基于元路径嵌入的移动应用需求偏好分析方法

随着互联网和移动应用平台的快速发展,围绕移动应用所产生的海量用户数据已经成为精确分析用户需求偏好的重要数据源.尽管已有不少学者从这些数据中分析和挖掘用户需求,但现有的方法通常只研究了数据的少数维度的特征,未能有效地挖掘多维移动应用信息以及他们之间的关联.提出一种基于元路径嵌入的移动应用需求偏好分析方法,能够为用户进行个性化移动应用推荐.具体地,首先分析移动应用的文本信息中的语义主题,挖掘用户需求偏好的分析维度.其次,将移动应用信息的语义特征构建了一个融合移动应用多维信息的概念模型,涵盖了能够表征用户需求偏好的多维度数据.基于概念模型的语义,设计了一组有意义的元路径集合,以精确地捕捉用户需求偏好的语义.最后,通过使用元路径嵌入技术进行用户行为画像,进而实现个性化的移动应用推荐.使用苹果应用商店包括1507个移动应用和153501条用户评论的真实数据集进行实验评估.实验结果表明所提的方法在各指标上均优于现有模型,其中平均F1值提升0.02,平均归一化折损累计增益(normalized discounted cumulative gain,NDCG)提升0.1.     

Node coordination mechanism based on distributed estimation and control in wireless sensor and actuator networks

Wireless sensor and actuator networks （WSANs） have a wide range of applications. To perform effective sensing and acting tasks, multiple coordination mechanisms among the nodes are required. As attempt in this direction, this paper describes collaborative estimation and control algorithms design for WSANs. First, a sensor-actuator coordination model is proposed based on distributed Kalman filter in federated configuration. This method provides the capability of fault tolerance and multi-source information fusion. On this basis, an actuator-actuator coordination model based on even-driven task allocation is introduced. Actuators utilize fused sensory information to adjust their action that incur the minimum energy cost to the system subject to the constraints that user＇s preferences regarding the states of the system are approximately satisfied. Finally, according to system requirements, a distributed algorithm is proposed to solve the task allocation problem. Simulations demonstrate the effectiveness of our proposed methods.     

Designing multimodal tracks for mobile users in unfamiliar urban environments

While there have been many studies of navigation design guidelines for integrating maps in mobile devices, we argue that the research focus should turn more towards the design of multimodal navigation aids, which would reflect the impact of typical human wayfinding behaviour. Therefore, we suggest an interdisciplinary design approach, i.e. building on urban design practice that focuses on supporting the navigation of pedestrians in urban areas. The design implications are explored in this study with the subjects being pedestrians accomplishing wayfinding tasks in an unfamiliar, urban area. The main areas that this paper contributes to are: investigating the design implications of multimodal navigation aids; evaluating these in the context of mobile wayfinding tasks; and reflecting the results according to the user's wayfinding strategies and spatial anxiety. It is concluded that through designing multimodal tracks into mobile navigation applications we can help users to find their way in unfamiliar, urban environments.     

Tuning user profiles based on analyzing dynamic preference in document retrieval systems

Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. In this paper a model for user profile tuning in document retrieval systems is considered. Methods for tuning the user profile based on analysis of user preferences dynamics are experimentally evaluated to check whether with growing history of user activity the created user profile can converge to his preferences. As the statistical analysis of series of simulations has shown, proposed methods of user profile actualization are effective in the sense that the distance between user preferences and his profile becomes smaller and smaller along with time.     

Wayfinding behaviour in a multi-level building: A comparative study of HMD VR and Desktop VR

Virtual Reality (VR) provides the possibility to study pedestrian wayfinding behaviour in multi-level buildings. Although VR has been applied increasingly to study pedestrian behaviour, it has remained unclear how different VR technology would affect behavioural outcomes in a multi-level building. The study compares the adoption of different VR technologies for pedestrian wayfinding studies, via investigating the difference in pedestrian wayfinding behaviour and user experience. Wayfinding experiments with two groups of participants were conducted using either HMD VR or Desktop VR. Pedestrian movement trajectory data were collected via the VR system and user experience data were recorded via a questionnaire. These data allow for direct comparison and detailed analysis of pedestrian behaviour and user experience between the adoption of two VR technologies. The results showed that technological differences have a significant impact on wayfinding task performance and head rotation change. However, the route choice, exit choice and user experience were overall similar between the two groups. These results provide empirical evidence supporting researchers to choose between immersive and non-immersive VR when study pedestrian wayfinding behaviour.     

