waashk/dblp huggingface dataset:
se-155 dbsa: a device-based software architecture for data mining. in this paper a new architecture for a variety of data mining tasks is introduced. the device-based software architecture (dbsa) is a highly portable and generic data mining software framework where processing tasks are modeled as components linked together to form a data mining application. the name of the architecture comes from the analogy that each processing task in the framework can be thought of as a device. the framework handles all the devices in the same manner, regardless of whether they have a counterpart in the real world or whether they are just logical devices inside the framework. the dbsa offers many reusable devices, ready to be included in applications, and the application programmer can easily code new devices for the architecture. the framework is bundled with connections to several widely used external tools and languages, making prototyping new applications easy and fast. in the paper we compare dbsa to existing data mining frameworks, review its design and present a case study application implemented with the framework. the paper shows that the dbsa can act as a base for diverse data mining applications.
ressd: a software layer for resuscitating ssds from poor small random write performance. nand flash-based solid state drives have emerged as revolutionary storage media during recent years. however, the wide-spread of ssd technology is currently obstructed by the fact that the random write bandwidth is lower than the sequential write bandwidth by several orders of magnitude. this paper proposes a novel software layer called ressd whose purpose is to resuscitate ssds from poor small random write performance with low memory usage. ressd works as a virtual block device on ssd which does not require any modifications of the operating system kernel and applications. by inspecting all incoming requests, ressd identifies small random writes which have potential to degrade ssd's performance significantly and transforms them into sequential and ordered-sequential writes which are more favorable to ssds. our evaluation results with postmark show that ressd improves the overall performance by up to 72% using a few megabytes of kernel memory.
virtual protocol stack interface for multiple wireless sensor network simulators. we present vpsi, which is a virtual protocol stack interface for wireless network simulators. vpsi provides two features; 1) a unified programming interface for developing network protocols, and 2) separation of simulator engine and the network protocols. by using vpsi, developers do not need to understand multiple different simulator engines. we implemented and adapted vpsi on existing well-known simulation packages, and we observed that vpsi works well with different wireless network simulators.
towards a god-theory for organizational engineering: continuously modeling the continuous (re)generation, operation and deletion of the enterprise. much time is lost, in organizations, in the handling of unknown exceptions because organizational models are not current or coherent with reality and there is a lack of concepts and methods in organizational engineering (oe), for a continuous and timely update of models of organizational reality. to address these problems, a renowned methodology for oe -- demo (design and engineering methodology for organizations) and its underlying theory are improved and extended enabling a precise and integrated modeling of three aspects that we consider to be part of the function perspective of an organization: (1) viability -- specification of vital norms of operation that ensure the viability of the organization, dysfunctions and their causing exceptions, (2) change -- specification of the organizational engineering processes responsible for generation, operation and discontinuation of organizational artifacts (oas) -- for example, business rules or organizational actors -- in order to solve dysfunctions and (3) architecture -- specification of design rules that guide the referred engineering processes, restricting the "shape" of their end result -- oas.
coordination in open and dynamic environments with tucson semantic tuple centres. with the goal of addressing open and dynamic scenarios like the web, in this paper we introduce a semantic-oriented extension of the tuple space model. in our model, each tuple space is equipped with the ontological description of the coordination domain (e.g. expressed in w3c's owl language). tuples represent individuals of the domain along with their properties, and retrieval templates correspond to queries used to check whether an individual is the instance of an ontological concept (as typical in description logics, the logic of semantic reasoning). differently from existing approaches in literature, we obtain a model that is fully expressive -- compared to owl -- with a smooth extension of the standard syntactic setting of tuple spaces, namely, by an intuitive extension of the language for tuples and tuple templates. we discuss an incarnation of this model in the tucson coordination model and infrastructure.
