ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

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A new paradigm based on agents applied to free-hand sketch recognition

Important advances in natural calligraphic interfaces for CAD (Computer Aided Design) applications are being achieved, enabling the development of CAS (Computer Aided Sketching) devices that allow facing up to the conceptual design phase of a product. Recognizers play an important role in this field, allowing the interpretation of the user’s intention, but they still present some important lacks. This paper proposes a new recognition paradigm using an agent-based architecture that does not depend on the drawing sequence and takes context information into account to help decisions. Another improvement is the absence of operation modes, that is, no button is needed to distinguish geometry from symbols or gestures, and also “interspersing” and “overtracing” are accomplished.     

User-defined gestures for controlling primitive motions of an end effector

In designing and developing a gesture recognition system, it is crucial to know the characteristics of a gesture selected to control, for example, an end effector of a robot arm. We conducted an experiment to collect a set of user-defined gestures and investigate characteristics of the gestures for controlling primitive motions of an end effector in human–robot collaboration. We recorded 152 gestures from 19 volunteers by presenting virtual robotic arm movements to the participants, and then asked the participants to think about and perform gestures that would cause the motions. It was found that the hands were the parts of the body used most often for gesture articulation even when the participants were holding tools and objects with both hands: a number of participants used one- and two-handed gestures interchangeably, gestures were consistently performed by the participants across all pairs of reversible gestures, and the participants expected better recognition performance for gestures that were easy to think of and perform. These findings are expected to be useful as guidelines in creating a gesture set for controlling robotic arms according to natural user behaviors.     

Self-attention generative adversarial networks applied to conditional music generation

The task of audio and music generation in the waveform domain has become possible due to recent advances in deep learning. Generative Adversarial Networks (GANs) are a type of generative model that has achieved success in areas such as image, video and audio generation. However, realistic audio generation with GANs is still a challenge, thanks to the specific characteristics inherent to this kind of data. In this paper we propose a GAN model that employs the self-attention mechanism and produces small chunks of music conditioned by instrument. We compare our model to a baseline and run ablation studies in order to demonstrate its superiority. We also suggest some applications of the model, particularly in the area of computer assisted composition.

     

An operational process for goal-driven definition of measures

We propose an approach (GOM/MEDEA) for defining measures of product attributes in software engineering. The approach is driven by the experimental goals of measurement, expressed via the GQM paradigm, and a set of empirical hypotheses. To make the empirical hypotheses quantitatively verifiable, GQM/MEDEA supports the definition of theoretically valid measures for the attributes of interest based on their expected mathematical properties. The empirical hypotheses are subject to experimental verification. This approach integrates several research contributions from the literature into a consistent, practical, and rigorous approach.     

Polynomial trend surface analysis applied to AVHRR images to improve definition of arctic leads

Polynomial trend surface analysis was applied to three AVHRR images to determine whether regional trends in image radiance can be removed with this procedure. Results suggest that trend surface techniques can be effective in removing region-scale variation in image radiances that are related to uneven illumination, intermittent cloud cover, and variation in the surface temperature field. The dominant effects of illumination in Channel 2 (visible) data, caused by variable sun angle and proximity of the scene to the terminator, can be minimized by removing (subtracting) the first- and second-order trend surfaces from the raw image. These low-order surfaces also remove regional variation in the surface temperature field, which leads to marginal improvement in binary images derived from Channel 4 (infrared) data. Optimum results for both Channel 2 and Channel 4 data are achieved when the third- and fourth-order surfaces are subtracted to remove local temperature and illumination anomalies that occur at smaller spatial scales, primarily in the vicinity of clouds. Application of higher order surfaces fails to improve image quality. There is some indication that the topography of these higher-order surfaces in part maps regional variation in lead density. Use of a best-fit criterion based on a strict variance technique (such as the least-squares method) to define the trend surface limits the effectiveness of the technique in this application. Criteria that allow for data to be weighted based on their distance from the plane about which they cluster are more appropriate to the structure of AVHRR radiance data typical of images that show sea ice. A criterion that incorporates a rule system based on fuzzy logic offers an alternative means of assessing goodness-of-fit that might prove appropriate in this application.     

jBPM流程定义文件自动生成算法

基于jBPM的工作流管理系统在执行前要将流程定义文件发布到系统中,流程定义文件生成的传统做法是使用eclipse图形化工具手工进行绘制。通过对工作流设计阶段的流程任务节点数据进行分析,提出根据设计节点数据自动生成流程定义文件和流程图的算法。算法具体内容是对流程中的节点数据进行键值对的分析、处理,以确定任务节点在流程图的位置,然后生成流程定义文件和流程图,以供jBPM工作流管理系统发布、执行。最后通过装甲车辆测试方案生成系统验证了该算法的可行性和实用性。     

Towards a communication model applied to the interface design process

The design of computational artifacts involves many communication acts occurring through different channels; usability tests, for example, have been used as a main communication channel between designers and users. Other channels used during the design process include requirement analysis, scenario construction, prototyping, etc. This work takes the Westley and MacLehan communication model and projects it into the context of interactive artifact design, resulting in a model of fractal nature. Some channels already present during the design process fit in well with the model we are proposing. It also reveals that other channels have not been explored in the global process of communication. Thus, we show how a semiotic-based analysis can unify techniques, methods and tools that support design, as well as make explicit the necessary interactions among designers, users and computers through different channels.     

