Situational method engineering: combining assembly-based and roadmap-driven approaches

Because the engineering situation of each information system development (ISD) project is different, engineering methods need to be adapted, transformed or enhanced to satisfy the specific project situation. Contributions, in the field of situational method engineering (SME), aim at providing techniques and tools allowing to construct project-specific methods instead of looking for universally applicable ones. In addition to the engineering method tailoring, necessary to fit the project situation, a customization of the engineering method for each engineer participating in the project is also required. Such a configuration allows a better understanding of the method by focusing on guidelines related to the project engineer’s daily tasks. It also increases his/her involvement in the ISD method realization. To achieve this twofold objective (ISD method tailoring and customization), we propose a framework for SME combining an assembly-based approach for project-specific method construction and a roadmap-driven approach for engineer-specific method configuration. The first step of our process provides support to build a new method that is most suitable for the current ISD project situation, whereas the second step aims at choosing the most adapted path (roadmap) to satisfy the requirements of a particular project engineer within the project-specific method. The two core elements of our SME framework are the method chunks repository and the reuse frame. The former concerns reusable method components definition and storage whereas the latter deals with the characterization of the project situation and the project engineer’s profile. In this paper we start first by presenting our SME framework and its core elements: the method chunk repository and the reuse frame. Then we show how to take advantage of them through our two-step process combining assembly-based method construction and roadmap-driven method configuration.     

Effective approaches to combining lexical and syntactical information for code summarization

Natural language summaries of source codes are important during software development and maintenance. Recently, deep learning based models have achieved good performance on the task of automatic code summarization, which encode token sequence or abstract syntax tree (AST) of code with neural networks. However, there has been little work on the efficient combination of lexical and syntactical information of code for better summarization quality. In this paper, we propose two general and effective approaches to leveraging both types of information: a convolutional neural network that aims to better extract vector representation of AST node for downstream models; and a Switch Network that learns an adaptive weight vector to combine different code representations for summary generation. We integrate these approaches into a comprehensive code summarization model, which includes a sequential encoder for token sequence of code and a tree based encoder for its AST. We evaluate our model on a large Java dataset. The experimental results show that our model outperforms several state-of-the-art models on various metrics, and the proposed approaches contribute a lot to the improvements.     

Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches

Sentiment polarity detection is one of the most popular tasks related to Opinion Mining. Many papers have been presented describing one of the two main approaches used to solve this problem. On the one hand, a supervised methodology uses machine learning algorithms when training data exist. On the other hand, an unsupervised method based on a semantic orientation is applied when linguistic resources are available. However, few studies combine the two approaches. In this paper we propose the use of meta-classifiers that combine supervised and unsupervised learning in order to develop a polarity classification system. We have used a Spanish corpus of film reviews along with its parallel corpus translated into English. Firstly, we generate two individual models using these two corpora and applying machine learning algorithms. Secondly, we integrate SentiWordNet into the English corpus, generating a new unsupervised model. Finally, the three systems are combined using a meta-classifier that allows us to apply several combination algorithms such as voting system or stacking. The results obtained outperform those obtained using the systems individually and show that this approach could be considered a good strategy for polarity classification when we work with parallel corpora.     

New approaches to relaxed stabilization conditions and H-infinity control designs for T-S fuzzy systems

This paper focuses on the problem of fuzzy control for a class of continuous-time T-S fuzzy systems.New methods of stabilization design and H infinity control are derived based on a relaxed approach in...     

Periodic motions of the Pendubot via virtual holonomic constraints: Theory and experiments

This paper presents a new control strategy for an underactuated two-link robot, called the Pendubot. The goal is to create stable oscillations of the outer link of the Pendubot, which is not directly actuated. We exploit a recently proposed feedback control design strategy, based on motion planning via virtual holonomic constraints. This strategy is shown to be useful for design of regulators for achieving: stable oscillatory motions, a closed-loop-design-based swing-up, and propeller motions. The theoretical results are verified via successful experimental implementation.     

Networking communities from the bottom up: grassroots approaches to overcoming the digital divide

Achieving meaningful usage of the Internet is more than attaining access: multiple social and technological insufficiencies must be overcome and continually readdressed. A wide variety of approaches have been undertaken to address these issues, both to enable individuals to cross the ‘digital divide’ and also to enhance community interactions. In this paper, we focus on one approach–grassroots networked communities. These are communities of locality that have developed their own computer network infrastructure with minimal external support. We analyse eight examples from the UK and present survey results, identifying key characteristics and modes of operation. We argue that such initiatives may offer a viable method of overcoming multiple digital insufficiencies and ensure sustainable and meaningful ICT usage. Further research has commenced to analyse how the presence of social software and near-ubiquitous computer network access affects the sharing and storage of information within communities of locality.     

