waashk/dblp huggingface dataset:
a framework for cross-disciplinary hypothesis generation. the complexity of cross-disciplinary knowledge discovery is two-fold: integration of vast amount of information in disparate silos, and dissemination of discovery to stakeholders with different interests. here we propose a framework that combines semantic web technology, graph algorithms, and user profiling to discover and prioritize novel associations among biomedical entities across disciplines. a proof-of-concept system was developed and tested through case studies tailored for three different user groups involved in colorectal cancer (crc). in this document, we describe in detail the major components of the system and summarize the results of the case studies. the results demonstrate the potential of user profiling and semantic graphs in discovering novel associations that are intellectually engaging to a cross-disciplinary audience.
expression microarray classification using topic models. classification of samples in expression microarray experiments represents a crucial task in bioinformatics and biomedicine. in this paper this scenario is addressed by employing a particular class of statistical approaches, called topic models. these models, firstly introduced in the text mining community, permit to extract from a set of objects (typically documents) an interpretable and rich description, based on an intermediate representation called topics (or processes). in this paper the expression microarray classification task is cast into this probabilistic context, providing a parallelism with the text mining domain and an interpretation. two different topic models are investigated, namely the probabilistic latent semantic analysis (plsa) and the latent dirichlet allocation (lda). an experimental evaluation of the proposed methodologies on three standard datasets confirms their effectiveness, also in comparison with other classification methodologies.
hybrid evolutionary quantum inspired method to adjust time phase distortions in financial time series. this work presents a hybrid evolutionary quantum inspired method to adjust time phase distortions present in financial time series, overcoming the random walk dilemma for financial prediction. it is composed of a qubit multilayer perceptron (qumlp) with a quantum inspired evolutionary algorithm (qiea), which is able to evolve the complete qumlp architecture and parameters, as well as searches for the best time lags to describe the time series generator phenomenon. an experimental analysis is conducted with the proposed approach through two real world financial time series, and the obtained results are discussed and compared to results found with classical models in literature.
eigenvector-based clustering using aggregated similarity matrices. clustering of high dimensional data is often performed by applying singular value decomposition (svd) on the original data space and building clusters from the derived eigenvectors. often no single eigenvector separates the clusters. we propose a method that combines the self-similarity matrices of the eigenvector in such a way that the concepts are well separated. we compare it with a k-means approach on public domain data sets and discuss when and why our method outperforms the k-means on svd method.
evidential reasoning for the treatment of incoherent terminologies. many reasoning algorithms and techniques require consistent terminologies to be able to operate correctly and efficiently. however, many ontologies become inconsistent during their evolution and lifecycle. many methods have been proposed to handle inconsistent terminologies including those that tolerate or repair inconsistencies. most of these approaches focus on the syntactic properties of ontology terminologies and attempt to address inconsistency from that perspective and satisfy postulates such as the principle of minimal change. in this paper, we will employ evidential reasoning to take into account assertional statements of an ontology as observations and probable indications for the correctness and validity of one axiom over other competing axioms. we will show how ontology assertions are beneficial in ranking axioms to be used in reiter's hitting set algorithm.
a review of approaches for representing rcc8 in owl. this paper investigates several approaches for qualitative spatial knowledge representation on the semantic web, by using rcc8 relations. we discuss several issues arising when representing rcc8 in owl dl, e.g., the lack of required features like role reflexivity, role boolean operators, and role inclusion axioms. we conclude that, although some of these features are to be included in the new version of the owl standard, owl 2, this language still lacks the expressive power to support role negations, conjunctions, and disjunctions, and complex role inclusion axioms.
a business driven cloud optimization architecture. in this paper, we discuss several facets of optimization in cloud computing, the corresponding challenges and propose an architecture for addressing those challenges. we consider a layered cloud where various cloud layers virtualize parts of the cloud infrastructure. the architecture takes into account different stakeholders in the cloud (infrastructure providers, platform providers, application providers and end users). the architecture supports self-management by automating most of the activities pertaining to optimization: monitoring, analysis and prediction, planning and execution.
