waashk/dblp huggingface dataset:
combining symbolic and numerical solvers to simplify indecomposable systems solving. in computer-aided design, solvers always attempt to decompose geometric constraint systems into smaller ones in order to make faster the resolution. however, this scheme often fails in the case of 3d geometric constraint systems since they are hardly decomposable. we have studied a new method which uses jointly two solvers, a symbolic one and a numerical one, in order to solve a system s: system s is transformed into a parametric system s' "almost" equivalent to s and such that system s" is symbolically solvable and the numerical solver computes solutions of s from solutions of s".
using simplified event calculus in digital investigation. in a hypothesis-based approach to digital investigation, the investigator formulates his hypothesis about which events took place, and tests them using the evidence available. a formalism for the description of the investigated system is useful in the hypothesis formulation and testing. simplified event calculus, a form of propositional logic, can be used to define and test hypotheses in a digital investigation. when a system is modelled in this logic, observed states can be used to find action hypotheses and test them in the model. this can assist investigators and fact-finders in reconstruction of events from digital evidence. the logic can also be used to derive invariants for a system that can be utilized in tools checking evidence from these systems for consistency.
semi-supervised visual clustering for spherical coordinates systems. in this paper we propose a method that combines the advanced data analysis of the automatic statistical methods and the flexibility and manual parameter tuning of interactive visual clustering. we present the semi-supervised visual clustering (ssvc) interface; its main contribution is the learning of the optimal projection distance metric for the star and spherical coordinate visualization systems. beyond the conventional manual setting, it couples the visual clustering with the automatic setting where the projection distance metric is learned from the available set of user feedbacks in the form of either item similarities or direct item annotations. moreover, ssvc interface allows for the hybrid setting where some parameters are manually set by the user while the remaining parameters are determined by the optimal distance algorithm.
anomaly detection algorithms in logs of process aware systems. today, there is a variety of systems that support business process as a whole or partially. however, normative systems are not appropriate for some domains (e.g. health care) as a flexible support is needed to the participants. on the other hand, while it is important to support flexibility in these systems, security requirements can not be met whether these systems do not offer extra control. this paper presents and assesses two anomaly detection algorithms in logs of process aware systems (pas). the detection of an anomalous trace is based on the "noise" which a trace makes in a process model discovered by a process mining algorithm. this paper argues that these methods can support the coexistence of security and flexibility when aggregated to a pas.
segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography cad schemes. breast cancer is one of the most important cause to mortality rate among women. computer-aided detection (cad) schemes have been developed as a tool in detecting early breast cancer. this can be an important tool in mammography since previous studies have been indicated that the detection of breast cancer can be increased up to 20% when assisted by a cad scheme. one of the main stages of such process is thus the segmentation of structures of interest, as the suspect masses. however, when evaluating mammograms obtained from dense breasts, a cad scheme efficacy can be very reduced due to the poor contrast of such type of image. this work attempts hence to this challenge, by describing a methodology for segmenting suspect masses in dense breast images as a part of a cad scheme under development. this methodology is based on the watershed transformation, which is combined with two other procedures -- a histogram equalization, working as pre-processing for enhance images contrast, and a labeling procedure intended to reduce noise. tests with a set of 252 regions of interest extracted from 130 digitized mammograms have registered a scheme sensibility of 92% with about 90% of specificity. these results are promising when applied to dense breast images, which can improve significantly the performance of a processing scheme for such type of cases in mammography.
events must be complete in event processing! current event detection systems are based on event completeness, which alone is insufficient for supporting new and advanced applications. in this paper, we discuss the need for events that are not complete. we briefly discuss their modeling and specification.
