waashk/dblp huggingface dataset:
using a product line for creating component systems. component systems have become a wide-spread technology and found their place in several application domains. each component system has its specifics and particularities that reflect its focus and the application domain it is intended for. although important, the diversity of component systems leads to a number of problems including having different tools for each systems, unnecessary duplication of functionality and problems with integration when several domains are to be targeted. based on categorization of component application domains, we propose a "meta-component system", which provides a software product line for creating custom component systems. we focus especially on the deployment and execution environment, which is where most diversities are found. we demonstrate the usage of the "meta-component system" and propose how it is to be realized by two core concepts of sofa 2, namely connector generator and microcomponents.
structuring a process management center of excellence. in view of the growing complexity and scope of processes in organizations, it is increasingly necessary to structure their process management tasks. organizations have sought guidelines to structure a process center of excellence - pce. however, there is still no consensus on how to structure this kind of organizational unit. this paper proposes the organizational structure and main macro-processes entailed in setting up a pce.
a sentence level probabilistic model for evolutionary theme pattern mining from news corpora. some recent topic model-based methods have been proposed to discover and summarize the evolutionary patterns of themes in temporal text collections. however, the theme patterns extracted by these methods are hard to interpret and evaluate. to produce a more descriptive representation of the theme pattern, we not only give new representations of sentences and themes with named entities, but we also propose a sentence-level probabilistic model based on the new representation pattern. compared with other topic model methods, our approach not only gets each topic's distribution per term, but also generates candidate summary sentences of the themes as well. consequently, the results are easier to understand and can be evaluated using the top sentences produced by our probabilistic model. experimentation with the proposed methods on the tsunami dataset shows that the proposed methods are useful in the discovery of evolutionary theme patterns.
ubiquitous services in home networks offered through digital tv. this paper presents some industry proposals for the interoperability between devices in home environments and reports our efforts on exploiting the computational aspects related to the offer of ubiquitous services in the context of household environments. it explores the communication ability between the set-top box and other devices of household environments and of personal use through the development of a software module for transparent replication of audiovisual digital tv content, in home networks based on universal plug and play (upnp&trade;) / digital living network alliance (dlna). the implementation was carried out on an environment emulating a set-top box according to the brazilian standard for dtv.
exploiting join cardinality for faster hash joins. hash joins combine massive relations in data warehouses, decision support systems, and scientific data stores. faster hash join performance significantly improves query throughput, response time, and overall system performance. in this work, we demonstrate how using join cardinality improves hash join performance. the key contribution is the development of an algorithm to determine join cardinality in an arbitrary query plan. we implemented early hash join and the join cardinality algorithm in postgresql. experimental results demonstrate that early hash join has an immediate response time that is an order of magnitude faster than the existing hybrid hash join implementation. one-to-one joins execute up to 50% faster and perform significantly fewer i/os, and one-to-many joins have similar or better performance over all memory sizes.
gpu-based computation of distance functions on road networks with applications. we present a gpu-based algorithm for computing discretized distance functions on road networks. as applications, we provide algorithms for computing discrete order-k network voronoi diagrams and for approximately solving k-nearest neighbor queries and aggregate k-nearest neighbor queries on road networks. finally, we present experimental results obtained with the implementation of our algorithms.
thin client architecture in support of remote radiology learning. we implemented a system for remote radiology learning which provides immediate feedback to the learner. using a thin remote client, expert readers are asked to answer questions about specified radiological findings. these scans are presented as realtime 2d and 3d presentations which allow the user to freely manipulate them using a thin java client with all 3d rendering performed on the server side. answers are stored on the server and are used to provide feedback to learners who are presented with the same questions, using the remote client. learners can practice on real datasets while receiving immediate feedback on their diagnosis and measurements. novel concepts introduced are (1) the use of server-side rendering in radiology learning, (2) providing immediate and specific feedback to trainees, (3) the ability to provide useful feedback when a definitive gold standard does not exist and (4) a thin, highly compatible client that runs on common, existing hardware which allows to have more people participating in very complex radiological evaluations, even if there are not at the same site.