an optimized capacity planning approach for virtual infrastructure exhibiting stochastic workload. traditionally, any capacity planning problem is modeled with deterministic workloads by considering the peak workload for resource allocation. in the context of businesses using cloud service, cloud provider could allocate resources for peak workload which could lead to under utilization of resource and charging users for unused yet provisioned resources. hence we came up with a better capacity planning algorithm which could ensure that we plan for peak usage but do not provision for it. in our approach, we modeled the problem as a stochastic optimization problem with the objective of minimizing the number of servers considering two important constraints a) stochastic nature of workloads and b) minimizing the application sla violations. we implemented the model using genetic algorithm and to address the stochastic nature of work loads, we reserved a free pool of resources in each server by the quantity determined by our algorithm. we evaluated the solution with real sever utilization data from a datacenter seeking consolidation. we did comparative analysis on the number of servers required suggested by our solution vs. peak work loads based solutions for various service levels. our results illustrate that reserving certain amount of resources in servers for addressing variability of workloads gives better results in terms of lesser number of servers compared to packing resources based on peak workloads for the same service levels.
dynamic task set partitioning based on balancing resource requirements and utilization to reduce power consumption. power consumption is a major design concern in current high-performance microprocessors. to deal with consumption, many systems apply dynamic voltage scaling (dvs) techniques which dynamically change the system speed depending on the workload characteristics. dvs costs in a multicore system can be reduced by sharing the same dvs regulator among the cores. in this context, to handle energy efficiently, the workload must be properly balanced among the cores. this paper proposes a new heuristic algorithm to balance the workload in a coarse-grain multicore system. the algorithm works on hard real-time tasks and dynamically drives the frequency/voltage level in order to guarantee real-time constraints. the proposed heuristic is aimed at improving the overlapping time between the memory and the processor while keeping utilization balanced among cores. energy savings depend on the range of frequency/voltage levels that dvs implements. experimental results show that the proposed heuristic reduces the energy consumption in almost 3 times with respect to a system with no dvs regulator and applying no heuristic.
a deidealisation semantics for kaos. kaos is a goal directed requirements engineering framework based on the decomposition and refinement of goals. decomposition and refinement continue until a point is reached at which agents, identifiable in the application environment, can be assigned responsibility for operations that manipulate variables over which they have control, and where the information for determining changes in the controlled variables resides in variables which the agent can monitor. although many of the 'refinements' that arise in the kaos process can be viewed as acceptable according to one or other model based refinement formalism, many cannot. those that cannot correspond to 'deidealisation' steps, not covered by conventional refinement formalisms. it is shown that such deidealisations can be seen as retrenchments, and the smooth interworking between refinement and retrenchment leads to a fuller formalisation of the kaos process than is otherwise possible.
programming assistance based on contracts and modular verification in the automation domain. in industrial automation, control software often has to get changed and adapted by domain experts and end users who have no or only limited software development expertise. this results in high demands on programming environments with respect to supporting, guiding, and supervising the programming tasks. in this paper we present an approach based on model checking and artificial intelligence techniques to guide domain experts in building control software which is guaranteed to obey specified contracts and constraints. the work is based on monaco which is a domain-specific language for programming automation solutions. as monaco employs a hierarchical component approach, the verification is done hierarchically where an upper component is verified against the contracts of its subcomponents. the verification approach is leveraged in different programming support systems which give immediate feedback about valid and invalid programs in an integrated development environment.
energy efficient management scheme for heterogeneous secondary storage system in mobile computers. flash memory is widely used because of its shock-resistance and power-efficient features. however, it cannot replace hard disks as secondary storage devices due to their greater cost per unit storage and low capability. in this paper, we propose an energy efficient heterogeneous secondary storage system management scheme for mobile systems. we employ flash memory device as a file cache of hard disk and extend existing data cache management algorithms to distribute files between two devices with consideration of file level cache restrictions. as a result, most file accesses are conducted in flash memory device and disk is spun-down to save energy. we develop a trace-driven simulator to evaluate our scheme in comparison with other alternatives. results demonstrate that with the help of our scheme, energy consumption of secondary storage system can be saved by up to 90% and i/o access time is improved. furthermore, the file cache management algorithms can result in high hit ratios.
complex objects ranking: a relational data mining approach. a key task in data mining and information retrieval is learning preference relations. most of methods reported in the literature learn preference relations between objects which are represented by attribute-value pairs or feature vectors (propositional representation). the growing interest in data mining techniques which are able to directly deal with more sophisticated representations of complex objects, motivates the investigation of relational learning methods for learning preference relations. in this paper, we present a probabilistic relational data mining method which permits to model preference relations between complex objects. preference relations are then used to rank objects. experiments on two ranking problems for scientific literature mining prove the effectiveness of the proposed method.
