waashk/dblp huggingface dataset:
analysis of collision probability in unsaturated situation. a large number of the mac protocols proposed for establishing wireless sensor networks are based on the 802.11 standard. the trade-off in these protocols is the control packet overhead and the retransmission cost due to collision without it. in this paper we evaluate the collision probability of a csma/ca mac protocol in an unsaturated situation as a function of nodes' sampling and transmission rates. we provide an accurate and comprehensive analytical model in which a finite number of nodes exist. we assume an ideal channel condition, independent collision probability of packets as well as infrequent communication between sensor nodes. we will demonstrate that the collision probability changes from 0 to 0.22 as the sampling rate changes from 0 to 0.94mbps. moreover, we will demonstrate that collision only begins after the sampling rate reaches 0.31mbps, which implies that for a sampling rate below this threshold, the control overhead can be avoided by altogether avoiding the collision avoidance mechanism.
filtering the shadows from poorly illuminated photos. this paper presents a new algorithm for filtering shadowy photos degraded by uneven light condition. the dark photos are harder to use for face recognition or even an analysis of the scene. corrections can be made using commercial tools as photoshop&trade; but this is done manually and it requires a specialized user. we propose an automatic method which inproves the visibility of the scenes. this method is based on wavelet analysis but it uses different filters.
a rough set approach to mining connections from information systems. mining data changes and connections from information systems (or databases) is made difficult by the different data behaviors and relationships across multiple data sets. when making a decision, such a dynamic and integrated knowledge base can be used to set useful rules (e.g., causality) that differ from the statistical associations in a single resource. in this paper, using techniques based on the rough set theory, we propose a change and connection mining algorithm for discovering a time delay between the quantitative changes in the data of two temporal information systems and for generating the association rules of changes from their connected decision table. we establish evaluation criteria for the connectedness of two temporal information systems with varying time delays by calculating weight-based accuracy and coverage of the association rules of changes, adjusted by a fuzzy membership function.
a knowledge-rich similarity measure for improving it incident resolution process. the aim of incident management is to restore a given it service disruption, simply called incident, to normal state as quickly as possible. in incident management, it is essential to resolve a new incident efficiently and accurately. however, typically, incident resolution process is largely manual, thus, it is time-consuming and error-prone. this paper proposes a new knowledge-rich similarity measure for improving this process. the role of this measure is to retrieve the most similar past incident cases for a new incident without human intervention. the solution information contained the retrieved incident cases can be utilized to resolve the new incident. the main feature of our similarity measure is to incorporate additional useful meta knowledge, outside of incident description that is the only exploited information in typical similarity measures used in cbr, to improve effectiveness. moreover, this measure exploits as much semantic knowledge as possible about features contained in previous incident cases. through an experimental evaluation, we show the effectiveness, technical coherence and feasibility of this measure using a real dataset.
a general energy optimization model for wireless networks using configurable antennas. we consider two fundamental problems of minimizing energy consumption and maximizing the lifetime of a given communication request in multi-hop wireless networks that use configurable antennas and have limited energy resources. we provide a globally optimal solution to this problem by developing a general optimization model that can apply to many antenna models including highly and moderately directional antenna types. our numerical experiments validate our formulations and the obtained results reveal the performance behavior as a function of antenna and multicast configurations for some network examples.
a new methodology for photometric validation in vehicles visual interactive systems. this work proposes a new methodology for automatically validating the internal lighting system of an automotive, i.e., assessing the visual quality of an instrument cluster (ic) from the point of view of the user. although the evaluation of the visual quality of a component is a subjective matter, it is highly influenced by some photometric features of the component, such as the light intensity distribution. the methodology proposed here uses this last photometric feature to classify regions in images of instrument cluster components as homogenous or not, while also taking into account the user subjective evaluation. in order to achieve that, we acquired a set of 107 ic component images, and preprocessed them. these same components were evaluated by a user to identify their non-homogenous regions. then, for each component region, we extracted a set of homogeneity descriptors. these descriptors were associated with the results of the user evaluation, and given to two machine learning algorithms. these algorithms were trained to identify a region as homogenous or not, and showed that the proposed methodology obtains precision above 95%.
flash-aware record management method. in this paper, we analyze how unique characteristics of flash memory affect the performance of clustering and non-clustering strategies for record management, and show that non-clustering is more suitable in flash memory environment. also, we identify the problems of the existing non-clustering method when applied to flash memory environment, and propose an effective method for record management in flash memory databases.