integrating standardized transaction protocols in service-oriented wireless sensor networks. despite much research in the area of wireless sensor networks in recent years, the programming of sensor nodes is still time-consuming and tedious. a new paradigm which seems to be qualified to simplify the programming of sensor networks is the service oriented architecture. the composition of simple services to more complex ones can be a convenient way to design applications. to enable the sophisticated techniques known from service oriented architectures like replication and migration of services, a transaction model for sensor networks is required. in this paper, we study the applicability of the standard commit protocols two phase commit (2pc) and transaction commit on timeout and show in experiments with real sensor nodes that 2pc can enable consistent service migration in wireless sensor networks.
improved spam filtering by extraction of information from text embedded image e-mail. the increase of image spam, a kind of spam in which the text message is embedded into an attached image to defeat spam filtering techniques, is becoming an increasingly major problem. for nearly a decade, content based filtering using text classification or machine learning has been a major trend of antispam filtering systems. a key technique being used by spammers is to embed text into image(s) in spam email. in [4], we proposed two levels of ontology spam filters: a first level global ontology filter and a second level user-customized ontology filter. however, that previous system handles only text e-mail and the percentage of attached images is increasing sharply. the contribution of the paper is that we add an image e-mail handling capability to the previous anti-spam filtering system, enhancing the effectiveness of spam filtering.
sqlprob: a proxy-based architecture towards preventing sql injection attacks. sql injection attacks (sqlias) consist of maliciously crafted sql inputs, including control code, used against database-connected web applications. to curtail the attackers' ability to generate such attacks, we propose an sql proxy-based blocker (sqlprob). sqlprob harnesses the effectiveness and adaptivity of genetic algorithms to dynamically detect and extract users' inputs for undesirable sql control sequences. compared to state-of-the-art protection mechanisms, our method does not require any code changes on either the client, the web-server or the back-end database. rather, our system uses a proxy that seamlessly integrates with existing operational environments offering protection to front-end web servers and back-end databases. to evaluate the overhead and the detection performance of our system, we implemented a prototype of sqlprob which we tested using real sql attacks. our experimental results show that we can detect all sql injection attacks while maintaining very low resource utilization.
an efficient and accurate lattice for pricing derivatives under a jump-diffusion process. derivatives are popular financial instruments that play essential roles in financial markets. however, most derivatives have no analytical formulas and must be priced by numerical methods such as lattice models. the pricing results generated by a lattice converge to the theoretical values, but they may converge slowly or even oscillate significantly due to the nonlinearity error. according to empirical studies, a lognormal diffusion process, which has been widely studied, does not capture the real world phenomena well. to address these problems, this paper proposes a novel lattice under the jump-diffusion processes. our lattice is accurate because it suppresses the nonlinearity error. it is more efficient due to the fact that the time complexity of our lattice is lesser than those of the other existing lattice models. numerous numerical calculations confirm the superior performance of our lattice model to the other existing methods.
self-organizing collaborative filtering in global-scale massive multi-user virtual environments. due to the huge amount of available information in today's society, it becomes more and more difficult for the consumer to locate the most useful information for a specific topic. recommender systems using collaborative filtering (cf) are a popular technique for reducing information overload and finding useful information on the internet. however, in massive global-scale multi-user virtual environments different approaches are required from those used within the currently dominant centralized infrastructures or lately investigated p2p approaches. within this paper we present a novel collaborative filtering algorithm used within the hyperverse -- a p2p-based self-organizing middleware service for massively distributed virtual worlds -- to generate and manage recommendations for hyperverse object favorites. due to its global extent considering users and possible ratings, using a monolithic database-backed recommendation service or huge profile- or item-rating-matrices does not scale in our scenario. the decentralized approach presented within this paper creates per user ratings in an adaptive and transparent way by comparing public favorites of passer-by users with personal peer data, weighted by self-adjusting buddy lists.
fast networking with socket-outsourcing in hosted virtual machine environments. this paper proposes a novel method of achieving fast networking in hosted virtual machine (vm) environments. this method, called socket-outsourcing, replaces the socket layer in a guest operating system (os) with the socket layer of the host os. socket-outsourcing increases network performance by eliminating duplicate message copying in both the host os and the guest os. furthermore, socket-outsourcing significantly enhances inter-vm communication within the same host os since it enables network packets to bypass the protocol stack in guest oses. socket-outsourcing was implemented in two representative operating systems (linux and netbsd) and on two virtual machine monitors (linux kvm and pansyvm). these virtual machine monitors provided support for socket-outsourcing through shard memory, event queues, and vm-specific remote procedure call between a guest os and a host os. the experimental results revealed that a guest os outsourcing the socket layer achieved the same network throughput as a native os using up to four gigabit ethernet links. moreover, the benchmark results obtained from an n-tier web application that generated a significant amount of inter-vm communication indicated that socket-outsourcing improved performance by up to 45 percent compared with conventional hosted vm environments.
