waashk/dblp huggingface dataset:
tucupi: a flexible workflow system based on overridable constraints. this work presents the idea and a prototype of workflow systems whose definition is based on constraints. the flexibility is reached through the less rigid definition of workflow definitions - the workflow is defined as a set of pre and post conditions of activities, which are selected dynamically as the process instance unfolds. the workflow system besides dispatching activities that have all their preconditions fulfilled to be executed, also helps users to decide which activity to chose through what if scenarios. the system also includes an access control model which not only represents which users have the authority to chose and execute the activities but also the authority to override the constraints. in particular, overriding constraints is itself an activity and thus may have pre and post conditions defined in other constraints. the paper present tucupi, a prototype of such constraint based wfms.
protected transmission of biometric user authentication data for oncard-matching. since fingerprint data are no secrets but of public nature, the verification data transmitted to a smartcard for oncard-matching need protection by appropriate means in order to assure data origin in the biometric sensor and to prevent bypassing the sensor. for this purpose, the verification data to be transferred to the user smartcard is protected with a cryptographic checksum that is calculated within a separate security module controlled by a tamper resistant card terminal with integrated biometric sensor.
surface reconstruction using shadow profilometry. digital image analysis has rich potential in application to post-disaster forensic investigations. the underlying concept is to reconstruct the exterior surfaces of object fragments that failed under load or were torn apart because of explosive forces. the surface reconstruction technique must be sufficiently accurate to capture detailed characteristics of small samples for identification and classification of the object under examination. this paper presents an inexpensive technique for constructing a digital image of a three dimensional surface of very small objects which preserves the minor details via two dimensional slices produced using shadow profilometry.
cleaning microarray expression data using markov random fields based on profile similarity. this paper proposes a method for cleaning the noise found in microarray expression data sets. while other methods either concentrate on the image processing phase, or apply normalization techniques to the resulting microarray raw expression values, we introduce a method based on markov random fields for the data set. the cleaning process is guided by genes with similar expression profiles. as a result, data cleaning is conducted using biological similarity of genes.
the loop fallacy and serialization in tracing intrusion connections through stepping stones. network based intruders seldom attack directly from their own hosts, but rather stage their attacks through intermediate "stepping stones" to conceal their identity and origin. to identify attackers behind stepping stones, it is necessary to be able to trace through the stepping stones and construct the correct intrusion connection chain.a complete solution to the problem of tracing stepping stones consists of two complementary parts. first, the set of correlated connections that belongs to the same intrusion connection chain has to be identified; second, those correlated connections need to be serialized in order to construct the accurate and complete intrusion connection chain. existing approaches to the tracing problem of intrusion connections through stepping stones have focused on identifying the set of correlated connections that belong to the same connection chain and have overlooked the serialization of those correlated connections.in this paper, we use set theoretic approach to analyze the theoretical limits of the correlation-only approach and demonstrate the gap between the perfect correlation-only approach and perfect solution to the tracing problem of stepping stones. in particular, we identify the serialization problem and the loop fallacy in tracing connections through stepping stones. we formally demonstrate that even with perfect correlation solution, which gives us all and only those connections that belong to the same connection chain, it is still not adequate to serialize the correlated connections in order to construct the complete intrusion path deterministically. we further show that correlated connections, even with loops, could be serialized deterministically without synchronized clock. we present an efficient intrusion path construction method based on adjacent correlated connection pairs.
modeling and analyzing applications with domain-specific languages by reflective rewriting: a case study. in this paper, we propose to model and analyze applications with domain-specific languages by reflection. we argue that both tasks can be significantly simplified by using a reflective modeling language. to make our arguments, we model and analyze a model checker in the reflective language maude. the simplicity of our methodology suggests our methodology is useful for such applications.
a density based approach to classification. this paper presents a novel method for classification, which is density based and makes use of the models built by the lattice machine (lm) [5, 7]. density is a natural concept to use in clustering and the lm is a relatively new method for supervised learning developed in recent years. the lm approximates data resulting in, as a model of data, a set of hyper tuples that are equilabelled, supported and maximal. the method presented in this paper uses the lm model of data to classify new data with a view to maximising the density of the model. in order for the method to have wide applicability a measure of density is introduced for hyper tuples and relations.experiments were carried out with both public and proprietary data. experimental results show that our method outperforms the classification method in the lm literature and it is comparable to the c5.0 classification algorithm. it is also shown that our method works quite well in an application-stock market data mining.
