waashk/dblp huggingface dataset:
oasnet: an optimal allocation approach to influence maximization in modular social networks. influence maximization in a social network is to target a given number of nodes in the network such that the expected number of activated nodes from these nodes is maximized. a social network usually exhibits some degree of modularity. previous research efforts that made use of this topological property are restricted to random networks with two communities. in this paper, we firstly transform the influence maximization problem in a modular social network to an optimal resource allocation problem in the same network. we assume that the communities of the social network are disconnected. we then propose a recursive relation for finding such an optimal allocation. we prove that finding an optimal allocation in a modular social network is np-hard and propose a new optimal dynamic programming algorithm to solve the problem. we name our new algorithm oasnet (optimal allocation in a social network). we compare oasnet with equal allocation, proportional allocation, random allocation and selecting top degree nodes without any allocation strategy on both synthetic and real world datasets. experimental results show that oasnet outperforms these four heuristics.
enhancing semantic search using case-based modular ontology. in this paper, we present a semantic search approach based on case-based modular ontology. our work aims to improve ontology-based information retrieval by the integration of the traditional information retrieval, the use of ontology and the case based reasoning (cbr). in fact, our recommender approach uses the cbr with ontology for case representation and indexing. ontology-based similarity is used to retrieve similar cases and to provide end users with alternative recommendations. the main contribution of this work is the use of a cbr mechanism and an ontological representation for two purposes: resource retrieval from web and ontology enrichment from cases.
separating the reputation and the sociability of online community users. in online communities or blogospheres, the users publish their posts, and then their posts get feedback actions from other users in the form of a comment, a trackback or a recommendation. these interactions form a graph in which the vertices represent a set of users while the edges represent a set of feedbacks. thus, the problem of users' rankings can be approached in terms of the analysis of the social relationships between the users themselves within this graph. pagerank and hits have often been applied for users' rankings, especially for users' reputation, but there has been no consideration of the fact that the user's sociability can affect the user's reputation. to address this problem, in this paper, we newly propose two different factors that affect the score of every user: the user's reputation and the user's sociability. furthermore, we present novel schemes that effectively and separately can estimate the reputation and the sociability of the users. our experimental results show that: 1) our schemes can effectively separate the user's pure reputation from the user's sociability 2) pure reputation, as it stands alone or when it is combined with sociability, is capable of producing more optimal user ranking results than can the previous works.
mopsi location-based search engine: concept, architecture and prototype. traditional location-based services use databases where all entries have been explicitly georeferenced beforehand. we propose an alternative approach based on web search and using ad-hoc georeferencing of web-pages. we denote it as location-based search engine and emphasize its seemingly small but significant distinction from traditional location-based services. we outline how to construct such search engine and prove its effectiveness using a prototype called mopsi search.
improving the efficiency of dynamic malware analysis. each day, security companies see themselves confronted with thousands of new malware programs. to cope with these large quantities, researchers and practitioners alike have developed dynamic malware analysis systems. these systems automatically execute a program in a controlled environment and produce a report describing the program's behavior. during the last three years, the number of malware programs appearing each day has increased by a factor of ten, and this number is expected to continue to grow. to keep pace with these developments without causing even more hardware costs for operating dynamic analysis systems, we have developed a technique that drastically reduces the overall analysis time. our solution is based on the insight that the huge number of new malicious files is due to mutations of only a few malware programs. to save analysis time, we suggest a technique that avoids performing a full analysis of the same polymorphic file multiple times. in an experiment conducted on a set of 10,922 randomly chosen executable files, our prototype implementation was able to avoid a full dynamic analysis in 25.25 percent of the cases.
a multi-objective optimization approach for fault-tolerance provisioning in multi-radio hybrid wireless-optical broadband access networks. wireless-optical broadband-access networks (wobans), currently being deployed at the access section, should integrate fault-tolerance in their design so that geographically continuous wireless coverage can be provided without service breaks. in this paper we propose a joint wireless and optical fault-tolerance planning approach for wobans having multiple radios in each router. the problem is formalized, as a multi-objective optimization problem, and a heuristic is proposed to solve this problem. two fault-tolerance planning scenarios, where gateways are arranged differently in the risk groups, are analysed. it is shown that multi-radio routers can be exploited to improve the performance of wobans, providing wireless and optical fault-tolerance. results also indicate that, when using our approach, a small increase in the number of radios can significantly decrease the total capacity required to provide any degree of fault-tolerance.
