles of a semantic service-oriented architecture (ssoa). ppepr solves the problem of interoperability at the semantic level. a key focus of ppepr is that once a patient information is captured, should be available for use across all potential care processes.
a system for modal and deontic defeasible reasoning. defeasible reasoning is a well-established nonmonotonic reasoning approach that has recently been combined with semantic web technologies. this paper describes modal and deontic extensions of defeasible logic, and shows how these extensions can bbe used for modelling multi-agent systems and policies.