hyperrelations in version space. a version space is a set of all hypotheses consistent with a given set of training examples, delimited by the specific boundary and the general boundary. in existing studies [4, 5, 3] a hypothesis is a conjunction of attribute-value pairs, which is shown to have limited expressive power [6].in this paper we investigate version space in a more expressive hypothesis space, where a hypothesis is a hyperrelation, which is in effect a disjunction of conjunctions of disjunctions of attribute-value pairs. we propose to use an inductive bias, e-set, which turns our attention to equilabelled, supported, and maximal hypertuples. we characterise version space in such a hypothesis space under this bias and show the relationship between the specific boundary and general boundary with respect to unequivocal data, a special subset of the data space. we present experimental results on some public datasets.
guiding motif discovery by iterative pattern refinement. in this paper, we demonstrate that the performance of a motif discovery algorithm can be significantly improved by embedding it into a novel framework that effectively guides the motif discovery process. the framework is also general enough to allow any statistical motif discovery algorithm to be used. motivation for this research comes from the fact that the statistical significance of patterns depends on the background probability which is largely determined by input sequences. our framework guides motif discovery by inputting subsequences to an existing motif discovery algorithm, rather than using entire sequences. subsequences are determined by motifs discovered using existing motif discovery and search algorithms. then this technique is iteratively applied until convergence. a starting set of patterns is discovered by a simple, but effective pattern set generation algorithm. our framework was implemented using meme and mast and tested with 108 prosite patterns. the result demonstrates that our framework significantly improves the performance of meme.
rapidly prototyping implementation infrastructure of domain specific languages: a semantics-based approach. domain specific languages (dsls) are high level languages designed for solving problems in a particular domain, and have been suggested as means for developing reliable software systems. however, designing of a domain specific language is a difficult task. the design of a domain specific language will evolve as it is used more and more and experienced is gained by its designers. being able to rapidly develop the implementation infrastructure (interpreter, compiler, debugger, profiler, etc.) of a domain specific language is thus of utmost importance so that as the language evolves, the implementation infrastructure can keep pace. in this paper we present a framework for automatically generating interpreters, compilers, debuggers, and profilers from semantic specification of a domain specific language. we illustrate our approach via the scr language, a language used by the us defense department for developing control systems.
aodv compatible routing with extensive use of cache information in ad-hoc networks. recently, there has been an increasing interest in ad-hoc networks, which are dynamically constructed by collections of mobile hosts without using any existing network infrastructure or centralized administration. each mobile host plays a role of a router and relays packets for multihop network communications. a recent trend in ad-hoc network routing is the reactive on-demand philosophy where routes are established only when required. most of the protocols in this category, however, show a long latency in route discovery since the cached routing information will be invalidated even if it is still effective. in this paper, we propose a scheme which is compatible to the existing ad-hoc on-demand distance vector (aodv) protocol and prevents the cached routing information from becoming invalidated without using any extra control message. we also verify the effectiveness of the newly proposed scheme by simulation experiments.
tree-based clustering for gene expression data. data clustering methods have been proven to be a successful data mining technique in analysis of gene expression data and many other types of data. however, some concerns and challenges still remain, e.g., in gene expression clustering. in this paper, we propose an efficient clustering method using attractor trees. the combination of the density-based approach and the similarity-based approach considers clusters with diverse shapes, densities, and sizes. experiments on gene expression datasets demonstrate that our approach is efficient and scalable with competitive accuracy.
distributed collaborative filtering for peer-to-peer file sharing systems. collaborative filtering requires a centralized rating database. however, within a peer-to-peer network such a centralized database is not readily available. in this paper, we propose a fully distributed collaborative filtering method that is self-organizing and operates in a distributed way. similarity ranks between multimedia files (items) are calculated by log-based user profiles and are stored locally at these items in so-called buddy tables. this intuitively creates a semantic overlay to organize multimedia files. based on this semantic overlay and the items that a user has downloaded previously (indicating the profile of the user), recommendations can be performed and the recommended items can be easily located. we have tested our distributed collaborative filtering approach and compared it to centralized collaborative filtering, showing that it has similar performance. it is therefore a promising technique to facilitate filtering for relevant multimedia data in p2p networks.