adaptive fuzzy-valued service selection. service composition concerns both integration of heterogeneous distributed applications and dynamic selection of services. qos-aware selection enables a service requester with certain qos requirements to classify services according to their qos guarantees. in this paper we present a method that allows for a fuzzy-valued description of qos parameters. fuzzy sets are suited to specify both the qos preferences raised by a service requester such as 'response time must be as lower as possible and cannot be more that 1000ms' and approximate estimates a provider can make on the qos capabilities of its services like 'availability is roughly between 95% and 99%'. we propose a matchmaking procedure based on a fuzzy-valued similarity measure that, given the specifications of qos parameters of the requester and the providers, selects the most appropriate service among several functionally-equivalent ones. we also devise a method for dynamical update of service offers by means of runtime monitoring of the actual qos performance.
adapting the tf idf vector-space model to domain specific information retrieval. the default implementation in lucene, an open-source search engine, is the well-known vector-space model with tf idf weighting. the objective of this paper is to propose and evaluate additional techniques that can be adapted to this search model, in order to meet the particular needs of domainspecific information retrieval (ir). in this paper, we suggest certain specificity measures derived from either information theory or corpus-based linguistics. as an additional feature we suggest accounting for the number of search terms that a query and retrieved documents have in common. to integrate these methods we design and implement four extensions to the classical tf idf model and then evaluate the new ir models by applying them to four different domain-specific collections and comparing them to results found by a probabilistic retrieval model. the results tend to demonstrate that the adapted vector-space models clearly outperform the baseline approach (tf idf) and that performance levels obtained even surpass those found in the okapi model.
identification and control of intrinsic bias in a multiscale computational model of drug addiction. personalized medicine is rapidly evolving with the objective of providing a patient with medications based on the "use of genetic susceptibility or pharmacogenetic testing to tailor an individual's preventive care or drug therapy" [1]. it is reasonable to foresee that this domain will incorporate sources of biological knowledge other than genetics including computational modeling of diseases. for this purpose, a critical issue is how to identify and control systematic biases that may arise. in this paper, a multiscale computational model of drug addiction is presented and the interpretations of the simulated behavioral profiles of a virtual subject are discussed. these outcomes are analyzed using mathematical analytical techniques with particular attention directed to minimization of systematic biases. the simulations exemplify how a structural analysis of the model, prior to the actual simulations, may benefit the overall framework in terms of accuracy. while this paper focuses on an equation-based model for drug addiction, a similar methodology could be applied to other types of computational models for other diseases.
mining models of exceptional objects through rule learning. a new technique, sniper, is proposed for learning a model that deals with continuous values of exceptionality. specifically, given some training objects associated with a continuous attribute f, sniper induces a rule-based model for the identification of those objects likely to score the maximum values for f. the purpose of sniper differs from the one pursued in regression problems, since its main objective is to retrieve those objects more likely to score the highest values of f. although there are opportunities for improvement, the results of a preliminary evaluation are encouraging. sniper is competitive in the quality of the attained results with respect to some established competitors, while outperforming them when the exceptional objects are very rare. additionally, sniper is much faster in the induction of a model of object exceptionality.
a requirements elicitation framework and tool for sourcing business-it aligned e-services. this paper describes a multiple perspectives goal-oriented requirements elicitation framework aimed at identifying it service requirements for service sourcing in a service-oriented software marketplace (e-services marketplace). the framework is based on decomposing strategic goals into tactical and operational it goals adopting a multiple perspectives elicitation approach that facilitates business-it alignment in the elicitation/mapping of the service requirements and in the choice of the target service offerings. the framework is applicable in a scenario where there is a marketplace of software service providers (e.g. asp's or web service yellow pages) and the sourcing organization is interested in developing service requirements and searching for services available in the marketplace that are aligned with the organizational goals. the framework is supported by a tool that helps the requirements analyst in the process of developing multiple perspectives service goal trees and also in the process of matching service goals to keywords describing service offerings available in the e-service marketplace.