processing complex similarity queries in peer-to-peer networks. similarity search for content-based retrieval (where content can be any combination of text, image, audio/video, etc.) has gained importance in recent years, also because of the advantage of ranking the retrieved results according to their proximity to a query. however, to use similarity search in real world applications, we need to tackle the problem of huge volumes of such mixed multimedia data (e.g., coming from web sites) and the problem of their distribution on multiple cooperating nodes. the proposed approach is being used in two running projects: sapir and nep4b. in this paper we approach this problem by considering a scenario of a network of autonomous peers maintaining a local collection of metric objects (i.e., mixed mode multimedia content). this network forms a distributed peer-to-peer (p2p) search engine for similarity search based on the paradigm of routing index. each peer in the network thus maintains both an index of its local resources and a table for every neighbor, summarizing the objects that are reachable from it. the paper presents techniques that aim to make our p2p similarity-based search system viable, trading approximate results for scalable solutions. results of simulations that use real collections of images are discussed.
using association rules for energy conservation in wireless sensor networks. frequent radio transmissions among sensors, or from sensors to the basestation, have always been a major energy drain. one of the approaches to reduce the data transmitted to the basestation is to shift the bulk of data processing to networked sensor nodes; for instance, sensors to send only data aggregates to reduce the overall amount of data exchanged. sensor nodes, however, are quite limited in terms of their energy and processing power, and as such, traditional centralised data mining algorithms are infeasible to be directly implemented on sensors. in this paper, we modify apriori to find strong rules from sensor readings in a sensor network and using these rules, autonomously control sensor network operations or supplement sensor operations with a rule knowledge base. for example, triggers activated from the rules could be used to sleep sensors or reduce data transmissions to conserve sensor energy. our work here includes a detailed implementation of a lightweight rule learning algorithm for a resource-constrainted sensor network, with simulation results for a group node setup running the algorithm.
towards the composition of stateful and independent semantic web services. most of the work on automated semantic web service composition has focused so far on two main levels of composition i.e., functional level and process level composition (respectively flc and plc from now). the former level of composition considered web services as atomic components that can be executed in a single request-response step whereas the latter level studies in more details the protocol and the behavioural features of web services. since plc i.e., a time and particularly space consuming level of composition makes difficult the scalability of composition-based applications, it seems interesting to restrict the composition of stateful but (only) independent web services. such a restriction make possible the composition of a large number of web services in industrial scenarios, the whole with convincing results. in this paper we suggest to study the advantages to apply flc together with &beta;-composition in order to perform an automated end to end composition of stateful and independent services. in particular we focus on computational complexity results concerning the two models of composition i.e., the well-known plc vs. the newest flc+&beta;-composition. moreover we prove that flc is an interesting and necessary level of composition to significantly reduce computational complexity not only in space but also in time.
web service security management using semantic web techniques. the importance of the web service technology for business, government, among other sectors, is growing. its use in these sectors demands security concern. the web services security standard is a step towards satisfying this demand. however, in the current security approach, the mechanism used for describing security properties of web services restricts security policy specification and intersection. in environments that include loosely-coupled components, a rich description of components is needed to determine whether they can interact in a secure manner. the goal of this paper is to propose a security approach for web services, which combines web services policy framework policies and a web ontology language ontology to overcome the limitation of the current syntactic approach. the main contribution of this paper is an extended approach based on semantics-enriched security policies.
multiprocessor frequency locking for real-time task synchronization. energy-efficient real-time task scheduling is complicated by the deployment of co-processors. the problem complexity comes from the complexity in multiprocessor task scheduling and the difficulty in the trading of priority inversion with energy consumption. in this paper, a multiprocessor task synchronization protocol is proposed by locking the frequency of each (co-)processor and synchronizing resource sharing of tasks in a systematic way and with cross-(co)processor considerations. the well-known priority ceiling protocol is taken as an example for its extension on energy-efficient real-time multiprocessor scheduling. the objective is to minimize the energy consumption of a task set and, at the same time, meet the task deadlines. the capability of the proposed methodology is evaluated by a series of experiments, for which encouraging results are presented.
supporting the development of context-aware agent-based systems for mobile networks. distributed applications involving mobile devices interconnected by wireless networks may benefit both from multiagent technology and context-aware programming techniques. we present a system running on a agent platform to allow access to services that support the development of context-aware applications for mobile devices. the resulting middleware supports the easy and fast development of context-aware applications based on the multi-agent paradigm.