variable selection and ranking for analyzing automobile traffic accident data. variable ranking and feature selection are important concepts in data mining and machine learning. this paper introduces a new variable ranking technique named sum max gain ratio (smgr). the new technique is evaluated within the domain of traffic accident data and against a more generalized dataset. in certain cases, smgr is empirically shown to provide similar results to established approaches with significantly better runtime performance.
escape analysis for synchronization removal. in this paper we introduce our escape analysis framework for java, which is a kind of flow-insensitive, inter-procedural, and context-sensitive data flow analysis. and we present an efficient static intra-procedural algorithm for inferring the set of types that can occur at runtime for each object. with intra-procedural type information, virtual method resolution, callee-stub inline, exception analysis, and thread allocation site analysis are implemented. virtual method resolution and callee-stub inline effectively reduce the call graph and scc (strongly connected components). in exception analysis, we present a more precise analysis algorithm, especially in the inter-procedural exception in scc.our escape analysis can be applied to the whole java program and java libraries to identify unnecessary synchronization in java and remove them. we have developed an implementation on intel's open runtime platform. for our benchmarks, 7.3% to 99% (with an average of 54%) of synchronization operations are eliminated.
an approach for identifying attribute correspondences in multilingual schemas. in this paper, we focus on a new problem in database integration---attribute correspondence identification in multilingual schemas, and give a rule-based method for the problem. attribute correspondence identification in multilingual schemas involves the study of integrating schemas, designed in various languages, and returns the correspondences among those schemas. we first analyze the problem through two schemas of a large financial corporate of china. based on the relationships of the attribute names of the schemas, a method of name-based attribute correspondence identification is proposed. and we give a computer-aided system to deal with the problem according to the method. the components and identifying procedure of the method have been discussed in detail in the paper. we have implemented a prototype, and the tool shows its effectiveness in application.
optimizing relational store for e-catalog queries: a data mining approach. a frequent use of database management systems in electronic commerce is to provide electronic product catalogs (e-catalogs) that allow users to search for products of interest via constraints on attributes. an intuitively straightforward representation of e-catalogs is to use one table for the whole e-catalog as it is conceptually easy to maintain and query. however, for any e-commerce business with a reasonably large number of products and product types, its e-catalog usually involves a large number of attributes due to the great variety of the products, and at the same time, contains a large number of null values due to the fact that each product only has values under a relatively small number of attributes. because of these properties, the above intuitive method does not work well in current relational database systems. techniques have been proposed in the literature to deal with this problem, namely binary and vertical schemas. however, these techniques fail to take advantage of inherent properties of realistic e-catalogs to provide superior performance. this paper proposes a novel decomposition method for e-catalogs based on association rule discovery, a data mining technique. the method discovers groups of attributes that frequently appear together, i.e., are frequently used together to describe products, and generates schemas that contain these groups. this paper also reports experimental results showing the efficiency of the method.
reordering b-tree files. in this investigation, we address a reordering technique that improves sequential processing to b-tree files dramatically. we show conventional reorganization is not fully helpful, and propose reordering of blocks reflecting logical order. first we obtain all the data in a logical order, and then we put them into pre-order. here we show some experimental results that says how this technique works well.
comparing table views for small devices. users expect access to web data from a wide range of devices, both wired and wireless. many users switch back and forth between devices, including laptops and personal data assistants (pdas), and expect to be able to continue working with that data. the goal of this research is to inform the design of applications that support the user by providing reasonably seamless migration of web data among internet-compatible devices with minimal loss of effectiveness and efficiency. earlier studies concentrated on the dynamic transformation of text content, lists, and forms embedded in web pages for access on a range of devices from desktop to handheld. this study focuses on the transformation of large tables onto small mobile devices. in this paper we report on the results of two user studies that examine effectiveness and efficiency of three basic models for the display of tables, originally intended for use on larger screen, on pda size screens for both simple and complex tasks.
combining analysis and synthesis in a model of a biological cell. we have previously described a top-down analytical approach, cell assembly kit (cellak), based on the object-oriented (oo) paradigm and the unified modeling language (uml) and real-time object-oriented methodology (room) formalisms, for developing models and simulations of cells and other biological entities. in this approach, models consist of a hierarchy of containers (ex: cytosol), active objects with behavior (ex: enzymes, lipid bilayers, transport proteins), and passive small molecules (ex: glucose, pyruvate). in this paper we describe the substrate catalyst link (scl) bottom-up synthesis approach [17], the concept of autopoiesis on which it is based, and what we have learned in trying to integrate this approach into cellak. the enhanced cellak architecture consists of a network of active objects (polymers), each of which has behavior that causally depends partly on its own fine-grained structure (monomers), where this structure is constantly changing through interaction with other active objects.
