waashk/dblp huggingface dataset:
a case study of pattern-based software framework to improve the quality of software development. in recent years, development of the software industry and demand for software systems have increased rapidly, but developers often does not know whose suggestion to follow regarding methodologies of software engineering. one reason for that is the difficulty in applying new software engineering technologies. developers take a long time to train. another reason is the difficulty in integrating case toolsets. so many indeterminate factors make the development process more and more complex. on the other hand, software development is too customized, and software reuse is difficult. the reasons above are the cause for software development and maintenance to become more complex and difficult to control. in this paper we explore the importation of a software pattern-based framework, and the development of an erp/support chain system. based on software patterns, developers can separate development and business so as to reduce problems caused by the developer's lack of business experience. the quality of the product can thus be enhanced, software development costs be reduced, and software maintenance be improved.
method for fast compression of program codes for remote updates in embedded systems. this paper presents the technology for remote system updating that allows software updates in embedded systems such as mobile phones and car navigation systems. software updates are carried out using binary difference, software construction, and fast compression technologies. in this paper, we focus on a fast bpe (byte pair encoding) method of encoding program codes in embedded systems and show the index of relations between the updating time and the downloaded data size.
applying reo to service coordination in long-running business transactions. this paper presents an approach to formal modeling of long-running business transactions. our solution is based on the channel-based exogenous coordination language reo, which is an expressive, compositional and semantically precise design language that admits formal reasoning.
reasoning about comprehensions with first-order smt solvers. this paper presents a technique for translating common comprehension expressions (sum, count, product, min, and max) into verification conditions that can be tackled by two off-the-shelf first-order smt solvers. since a first-order smt solver does not directly support the bound variables that occur in comprehension expressions, the challenge is to provide a sound axiomatisation that is strong enough to prove interesting programs and, furthermore, that can be used automatically by the smt solver. the technique has been implemented in the spec# program verifier. the paper also reports on the experience of using spec# to verify several challenging programming examples drawn from a textbook by dijkstra and feijen.
learning the ontological theory of an information extraction system in the multi-predicate ilp setting. in recent years, numerous works have been carried out to design information extraction (ie) systems able to extract genic interaction networks from text. usually, the extraction procedure is completed by so-called extraction patterns, which are often limited to map textual fragments to a single semantic relation. such poor representations do not take into account the complexity of the data processed by biologists. ie systems need sophisticated representations, encoded with ontologies, allowing the definition of multiple relations, and of the (possibly recursive) dependencies between them. up to now, machine learning techniques used to acquire extraction patterns, i.e. binary or multi-class learners, reflect those representation restrictions. they assume independence between target predicates, and do not handle recursion. in this paper, we use inductive logic programming in a multi-predicate setting to learn extraction patterns fitted to an ontological context. multi-predicate ilp is an important paradigm which allows to learn recursive theories. we experimented our framework on a bacillus subtilis bacterium text corpus, in which we reach a global recall of 67.7% and a precision of 75.5% in ten-fold cross-validation.
model interfaces for two-way obliviousness. a key problem in software development is producing systems that are maintainable even as the concerns at play evolve. aspect-oriented programming (aop) seeks to foster maintainability by isolating the specifications of cross-cutting concerns, allowing them to be modified in relative isolation from the rest of the system. research in aspect-oriented modeling (aom) aims to develop a model-layer analogue of aop, allowing integration with accepted modeling practices. aspects usually allow developers of the primary model to be oblivious to the aspects that modify the primary model; because of this, aspects can be closely coupled to potentially transient details of the primary model. when those details change, the aspects that depend on them may no longer have the desired effect. in this paper, we introduce model interfaces as a solution to the problem of obliviousness by extending a graph-transformational approach to aom.
conceptualization and implementation of a microscopic pedestrian simulation platform. this work reports on our first steps toward the implementation of a platform for pedestrian simulation in multimodal exchange interfaces. whereas most approaches in pedestrian simulation are aimed at studying interactions among people using a common environment with different purposes and under different mobility restrictions, our research is focused on how those interactions will ultimately affect transportation operations at multimodal stations. this paper starts with a general overview of pedestrian simulation, focusing on the microscopic representation of individuals and their interoperability that will be the basis for the system architecture briefly presented.