photoland: a new image layout system using spatio-temporal information in digital photos. this paper describes photoland, a system that visualizes hundreds of photos on a 2d grid space to help users manage their photos. it closely places similar photos in the grid based on temporal and spatial information. most photograph management systems use a scrollable view based on a sequential grid layout that arranges the thumbnails of photos in some default order on the screen. our system decreases drag and drop mouse interaction when they classify their photos into small groups comparing to the sequential grid layout. we conducted experiments to evaluate our system. they consider four features: space efficiency, temporal stability and spatial consistency between neighbor photos of the grid, and cluster similarity. although photoland decreases space efficiency, it improves other features compared to the sequential grid layout.
of mice and terms: clustering algorithms on ambiguous terms in folksonomies. developed using the principles of the model-view-controller architectural pattern, folksengine is a parametric search engine for folksonomies that allows us to test arbitrary search improvement algorithms by specifying them in three phases: expansion, where the original query is converted in multiple ones according to semantic rules associated to the query terms, search, executing the queries on a standard folksonomy search engine such as delicious, and ranking, sorting the results according to rules. in this paper we extend our previous studies using folksengine and offer a new query expansion algorithms based on natural language processing techniques, and a new view for the results based on semantic web technologies. we also describe some tests of the algorithms developed, in order to obtain a clear and effective evaluation of them.
enhancing document structure analysis using visual analytics. during the last decade national archives, libraries, museums and companies started to make their records, books and files electronically available. in order to allow efficient access of this information, the content of the documents must be stored in database and information retrieval systems. state-of-the-art indexing techniques mostly rely on the information explicitly available in the text portions of documents. documents usually contain a significant amount of implicit information such as their logical structure which is not directly accessible (unless the documents are available as well-structured xml-files) and is therefore not used in the search process. in this paper, a new approach for analyzing the logical structure of text documents is presented. the problem of state-of-the-art methods is that they have been developed for a particular type of documents and can only handle documents of that type. in most cases, adaptation and re-training for a different document type is not possible. our proposed method allows an efficient and effective adaptation of the structure analysis process by combining state-of-the-art machine learning with novel interactive visualization techniques, allowing a quick adaptation of the structure analysis process to unknown document classes and new tasks without requiring a predefined training set.
a data perturbation approach to sensitive classification rule hiding. this paper focuses on privacy preservation in classification rule mining. the subject at hand is approached through the proposition of a data perturbation approach for hiding sensitive classification rules in categorical datasets. such a methodology is absolutely necessary in case the data needs to be published on the web so that it is amply available for public use as opposed to other approaches like output perturbation or cryptographic techniques that restrict the usability of the data in different ways. this methodology is based upon the unique characteristics of sequential covering classification algorithms. it modifies the tuples of sensitive rules of a dataset d in such a way that these are distributed to the "more important" non-sensitive rules. in addition it assures that the tuples belonging to the sensitive rules are assigned to the non-sensitive rules in proportion to their rank in the ruleset. in that way, it is ensured that not only the sensitive rules are hidden but also that the current structure of the ruleset, thus the information value of the dataset, is preserved. moreover a modification of the basic method which exhibits an alternative distribution procedure is also presented. finally, a series of experiments are executed in order to evaluate the validity and effectiveness of the proposed approaches against existing similar ones.
collaborative interaction through spatially aware moving displays. in many real life situations, people work together using each their own computers. in practice, besides personal communication, such situations often involve exchanging documents and other digital objects. since people are working in a common physical space, it is a natural idea to enlarge the virtual space to a common area where they can exchange objects while taking advantage of the collaborators' physical proximity. in this work we propose a way to allow collaboration through the interaction with objects in a common virtual workspace built on the top of tablet pcs. the concepts of dynamic multiple displays and real world position tracking are implemented exploiting the tablet's embodied resources such as webcam, touch-screen and stylus. also, a multiplayer game was implemented to show how users can exchange information through intercommunicating tablets. we performed user tests to demonstrate the feasibility of collaborative tasks in such environment, and drawn conslusions regarding the impact of the new paradigm of extended multi-user workspaces.
ontologies and summarizability in olap. summarizability, i.e. the correctness of aggregation operations, is essential for olap analysis. summarizability has commonly been studied in the context of dimension hierarchies, but the role of semantics of measure attributes and aggregation functions (sum, avg, min, max, count) has received less research interest. in this paper, we focus on the relationship between measure and dimension attributes and its effect on summarizability. we define the concept of measure-dimension consistency and show how it can be concluded from an olap ontology constructed by using semantic web technologies. measure-dimension consistency can be used both for olap cube construction and queries and it is also very useful when integrating data over the internet.