distributed discrimination of media moments and media intervals: a watch-and-comment approach. the watch-and-comment paradigm has been proposed as the seamless capture of comments made by users when appreciating a video, which is watched using a computer or a tv set, for example. the comments can be associated with the original media in several ways so as to generate interactive videos automatically -- the interactive video corresponding to the original video annotated with the captured comments. in this paper we explore the application of the watch-and-comment approach in situations in which the annotations are represented by operators used for the discrimination of instants and time intervals within continuous media. this particular type of annotation can be performed collectively by several users, over distinct instances of media and, for recorded media, at different times. once these discriminations are available, combinations of the annotations may be employed by several classes of applications such as those which exploit collaborative selection, voting, cutting, classification and evaluation. we show the feasibility of the operators by implementing a proof-of-concept prototype on a digital television platform: the prototype supports multiple geographically distributed users who, using distinct input devices, perform collaborative tasks not planned in the original interactive-tv applications.
local lemma: a new strategy of pruning in sat solvers. this paper proposes a search tree pruning strategy for sat solving. it is called local lemma, because it generates lemmas from explored subtrees and these lemmas are valid only in a part of the search tree. the paper explains the basic principle of the strategy, illustrates it with an example, and presents some experimental results.
a committee machine implementing the pattern recognition module for fingerspelling applications. in several countries, deaf communities adopt the sign language as their official and natural language. this fact inserts a new field for the software application development that can improve sign language dissemination and social inclusion of deaf people. alphabetic character-based applications, like games and educational softwares, can be adapted to run as fingerspelling-based applications, in which the inputs are signs (static images or videos) rather than letters (typed letters). in this paper, we present a pattern recognition module, implemented by committee machine, for fingerspelling applications. the committee experts are built with supervised and unsupervised fuzzy learning vector quantization models using the "boosting by filtering" strategy. the module was tested in a specific sign language context considering hand configurations and hand movements.
recognizing affect from non-stylized body motion using shape of gaussian descriptors. in this paper, we address the problem of recognizing affect from non-stylized human body motion. we utilize a novel feature descriptor which is based on the shape of signal probability density function framework to represent the motion capture data. combining the feature representation scheme with support vector machine classifier, we detect implicitly communicated affect in human body motion. we test our algorithm using a comprehensive database of affectively performed motion. experiment results show state-of-the-art performance compared with the existing methods.
a machine-checked soundness proof for an efficient verification condition generator. verification conditions (vcs) are logical formulae whose validity implies the correctness of a program with respect to a specification. the technique of checking software properties by specifying them in a program logic, then generating vcs, and finally feeding these vcs to a theorem prover, is several decades old. it is the underlying technology for state-of-the-art program verifiers such as the spec# programming system, or esc/java. the classic way of computing vcs is by means of dijkstra's weakest precondition calculus. however, modern verification condition generators (vcgens), including spec# and esc/java's vcgens, are based on an optimized version of this algorithm, that avoids an exponential growth of the vcs in the length of the program to be verified. for this optimized vcgen algorithm, only informal soundness arguments are available. the main contribution of this paper is a fully formal, machine-checked proof of the soundness of such an efficient vcgen algorithm.
software tools for requirements management in an erp system context. identification and specification of business requirements are extremely important when development of enterprise resource planning systems (erps) take place. our aim is to present some prescriptive thoughts about requirements management useful when developing future requirements management software tools in the erp system realm. from an initial test we suggest further research in this area that could end up in some more practical guidelines on how to develop a requirement management software tool that can be used when developing future erps.
fast file-type identification. this paper proposes two techniques to reduce the classification time of content-based file type identification. the first is a feature selection technique, which uses a subset of highly-occurring byte patterns in building the representative model of a file type and classifying files. the second is a content sampling technique, which uses a subset of file content in obtaining its byte-frequency distribution. our initial experiments show that the proposed approaches are promising even the simple 1-gram features are used for the classification.
feature interaction networks. a quantitative approach for measuring and describing feature interactions in object-oriented software components based on source code inspection is presented. the methodical arsenal is borrowed from the field of network analysis. based on data gathering through source code harvesting, network representations of feature implementations (i.e., feature interaction networks, fins) are constructed. by applying established network statistics, various properties of interaction structures between features can be captured, e.g. the scatteredness, the degree of crosscutting (scattering), and the scattering concentration. this approach contrasts with related proposals based on frequency and dispersion measures.