The quest for runware: on compositional, executable and intuitive models

We believe that future models of complex software and systems will combine the crucial traits of intuitiveness, compositionality, and executability. The importance of each of these to modeling is already well recognized, but our vision suggests a far more powerful synergy between them. First, models will be aligned with cognitive processes used by humans to think about system behavior and will be understood, and perhaps creatable, by almost anyone. Second, one will be able to build models incrementally, adding to, refining or sculpting away already-specified behaviors without changing most existing parts of the model. Third, there will be powerful ways to execute such intuitive and compositional models, in whole or in part, at any stage of the development. The presence of these three traits in a single artifact will blur the boundaries between natural-language requirements, formal models, and actual software, bringing in its wake a major advance in the way systems are built, and in their cost and quality. We propose the term runware to refer to this kind of higher level artifact.     

Choosing small sets of policy-relevant scenarios by combining vulnerability and diversity approaches

Computer simulation models can generate large numbers of scenarios, far more than can be effectively utilized in most decision support applications. How can one best select a small number of scenarios to consider? One approach calls for choosing scenarios that illuminate vulnerabilities of proposed policies. Another calls for choosing scenarios that span a diverse range of futures. This paper joins these two approaches for the first time, proposing an optimization-based method for choosing a small number of relevant scenarios that combine both vulnerability and diversity. The paper applies the method to a real case involving climate resilient infrastructure for three African river basins (Volta, Orange and Zambezi). Introducing selection criteria in a stepwise manner helps examine how different criteria influence the choice of scenarios. The results suggest that combining vulnerability- and diversity-based criteria can provide a systematic and transparent method for scenario selection.     

Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in the T_s–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI, T_s, F_vegetation, F_soil, temperature (T), precipitation at time t (P_t, P_{t – 1}, P_{t – 2}), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R²) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between ?2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations.     

A robust stabilization problem of fuzzy control systems and itsapplication to backing up control of a truck-trailer

A robust stabilization problem for fuzzy systems is discussed in accordance with the definition of stability in the sense of Lyapunov. We consider two design problems: nonrobust controller design and robust controller design. The former is a design problem for fuzzy systems with no premise parameter uncertainty. The latter is a design problem for fuzzy systems with premise parameter uncertainty. To realize two design problems, we derive four stability conditions from a basic stability condition proposed by Tanaka and Sugeno: nonrobust condition, weak nonrobust condition, robust condition, and weak robust condition. We introduce concept of robust stability for fuzzy control systems with premise parameter uncertainty from the weak robust condition. To introduce robust stability, admissible region and variation region, which correspond to stability margin in the ordinary control theory, are defined. Furthermore, we develop a control system for backing up a computer simulated truck-trailer which is nonlinear and unstable. By approximating the truck-trailer by a fuzzy system with premise parameter uncertainty and by using concept of robust stability, we design a fuzzy controller which guarantees stability of the control system under a condition. The simulation results show that the designed fuzzy controller smoothly achieves backing up control of the truck-trailer from all initial positions     

WTFM, software for well test analysis in fractured media combining fractional flow with double porosity and leakance approaches

The identification of the hydraulic characteristics and transport properties of fractured reservoirs requires the development of specific models that account for (i) the medium heterogeneity, e.g. the presence of major conductive fractures that delimit capacitive matrix blocks, with weakly open, dead end or isolated fractures, and (ii) for the geometrical arrangement of the major conductive fractures network, which dominates the flow at the scale of the well tests.Well Tests in Fractured Media (WTFM) software takes into account these two main features by combining radial flow generalized to fractional dimension, with the theory of double-porosity, including diffusivity in the second porosity, transient inter-porosity flow and inter-porosity skin effect, and with leakance. The implementation of this nD model, with n fractional, extends usefully the domain of application of the usual 1D/2D/3D double-porosity/leakance models for a large range of connection levels of fracture networks. Although the fractures geometry and properties are not considered one by one, or by directional families, they are taken into account by averaged properties and by the impact that the whole network has on the hydrodynamic behaviour. The accuracy of the coupled transient behaviours analysis is augmented by taking into account wellbore storage and skin effects. All together, the use of these different options allows matching a wide range of pumping test curves, characteristics of distinctive behaviours, with a limited number of parameters. Distinctive well test experiments, in both sedimentary and crystalline rocks, are presented for enlightening how the pertinent use of the model options improves predictions.     

Traditional, semantic, and hypersemantic approaches to datamodeling

An overview is given of past present data-modeling trends, and future directions are identified. The three traditional and commonly used data models that gained wide acceptance in the late 1960s and early 1970s and are used extensively today, namely the relational, hierarchical, and network models, are reviewed. Semantic data models that attempt to enhance the representation of operational information by capturing more of the meaning about data values and relationships are described. Enhancements to semantic data models that characterize hypersemantic data models and emphasize capturing inferential relationships are discussed     

Human pose,hand and mesh estimation using deep learning: a survey

Human pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3D HPE approaches.