two-dimensional non-photorealistic drawings on mobile devices. this paper presents fast approaches to simulate four different 2d non-photorealistic drawing styles (i.e., daub, printmaking, line drawing and embossing) on mobile phones, through exploiting several image processing techniques. taking into careful consideration of the limited hardware support and capabilities of mobile phones, we keep the drawing methods of low complexity. to reduce computation, we consider only important features that best represent each drawing style, and select the corresponding image processing techniques that can achieve the desired effect.
ls(graph & tree): a local search framework for constraint optimization on graphs and trees. ls(graph & tree) is a local search framework which aims at simplifying the modeling of constraint satisfaction optimization problems on graphs (csop on graphs or gcsop). optimum constrained trees (oct) problems (a subclass of csop on graphs) in which we need to find an optimum subtree with additional constraints of a given weighted graph arise in many real-life applications. this paper introduces the ls(graph & tree) framework and local search abstractions for oct problems. these abstractions are applied to model and solve the edge weighted k-cardinality tree (kct) problem. the modeling as well as experimental results show the significance of the abstractions.
exploiting weak dependencies in tree-based search. in this work, our objective is to heuristically discover a simplified form of functional dependencies between variables called weak dependencies. once discovered, these relations are used to rank the variables. our method shows that these relations can be detected with some acceptable overhead during constraint propagation. more precisely, each time a variable y gets instantiated as a result of the instantiation of x, a weak dependency (x, y) is recorded. as a consequence, the weight of x is raised, and the variable becomes more likely to be selected by the variable ordering heuristic. experiments on a large set of problems show that on the average, the search trees are reduced by a factor 3 while runtime is decreased by 31% when compared against dom-wdeg, one of the best dynamic variable ordering heuristic.
sources of error in a rigid body simulation of rigid parts on a vibrating rigid plate. we present a simulation study of an important rigid body contact problem. the system in question is composed of a rigid plate and a single rigid body (or particle). the plate follows a prescribed periodic motion of small amplitude and high frequency, such that the net force applied to the part appears to be from a time-independent, position-dependent velocity field in the plane of the plate. theoretical results obtained by vose et al. were found to be in good agreement with simulation results obtained with the stewart-trinkle time-stepping method. in addition, simulations were found to agree with the qualitative experimental results of vose et al. after such verification of the simulation method, additional numerical studies were done that would have been impossible to carry out analytically. specifically, we were able to demonstrate the convergence of the method with decreasing step size (as predicted theoretically by stewart). further analytical and numerical studies will be carried out in the future to develop and select robust simulation methods that best satisfy the speed and accuracy requirements of different applications.
multi-step attack modelling and simulation (msams) framework based on mobile ambients. attackers take advantage of any security breach to penetrate an organisation perimeter and exploit hosts as stepping stones to reach valuable assets, deeper in the network. the exploitation of hosts is possible not only when vulnerabilities in commercial off-the-shelf (cots) software components are present, but also, for example, when an attacker acquires a credential on one host which allows exploiting further hosts on the network. finding attacks involving the latter case requires the ability to represent dynamic models. in fact, more dynamic aspects are present in the network domain such as attackers accumulate resources (i.e. credentials) along an attack, and users and assets may move from one environment to another, although always constrained by the ruling of the network. in this paper we address these dynamic issues by presenting msams (multi-step attack modelling and simulation), an implemented framework, based on mobile ambients, to discover attacks in networks. the idea of ambients fits naturally into this domain and has the advantage of providing flexibility for modelling. additionally, the concept of mobility allows the simulation of attackers exploiting opportunities derived either from the exploitation of vulnerable and non-vulnerable hosts, through the acquisition of credentials. it also allows expressing security policies embedded in the rules of the ambients.