A CNN-based personalized system for attention detection in wayfinding tasks

Firefighters are often exposed to extensive wayfinding information in various formats owing to the increasing complexity of the built environment. Because of the individual differences in processing assorted types of information, a personalized cognition-driven intelligent system is necessary to reduce the cognitive load and improve the performance in the wayfinding tasks. However, the mixed and multi-dimensional information during the wayfinding tasks bring severe challenges to intelligent systems in detecting and nowcasting the attention of users. In this research, a virtual wayfinding experiment is designed to simulate the human response when subjects are memorizing or recalling different wayfinding information. Convolutional neural networks (CNNs) are designed for automated attention detection based on the power spectrum density of electroencephalography (EEG) data collected during the experiment. The performance of the personalized model and the generalized model are compared and the result shows a personalized CNN is a powerful classifier in detecting the attention of users with high accuracy and efficiency. The study thus will serve a foundation to support the future development of personalized cognition-driven intelligent systems.     

Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments

This paper tackles a privacy breach in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. For example, a user who wants to issue a query asking about her nearest gas station has to report her exact location to an LBS provider. However, many recent research efforts have indicated that revealing private location information to potentially untrusted LBS providers may lead to major privacy breaches. To preserve user location privacy, spatial cloaking is the most commonly used privacy-enhancing technique in LBS. The basic idea of the spatial cloaking technique is to blur a user’s exact location into a cloaked area that satisfies the user specified privacy requirements. Unfortunately, existing spatial cloaking algorithms designed for LBS rely on fixed communication infrastructure, e.g., base stations, and centralized/distributed servers. Thus, these algorithms cannot be applied to a mobile peer-to-peer (P2P) environment where mobile users can only communicate with other peers through P2P multi-hop routing without any support of fixed communication infrastructure or servers. In this paper, we propose a spatial cloaking algorithm for mobile P2P environments. As mobile P2P environments have many unique limitations, e.g., user mobility, limited transmission range, multi-hop communication, scarce communication resources, and network partitions, we propose three key features to enhance our algorithm: (1) An information sharing scheme enables mobile users to share their gathered peer location information to reduce communication overhead; (2) A historical location scheme allows mobile users to utilize stale peer location information to overcome the network partition problem; and (3) A cloaked area adjustment scheme guarantees that our spatial cloaking algorithm is free from a “center-of-cloaked-area” privacy attack. Experimental results show that our P2P spatial cloaking algorithm is scalable while guaranteeing the user’s location privacy protection.     

Human-centric product conceptualization using a design space framework

Designing products to fit human needs, preferences and capabilities is an essential key to competitiveness. In contested product markets, the management of user-related knowledge is therefore critical. Studies have shown that the identification and implementation of user requirements are significant issues for determining successful product development, especially during the conceptual design phase. User requirements represented in a single or limited level of abstraction is inadequate for effective incorporation into the conceptual design process. Such representation of user needs is argued here to be associated with issues such as the errors of problem framing, which is a cause of inadmissible, uncreative or sub-optimal designs. In this paper, a human-centric knowledge organization structure, Design Space Framework, is established to facilitate the consistent incorporation of user information into the length of the product conceptualization process. The role of this structure in human-centric design is illustrated in a case study.     

A Movie Recommendation System – An Application of Voting Theory in User Modeling

Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.     

The pragmatic quality of Resources‐ Events‐Agents diagrams: an experimental evaluation

The Resources‐Events‐Agents (REA) model is a semantic data model for the development of enterprise information systems. Although this model has been proposed as a benchmark for enterprise information modelling, only few studies have attempted to empirically validate the claimed benefits of REA modelling. Moreover, these studies focused on the evaluation of REA‐based system implementations rather than directly assessing the REA‐modelled conceptual schemas that these systems are based on. This paper presents a laboratory experiment that measured the user understanding of diagrammatic conceptual schemas developed using the REA model. The theoretical foundation for the hypotheses are cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding conceptual schemas. The results of the experiment indicate a more accurate understanding of the business processes and policies modelled when users recognize the REA model’s core pattern of enterprise information in the diagram. The implication for modelling practice is that the use of the REA model improves the requirements engineering process by facilitating the user validation of conceptual schemas produced by analysts, and thus helps ensuring the quality of the enterprise information system that is developed or implemented.     