uml-based design test generation. in this paper we investigate and propose a fully automated technique to perform conformance checking of java implementations against uml class diagrams. in our approach, we reused the designwizard java api that allows us to write design rules as junit tests, i.e., to write them as code directly in the programming language. we fully pursued mda as the approach for generating the design tests and hence we used several mda artifacts, such as metamodels, models and transformations. a proof of concept of the technique has been implemented and evaluated. we performed several experiments on simple scenarios. simple designs involving classes, associations, inheritance have been checked. compared to previous related work, the advantage of our approach lies in the fact that we automatically generate design tests from uml class diagrams to java code that play the dual role of design test and implementation language. thus, we check the conformance between the design and the implementation.
contextualizing normative open multi-agent systems. open mass can be extremely dynamic due to heterogeneous agents that migrate among them to obtain resources or services not found locally. in order to prevent malicious actions and to ensure agent trust, open mas should be enhanced with normative mechanisms. however, it is not reasonable to expect that foreign agents will know in advance all the norms of the mas in which they will execute. thus this paper presents dynacrom, our approach for addressing these issues. from the individual agents' perspective, dynacrom is an information mechanism so that agents become context norm-aware; from the system developers' perspective, dynacrom is a methodology for norm management in regulated mass. notwithstanding the ultimate goal of a regulated mas is to have an enforcement mechanism, we also present in the paper the integration of dynacrom and scaar. scaar is the current solution of dynacrom for norm enforcement.
atmospheric temperature retrieval from satellite data: new non-extensive artificial neural network approach. in this paper, vertical temperature profiles are inferred by neural networks based inverse procedure from satellite data, non-linear function estimation. a new approach to classical radial basis function neural network is trained using data provided by the direct model characterized by the radiative transfer equation (rte). the neural network results are compared to the ones obtained from classical neural networks radial basis function and traditional method to solve inverse problems, the regularization. in addition, real radiation data from the hirs/2 - high resolution infrared radiation sounder - is used as input for the neural networks to generate temperature profiles that are compared to measured temperature profiles from radiosonde. analysis of the new approach results reveals the generated profiles closely approximate the results obtained with classical neural networks and regularized inversions, [5] [15], thus showing adequacy of neural network based models in solving the inverse problem of temperature retrieval from satellite data. the advantages of using neural network based systems are related to their intrinsic features of parallelism; after trained, the networks are much faster than regularized approaches, and hardware implementation possibilities that may imply in very fast processing systems.
integrating coercion with subtyping and multiple dispatch. coercion can greatly improve the readability of programs, especially in arithmetic expressions. however, coercion interacts with other features of programming languages, particularly subtyping and overloaded functions and operators, in ways that can produce surprising behavior. we study examples of such surprising behavior in existing languages. this study informs the design of the coercion mechanism of fortress, an object-oriented language with multiple dynamic dispatch, multiple inheritance and user-defined coercion. we describe this design and show how its restrictions on overloaded declarations prevent ambiguous calls due to coercion.
application of divide-and-conquer algorithm paradigm to improve the detection speed of high interaction client honeypots. we present the design and analysis of a new algorithm for high interaction client honeypots for finding malicious servers on a network. the algorithm uses the divide-and-conquer paradigm and results in a considerable performance gain over the existing sequential algorithm. the performance gain not only allows the client honeypot to inspect more servers with a given set of identical resources, but it also allows researchers to increase the classification delay to investigate false negatives incurred by the use of artificial time delays in current solutions.
automated segmentation and volumetric analysis of brain components on mr imaging. as the world population has been growing old, the incidence of several brain diseases, including dementia, has risen. as a consequence, a greater demand for automated segmentation methods and volumetric analysis of the brain structure and its components has increased. this work is based on the use of image processing algorithms and previous knowledge statistical models for automated segmentation over 386 magnetic resonance exams. we analyze brain components considering two different image subsets: non-demented and demented individuals. our results demonstrate gray and white matter loss, and csf enlargement with aging.
