waashk/dblp huggingface dataset:
editorial message: special track on software engineering: methods, practices, and tools. for the third time in a sequence the annual acm sac symposium is hosting this software engineering track. a few changes have taken place since last year: from the original se team only stefan gruner is continuing to organise this track whilst alessandra cavarra and sung shin have come in as new members of the team. also the tracks original subtitle, "applications, practices and tools", has been modified to "methods, practices and tools" in order to emphasise our focus on sound solutions.
tool support for plagiarism detection in text documents. we propose a software tool that supports the detection of plagiarism. the application domain of the tool are those cases in which a stylometric approach seems appears viable. the tool is language-sensitive. it takes as input two sufficiently long pieces of english text under the hypothesis that they stem from different authors, and it raises a plagiarism warning in case that significant stylistic similarities in the two texts can be found.
editorial message. for the fourth time in a sequence the annual acm-sac symposium is hosting this software engineering track. a few changes have taken place since last year: from last year's se-track team only sung shin and stefan gruner are continuing to organise this track and, at the occasion of the 21st sac symposium, the track's previous subtitle "methods, practices and tools" was modified to "sound solutions for the 21st century". moreover: for the very first time this track is not only supported by the acm via the sac symposium but also endorsed by two further software engineering societies, namely formal methods europe (fme) and the european association of software science and technology (easst).
improving courseware quality through life-cycle encompassing quality assurance. the quality of courseware development is affected by four factors: content and instructional issues; managment; technical and graphical issues; and concerns of the customer. in this paper we describe intview, a courseware development method that integrates these four factors throughout the whole development life-cycle. by combining existing courseware quality assurance methodologies with software engineering techniques such as inspections and tests the interests of the participating roles are balanced. both the intview methodology and the quality assurance techniques are described and the results of some preliminary case studies are reported.
a formal framework to generate xpdl specifications from uml activity diagrams. the xml process definition language (xpdl) is a standardized language allowing process definitions interchange between a variety of tools ranging from workflow management systems to modeling and simulation tools. on the other hand, uml activity diagrams offer a convenient notation to depict synthetic and intuitive views of systems that facilitate stakeholder communication. however, there is currently no tool able to animate activity diagrams using workflow management systems. moreover, despite standardization efforts, diagram interchange across different modeling tools remains an issue. hence, transforming uml activity diagrams into xpdl specifications would preserve stakeholder communication while enabling animation tool support. this paper presents a formal and optimal transformation of uml activity diagrams into xpdl specifications. this mapping is described through a set of formal translation rules defined from the uml activity metamodel to the xpdl metamodel. we demonstrate that the defined mapping process preserves some structural properties specified on the translated uml activity diagram.
multilure active contours. this paper presents a variation of the classic algorithm of active contours in order to improve the visualization of 3d reconstruction of anatomical structures in medicine. this 3d reconstructions are made from the obtained contours. this variation of active contour enables a better behavior of them.other authors have already used snakes (active contours) for segmenting shapes in medical images. but they usually fail in accurately adjust to the desired contour.the proposed variation introduce a new kind of energy in the classical formula of active contours. this new energy is based in the computation of distance to several supervised lures that attract the snake points. once the lures are selected, the active contours work in a fully automated way. this new variation of active contours have been tested on ct medical images to segment contours of different bone structures. the results show that the proposed active contour is superior in results to the classical method. we have used a multiple criteria decision making framework for obtaining the final conclusions of this survey.the new variation proposed for active contours works better for detecting vertebrae contours in ct images. in addition, a new energy and use of lures for active contours have been introduced.
design and implementation of a real-time notification service within the context of embedded orb and the can bus. many distributed real-time and embedded (dre) applications require a scalable event-driven communication model that decouples suppliers from consumers and simultaneously supports advanced quality of service (qos) properties. this article focuses on the design of such a service complying with the omg notification service standard. it is optimized for the can bus, a widely used interconnect, where real-time characteristics are a requirement. a new protocol for the efficient distribution of events in a can-based distributed control system is presented, a protocol which is tailored to the can bus and produces very low overhead by utilizing can-specific features.