eliciting required characteristics for usable requirements engineering approaches. it has been reported that many software companies do not use existing requirements engineering approaches. this indicates that there is room and opportunity for improving the usability of existing requirements engineering approaches. this paper describes a market study intended to elicit a set of characteristics that could improve the usability of requirements engineering approaches. the survey is aimed toward software stakeholders such as developers, designers, customers, and managers at various software companies. the survey results are used to define a set of desirable characteristics for usable requirements engineering approaches and to suggest a set of guidelines that could help achieve the desirable characteristics.
preciso: a reengineering process and a tool for database modernisation through web services. a common trend in service oriented architecture (soa) is to consider information systems exposing software as services. this current approach is not only applied to new software developments, but also it is related to the maintenance of legacy systems. nowadays, a cornerstone of information systems are relational databases, which constitute meaningful sources of services. these services can provide database's information in soa scenarios. this paper presents a reengineering process to recover and implement web services in automatic manner from relational databases. this process follows the adm approach (architecture-driven modernization). in this paper authors present a case study that has been carried out using a tool built to support the process. this tool is used to generate a set of web services which are integrated into a web development allowing to modernise the legacy database in a soa context. this case study has been carried out in the context of software company indra.
a gradient oriented recombination scheme for evolution strategies. this paper proposes a novel recombination scheme for evolutionary algorithms, which can guide the new population generation towards the maximum increase of the objective function. given the current sample points and their function evaluations, the shepard's interpolation method is used to approximate the underlying objective function in that local region. we then compute the gradient of the estimated function which in consequence leads to an iterative process, called the mean shift, for searching the local function optimum. in each mean shift step, we calculate the weighted mean of sample points in the kernel window, followed by shifting the location of the kernel to the computed mean. such iterative process eventually converges to the point at which the estimated objective function has zero gradient. we use the converged point as the output of our recombination operator. experimental results show that such gradient based recombination scheme can improve the efficiency of optimization search in evolutionary algorithms.
jointly optimizing data acquisition and delivery in traffic monitoring vanets. vehicular ad hoc networks (vanets) are envisaged to become a flexible platform for monitoring road traffic, which will gradually replace more cumbersome fixed sensor deployments. the efficacy of vehicle-assisted traffic monitoring systems depends on the freshness of traffic data that they can deliver to users, and the bandwidth used to do so. clearly, high data freshness will allow users to estimate trip times accurately, and to select the fastest route to a destination. low bandwidth utilization will allow the traffic monitoring application to coexist symbiotically with a wide variety of vehicle-based applications, ranging from road safety to advertising and entertainment. in this paper, we investigate the problem of minimizing the bandwidth utilization of a vehicle-assisted traffic monitoring system, whilst adhering to user-defined requirements for data freshness. the novelty of our approach is that we jointly optimize two intertwined aspects of traffic monitoring: data acquisition and data forwarding. we investigate how their combined operation trades data freshness for bandwidth utilization, and we propose a novel mechanism that fine-tunes their parameters to optimize the overall system performance. our mechanism is evaluated using realistic vehicular traces on a real city map.
a new probabilistic generative model of parameter inference in biochemical networks. we present a new method for estimating rate coefficients and level of noise in models of biochemical networks from noisy observations of concentration levels at discrete time points. its probabilistic formulation, based on maximum likelihood estimation, is key to a principled handling of the noise inherent in biological data, and it allows for a number of further extensions, such as a fully bayesian treatment of the parameter inference and automated model selection strategies based on the comparison between marginal likelihoods of different models. we developed kinfer (knowlegde inference), a tool implementing our inference model. kinfer is downloadable for free at http://www.cosbi.eu.
privacy preserving churn prediction. churn prediction is an important component of customer retention to predict whether a current customer decides to take business elsewhere or voluntarily terminates service, so marketing campaigns can target at the potential churners for retention efforts. in this paper we provide a strategy to protect customers' privacy in churn prediction. first of all, we demonstrate how to use data distortion to mask a telecom customer dataset, and then apply churn prediction methods to the distorted data. since the distorted data are so different from the original data the privacy of customer is preserved, but the prediction methods we proposed will not compromise the accuracy of churn prediction. the performance of several data distortion methods are compared and evaluated.