     

Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine

Neural Computing and Applications - The determination of the energy characteristics of a Kaplan hydraulic turbine is based on numerous measuring points during extensive and expensive experimental...     

Interactive real-time physics: An intuitive approach to form-finding and structural analysis for design and education

Real-time physics simulation has been extensively used in computer games, but its potential has yet to be fully realized in design and education. We present an interactive 3D physics engine with a wide variety of applications.In common with traditional FEM, the use of a local element stiffness matrix is retained. However, unlike typical non-linear FEM routines elements forces, moments and inertia are appropriately lumped at nodes following the dynamic relaxation method. A semi-implicit time integration scheme updates linear and angular momentum, and subsequently the local coordinate frames of the nodes. A co-rotational approach is used to compute the resultant field of displacements in global coordinates including the effect of large deformations. The results obtained compare well against established commercial software.We demonstrate that the method presented allows the making of interactive structural models that can be used in teaching to develop an intuitive understanding of structural behaviour. We also show that the same interactive physics framework allows real-time optimization that can be used for geometric and structural design applications.     

结合全局和双核局部拟合的活动轮廓分割模型

针对可缩放区域拟合（RSF）模型对初始轮廓敏感的缺点,提出了一种结合全局和局部图像信息的变分水平集活动轮廓模型。该模型设计了一个灰度域上的核函数,将其与RSF模型空域核的线性组合作为局部能量项,弥补了采样权值仅与空间距离有关的缺陷,提高了分割精度;构造了带有自适应全局指示函数的面积项,作为全局拟合力,提高了模型的收敛速度且避免陷入局部极小值;使用了高斯滤波方法,规则水平集函数,使其保持光滑,并避免了复杂的重新初始化过程。实验结果表明,该模型初始化灵活,对灰度不均匀图像有很好的分割效果     

A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used – a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.     

Visual-model-based,real-time 3D pose tracking for autonomous navigation: methodology and experiments

This paper presents a novel 3D-model-based computer-vision method for tracking the full six degree-of-freedom (dof) pose (position and orientation) of a rigid body, in real-time. The methodology has been targeted for autonomous navigation tasks, such as interception of or rendezvous with mobile targets. Tracking an object’s complete six-dof pose makes the proposed algorithm useful even when targets are not restricted to planar motion (e.g., flying or rough-terrain navigation). Tracking is achieved via a combination of textured model projection and optical flow. The main contribution of our work is the novel combination of optical flow with z-buffer depth information that is produced during model projection. This allows us to achieve six-dof tracking with a single camera. A localized illumination normalization filter also has been developed in order to improve robustness to shading. Real-time operation is achieved using GPU-based filters and a new data-reduction algorithm based on colour-gradient redundancy, which was developed within the framework of our project. Colour-gradient redundancy is an important property of colour images, namely, that the gradients of all colour channels are generally aligned. Exploiting this property provides a threefold increase in speed. A processing rate of approximately 80 to 100 fps has been obtained in our work when utilizing synthetic and real target-motion sequences. Sub-pixel accuracies were obtained in tests performed under different lighting conditions.

Scaling up multi-touch selection and querying: Interfaces and applications for combining mobile multi-touch input with large-scale visualization displays

We present a mobile multi-touch interface for selecting, querying, and visually exploring data visualized on large, high-resolution displays. Although emerging large (e.g., ～10 m wide), high-resolution displays provide great potential for visualizing dense, complex datasets, their utility is often limited by a fundamental interaction problem – the need to interact with data from multiple positions around a large room. Our solution is a selection and querying interface that combines a hand-held multi-touch device with 6 degree-of-freedom tracking in the physical space that surrounds the large display. The interface leverages context from both the user's physical position in the room and the current data being visualized in order to interpret multi-touch gestures. It also utilizes progressive refinement, favoring several quick approximate gestures as opposed to a single complex input in order to most effectively map the small mobile multi-touch input space to the large display wall. The approach is evaluated through two interdisciplinary visualization applications: a multi-variate data visualization for social scientists, and a visual database querying tool for biochemistry. The interface was effective in both scenarios, leading to new domain-specific insights and suggesting valuable guidance for future developers.     

多视频摘要技术:方法、应用及挑战

多视频摘要技术近年来受到了国内外学者的广泛关注,它是指通过对视频结构和内容的分析,从多个相关视频文件中提取出有意义的部分,将它们以一定方式进行组合,形成简洁的、能够充分表现语义内容的概要,目的是提供快捷的浏览和查询服务。目前该技术还处于起步阶段,实际的应用系统很少,有关体系结构、技术方法仍需要深入研究,更多的应用方向还有待开拓。在介绍研究意义的基础上,对多视频摘要的研究现状和主要方法进行了归纳、评述,探讨了一些应用方向,指出了目前研究面临的重点问题以及发展趋势。