exploring, visualizing and slicing the soul of xml documents. in this paper we introduce exvisxml, a visual tool to explore documents annotated with the mark-up language xml, in order to easily perform over them tasks as knowledge extraction or document engineering. exvisxml was designed mainly for two kind of users. those who want to analyze an annotated document to explore the information contained---for them a visual inspection tool can be of great help, and a slicing functionality can be an effective complement. the other target group is composed by document engineers who might be interested in assessing the quality of the annotation created. this can be achieved through the measurements of some parameters that will allow to compare the elements and attributes of the dtd/schema against those effectively used in the document instances. both functionalities and the way they were delineated and implemented will be discussed along the paper.
a generative pattern model for mining binary datasets. in many application fields, huge binary datasets modeling real life-phenomena are daily produced. these datasets record observations of some events, and people are often interested in mining them in order to recognize recurrent patterns. however, the discovery of the most important patterns is very challenging. for example, these patterns may overlap, or be related only to a particular subset of the observations. finally, the mining can be hindered by the presence of noise. in this paper, we introduce a generative pattern model, and an associated cost model for evaluating the goodness of the set of patterns extracted from a binary dataset. we propose an efficient algorithm, named gpm, for the discovery of the most relevant patterns according to the model. we show that the proposed model generalizes other approaches and supports the discovery of high quality patterns.
an automatic linking service of document images reducing the effects of ocr errors with latent semantics. robust information retrieval (ir) systems have been demanded due to the widespread and multipurpose use of document images, and the high number of document images repositories available nowadays. this paper presents a novel approach to support the automatic generation of relationships among document images by exploiting latent semantic indexing (lsi) and optical character recognition (ocr). the linkdi service extracts and indexes document images content, obtains its latent semantics, and defines relationships among images as hyperlinks. linkdi was experimented with document images repositories, and its performance was evaluated by comparing the quality of the relationships created among textual documents and among their respective document images. results show the feasibility of linkdi relating ocr output with high degradation.
fraud detection in reputation systems in e-markets using logistic regression. reputation systems are specially important in e-markets, where they help buyers to decide whether or not to purchase a product. this work addresses the task of finding attempts to deceive reputation systems in e-markets. our goal is to generate a list of users (sellers) ranked by the probability of fraud. first we describe characteristics related to transactions that may indicate frauds evidence and they are expanded to the sellers. we describe results of a simple approach that ranks sellers by counting characteristics of fraud. then we incorporate characteristics that cannot be used by the counting approach, and we apply logistic regression to both, improved and not improved. we use real data from a large brazilian e-market to train and evaluate our methods and the improved set with logistic regression performes better. the list with 32.1% of topmost probable fraudsters against the reputation system was selected. we increased by 110% the number of identified fraudsters against the reputation system and no false positives were confirmed.
semantics for intelligent delivery of multimedia content. this paper describes a new generic metadata model, called cam metamodel, that merges altogether information about content, services, physical and technical environment in order to enable homogenous delivery and consumption of content. we introduce a metadata model that covers all these aspects and which can be easily extended so as to absorb new types of models and standards. we ensure this flexibility by introducing an abstract metamodel, which defines structured archetypes for metadata and metadata containers. the metamodel is the foundation for the technical metadata specification. we also introduce new structures in the abstract and core metamodels supporting the management of distributed community created metadata.
structural handwritten and machine print classification for sparse content and arbitrary oriented document fragments. discriminating handwritten and printed text is a challenging task in an arbitrary orientation scenario. the task gets even tougher when the text content is by nature sparse in the document, e.g. in torn document pieces. we here propose a system for discriminating handwritten and printed text in the context of sparse data and arbitrary orientation. a chain-code feature is used with support vector machine (svm) classifier for the purpose. prior to feature extraction and classification some preprocessing steps (like region growing and angle estimation using principle component analysis) are performed in order to resolve the arbitrary orientation issue. we got promising results of 96.90% accuracy, even when the document consists of sparse data with arbitrary orientation.