mocha-pi, an exogenous coordination calculus based on mobile channels. in this paper we present mocha-&pi;, an exogenous coordination calculus that is based on mobile channels. a mobile channel is a coordination primitive that allows anonymous point-to-point communication between processes. our calculus is an extension of the well-known &pi;-calculus. the novelty of mocha-&pi; is that its channels are a special kind of process that allow other processes to communicate with each other and impose exogenous coordination through user defined channel types. also new, is the fact that in our calculus channels are viewed as resources. processes must compete with each other in order to gain access to a particular channel. this makes the calculus more in line with existing systems. an immediate application of this calculus is the modeling of the mocha middleware, a distributed system that coordinates components using mobile channels.
mode-directed preferences for logic programs. preference logic programming (plp) is an extension of constraint logic programming for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. plp essentially separates the programming of a problem itself from the criteria specification of its solution selection. in this paper we provide a syntax for plp based upon mode-directed preferences and a semantics based upon herbrand models and fixed-point theory. our method uses mode declarations to designate certain predicates as optimization predicates, and uses preference rules for stating the criteria for determining their optimal solutions. this paper also presents an elegant and easy method of executing preference logic programs in terms of tabled prolog. automatic transformation is applied to embed the preferences into the problem specification for efficient evaluation. we show that the procedural semantics of a preference logic program is equivalent to its declarative semantics.
on the use of spectral filtering for privacy preserving data mining. randomization has been a primary tool to hide sensitive private information during privacy preserving data mining. the previous work based on spectral filtering, show the noise may be separated from the perturbed data under some conditions and as a result privacy can be seriously compromised. in this paper, we explicitly assess the effects of perturbation on the accuracy of the estimated value and give the explicit relation on how the estimation error varies with perturbation. in particular, we derive one upper bound for the frobenius norm of reconstruction error. this upper bound may be exploited by attackers to determine how close their estimates are from the original data using spectral filtering technique, which imposes a serious threat of privacy breaches.
fingerprinting relational databases. in this paper, we propose a fingerprinting solution to protect valuable numeric relational data from illegal duplications and redistributions. we introduce a twice-embedding scheme. in the first embedding process, we embed a unique fingerprint to identify each recipient to whom the relational data is distributed. the embedding process is controlled by a secret key. meanwhile, the fingerprint can be detected using the same secret key to prove ownership at a numerical confidence level. the second embedding process is designed for verifying the extracted fingerprint and giving a numerical confidence level. thus, once a suspect copy is found, numerical confidence level can be provided both to identify the owner and the illegal distributor. the experiment shows that our solution is effective and robust to various attacks.
experiments with una for solving linear constraints in real variables. linear constraints arise in formulation of several computationally challenging problems such as weather modeling, underground water modeling, air pollution modeling etc. the constraints may correspond to multiple observations that place upper or lower bounds on linear combinations of variables. computing a feasible solution or solving these inequalities in least squares sense is a fundamental problem in many applications.in this paper, we present a strikingly simple numerical algorithm called una (unified numerical approach) that computes a feasible solution of linear inequalities or solves them in a least squares sense in case they are inconsistent. we compare the performance of una with bramley-winnicka algorithm, which is the best known algorithm to solve linear inequalities in a least squares sense. we also give experimental performance comparison of una with commercial linear programming based packages xa and cplex. our experiments show that una algorithm is faster than bramley-winnicka algorithm for solving large constraint sets in least squares sense. our experiments also show that for large constraint sets although cplex performs better than una, una performs far better than xa. in addition, the una algorithm is so simple that its implementation in c programming language is only 170 lines of code and its implementation using matlab is 80 lines of matlab script. our results show that in-spite of its simplicity, it is a powerful algorithm for solving linear inequalities in real variables.