a service-based context management framework for cross-enterprise collaboration. the more service based applications appear in different business areas such as location based services (lbs) and field service management (fsm), the more important it becomes to support context-awareness in service based systems. exposition, collection, management and consumption of context information are the main processes to be addressed in a service based context management framework. this paper presents a framework, namely cosco, tackling these processes to enrich service applications with context-aware decisions. the presented framework follows the service oriented architecture (soa) principles. adopting soa eases the deployment of our framework into highly dynamic environments where the source of context information can show high level of variability. as a general guideline, we also provide an empirical study of our framework to elicit the performance with different deployment scenarios. the results of our study can help system administrators deploy systems with different capabilities. a context-aware calendar application built by using our framework is also presented at the end of the paper.
oncoviz: a user-centric mining and visualization tool for cancer-related literature. a plethora of health informatioin available on the internet potentially benefits a variety of people, including patients and care providers. however, due to the sheer volume of such information, the benefits have not been fully realized. in this poster, we present a user-centric text information retrieval and knowledge discovery tool, called "oncoviz," for cancer-related literature and navigation of the extracted knowledge as associations using a novel interactive information visualization tool. based on our current results, it shows that a user-centric, integrated information extraction and visualization tool indeed can provide valuable information to users, such as cancer patients and care providers.
evaluation of different feature sets in an ocr free method for word spotting in printed documents. this paper presents the evaluation of tree feature sets in an ocr free word spotting method under a strong experimental protocol. different feature sets are evaluated under the same experimental conditions. in addition, a tuning process in the document segmentation step is proposed which provides a significant reduction in terms of the processing time. for this purpose, a complete ocr-free method for word spotting in printed documents was implemented, and a document database containing document images and their corresponding ground truth text files was created. a strong experimental protocol based on 800 document images allows us to compare the results of the three feature sets used to represent the word image.
asynchronous byzantine consensus with 2f+1 processes. byzantine consensus in asynchronous message-passing systems has been shown to require at least 3f + 1 processes to be solvable in several system models (e.g., with failure detectors, partial synchrony or randomization). recently a couple of solutions to implement byzantine fault-tolerant state-machine replication using only 2f + 1 replicas have appeared. this reduction from 3f + 1 to 2f + 1 is possible with a hybrid system model, i.e., by extending the system model with trusted/trustworthy components that constrain the power of faulty processes to have certain behaviors. despite these important results, the problem of solving byzantine consensus with only 2f + 1 processes is still far from being well understood. in this paper we present a methodology to transform crash consensus algorithms into byzantine consensus algorithms with different characteristics, with the assistance of a reliable broadcast primitive that requires trusted/trustworthy components to be implemented. we exemplify the methodology with two algorithms, one that uses failure detectors and one that is randomized. we also define a new flavor of consensus and use it to solve atomic broadcast, showing the practical interest of the transformations.
integration of a human face annotation technology in an audio-visual search engine platform. search-based web applications are configurable and customizable platforms for multimedia search over the web. this paper describes the results of the integration of an annotation component for automatic face recognition within a configurable multimedia web search platform. we integrate a third-party image analysis technology for human face recognition, by extending it in order to cope with the requirements of video annotation solutions on the web and wrapping it according to the platform's integration specification. as a result, the annotator outcomes are indexed within the platform and can be used for multi-modal and content-based search. we evaluate our experience in terms of development effort required, indexing performances, and perceived quality of the annotation results.
an investigation into the notion of non-functional requirements. although non-functional requirements (nfrs) are recognized as very important contributors to the success of software projects, studies to date indicate that there is still no general consensus in the software engineering community regarding the notion of nfrs. this paper presents the result of an extensive and systematic analysis of the extant literature over three nfrs dimensions: (1) definition and terminology; (2) types; and (3) relevant nfrs in various types of systems and application domains. two different perspectives to consider nfrs are described. a comprehensive catalogue of nfrs types as well as the top five nfrs that are frequently considered are presented. this paper also offers a novel classification of nfrs based on types of systems and application domains. this classification could assist software developers in identifying which nfrs are important in a particular application domain and for specific systems.