abba: adaptive bicluster-based approach to impute missing values in binary matrices. missing values frequently pose problems in binary matrices analysis since they can hinder downstream analysis of the datasets. despite the presence of many imputation methods that have been developed to substitute missing values with estimated values, these available techniques have some common disadvantages: they need to fix some parameters (e.g., number of patterns, number of rows to consider) to estimate missing values---with little theoretical support to determine these parameters---; and, missing values need to be recomputed from scratch as parameters change. in this paper we propose a novel algorithm (abba: adaptive bicluster-based approach) that does not have the above limitations. further, a formal framework that justifies the rationales behind abba is detailed. finally, experimental results over both synthetic and real data confirm the viability of our approach and the quality of the results, that overcomes the ones achieved by the main competing algorithm (knn).
filtering spams using the minimum description length principle. spam has become an increasingly important problem with a big economic impact in society. spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. in this paper, we present a novel approach to spam filtering based on the minimum description length principle. the proposed model is fast to construct and incrementally updateable. additionally, we offer an analysis concerning the measurements usually employed to evaluate the quality of the anti-spam classifiers. in this sense, we present a new measure in order to provide a fairer comparison. furthermore, we conducted an empirical experiment using six well-known, large and public databases. finally, the results indicate that our approach outperforms the state-of-the-art spam filters.
performance and extension of user space file systems. several efforts have been made over the years for developing file systems in user space. many of these efforts have failed to make a significant impact as measured by their use in production systems. recently, however, user space file systems have seen a strong resurgence. fuse is a popular framework that allows file systems to be developed in user space while offering ease of use and flexibility. in this paper, we discuss the evolution of user space file systems with an emphasis on fuse, and measure its performance using a variety of test cases. we also discuss the feasibility of developing file systems in high-level programming languages, by using as an example java bindings for fuse that we have developed. our benchmarks show that fuse offers adequate performance for several kinds of workloads.
a simulation of large-scale groundwater flow on cuda-enabled gpus. this paper presents a simulation method for large-scale groundwater flow on cuda-enabled gpus. the discretization method for a three-dimensional groundwater flow equation is introduced. when using the preconditioned conjugate gradient algorithm to solve the discretized equation, the implementing methods for the sparse matrix-vector multiplication and the vector inner product on cuda-enabled gpus are given. the experimental results show that gpus can speed up the groundwater simulation significantly.
capacon: access control mechanism for inter-device communications through tcp connections. we present capacon, an access control mechanism for interdevice communications through tcp connections. capacon provides capability-based access control for a system composed of devices. using capacon, an administrator does not need to set access control policies for each device and can manage these policies outside the system. a capability consists of an object device identifier and the list of permitted operations for that object device. subject devices that maintain capabilities can access object devices corresponding with those capabilities. to protect a capability from being fabricated, capacon uses a digital signature. capacon can be used without modifying existing device programs. we analyzed the safety of capabilities in capacon, and measured network throughputs and processing times of capacon. these experimental results show the practicality of capacon.
probabilistic context prediction using time-inferred multiple pattern networks. we propose a probabilistic method for context prediction of mobile users based on their historic context data. the proposed method predicts general context based on the probability theory through a novel graphical data structure, which is a kind of weighted directed multi-graphs. user context data are transformed into the new graphical structure, in which each node represents a context or a combined context and each directed edge indicates a context transfer with the time weight inferred from corresponding time data. the periodic property of context data is also considered. we bring a nice solution to context data with such property. through simulation, we could show the merits of the proposed method.
data-aware design and verification of service compositions with reo and mcrl2. service-based systems can be modeled as stand-alone services coordinated by external connectors. reo is a channel-based coordination language with well-defined semantics that enables a compositional construction of complex connectors from a set of primitive channels. it has been successfully applied in the area of web service composition specification as well as in business process modeling. in this paper, we present a mapping from reo to mcrl2, a specification language based on the process algebra acp, extended with data and time. the mapping enables verification of reo process models and service compositions using the mcrl2 model checking facilities. the supporting eclipse coordination tools suite provides a user-friendly environment for the modeling and verification process.