modelling organisational practice in user requirements. modelling of organisational practice is an important yet notoriously difficult aspect of requirements engineering. this paper presents an approach which utilises "lightweight" ethnographic analyses in modelling the knowledge that exists in an organisation, together with how and where knowledge is communicated amongst organisational members. we view organisational practice in terms of the tasks that the organisation carries out, modelling and analysing what knowledge is required to perform these tasks.
dynamic on-demand updating of data in real-time database systems. the amount of data handled by real-time and embedded applications is increasing. also, applications normally have constraints with respect to freshness and timeliness of the data they use, i.e., results must be produced within a dead-line using accurate data. this calls for data-centric approaches when designing embedded systems, where data and its meta-information (temporal correctness requirements etc) are stored centrally. the focus of this paper is on maintaining data freshness in soft real-time embedded systems and the target application is vehicular systems. the contributions of this paper are three-fold. we (i) define a specific notion of data freshness by adopting data similarity in the value-domain of data items using data validity bounds that express required accuracy of data, (ii) present a scheme for managing updates in response to changes in the data items; and (iii) present a new on-demand scheduling algorithm, on-demand depth-first traversal denoted oddft, for enforcing data freshness by scheduling and executing update transactions. performance experiments show that, by using our updating scheme and introduced notion of data freshness in the value-domain, computational work imposed by updates is reduced for both the new oddft and well-established on-demand algorithms. moreover, oddft improves the consistency of produced results compared to well-established algorithms.
a semantic model for safe protocol interaction. most communication subsystems support modular and reconfigurable communication protocols based on the lego block model. in this model, complex protocols are built as collections of simpler protocols. however, protocol behavior is often expressed via informal descriptions and few work has been done to develop the underlying semantics that enables us to model and reason about protocol interactions. without it, it is very difficult to identify critical properties that must be met for correct operation. in this paper we present a communication framework based on a semantic model of distributed object reflection and we illustrate how this model can be used to formalize and reason about interactions between communication protocols. we evaluate the overhead and feasibility of our approach by developing an abstract executable specification of the communication framework in maude [3] and a maude api to systematically translate maude code into java. this gives us two versions of our model to play with: an abstract one for analysis and a concrete one for real-world applications.
two-phase clustering strategy for gene expression data sets. in the context of genome research, the method of gene expression analysis has been used for several years. related microarray experiments are conducted all over the world, and consequently, a vast amount of microarray data sets are produced. having access to this variety of repositories, researchers would like to incorporate this data in their analyses to increase the statistical significance of their results. in this paper, we present a new two-phase clustering strategy which is based on the combination of local clustering results to obtain a global clustering. the advantage of such a technique is that each microarray data set can be normalized and clustered separately. the set of different relevant local clustering results is then used to calculate the global clustering result. furthermore, we present an approach based on technical as well as biological quality measures to determine weighting factors for quantifying the local results proportion within the global result. the better the attested quality of the local results, the stronger their impact on the global result.
sequence modelling for sentence classification in a legal summarisation system. we describe a set of experiments using a wide range of machine learning techniques for the task of predicting the rhetorical status of sentences. the research is part of a text summarisation project for the legal domain for which we use a new corpus of judgments of the uk house of lords. we present experimental results for classification according to a rhetorical scheme indicating a sentence's contribution to the overall argumentative structure of the legal judgments using four learning algorithms from the weka package (c4.5, na&iuml;ve bayes, winnow and svms). we also report results using maximum entropy models both in a standard classification framework and in a sequence labelling framework. the svm classifier and the maximum entropy sequence tagger yield the most promising results.
reusable subsystems: domain-based approach. the goal of achieving a generic architecture for reusable software subsystems remains elusive. this work proposes a model, based on domain engineering, to support improved technique for designing reusable subsystems. the model is based on the concepts of "atomic domain" and the "wrapper" of an atomic domain. an atomic domain signifies a reusable subsystem, and it exhibits specific properties that serve as a basis for building reusable subsystems. the wrapper supports software reuse and management through a number of interface mechanisms, including taxonomy, application programmer interface, manager, communication, and control. implementations of illustrative atomic domains are provided.