identification of fundamental building blocks in protein sequences using statistical association measures. protein sequence data is abundant, yet derivation of structural features from sequence alone is generally restricted to prediction of domain architecture, secondary structure elements and motifs. precise feature boundaries cannot be determined reliably, and it is unknown to what extent these features constitute fundamental building blocks of protein sequences, a question with particular relevance to protein folding. here we propose a statistical approach using mutual information, a measure of association, to predict feature boundaries. in this approach, proteins are viewed as strings of adjacent, non-overlapping features, where each feature is a subsequence of the protein, and the union of the features is the entire protein. mutual information values are measured between nearby amino acids along sequences, and low values are indicators for feature boundaries. these boundaries are then predicted using a flexible partitioning algorithm. the algorithms presented in this paper were tested on the gpcr protein family and subfamilies. a comparison with segment boundaries implied indirectly from secondary structure prediction and expert knowledge demonstrates that the algorithm can be used to statistically predict feature positions in protein sequences generically, without assumptions on the feature type to be detected. access to the data used and algorithms presented in this paper are available at flan.blm.cs.cmu.edu.
policy-driven reflective enforcement of security policies. practical experience has shown that separating security enforcement code from functional code using separation of concerns techniques such as behavioural reflection leads to improvements in code undestandability and maintainability. however, using these techniques at requires providing a consistent and declarative way to specify policies. we have developed a prototype tool that allows the use of ponder policies that are enforced by the kava metaobject protocol. this prototype translates high-level policies into configuration files used to enforce the policies upon java applications.
improving collaborative filtering with trust-based metrics. despite its success, similarity-based collaborative filtering suffers from some significant limitations, such as scalability and sparsity. this paper introduces trust to the domain of collaborative filtering to overcome these limitations. compared with the similarity-based cf, introduction of trust does improve the performance of cf in terms of coverage, prediction accuracy, and robustness in the presence of attacks. experimental results based on a real dataset are illustrated as evidences to support our claim.
profiling and mapping of parallel workloads on network processors. network processors are embedded system-on-a-chip multiprocessors that are optimized to perform simple packet processing tasks at data rates of several gigabits per second. to meet the performance demands of increasing link speeds and more complex network applications, network processors are implemented with several dozens of processor cores and execute multiple packet processing applications in parallel. the complexity of such systems makes it increasingly difficult for application developers to map applications to the various system resources and achieve optimal performance. we propose an automated profiling and mapping methodology for these highly parallel, embedded systems that starts out with a simple uniprocessor implementation of the networking application. an architecture independent representation of the runtime behavior of the application is used to map and schedule different processing steps to the underlying hardware. an analytic performance model is used in the process to estimate system performance and to find an near-optimal solution through iteration.
pvin: a scalable and flexible system for visualizing pedigree databases. we describe the design and implementation of pvin (pedigree visualization and navigation), a scalable and flexible software system that enables the visualization, analysis, and printing of hierarchical relations typically stored in relational databases. although the concept of visualizing and printing pedigree databases is not new, we have developed a novel implementation based on modern approaches for several important reasons: (1) our university's center of hereditary diseases has accumulated very large amounts of hereditary information from various populations for ongoing research projects, and has difficulty managing and effectively printing the associated pedigree trees with legacy fortran software; (2) the size of some of these databases (over 40,000 entries covering seven generations) is too large for existing commercial pedigree software to handle; and (3) our researchers and support staff need more effective ways to perform visual analysis tasks, such as the comparison of multiple pedigrees and the cross-referencing of individuals that appear in multiple families (through re-marriage.) the pvin system addresses these fundamental problems while also providing a number of additional features and functions, including: context-free drawing routines that enable rendering onto screen and printer contexts interchangeably; a generic framework that allows the system to interface with multiple databases and database servers; a multiple view user interface that provides side-by-side comparisons and "focus+context" rendering; and advanced node searching and cross-referencing capabilities.