capturing truthiness: mining truth tables in binary datasets. we introduce a new data mining problem: mining truth tables in binary datasets. given a matrix of objects and the properties they satisfy, a truth table identifies a subset of properties that exhibit maximal variability (and hence, complete independence) in occurrence patterns over the underlying objects. this problem is relevant in many domains, e.g., in bioinformatics where we seek to identify and model independent components of combinatorial regulatory pathways, and in social/economic demographics where we desire to determine independent behavioral attributes of populations. we outline a family of levelwise approaches adapted to mining truth tables, algorithmic optimizations, and applications to bioinformatics and political datasets.
htilde: scaling up relational decision trees for very large databases. nowadays, many organizations have relational databases with millions of records and an important question is how to extract information from them. this work proposes htilde (hoeffding tilde) to handle very large relational databases, based on the inductive logic programming (ilp) system tilde (top-down induction of logical decision trees) and the propositional very fast decision tree (vfdt) learner. it is an incremental and anytime algorithm that uses the hoeffding bound to find out the amount of examples that must be considered for choosing the best test for a node. the results show that, compared to tilde, htilde generates theories from very large relational datasets more efficiently without harming their quality measures (f-measure, precision, recall and accuracy). also, htilde learns less complex theories than tilde.
similarity measures for trajectory of moving objects in cellular space. while most gis (geographical information system) are based on euclidean space, cellular space can be used as an alternative type of space for a large number of gis applications. in order to analyze the pattern of moving objects in cellular space, we need new definitions of similarity between their trajectories since the trajectories in cellular space significantly differ from those in euclidean space. in this paper, we study the properties of moving object in cellular space. based on these observations, we propose several similarity measures between trajectories in cellular space. we analyze the differences of the proposed measures by experiments.
privacy-preserving linear programming. with the rapid increase in computing, storage and networking resources, data is not only collected and stored, but also analyzed. this creates a serious privacy problem which often inhibits the use of this data. in this paper, we focus on the problem of linear programming, which is the most important sub-class of optimization problems. we consider the case where the objective function and the constraints are partitioned between two parties with one party holding the objective while the other holds the constraints. we propose a very efficient and secure transformation based solution that has the significant added benefit of being independent of the specific linear programming algorithm used.
evolving morphologies and gaits of physically realistic simulated robots. this paper describes our research and experiments with autonomous robots, in which were used genetic algorithms to evolve stable gaits of simulated legged robots in a physically based simulation environment. in our approach, gaits are defined using two different methods: a finite state machine based on the joint angles of the robot legs; and an elman's recurrent neural network. the parameters for both methods are optimized using genetic algorithms, and the proposed model also allows the evolution of the robot body morphology. several experiments are described, and the obtained results show that it is possible to generate stable gaits and efficient morphologies using machine learning techniques.
improving functional verification of embedded systems using hierarchical composition and set theory. during functional verification, complex interactions between multiple modules that compose a digital circuit design can reveal hard-to-find bugs. functional coverage specifications must be precise to assure these interactions occur during the simulation. we are proposing a technique for improving the functional verification specification of individual modules, preserving the occurrence of these interactions scenarios in the composition phase. we obtain these new specifications in a deductive way, by means of set theory. using experimental results, we show how our work can contribute to error detection and save functional verification time.
a consumer/producer approach to risk-driven software reliability and testing. we introduce the concept of using both consumer software and producer software in the analysis of risk as it relates to test criteria and strategies. there are two different versions of the software, one representing the consumer's viewpoint, which requires reliable software at reasonable cost, and the other representing the producer's viewpoint, trying to produce adequate software with the lowest effort.
kvmsec: a security extension for linux kernel virtual machines. virtualization is increasingly being used in regular desktop pcs, data centers and server farms. one of the advantages of introducing this additional architectural layer is to increase overall system security. in this paper we propose an architecture (kvmsec) that is an extension to the linux kernel virtual machine aimed at increasing the security of guest virtual machines. kvmsec can protect guest virtual machines against attacks such as viruses and kernel rootkits. kvmsec enjoys the following features: it is transparent to guest machines; it is hard to access even from a compromised virtual machine; it can collect data, analyze them, and act consequently on guest machines; it can provide secure communication between each of the guests and the host; and, it can be deployed on linux hosts and at present supports linux guest machines. these features are leveraged to implement a real-time monitoring and security management system. further, differences and advantages over previous solutions are highlighted, as well as a concrete roadmap for further development.