video summarization by k-medoid clustering. in this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. this algorithm is based on the k-medoid clustering algorithms to find the best representative frame for each video shot. this algorithm, which is applicable to all types of descriptors, consists of extracting key frames by similarity clustering according to the given index. in our proposal, the distance between frames is calculated using a fast full search block matching algorithm based on the frequency domain. the proposed approach is computationally tractable and robust with respect to sudden changes in mean intensity within a shot. additionally, this approach produces different key frames even in the presence of large motion. the experiments results show that our algorithm extracts multiple representatives frames in each video shot without visual redundancy, and thus it is an effective tool for video indexing and retrieval.
translating the object constraint language into the java modelling language. the object constraint language ocl is a textual specification language that could be used for constraining the modelling elements that occur in uml diagrams. typical constraints include class invariants and preconditions and postconditions of operations. the java modelling language (jml) is a behavioural interface specification language designed for specifying java classes and interfaces. this paper defines a translation of ocl expressions and constraints into the java modelling language. the objective of this translation is to be able to map uml object-oriented designs with ocl constraints to java classes and interfaces annotated with jml specifications, and to carry out logical reasoning about such classes and interfaces using jml tools which include a run time assertion checker and an interactive prover based on pvs.
rule-based word clustering for document metadata extraction. text classification is still an important problem for unlabeled text; citeseer, a computer science document search engine, uses automatic text classification methods for document indexing. text classification uses a document's original text words as the primary feature representation. however, such representation usually comes with high dimensionality and feature sparseness. word clustering is an effective approach to reduce feature dimensionality and feature sparseness, and improve text classification performance. this paper introduces a domain rule-based word clustering method for cluster feature representation. the clusters are formed from various domain databases and the word orthographic properties. besides significant dimensionality reduction, such cluster feature representations show a 6.6% absolute improvement on average on classification performance of document header lines and a 8.4% absolute improvement on the overall accuracy of bibliographic fields extraction, in contrast to feature representation just based on the original text words. our word clustering even outperforms the distributional word clustering in the context of document metadata extraction.
a hierarchical naive bayes mixture model for name disambiguation in author citations. because of name variations, an author may have multiple names and multiple authors may share the same name. such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. this paper presents a hierarchical naive bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. this method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. the approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and dblp computer science bibliography.
aspect-specification based on structural type information. aspects developed in aspect-oriented systems often need to hook onto multiple objects that share common structural characteristics - such as attributes and operations. in strongly-typed aspect-oriented systems like aspectj these objects need to be of common type so that pointcuts may designate them and pieces of advice may interact with them. such type-systems are typically based on nominal types, therefore, aspects cannot interact with objects according to their structural information in a common way. this paper argues that specifying aspects based on a nominal type system is not sufficient and shows that aspect-specifications based on structural characteristics overcome this problem. a corresponding extension of the nominal type systems is proposed and illustrated by means of structural types and compound types.
location management of data items in mobile ad hoc networks. in this paper, we address techniques to manage locations of data items or replicas in mobile ad hoc networks and efficiently forward access requests to the locations. in order to manage locations of data items efficiently, a key issues is predicting the locations of data items that dynamically change. to predict the locations of data items, we use the information on replica allocation at every relocation time and the logs of past data accesses.
iterative querying in web-based database applications. web applications are increasingly relying on databases. the traditional method offered by most web sites of submitting a query across the internet and getting a response from a server packed in a hyper text markup language page is no longer satisfying [1][3][4][5]. this is due to the result set: it is often too large or empty. this forces the user to try and guess which constraints should be relaxed or tightened in order to obtain the desired result set. and so, the user goes through several submit/response cycles. web applications that require this sort of interactive exploration of databases need to use a model that is different from the submit/respond model described above. in this work, we propose an iterative querying model that integrates querying with result browsing.
editorial message: special track on database theory, technology, and applications. the world nowadays revolves around dealing with data presented in various formats. so it is inevitable that researchers focus their work on advancing the state of managing information. from here, the importance of database technology ranks amongst the hottest areas of research. this year the track has received many papers covering different areas of databases.