基于AOV网的抽象工作流模型

针对项目管理中的工作流控制需求,提出一个基于顶点活动(AOV)网的抽象工作流模型。给出该模型的形式化定义以及各结点的时序关系,阐述工作流的设计与执行规则,包括分支设计规则和回路设计规则,定义工作流图分支结构中汇聚结点的时序同步规则,并给出一个优化的回路检测方法。实例应用结果证明了该模型的有效性。     

Error analysis of task performance with laptop in vibration environment

The present study investigates influences of vibration directions, vibration magnitudes, object sizes, object distances and angles of approach on producing errors while performing pointing and clicking activity on a laptop monitor similar to passengers working on moving trains. Attempts made outside the boundary of an object while performing the activity is registered as an error. The number of errors produced under different exposure conditions is computed. Higher vibration magnitudes, smaller size of the objects, and diagonal angular movements are found to create more errors and affected accuracy of the activity and hence showed difficulties to perform the activity.     

考虑用户活跃度和项目流行度的基于项目最近邻的协同过滤算法

项目相关性度量是基于项目最近邻的协同过滤算法的关键。已有的项目相关性度量方法在数据集稀疏或推荐低流行度产品时会面临较大挑战,因此提出一种考虑用户活跃度和项目流行度的基于项目最近邻的协同过滤算法。该算法在度量两个项目的相关性时,若有记录只对两个项目之一有评分,则利用该记录所对应的评分用户的活跃度和被评价项目的流行度进行相关性惩罚,从而提高数据稀疏环境下低流行度产品被推荐的概率。实验表明,所提算法在保证评分预测精度的情况下提升了推荐结果的多样性和新颖性。     

Tool choice in innovation diffusion: A human activity readiness theory

The short product life cycle of information and communication technology (ICT) applications makes it critical for developers to expedite the diffusion process by reducing user resistance. At the individual level, the adoption of an ICT application largely depends on how a person is ready to use the computerized tool in place of the corresponding traditional method for a certain purpose. To find out how to facilitate the transition, this study investigates the human choice behavior involved in technology adoption with the Activity Theory that allows for the inclusion of different tools in one unit of analysis. The understanding of user situated experiences from the activity perspective leads to hypothesized relationships between user-, tool- and task-related factors and the dependent variable in terms of tool readiness. The empirical results support that tool experiences have stronger effects on specific tool readiness at the within-subject level, user characteristics have weaker effects on general tool readiness at the between-subject level, and task situations influence both levels of tool readiness. The findings provide insights on how to facilitate innovation diffusion for different tools, tasks and users.     

Digital Game Dynamics Preferences and Player Types

In this study, we examine digital game preferences by identifying game dynamics, i.e. player–game interaction modes, of 700 contemporary digital games, and players' (N = 1717) desire to play games with specific types of dynamics. Based on statistical analysis of the data, 5 game dynamics preference categories (“assault,” “manage,” “journey,” “care,” and “coordinate”) and 7 player types were revealed. The results show that identifying player types requires including both preferred and undesired game dynamics categories in the analysis. The findings unveil digital gaming as a more multifaceted phenomenon than common stereotypes suggest. The original game preferences model we present in this study can be conceptualized as a complementary approach for motivations to play and player behavior studies.     

A Novel Framework for Human Activity Recognition with Time Labelled Real Time Sensor Data

Human activity recognition is an effective approach for identifying the characteristics of historical data. In the past decades, different shallow classifiers and handcrafted features were used to identify the activities from the sensor data. These approaches are configured for offline processing and are not suitable for sequential data. This article proposes an adaptive framework for human activity recognition using a deep learning mechanism. This deep learning approach forms the deep belief network (DBN), which contains a visible layer and hidden layers. The processing of raw sensor data is performed by these layers and the activity is identified at the top most layers. The DBN is tested using the real time environment with the help of mobile devices that contain an accelerometer, a magnetometer, and a gyroscope. The results are analyzed with the metrics of precision, recall, and the F1-score. The results proved that the proposed method has a higher F1_score when compared to the existing approach.     

Fast human activity recognition based on structure and motion

We present a method for the recognition of human activities. The proposed approach is based on the construction of a set of templates for each activity as well as on the measurement of the motion in each activity. Templates are designed so that they capture the structural and motion information that is most discriminative among activities. The direct motion measurements capture the amount of translational motion in each activity. The two features are fused at the recognition stage. Recognition is achieved in two steps by calculating the similarity between the templates and motion features of the test and reference activities. The proposed methodology is experimentally assessed and is shown to yield excellent performance.