An integrative approach to requirements analysis: How task models support requirements reuse in a user-centric design framework

Many software systems fail to address their intended purpose because of a lack of user involvement and requirements deficiencies. This paper discusses the elaboration of a requirements-analysis process that integrates a critical-parameter-based approach to task modeling within a user-centric design framework. On one hand, adapting task models to capture requirements bridges the gap between scenarios and critical parameters which benefits design from the standpoint of user involvement and accurate requirements. On the other hand, using task models as a reusable component leverages requirements reuse which benefits design by increasing quality while simultaneously reducing development costs and time-to-market. First, we present the establishment of both a user-centric and reuse-centric requirements process along with its implementation within an integrated design tool suite. Secondly, we report the design, procedures, and findings of two user studies aimed at assessing the feasibility for novice designers to conduct the process as well as evaluating the resulting benefits upon requirements-analysis deliverables, requirements quality, and requirements reuse.     

Benchmarking stereo-derived DSMs to lidar by land cover and slope for resource development in northern Ontario,Canada

Topographic and elevation data are essential in the development of supporting infrastructure around mining sites. The de facto standard for acquiring elevation data is through light detection and ranging (lidar). The high labour and monetary cost of acquiring lidar has fostered more cost-effective approaches for creating elevation models that use stereo photogrammetry. To assess the accuracy of stereo-photogrammetry-derived elevation models and their potential application, we benchmark satellite (Worldview-2) and aircraft (South Central Ontario Orthoimagery Project; SCOOP) stereo-derived digital surface models (DSMs) against a lidar-derived DSM. Our results show that both stereo-derived DSMs have strong monotonic correlations with lidar across a range of land-cover types and slopes. The overall vertical accuracy of Worldview-2 and SCOOP DSMs are similar and do not meet the United States National Digital Elevation Program (NDEP) standards. However, accuracy assessment across land-cover types and slope categories show that specific land cover types (i.e. grass, row crops/pasture, sparse vegetation and marsh) on gently sloping terrain compare well to lidar data and meet NDEP accuracy standards. We situate the presented research in the context of northern resource development and discuss opportunities to improve the vertical accuracy of stereo-derived DSMs, for example, through unmanned aerial systems.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

Visually Perceived Distance Judgments: Tablet-Based Augmented Reality Versus the Real World

Does visually perceived distance differ when objects are viewed in augmented reality (AR), as opposed to the real world? What are the differences? These questions are theoretically interesting, and the answers are important for the development of many tablet- and phone-based AR applications, including mobile AR navigation systems. This article presents a thorough literature review of distance judgment experimental protocols, and results from several areas of perceptual psychology. In addition to distance judgments of real and virtual objects, this section also discusses previous work in measuring the geometry of virtual picture space and considers how this work might be relevant to tablet AR. Then, the article presents the results of two experiments. In each experiment, observers bisected egocentric distances of 15 and 30 m in tablet-based AR and in the real world, in both indoor corridor and outdoor field environments. In AR, observers bisected the distances to virtual humans, while in the real world, they bisected the distances to real humans. This is the first reported research that directly compares distance judgments of real and virtual objects in a tablet AR system. Four key findings were: (1) In AR, observers expanded midpoint intervals at 15 m, but compressed midpoints at 30 m. (2) Observers were accurate in the real world. (3) The environmental setting—corridor or open field—had no effect. (4) The picture perception literature is important in understanding how distances are likely judged in tablet-based AR. Taken together, these findings suggest the depth distortions that AR application developers should expect with mobile and especially tablet-based AR.     

Physical ergonomic hazards in highway tunnel construction: overview from the Construction Occupational Health Program

This report provides an overview of physical ergonomic exposures in highway construction work across trades and major operations. For each operation, the observational method “PATH” (Posture, Activity, Tools and Handling) was used to estimate the percentage of time that workers spent in specific tasks and with exposure to awkward postures and load handling. The observations were carried out on 73 different days, typically for about 4 h per day, covering 120 construction workers in 5 different trades: laborers, carpenters, ironworkers, plasterers, and tilers. Non-neutral trunk postures (forward or sideways flexion or twisting) were frequently observed, representing over 40% of observations for all trades except laborers (28%). Kneeling and squatting were common in all operations, especially tiling and underground utility relocation work. Handling loads was frequent, especially for plasterers and tilers, with a range of load weights but most often under 15 pounds. The results of this study provide quantitative evidence that workers in highway tunnel construction operations are exposed to ergonomic factors known to present significant health hazards. Numerous opportunities exist for the development and implementation of ergonomic interventions to protect the health and safety of construction workers.     

Removal of partial occlusion from single images

This paper examines large partial occlusions in an image which occur near depth discontinuities when the foreground object is severely out of focus. We model these partial occlusions using matting, with the alpha value determined by the convolution of the blur kernel with a pinhole projection of the occluder. The main contribution is a method for removing the image contribution of the foreground occluder in regions of partial occlusion, which improves the visibility of the background scene. The method consists of three steps. First, the region of complete occlusion is estimated using a curve evolution method. Second, the alpha value at each pixel in the partly occluded region is estimated. Third, the intensity contribution of the foreground occluder is removed in regions of partial occlusion. Experiments demonstrate the method's ability to remove the effects of partial occlusion in single images with minimal user input.     

Detecting global predicates in distributed systems with clocks

Summary. This paper proposes a framework for detecting global state predicates in systems of processes with approximately-synchronized real-time clocks. Timestamps from these clocks are used to define two orderings on events: “definitely occurred before” and “possibly occurred before”. These orderings lead naturally to definitions of 3 distinct detection modalities, i.e., 3 meanings of “predicate held during a computation”, namely: (“ possibly held”), (“ definitely held”), and (“ definitely held in a specific global state”). This paper defines these modalities and gives efficient algorithms for detecting them. The algorithms are based on algorithms of Garg and Waldecker, Alagar and Venkatesan, Cooper and Marzullo, and Fromentin and Raynal. Complexity analysis shows that under reasonable assumptions, these real-time-clock-based detection algorithms are less expensive than detection algorithms based on Lamport's happened-before ordering. Sample applications are given to illustrate the benefits of this approach. Received: January 1999 / Accepted: November 1999     

Design and implementation of a vehicle interface protocol using an IEEE 1394 network

A wide variety of in-vehicle devices such as camera sensors, navigation systems, telematics and communication equipments have been incorporated into a vehicle to realize Intelligent Transport Systems (ITS) applications. Because an efficient standardized network is required, ITS Data Bus (IDB) has been discussed to carry high-speed multimedia data for audio, video and other real-time ITS applications. For connecting devices in a standardized manner, the IDB network has architecture with a gateway called vehicle interface which is located between automaker’s proprietary network and the standardized IDB network. IEEE 1394 (also known as iLink or FireWire), which can transport multimedia data for consumer electronics, is a good candidate for IDB network. In this paper, we analyze the issues for existing AV/C protocol (application layer protocol over IEEE 1394) to comprise the IDB network. In addition, we designed and implemented the vehicle interface protocol as a higher layer of IEEE 1394 to address the AV/C protocol issues for realizing the whole IDB network architecture.     

Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.