mining functional associated patterns from biological network data. the recent development of high-throughput biological techniques for functional genomics have generated a large quantity of new biological network data. analyzing these networks provides novel insights in understanding basic mechanisms controlling cellular processes. in this paper, we integrate protein interaction and microarray data and transform the un-weighted protein-protein interaction network to its weighted correspondent. we then present a novel graph mining problem, mining functional associated patterns across the weighted genome-wide network. the central idea of the problem is to detect groups of objects having highly associated with each other in interaction networks, and hypothesize these groups denote function modules. we develop an efficient algorithm, maps, which exploits several pruning techniques to mine maximal functional associated patterns. a systematic performance study is reported on protein-protein interaction networks and gene coexpression data. the experimental results show that the proposed method is efficient and has good predictive performance.
simulating human intuitive decisions by q-learning. simulations have become a great tool for research in the natural sciences. however their potential has not been reached far enough in the social sciences. this is in part due to the difficulty in simulating human decision making and reproducing human-like behavior. recent advances in neo-classical decision making have defined specific differences between the decision making capabilities of rational agents and humans as well as speculations into the cause. presented is a q-learning model for simulating human-like decision making based upon the intuition deliberation model proposed by psychologists kahneman and tversky. the model is tested against the classic economic bargaining game. in this game humans and rational agents consistently converge onto distinctly different strategies. our experiments show that a selfish agent defers from the strategy of the rational agent and is more similar to human strategy.
the visual authoring tool of flash-based component for interactive item template. current on-line testing systems are lacks of supplying versatile interactive answering alternatives such as click and select, drag and drop, link, collide and magnetize. this research have integrated the reusable component and framework technology, and composed an interactive item template software component construction. each interactive item template component ("iitc" in abbreviation) which is created by using flash actionscript technology symbolizes a different operation and interactive scenario. we also define a script language in xml format to flatten or unflatten the item objects. meanwhile, c/c++ language was employed to develop corresponding visual item authoring system which can dynamically load iitc to add new types of item template to create new items. under our software component model, software component developer can easily cultivate iitc with the rich multimedia function provided by actionscript language. visual item authoring system with dynamic loading iitc offers user a simple way to use iitc. the script language of iitc has also added diversity to the interactive scenario of each iitc.
flexible self-healing gradients. self-healing gradients are distributed estimates of the distance from each device in a network to the nearest device designated as a source, and are used in many pervasive computing systems. with previous self-healing gradient algorithms, even the smallest changes in the source or network can produce small estimate changes throughout the network, leading to high communication and energy costs. we observe, however, that in many applications, such as routing and geometric restriction of processes, devices far from the source need only coarse estimates, and that a device need not communicate when its estimate does not change. we have therefore developed flex-gradient, a new self-healing gradient algorithm with a tunable trade-off between precision and communication cost. when distance is estimated using flex-gradient, the constraints between neighboring devices are flexible, allowing estimates to vary by an amount proportional to a device's distance to the source. frequent small changes in the network or source thus cause frequent estimate changes only within a distance proportional to the magnitude of the change, as verified in simulation on a network of 1000 devices. this can enable drastic reductions in the communication and energy cost of gradient-based algorithms.
mobility aware path maintenance in ad hoc networks. current research on routing in ad hoc networks consider route discovery as the main problem and literature describes numerous on-demand protocols that discover a path between a source-destination pair. in contrast, the route maintenance phase is relatively neglected, and most protocols resort to route discovery as a result of route failure. we argue that this approach is inefficient, due to the fact that these protocols treat route failures in the same way as route discovery, and hence, the quality of service suffers from the additional delays resulting from the route rediscovery overhead. for data sessions over long periods of time, there is a good chance that the initial route discovered will fail before the session ends. thus, an intelligent protocol should be more proactive in maintaining routes so that packets may be readily switched to an alternate route when the route failure occurs, rather than wait for the route to break to restart the route discovery process. in this paper, we describe an efficient protocol that carries out route maintenance using path reliability information, and analyze the effect of using reliability information in path selection. simulations results show that our approach outperforms aodv in terms of increased throughput and reduced overhead. our approach can be an useful enhancement to on-demand routing protocols to increase their overall efficiency.