new theoretical findings in multiple personalized recommendations. within personalized marketing, a recommendation issue known as multicampaign assignment is to overcome a critical problem, known as the multiple recommendation problem which occurs when running several personalized campaigns simultaneously. this paper mainly deals with the hardness of multicampaign assignment, which is treated as a very challenging problem in marketing. the objective in this problem is to find a customer-campaign matrix which maximizes the effectiveness of multiple campaigns under some constraints. we present a realistic response suppression function, which is designed to be more practical, and explain how this can be learned from historical data. moreover, we provide a proof that this more realistic version of the problem is np-hard, thus justifying to use of heuristics presented in previous work.
mining interesting sets and rules in relational databases. in this paper we propose a new and elegant approach toward the generalization of frequent itemset mining to the multi-relational case. we define relational itemsets that contain items from several relations, and a support measure that can easily be interpreted based on the key dependencies as defined in the relational scheme. we present an efficient depth-first algorithm, which mines relational itemsets directly from arbitrary relational databases. several experiments show the practicality and usefulness of the proposed approach.
implementing software product lines using traits. a software product line (spl) is a set of software systems with well-defined commonalities and variabilities that are developed by managed reuse of common artifacts. in this paper, we present a novel approach to implement spl by fine-grained reuse mechanisms which are orthogonal to class-based inheritance. we introduce the featherweight record-trait java (frtj) calculus where units of product functionality are modeled by traits, a construct that was already shown useful with respect to code reuse, and by records, a construct that complements traits to model the variability of the state part of products explicitly. records and traits are assembled in classes that are used to build products. this composition of product functionalities is realized by explicit operators of the calculus, allowing code manipulations for modeling product variability. the frtj type system ensures that the products in the spl are type-safe by type-checking only once the records, traits and classes shared by different products. moreover, type-safety of an extension of a (type-safe) spl can be guaranteed by checking only the newly added parts.
evolutionary metaheuristic for biclustering based on linear correlations among genes. a new measure to evaluate the quality of a bicluster is proposed in this paper. this measure is based on correlations among genes. moreover, a new evolutionary metaheuristic based on scatter search, which uses this measure as the fitness function, is presented to obtain biclusters that contain groups de highly-correlated genes. later, an analysis of the correlation matrix of these biclusters is made to select these groups of genes that define new biclusters with shifting and scaling patterns. experimental results from human bcell lymphoma are presented.
interaction refinement in the design of business collaborations. to tackle the complexity of inter-organizational business collaborations, the design of such collaborations may require explicit modelling at multiple abstraction levels. at a high abstraction level, the collaboration is specified as a single abstract interaction; and at a lower abstraction level, the collaboration is specified as a composition of more-concrete interactions. interaction refinement is a design operation in which an abstract interaction is replaced with multiple related more-concrete interactions. the availability of an interaction design concept that is suitable for modelling interactions at multiple abstraction levels would facilitate the refinement. supporting design operations are necessary to check whether an interaction refinement is correct or conforms to the abstract interaction it replaces. this paper presents an interaction design concept and associated operations to support the design of business collaborations at successive abstraction levels.
investigating the influence of social computing applications on website quality. social computing applications hold immense potential in enriching communication, enabling collaboration and fostering innovation. however, little work has been done to studying social computing applications in library websites. this paper therefore seeks to investigate the prevalence of social computing applications in library websites, and in particular, examine if the presence of such applications enhance the quality of these sites. in our work, 120 libraries' websites, from north america, europe and asia, divided equally between public and academic, were sampled and their content analyzed. prevalence of social computing applications was found to vary across regions but only marginally by library type. further, the presence of social computing applications was found to correlate to the overall quality of library websites. the paper concludes by highlighting implications for both librarians and scholars interested to delve deeper into the implementation of social computing applications.
semi-join computation on distributed file systems using map-reduce-merge model. semi-join is the most used technique to optimize the treatment of complex relational queries on distributed architectures. however, the overhead related to semi-join computation can be very high due to data skew and to the high cost of communication in distributed architectures. internet search engines needs to process vast amounts of raw data every day. hence, systems that manage such data should assure scalability, reliability and availability issues with reasonable query processing time. hadoop and google's file system are examples of such systems. in this paper, we present a new algorithm based on map-reduce-merge model and distributed histograms for processing semi-join operations on such systems. a cost analysis of this algorithm shows that our approach is insensitive to data skew while reducing communication and disk input/output costs to a minimum.