mining spam email to identify common origins for forensic application. in recent years, spam email has become a major tool for criminals to conduct illegal business on the internet. therefore, in this paper we describe a new research approach that uses data mining techniques to study spam emails with the focus on law enforcement forensic analysis. after we retrieve useful attributes from spam emails, we use a connected components clustering algorithm to form relationships between messages. these initial clusters are then refined by using a weighted edges model where membership in the cluster requires the weight to exceed a chosen threshold. the results of the cluster membership are validated by whois data, by the ip address of the computer hosting the advertised sites, and through comparison of graphical images of website fetches. this technique has been successful in identifying relationships between spam campaigns that were not identified by human researchers, enabling additional data to be brought into a single investigation.
digital audio watermarking evaluation within the application field of perceptual hashing. digital watermarking is a growing research area to mark digital content by embedding information into the content itself. perceptual hashing is used to identify a specific content or to identify integrity violation up to a specific threshold. the evaluation of digital watermarking algorithms provides a fair and automated analysis of specific watermarking schemes for selected application fields. in this paper, we enhance an existing theoretical framework to provide normalized comparable application oriented evaluation results of embedding transparency and we present first practical evaluation results of digital audio watermarking schemes in the application scenario of perceptual hashing. thereby, the internals of the evaluation profile for the embedding transparency of the digital watermarking schemes are enhanced with a normalized perceptual hash difference grade, used to provide an objectively comparison and to give a recommendation of the usability and applicability of the digital audio watermarking schemes within the application scenario of perceptual hashing. first evaluation results are presented with an inter algorithm evaluation and analysis for five exemplary selected digital audio watermarking schemes, working in time, frequency and wavelet domain, and they give an objective recommendation for their applicability within this application scenario.
a web-based system for the collection and analysis of spectra signals for early detection of voice alterations. voice is the result of the coordination of the whole pneumophonoarticulatory apparatus. the analysis of the voice allows the identification of the diseases of the vocal apparatus and currently is carried out from an expert doctor through methods based on the auditory analysis. this paper presents a web-based system for the acquisition and automatic analysis of vocal signals. vocal signals are submitted by the users through a simple web-interface and are analyzed in real-time by using state-of-the art signal processing techniques, providing first-level information on possible voice alterations. the system offers different analysis functions to the doctors that may analyze suspected cases in detail. the system is currently being tested in the otorhinolaryngologist setting to carry out mass prevention via screening at a regional scale.
special track on software verification: editorial message. these are the proceedings of the software verification track at the 2008 acm symposium on applied computing. we had 33 submitted papers and only 10 have been selected for publication. the high number of submissions is a success for our track and shows the interest of the scientific community towards software verification.
a programming environment for web services. pews is a programming language for the definition of web service interfaces. pews programs can be used for the description of both simple and composite web services. simple web services can be built from scratch, by the combination of (wsdl) operations. each operation must be implemented as a java method. composite web services are constructed from the combination of existing web services, accessed by using their wsdl descriptions. pews combinators help to define the order in which web services and operations will be performed. pews has a human-readable syntax as well as a xml version, called xpews. the human-readable language is intended to help in the design of web services and in the formal reasoning about programs. xpews is used as an interface language between the front-end and back-end of the pews language processor. this paper presents the development of a computational environment for pews. the front-end of the environment is an eclipse plug-in. the use of the front-end can help reducing the time for development of the compositions, by the verification of codification errors and the generation xpews documents. the back-end of pews is responsible for the implementation of the web services described in a xpews document. the back-end produces java&trade; code (skeletons) to call the web service operations and performs them in the order defined by the xpews document.
secured tag identification using edsa (enhanced distributed scalable architecture). rfid technology has become increasingly popular in todays society and plays an important role in daily life. however, the exploitation of this technology requires practical and secure solutions to overcome certain issues. in the case of rfid systems, privacy protection and scalability are two conflicting goals. nevertheless, in this paper we propose a hexagonal cell based distributed architecture which ensures improved scalability while maintaining privacy. the hexagonal architecture allows readers to co-operate with one another to identify tags without compromising scalability. furthermore, this architecture uses serverless protocols for security assurance, cutting down set up and maintenance cost as well as traffic to server. to the best of our knowledge, we propose a combination of servered and serverless techniques within the same distributed architecture for the first time. our proposed distributed scalable architecture together with the secure serverless protocols can be used in numerous real life situations.