heterogeneous real-time embedded software optimization considering hardware platform. this paper classifies embedded software into four models according to hardware platform and execution time. we propose an algorithm, cmpch (cost minimization with probability for configurable hardware) that efficiently solves the configurable hardware, no-fixed execution time model, which is the most complicated model among the four models we proposed according to hardware platform and execution time. cmpch can solve other three models also. our approach fully takes advantage of configurable hardware and the soft real-time feature to improve the system performance. experimental results show our approach achieves significant cost-reduction comparing with previous work.
a class of multistep sparse matrix strategies for concept decomposition matrix approximation. in information retrieval, text documents are usually modeled as a term-document matrix which has high dimensional and space vectors. to reduce the high dimensions, one of the various dimensionality reduction methods, concept decomposition, has been developed by [3]. this method is based on document clustering techniques and least-square matrix approximation to approximate the matrix of vectors. gao and zhang [4] have indicated that the retrieval accuracy from the concept decomposition can be comparable to that from latent semantic indexing. however the numerical computation is expensive. in this paper we presented a class of multistep spare matrix strategies for concept decomposition matrix approximation. in this approach, a series of simple sparse matrices are used to approximate the decompositions. our numerical experiments show the advantage of such an approach in terms of storage costs and query time compared with other approaches while maintaining comparable retrieval quality.
scene map on wireless mobile platform. mobile devices equipped with gps provide powerful tools for location-aware applications. adding views to locations further augments users' perception of spaces. this work proposes a systematic approach to establish scene maps of a city with local, route, and object panoramas. the scene map is an ideal digest to access spatial information. we design comprehensive visual indexes for virtual travel and real guidance of an area based on pdas. the extended fields of view of various panoramas enhance the understanding of spaces on small mobile devices. densely embedded indexes from views allow free transitions and exploration in the urban area.
frequent spatio-temporal patterns in trajectory data warehouses. in this paper we present an approach for storing and aggregating spatio-temporal patterns by using a trajectory data warehouse (tdw). in particular, our aim is to allow the analysts to quickly evaluate frequent patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval. we resort to a tdw, based on a data cube model, having spatial and temporal dimensions, discretized according to a hierarchy of regular grids, and whose facts are sets of trajectories which intersect the spatio-temporal cells of the cube. the idea is to enrich such a tdw with a new measure: frequent patterns obtained from a data-mining process on trajectories. as a consequence these patterns can be analysed by the user at various levels of granularity by means of olap queries. the research issues discussed in this paper are (1) the extraction/mining of the patterns to be stored in each cell, which requires an adequate projection phase of trajectories before mining; (2) the spatio-temporal aggregation of patterns to answer roll-up queries, which poses many problems due to the holistic nature of the aggregation function.
an approach for component testing and its empirical validation. the lack of information limits component consumers to understand candidate components sufficiently in a way they can check if a given component fulfills its goal. thus, this paper presents an approach to support component testing aiming to reduce the lack of information between component producers and component consumers. additionally, the approach is covered by a case tool integrated in the development environment. an experimental study was performed in order to evaluate its efficiency and difficulties of its use. the experimental study indicates that the approach is viable and the tool support provides effort reduction to component producers and component consumers.
: a new function update scheme by arbitration between a remote call and a dynamic update for wireless sensor networks. in this paper, we propose arbiter-recall, which is a new kind of function code update management. when a function requires updating, arbiter-recall adaptively selects an energy-efficient method, deciding between a remote-call and a local-call after updating a function. the run-time decision is made by analyzing the cost of a remote-call, and a local-call with dynamic function update in terms of the energy consumption. simulation results show that arbiter-recall significantly reduces energy consumption compared to existing schemes.
constructing process views for service outsourcing. service outsourcing is a business paradigm in which an organization has a part of its business process performed by a service provider. the outsourced service can be specified in a public process view, which shields secret or irrelevant details from the internal business process of the provider. this way, the provider can reveal only public, relevant parts of its private business process to the client organization. to allow efficient monitoring and control by a consumer, a provider can offer a public activity as either invokable or observable.