Collecting data for comparability: benchmarking softwaredevelopment productivity

Whether you are benchmarking an organization or simply a project, it all boils down to one thing-data. Do you have the necessary data in your company, and is that data valid and comparable? How can you access data from other organizations? To help you answer these questions and avoid some common serious mistakes in the benchmarking process, the author has summarized her practical real-life experiences with software project data collection and benchmarking efforts in the guidelines     

The successful diffusion of innovations: guidance for softwaredevelopment organizations

Using the personal software process (PSP) as an example of an innovative information technology, the authors performed a field study of developers using the PSP approach on software development projects in industry. Their analysis of the results offers practical guidance on how software development organizations should support the diffusion of innovations into successful practice     

Local and global approaches for unsupervised image annotation

Image annotation is the task of assigning keywords to images with the goal of facilitating their organization and accessibility options (e.g., searching by keywords). Traditional annotation methods are based on supervised learning. Although being very effective, these methods require of large amounts of manually labeled images, and are limited in the sense that images can only be labeled with concepts seen during the training phase. Unsupervised automatic image annotation (UAIA) methods, on the other hand, neglect strongly-labeled images and instead rely on huge collections of unstructured text containing images for the annotation. In addition to not requiring labeled images, unsupervised techniques are advantageous because they can assign (virtually) any concept to an image. Despite these benefits, unsupervised methods have not been widely studied in image annotation, a reason for this is the lack of a reference framework for UAIA. In this line, this paper introduces two effective methods for UAIA in the context of a common framework inspired in the way a query is expanded throughout Automatic Query Expansion (AQE) in information retrieval. On the one hand, we describe a local method that processes text information associated to images retrieved when using the image to annotate as query, several methods from the state of the art can be described under this formulation. On the other hand, we propose a global method that pre-process offline the reference collection to identify visual-textual associations that are later used for annotation. Both methods are extensively evaluated in benchmarks for large-scale UAIA. Experimental results show the competitiveness of both strategies when compared to the state of the art. We foresee the AQE-based framework will pave the way for the development of alternative and effective methods for UAIA.     

Deterministic approaches for solving practical black-box global optimization problems

In many important design problems, some decisions should be made by finding the global optimum of a multiextremal objective function subject to a set of constrains. Frequently, especially in engineering applications, the functions involved in optimization process are black-box with unknown analytical representations and hard to evaluate. Such computationally challenging decision-making problems often cannot be solved by traditional optimization techniques based on strong suppositions about the problem (convexity, differentiability, etc.). Nature and evolutionary inspired metaheuristics are also not always successful in finding global solutions to these problems due to their multiextremal character. In this paper, some innovative and powerful deterministic approaches developed by the authors to construct numerical methods for solving the mentioned problems are surveyed. Their efficiency is shown on solving both the classes of random test problems and some practical engineering tasks.     

Wisdom: a software engineering method for small softwaredevelopment companies

Wisdom (Whitewater Interactive System Development with Object Models), a lightweight software engineering method, addresses the needs of small teams that develop and maintain interactive systems. Small companies leveraging on their communication, speed and flexibility can benefit from Wisdom's process, notation and project philosophy     

Multivariate global sensitivity analysis for dynamic models based on energy distance

In this paper, a new kind of multivariate global sensitivity index based on energy distance is proposed. The covariance decomposition based index has been widely used for multivariate global sensitivity analysis. However, it just considers the variance of multivariate model output and ignores the correlation between different outputs. The proposed index considers the whole probability distribution of dynamic output based on characteristic function and contains more information of uncertainty than the covariance decomposition based index. The multivariate probability integral transformation based index is an extension of the popularly used moment-independent sensitivity analysis index. Although it considers the whole probability distribution of dynamic output, it is difficult to estimate the joint cumulative distribution function of dynamic output. The proposed sensitivity index can be easily estimated, especially for models with high dimensional outputs. Compared to the classic sensitivity indices, the proposed sensitivity index can be easily used for dynamic systems and obtain reasonable results. An efficient method based on the idea of the given-data method is used to estimate the proposed sensitivity index with only one set of input-output samples. The numerical and engineering examples are employed to compare the proposed index and the covariance decomposition based index. The results show that the input variables may have different effect on the whole probability distribution and variance of dynamic model output since the proposed index and the covariance decomposition based index measure the effects of input variables on the whole distribution and variance of model output separately.     

Various approaches to speed up Mahalanobis distance classifier

In this paper, we propose various approaches to reduce the CPU time requirement of Mahalanobis distance classifier (MDC). Based on the results discussed in this paper, we have found that the Ensemble average approach and Quadratic form range theorem are superior to other algorithms. The algorithms based on these two approaches are found to give a maximum speed of 7 and 9 with MSS and TM data, respectively.     