an ontology-based semantic foundation for aris epcs. this paper presents an ontological analysis of the epc (event-driven process chain) business process modeling notation supported in the aris toolset. this ontological analysis provides an interpretation of the modeling elements in epc diagrams in terms of the ufo foundational ontology. this enables us to define the precise real-world semantics for business process models represented through epcs and allows us to identify problems affecting the clarity and expressiveness of epcs. in our analysis, we consider an up-to-date metamodel of the aris method that we have defined in our earlier work, which specifies epcs as currently supported by the aris toolset. to the best of our knowledge, the interpretation proposed here is presently the most complete ontological account for epcs.
assessing the impact of refactoring activities on the jhotdraw project. refactoring is a well-known technique for improving the maintainability of software products. however, it is not easy to justify the time and effort needed to refactor code as the benefits are difficult to quantify, especially the perception of improved maintainability. in this paper, we highlight some results of a retrospective case study undertaken to shed light on how refactoring affects maintainability of a software product. there are several findings. first of all, refactoring affects the amount of subsequent changes. furthermore, refactoring has a positive impact on the coupling relationships with dependent software applications.
web-based graphical querying of databases through an ontology: the wonder system. biological scientists have made large amounts of data available on the web, which can be accessed by canned or precomputed queries presented via web forms. to satisfy further information needs, users currently have to have a good understanding of sql and how the data is stored in the database. while accessing information at the ontological layer seems more appropriate, this poses two challenges: (1) to query data in databases and triple stores through an ontology with little performance overhead, and (2) to provide an intuitive web-based access to users that are not it experts. to address these issues, we draw upon the theory and technology developed for ontology-based data access for dl-lite. with an owl ontology and the dig-quonto reasoner as building blocks, we have developed an application that allows for graphical ontology browsing, query formulation, and answer retrieval via a web browser. we have evaluated our system for web-ontology based extraction of relational data (wonder) with an existing large genomics database about horizontal gene transfer and found that it meets both the scalability and the usability requirements.
smac: spatial map caching technique for mobile devices. applications for location-based services (lbs) are becoming more and more popular. however, the continuous access to the wireless internet to get map tiles from servers can significantly affect the quality of the user experience, or might be too expensive. in this paper, we present smac, spatial map caching, a caching technique devised for storing map tiles on the secondary memory of mobile devices. differently from classical caching techniques, such as lru, smac is based on the spatial locality principle that characterizes the way maps are shown to moving users.
towards modular i* models. the i* framework cannot effectively model crosscutting concerns, compromising modularity, reusability and evolution of the results. our approach embodies a specific notation to represent and compose aspectual i* models, using aspect-orientation to address modularity and composition of crosscutting concerns. this represents a step forward to improve separation of concerns in i*.
semantically enabled business process discovery. business process descriptions are usually stored in internal enterprise repositories. in order to be able to reuse business processes (a.k.a. bps), bp designers require some tools to help them to discover processes (or fragments of processes) in the repository based on these descriptions. in most cases discovery is a difficult task (the diversity of modeling languages, the process descriptions are very close to the it level being far from the business level, there is a lack of automatic tools, etc.). in this paper we investigate the use of semantics to alleviate the above mentioned problems providing with a method for the discovery of bps. we have developed an rdf vocabulary to annotate and store bps. first we use the vocabulary to annotate functional and non functional properties of basic activities of xml-based bp descriptions. then we build an rdf knowledge base following the developed rdf vocabulary by extracting, in an automatic way, these properties and the structural properties from the bp description. in addition, functional and non functional properties of structured activities are automatically computed and added to the rdf knowledge base. then the rdf knowledge base can be queried with sparql to achieve bps discovery. in addition, we present an implementation prototype.
mobile air pollution monitoring network. current methods of estimating air quality involve assigning a single value called the air quality index (aqi) to a large land area for a 24-hour period based on a very few, sparsely-located sensors. this produces a low-resolution image of the air quality in that region. we have devised a new mobile air quality monitoring network with the ability to provide high-resolution realtime pollution data at any location within the coverage area. we have prototyped sensors and proven the feasibility of the approach, and are currently testing a small-scale implementation using irregularly-sampled spatiotemporal measurements from mobile car-mounted sensors coupled with existing static data. this data feeds a web-based application, enabling users to view air quality in specific regions as well as estimate exposure over specified time periods and plan routes based on minimal exposure to a given pollutant.