Learning to embed music and metadata for context-aware music recommendation

Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.     

Analytic hierarchy process applied to synthetically evaluate the labour intensity of jobs

In this paper the systems engineering method is applied and the quantitative evaluation method of synthetical evaluation of labour intensity is briefly mentioned. A detailed application of the Analytic Hierarchy Process (AHP) is also discussed.     

Multiobjective process line optimization under uncertainty applied to papermaking

Multiobjective optimization methodology for the development of the papermaking process is considered. The aim is to find efficient and reliable solution procedures for the process line model consisting of sequential unit-process models; some of them based on physics, whereas others on experimental data. By the consequence of modeling procedures, nonphysical states or inherited from modeling data in statistical case, the unit-process models may suffer from undesired unreliability. To control the uncertainty resulting from the unit-process models, a new multiobjective optimization approach is introduced where both the papermaking targets as well as the uncertainty related unit-process models are simultaneously taken into account. We illustrate the solution process by numerical examples related to the quality of the produced paper.     

On the definition and design-time analysis of process performance indicators

A key aspect in any process-oriented organisation is the evaluation of process performance for the achievement of its strategic and operational goals. Process Performance Indicators (PPIs) are a key asset to carry out this evaluation, and, therefore, having an appropriate definition of these PPIs is crucial. After a careful review of the literature related and a study of the current picture in different real organisations, we conclude that there not exists any proposal that allows to define PPIs in a way that is unambiguous and highly expressive, understandable by technical and non-technical users and traceable with the Business Process (BP). In addition, like other activities carried out during the BP lifecycle, the management of PPIs is considered time-consuming and error-prone. Therefore, providing an automated support for them is very appealing from a practical point of view.     

LMI solutions for H-two and H-infinity decentralized controllers applied to an aerothermic process

In this paper, we present a linear matrix inequality (LMI)-based solution to implement H-two and Hinfinity decentralized robust control strategies. Appropriate parametrization of optimal H-two and H-infinity controllers is used. The general formulation of the decentralized control design leads to the optimal determination of both the state feedback gains and the observer gains of the decentralized controllers. This formulation is two folds: first, a centralized controller is obtained, and then, a simplified decentralized solution is derived by optimizing only the observer gains. The mathematical determination of these gains is formulated as an LMI optimization problem that can be easily solved using LMI solvers. As an experimental evaluation of these controllers, a real time application to an aerothermic process is carried out. A continuous-time model of the process obtained with a suitable direct continuous-time identification approach is elaborated. Results illustrating the real performance obtained from the H-two and H-infinity decentralized controllers are discussed and compared with the centralized ones.     

Concurrent engineering utilities for controlling interactions in process planning

This paper presents two concurrent engineering tools for serving in process planning. The first one, as design for manufacturing utility, evaluates critical interactions between several types of machining features (i.e., holes, pockets, etc.) on prismatic parts, and modifies original design (if possible) and reflects the modifications to all down- and up-stream stations in CAD/CAM integration. The second one is for group technology and flexibility, by which products are designed according to the existing manufacturing system. Methodologies of the developed systems are illustrated with several examples throughout the paper.     

A novel methodology of design for Additive Manufacturing applied to Additive Laser Manufacturing process

Nowadays, due to rapid prototyping processes improvements, a functional metal part can be built directly by Additive Manufacturing. It is now accepted that these new processes can increase productivity while enabling a mass and cost reduction and an increase of the parts functionality. However, the physical phenomena that occur during these processes have a strong impact on the quality of the produced parts. Especially, because the manufacturing paths used to produce the parts lead these physical phenomena, it is essential to considerate them right from the parts design stage. In this context, a new numerical chain based on a new design for Additive Manufacturing (DFAM) methodology is proposed in this paper, the new DFAM methodology being detailed; both design requirements and manufacturing specificities are taken into account. The corresponding numerical tools are detailed in the particular case of thin-walled metal parts manufactured by an Additive Laser Manufacturing (ALM) process.     

A grid procedure applied to the determination of parameters of a kinetic process

A fitting procedure for one trap and one recombination centre kinetic model is described here. The procedure makes use of a grid in the parameters space obtained by changing each parameter back and forth and calculating robust cost functions on the surfaces of this grid. The lengths of the changes are determined empirically. The best set of parameters is calculated by the projection on the grid surface with smallest cost function. The fitting procedure applied to the fit of one, two and three parameters of the kinetic model is analyzed. In all cases the optimization procedure shows reliable fitting within a feasible interval of processing time.