evaluating the usability of a mobile content sharing game. we propose marge, a game which incorporates multiplayer, pervasive gaming elements into mobile content sharing. a pilot study of marge was conducted, and in general, results suggest the potential of the application in terms of entertainment and content sharing value.
a simple role mining algorithm. complex organizations need to establish access control policies in order to manage access to restricted resources. role based access control paradigm has been introduced in '90 years aiming at simplifying the management of centralized access control. the definition of a good set of roles in order to match the organizational requirements of a company is a problem partially solved by role mining techniques, which return automatically a set of roles compatible with the permissions assigned to users. unfortunately, the problem of finding an optimal role set has been proved to be np-hard; so heuristics have been introduced in order to approximate the optimal solution. in this work we propose a novel heuristic and compare its results showing its efficiency and effectiveness.
experimental study on the impact of robust watermarking on iris recognition accuracy. watermarking has been suggested as a means to improve security of biometric systems or to add additional functionalities to such systems. we experimentally investigate the impact of applying a set of blind robust watermarking schemes on the recognition performance of two iris recognition algorithms. we find that different watermarking schemes result in a very different amount of impact rendering the choice of a particular watermarking scheme an important issue to be considered in the investigated context.
exploiting assumption-based verification for the adaptation of service-based applications. service-based applications (sbas) need to operate in a highly dynamic world, in which their constituent services could fail or become unavailable. monitoring is typically used to identify such failures and, if needed, to trigger an adaptation of the sba to compensate for those failures. however, existing monitoring approaches exhibit several limitations: (1) monitoring individual services can uncover failures of services. yet, it remains open whether those individual failures lead to a violation of the sba's requirements, which would necessitate an adaptation. (2) monitoring the sba can uncover requirements deviations. however, it will not provide information about the failures leading to this deviation, which constitutes important information needed for the adaptation activities. even a combination of (1) and (2) is limited. for instance, a requirements deviation will only be identified after it has occurred, e. g., after the execution of the whole sba, which then in case of failures might require costly compensation actions. in this paper we introduce an approach that addresses those limitations by augmenting monitoring techniques for individual services with formal verification techniques. the approach explicitly encodes assumptions that the constituent services of an sba will perform as expected. based on those assumptions, formal verification is used to assess whether the sba requirements are satisfied and whether a violation of those assumptions during run-time leads to a violation of the sba requirements. thereby, our approach allows for (a) pro-actively deciding whether the sba requirements will be violated based on monitored failures, and (b) identifying the specific root cause for the violated requirements.
tokdoc: a self-healing web application firewall. the growing amount of web-based attacks poses a severe threat to the security of web applications. signature-based detection techniques increasingly fail to cope with the variety and complexity of novel attack instances. as a remedy, we introduce a protocol-aware reverse http proxy tokdoc (the token doctor), which intercepts requests and decides on a per-token basis whether a token requires automatic "healing". in particular, we propose an intelligent mangling technique, which, based on the decision of previously trained anomaly detectors, replaces suspicious parts in requests by benign data the system has seen in the past. evaluation of our system in terms of accuracy is performed on two real-world data sets and a large variety of recent attacks. in comparison to state-of-the-art anomaly detectors, tokdoc is not only capable of detecting most attacks, but also significantly outperforms the other methods in terms of false positives. runtime measurements show that our implementation can be deployed as an inline intrusion prevention system.
extending relational algebra to handle bipolarity. this paper presents an extension of relational algebra suited to the handling of bipolar concepts. the type of queries considered involves two parts: a first one which expresses a (possibly flexible) constraint, and a second one that corresponds to a (possibly flexible) wish. the framework considered is that of bipolar fuzzy relations where each tuple is associated with a pair of degrees in the unit interval.
adaptability in xml-to-relational mapping strategies. one of the ways how to manage xml documents is to exploit tools and functions offered by (object-)relational database systems. the key aim of such techniques is to find the optimal mapping strategy, i.e. the way the xml data are stored into relations. currently the most efficient approaches, so-called adaptive methods, search a space of possible mappings and choose the one which suits the given sample data and query workload the most. in the paper we exploit a general heuristic method called ant colony optimization (aco) to solve the xml-to-relational mapping problem. we also adapt the algorithm so it can be used on a dynamic variant of the problem. the algorithms are evaluated in a set of experiments with a conclusion that the aco-based algorithms are suitable for the problem and can be even used as a basis of a dynamic mapping mechanism.