rdfswarms: selforganized distributed rdf triple store. we present and evaluate a distributed rdf storage that uses swarm algorithms to cluster similar rdf triples based on a configurable similarity measure.
maintaining data reliability without availability in p2p storage systems. peer-to-peer (p2p) storage is a promising technology to provide users with cheap and online persistence. however, due the instability of these infrastructures, p2p storage systems must introduce redundancy in order to guarantee a reliable storage service. besides, they need data repair algorithms to maintain this redundancy in front of permanent node departures. to ensure that such repairs can always be run, existing p2p storage systems aim to maintain 100% data availability. unfortunately, this solution seems to overkill in preventing data loses, introducing network and data overheads. in this paper we propose a new data repair algorithm able to guarantee a high reliable storage service without 100% data availability. the main idea is to ensure that objects are kept stored instead of maintaining them available. we analytically prove that our approach reduces considerably the total amount of redundancy. moreover, through simulation, we show how our approach significantly reduces the required number of repairs, decreasing both, the network and the storage overheads.
text line detection and segmentation: uneven skew angles and hill-and-dale writing. a line detection and segmentation technique is presented. the proposed technique is an improved version of an older technique. the experiments have been performed on the dataset of the icdar 2007 handwriting segmentation contest in order to be able to compare, objectively, the performance of the two techniques. the improvement between the older and newer version is more than 24% while the average extra cpu time cost is less than 200 ms per page.
trustworthy interaction balancing in mixed service-oriented systems. web-based collaboration systems typically require dynamic and context-based interactions between people and services. to support such complex interaction scenarios, we introduce a mixed service-oriented system that is composed of both humans and software services, collaborating and interacting to perform certain activities. as an example, consider a professional online help and support community spanning interactions between human participants and software-based services. trust between these members is essential for successful collaborations and has been extensively studied in the context of social and collaborative networks. in this paper, we discuss trust from a collaborative and social point of view instead of a security perspective. our approach follows an interaction monitoring and an interpretative rule-based trust inference model established on previous behavior. however, trust relations encourage network members to continue interacting with successful (and thus trusted) collaboration partners, and to avoid, or even refuse, interactions with unknown actors. this behavior has negative side-effects from a global community perspective. given the help and support environment, a small number of popular network members will become increasingly overloaded with support requests. we solve this load and interaction balancing problem by the means of trustworthy request delegations.
a discriminated correlation classifier for face recognition. in this paper, a discriminated correlation classifier is proposed to improve the performance of the two-dimensional (2-d) face recognition algorithm. until now, many methods have been proposed to address the problems encountered by face recognition system, such as small number problem, pose and illumination variation, etc. all these works are aiming at enhancing the performance of the face recognition system. however, as far as we know, few work are concerning about how to improve the classifier, whose performance directly determines the final recognition accuracy. so, in this paper, our motivation is to improve the performance of correlation classifier, which is widely used in face recognition problems, to improve the recognition accuracy. inspired by a correlation filter design method called minimal average correlation energy(mace) filter, we propose a novel classifier called discriminated semi-normalized correlation (dsnc) classifier using our discriminative learning method. compared with the classical discriminative learning methods that need many intra-class samples and can only be applied on close set recognition problems, our method needs only one intra-class sample and can be performed on open set face recognition problem. the validity of our method is tested on two benchmark face database(frgc2.0 and feret), and a private face database(thfaceid).
document retrieval using image features. this paper describes a new approach to document classification based on visual features alone. text-based retrieval systems perform poorly on noisy text. we have conducted series of experiments using cosine distance as our similarity measure, selecting varying numbers local interest points per page, and varying numbers of nearest neighbour points in the similarity calculations. we have found that a distance-based measure of similarity outperforms a rank-based measure except when there are few interest points. we show that using visual features substantially outperforms text-based approaches for noisy text, giving average precision in the range 0.4--0.43 in several experiments retrieving scientific papers.