Lessons learned in an industrial software lab [softwaredevelopment]

The author offers a personal account of the issues and challenges that have confronted the software developer over the last few decades. The failures and successes associated with formal methods, the realities of reuse, and the implementation of development process models and goals are discussed     

Will distance learning create a global university?

It is predicted that the development whereby universities have campuses connected to the central site by telecom links will continue until there are very few universities in the world, and perhaps only one. An exception is that some countries may try to maintain national universities for certain areas of study. The article discusses the possible consequences of such a development and the likelihood that a country would be successful in protecting its national institution.     

Innovative approaches to integrated global change modelling

Integrated models are important tools to investigate the interactions between planetary processes and the growing impacts of human populations – in short: global change. Current models still have significant shortcomings, notably in their representation of socio-economic processes and the feedbacks between these and the environmental system. They are also often not designed with sufficient transparency to enable participation of interested parties or effective communication with stakeholders and policy makers. These deficiencies are discussed and possible directions for improvement are identified. This Thematic Issue provides a collection of papers that offer a number of innovative ideas for remedying these shortcomings using novel methods and approaches.     

Practical applications of statistical process control [in softwaredevelopment projects]

Applying quantitative methods such as statistical process control (SPC) to software development projects can provide a positive cost-benefit return. The authors used SPC on inspection and test data to assess product quality during testing and to predict post-shipment product quality for a major software release     

Comparison of deterministic and stochastic approaches to global optimization

In this paper, we compare two different approaches to nonconvex global optimization. The first one is a deterministic spatial Branch‐and‐Bound algorithm, whereas the second approach is a Quasi Monte Carlo (QMC) variant of a stochastic multi level single linkage (MLSL) algorithm. Both algorithms apply to problems in a very general form and are not dependent on problem structure. The test suite we chose is fairly extensive in scope, in that it includes constrained and unconstrained problems, continuous and mixed‐integer problems. The conclusion of the tests is that in general the QMC variant of the MLSL algorithm is generally faster, although in some instances the Branch‐and‐Bound algorithm outperforms it.     

Sparse polynomial chaos expansions for global sensitivity analysis with partial least squares and distance correlation

Polynomial chaos expansion (PCE) has been proven to be a powerful tool for developing surrogate models in the field of uncertainty and global sensitivity analysis. The computational cost of classical PCE is unaffordable since the number of terms grows exponentially with the dimensionality of inputs. This considerably restricts the practical use of PCE. An efficient approach to address this problem is to build a sparse PCE. Since some basis polynomials in representation are highly correlated and the number of available training samples is small, the sparse PCE obtained by the original least square (LS) regression using these samples may not be accurate. Meanwhile, correlation between the non-influential basis polynomial and the important basis polynomials may disturb the correct selection of the important terms. To overcome the influence of correlation in the construction of sparse PCE, a full PCE of model response is first developed based on partial least squares technique in the paper. And an adaptive algorithm based on distance correlation is proposed to select influential basis polynomials, where the distance correlation is used to quantify effectively the impact of basis polynomials on model response. The accuracy of the surrogate model is assessed by leave-one-out cross validation. The proposed method is validated by several examples and global sensitivity analysis is performed. The results show that it maintains a balance between model accuracy and complexity.

     

Trimming local and global self-intersections in offset curves/surfaces using distance maps

A robust and efficient algorithm for trimming both local and global self-intersections in offset curves and surfaces is presented. Our scheme is based on the derivation of a rational distance map between the original curve or surface and its offset. By solving a bivariate polynomial equation for an offset curve or a system of three polynomial equations for an offset surface, all local and global self-intersection regions in offset curves or surfaces can be detected. The zero-set of polynomial equation(s) corresponds to the self-intersection regions. These regions are trimmed by projecting the zero-set into an appropriate parameter space. The projection operation simplifies the analysis of the zero-set, which makes the proposed algorithm numerically stable and efficient. Furthermore, in a post-processing step, a numerical marching method is employed, which provides a highly precise scheme for self-intersection elimination in both offset curves and surfaces. The effectiveness of our approach is demonstrated using several experimental results.     

Recent approaches to global optimization problems through Particle Swarm Optimization

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and ¹ errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.     