reparameterization based consistent graph-structured linear programs. a class of maximum a posteriori(map) formulations built on various graph models are of great interests for both theoretical and practical applications. recent advances in this field have extended the connections between the linear program (lp) relaxation and various tree-reweighted message passing algorithms. at both sides, many algorithms and their optimality certificates are proved, provided no conflict exists between the node marginal maximum and the corresponding edge marginal maximum. however, these conflicts are usually inevitable for general non-trivial markov random fields (mrfs). our work is aimed at reducing such conflicts by reparameterizing the original energy distributions in pairwise markov random field. all node potentials will be decomposed and attached to local edges according to their local graph structures. and thus, only edge marginals are needed in our linear program relaxation, and the node marginals are only used to exchange information among different parts of the graph. we incorporated this consistent graph-structured reparameterization into some latest lp optimality guaranteed proximal solvers, and the resulted algorithms outperform the original ones in convergence rate and also have a better behavior to converge to map optimality monotonously even for some highly noisy mrfs.
data stream anomaly detection through principal subspace tracking. we consider the problem of anomaly detection in multiple co-evolving data streams. in this paper, we introduce frahst (fast rank-adaptive row-householder subspace tracking). it automatically learns the principal subspace from n numerical data streams and an anomaly is indicated by a change in the number of latent variables. our technique provides state-of-the-art estimates for the subspace basis and has a true dominant complexity of only 5nr operations while satisfying all desirable streaming constraints. frahst successfully detects subtle anomalous patterns and when compared against four other anomaly detection techniques, it is the only with a consistent f1 &ge; 80% in the abilene datasets as well as in the isp datasets introduced in this work.
towards the modeling reactive and proactive agents by using mas-ml. the existence of mas where agents with different internal architectures interact promotes the need for a language capable of modeling these applications. this paper aims to extend the mas-ml language in order to support the modeling of proactive and reactive agents.
privacy preserving linear discriminant analysis from perturbed data. the ubiquity of the internet not only makes it very convenient for individuals or organizations to share data for data mining or statistical analysis, but also greatly increases the chance of privacy breach. there exist many techniques such as random perturbation to protect the privacy of such data sets. however, perturbation often has negative impacts on the quality of data mining or statistical analysis conducted over the perturbed data. this paper studies the impact of random perturbation for a popular data mining and analysis method: linear discriminant analysis. the contributions are two fold. first, we discover that for large data sets, the impact of perturbation is quite limited (i.e., high quality results may be obtained directly from perturbed data) if the perturbation process satisfies certain conditions. second, we discover that for small data sets, the negative impact of perturbation can be reduced by publishing additional statistics about the perturbation along with the perturbed data. we provide both theoretical derivations and experimental verifications of these results.
design-level metrics estimation based on code metrics. fault detection based on mining code and design metrics has been an active research area for many years. basically "module"-based metrics for source code and design level are calculated or obtained and data mining is used to build predictor models. however, in many projects due to organizational or software process models, design level metrics are not available and/or accurate. it has been shown that performance of these classifiers or predictors decline if only source code features are used for training them. based on best of our know knowledge no set of rule to estimate design level metrics based on code level metrics has been presented since it is believed that design level metrics have additional information and cannot be estimated without access to design artifacts. in this study we present a fuzzy modeling system to find and present these relationships for projects presented in nasa metrics data repository (mdp) datasets. interestingly, we could find a set of empirical rules that govern all the projects regardless of size, programming language and software development methodology. comparison of fault detectors built based on estimated design metrics with actual design metrics on various projects showed a very small difference in accuracy of classifiers and validated our hypothesis that estimation of design metrics based on source code attributes can become a practical exercise.
experiences in reading detection with eeg signals. this short paper introduces the use of neurophysiological signals, electroencephalogram (eeg), as a technique for reading detection. reading detection is a focused problem, yet with multiple facets and application relevance. the paper refers to the system architecture that supports the reading detection experiments, describes the sample tools designed for the experiment and discusses the results. the experiences demonstrate that the approach to reading detection is feasible. in addition, they highlight the technical and usability limitations of the approach, which are expected to be reduced both through system tuning and optimization, and through the technological evolution of the peripheral devices.