Multiple Adaptive Agents for Tactical Driving

Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However, there is currently no system for tactical-level reasoning. Such a system should address tasks such as passing cars, making exits on time, and merging into a traffic stream. Many previous approaches have attempted to hand construct large rule-based systems which capture the interactions between multiple input sensors, dynamic and potentially conflicting subgoals, and changing roadway conditions. However, these systems are extremely difficult to design due to the large number of rules, the manual tuning of parameters within the rules, and the complex interactions between the rules. Our approach to this intermediate-level planning is a system which consists of a collection of autonomous agents, each of which specializes in a particular aspect of tactical driving. Each agent examines a subset of the intelligent vehicle's sensors and independently recommends driving decisions based on their local assessment of the tactical situation. This distributed framework allows different reasoning agents to be implemented using different algorithms.When using a collection of agents to solve a single task, it is vital to carefully consider the interactions between the agents. Since each reasoning object contains several internal parameters, manually finding values for these parameters while accounting for the agents' possible interactions is a tedious and error-prone task. In our system, these parameters, and the system's overall dependence on each agent, is automatically tuned using a novel evolutionary optimization strategy, termed Population-Based Incremental Learning (PBIL).Our system, which employs multiple automatically trained agents, can competently drive a vehicle, both in terms of the user-defined evaluation metric, and as measured by their behavior on several driving situations culled from real-life experience. In this article, we describe a method for multiple agent integration which is applied to the automated highway system domain. However, it also generalizes to many complex robotics tasks where multiple interacting modules must simultaneously be configured without individual module feedback.     

Overlapping-multi-layer deadzone for alleviating over conservativeness in robot adaptive tracking

We present an overlapping-multi-layer deadzone approach to the adaptive tracking control of robotic manipulators in the presence of an unknown mass matrix and completely unmodeled disturbance torques. Even in the unrealistic case of a known mass matrix, over conservative bounds on the disturbance size lead to an unnecessarily large single-level deadzone and hence an unnecessarily large asymptotic tracking error. Additionally, a single-level deadzone is not assured to prevent the “bursting” phenomenon if the mass matrix is not known. The approach we propose herein is proved to cause convergence of the generalized tracking error to within a user-specified tolerance above the level of the smallest “valid” deadzone size (times a known bound on the square root of the ratio of the largest-possible eigenvalue of the mass matrix to the smallest-possible eigenvalue of the mass matrix), even though this size is not known a priori. The result is that we obtain provable convergence to a tracking error size that is much smaller than we would have with a single-level conservative deadzone. The reason deadzones of sufficient size are needed in the first place is as follows (and this applies even if sigma-modification or e1-modification are used). Even if the mass matrix were unrealistically known, without any deadzone, the following can happen, to the best of our knowledge. We can incur indefinite cycling between (i) and (ii): (i) time periods of Lyapunov function increase, due to a parameter error increase, while the generalized tracking error remains small, (ii) time periods of Lyapunov function decrease due to large reductions in the parameter error while the generalized tracking error simultaneously increases to large peak values before returning back to a small value. With indefinite cycling between (i) and (ii), there is no convergence of the generalized tracking error to small values. This phenomenon has been termed “bursting” by authors such as [P. Ioannou, J. Sun, Robust Adaptive Control, Prentice-Hall, Englewood Cliffs, NJ, 1996.] and is described as “one of the most annoying phenomena in adaptive control.” To the best of our knowledge, this paper, once published, will represent the first presentation of an overlapping-multi-layer deadzone method for adaptive tracking control of robotic manipulators with unknown mass matrix and disturbance torque.     

一种缓解协同过滤算法数据稀疏性的方法

协同过滤算法是推荐系统中最古老的算法之一,同时也是当今推荐系统中使用最广泛的一种算法。但是在简单,效率高的同时,协同过滤算法还存在数据稀疏性,冷启动等一些问题.本文针对其数据稀疏性的问题,提出了一种根据兴趣度预测用户未评分项目的方法。最后通过基于Netflix数据集的实验结果表明,该方法能够更好的处理稀疏矩阵,能缓解数据稀疏问题,从而提高了协同过滤算法的准确性。     

A robust global and local mixture distance based non-rigid point set registration

We present a robust global and local mixture distance (GLMD) based non-rigid point set registration method which consists of an alternating two-step process: correspondence estimation and transformation updating. We first define two distance features for measuring global and local structural differences between two point sets, respectively. The two distances are then combined to form a GLMD based cost matrix which provides a flexible way to estimate correspondences by minimizing global or local structural differences using a linear assignment solution. To improve the correspondence estimation and enhance the interaction between the two steps, an annealing scheme is designed to gradually change the cost minimization from local to global and the thin plate spline transformation from rigid to non-rigid during registration. We test the performance of our method in contour registration, sequence images and real images, and compare with six state-of-the-art methods where our method shows the best alignments in most scenarios.