On the nature of models for time-sensitive remote sensing

Remote sensing employs a range of conceptual and mechanistic models. Several conceptual models have been proposed to explain remote sensing systems (RSSs) and either directly prescribe or elucidate the configuration of such systems based on scene conditions and information requirements. This study interrogates the utility of these models for the design and practical implementation of RSSs to address time-sensitive information requirements and proposes a novel conceptual model, the remote sensing communication model, that places remote sensing within a decision support context.

Three pertinent remote sensing conceptual models are critically assessed based on their satisfaction of three basic requirements: (1) prescribe or explicate RSS configuration based on user information requirements, (2) elucidate dependencies between information type, accuracy, and timeliness, and (3) describe the effect of RSS configuration on the effectiveness of users’ decisions. The conceptual remote sensing models that are evaluated are not found to be appropriate for the design and configuration of time-sensitive RSSs (TSRSSs).

The remote sensing communication model employs the (information theory) communication model developed by Shannon and Weaver to elucidate the acquisition, transmission, processing, interpretation, and effectiveness of information derived from a TSRSS. Weaver’s three levels of effective communication are used to explain the varying value of information as a function of time and user characteristics and to elucidate the effect of RSS configuration on the ability of remote sensing-derived information to inform decisions in a timely manner. The concept of communication channel capacity is used to estimate the timeliness of RSSs, and a brief example of its implementation is presented.     

Changing information retrieval behaviours: an empirical investigation of users’ cognitive processes in the choice of location-based services

The introduction of smartphones and the accompanying profusion of mobile data services have had a profound effect on individuals’ lives. One of the most influential service categories is location-based services (LBS). Based on insights from behavioural decision-making, a conceptual framework is developed to analyse individuals’ decisions to use LBS, focusing on the cognitive processes involved in the decision-making. Our research is based on two studies. First, we investigate the use of LBS through semi-structured interviews of smartphone users. Second, we explore daily LBS use through a study based on diaries. The findings highlight that the decision to use LBS can be described by either a comparative mode based on the value of LBS in relation to other available options, or an intuitive mode in which past experiences trigger the use of heuristics. These modes in turn have positive influences on the continuance of LBS use and indicate changes in individuals’ information retrieval behaviours in everyday life. In particular, the distinct value dimension of LBS in specific contexts of use changes individuals’ behaviours towards accessing location-related information.     

Job scheduling algorithm based on Berger model in cloud environment

Considered the commercialization and the virtualization characteristics of cloud computing, the paper proposed for the first time an algorithm of job scheduling based on Berger model. In the job scheduling process, the algorithm establishes dual fairness constraint. The first constraint is to classify user tasks by QoS preferences, and establish the general expectation function in accordance with the classification of tasks to restrain the fairness of the resources in selection process. The second constraint is to define resource fairness justice function to judge the fairness of the resources allocation. We have expanded simulation platform CloudSim, and have implemented the job scheduling algorithm proposed in this paper. The experimental results show that the algorithm can effectively execute the user tasks and manifests better fairness.     

一种用户故事需求质量提升方法

敏捷开发采用用户故事表达用户需求.一般采用格式受限的自然语言编写,但在用户故事编写过程中经常出现一些表述上的缺陷.典型的缺陷包括缺失必要信息、意思表达含糊不清、故事间有重复或存在冲突等.这很大程度上影响了需求的质量,影响软件开发项目的进行.提出一种用户故事需求质量提升方法.从故事缺陷定位的角度出发,该方法构建了用户故事概念模型,并根据实际案例总结并提出11条用户故事应遵循的质量准则.从而提出故事结构分析、句法模式分析以及语法分析等技术,用于自动构建带场景用户故事的实例层模型,并根据准则进行故事缺陷检测,进而提升用户故事质量.在包含36个用户故事84个场景的实际项目中进行实验,自动检测出173个缺陷,缺陷检测的准确率和召回率分别达到88.79%和95.06%.     

Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms

Successful data warehouse (DW) design needs to be based upon a requirement analysis phase in order to adequately represent the information needs of DW users. Moreover, since the DW integrates the information provided by data sources, it is also crucial to take these sources into account throughout the development process to obtain a consistent reconciliation of data sources and information needs. In this paper, we start by summarizing our approach to specify user requirements for data warehouses and to obtain a conceptual multidimensional model capturing these requirements. Then, we make use of the multidimensional normal forms to define a set of Query/View/Transformation (QVT) relations to assure that the conceptual multidimensional model obtained from user requirements agrees with the available data sources that will populate the DW. Thus, we propose a hybrid approach to develop DWs, i.e., we firstly obtain the conceptual multidimensional model of the DW from user requirements and then we verify and enforce its correctness against data sources by using a set of QVT relations based on multidimensional normal forms. Finally, we provide some snapshots of the CASE tool we have used to implement our QVT relations.     

Enhancing human indoor cognitive map development and wayfinding performance with immersive augmented reality-based navigation systems

Augmented reality (AR) is an interactive experience where computer-generated perceptual information is superimposed into the real-world environment. Most existing research in AR-based wayfinding has focused on the technological aspects of developing AR-based software or devices to realize navigation. No previous investigations have focused on understanding the impact of immersive augmented reality (IAR)–based systems on human wayfinding performance from the cognitive perspective. Aimed at investigating the influence of IAR-based systems on people’s cognitive map development and their subsequent wayfinding performance as well as the effect of using three-dimensional (3D) layout models in IAR environments in addition to superimposed guideposts, an experiment was carried out in a building with a complex floor plan. A total of 54 university students were evenly divided into three groups: a control group with no IAR assistance, a second group using an IAR-based navigation system that includes only superimposed guideposts, and a third group using an IAR-based navigation system that includes both guideposts and a 3D layout model. Each participant was asked to conduct a spatial exploration task in the environment, sketch a floor map based on their spatial cognition, and perform a wayfinding task to find eight specific locations in the building. An analysis of the participants’ performance and responses to a number of self-evaluation questionnaires collected in the experiment indicates that IAR technology can help people develop their cognitive maps more effectively and can substantially improve their wayfinding performance with a much lower workload. A second finding is that adding a 3D layout model can enhance the effect of an IAR-based navigation system in terms of cognitive map development. The findings from this research extend the existing knowledge about IAR-based navigation and further verify that AR technology has the potential to reduce human workload for cognitive tasks. The results also could support its more effective application in various scenarios that require assisted wayfinding and cognitive map training, such as emergency evacuation drills.     

Effective design of electronic commerce environments: a proposedtheory of congruence and an illustration

We posit that the current electronic commerce (EC) systems are not user-oriented. Consumers in an EC environment find it difficult to deal with the inherent nature of virtual reality in their interaction. We propose a conceptual framework to address such an issue by drawing on insights from contingency research in information systems design and media choice behavior. The basic theme of this model, called the congruence model, is that for an EC systems environment to have favorable impact on a consumer, the system must have a good fit with both the products/services that are sold/provided and the users it supports. An initial validity of this conceptual framework is demonstrated by working with a few product and user types. A detailed evaluation of eight different commercial web sites dealing with four different product categories provides additional support to the proposed theory. We expect our conceptual model to spur significant future research on this important phenomenon     

Protecting query privacy in location-based services

The popularity of location-based services (LBSs) leads to severe concerns on users’ privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacy-preserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts—user profiles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users’ query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users’ query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users’ privacy requirements expressed in these metrics. By experiments, our metrics and algorithms are shown to be effective and efficient for practical usage.     

Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms

Successful data warehouse (DW) design needs to be based upon a requirement analysis phase in order to adequately represent the information needs of DW users. Moreover, since the DW integrates the information provided by data sources, it is also crucial to take these sources into account throughout the development process to obtain a consistent reconciliation of data sources and information needs. In this paper, we start by summarizing our approach to specify user requirements for data warehouses and to obtain a conceptual multidimensional model capturing these requirements. Then, we make use of the multidimensional normal forms to define a set of Query/View/Transformation (QVT) relations to assure that the conceptual multidimensional model obtained from user requirements agrees with the available data sources that will populate the DW. Thus, we propose a hybrid approach to develop DWs, i.e., we firstly obtain the conceptual multidimensional model of the DW from user requirements and then we verify and enforce its correctness against data sources by using a set of QVT relations based on multidimensional normal forms. Finally, we provide some snapshots of the CASE tool we have used to implement our QVT relations.     

基于连续查询的用户轨迹k-匿名隐私保护算法

随着移动服务和移动网络的持续发展,基于LBS的连续查询服务被广泛应用。基于单点的K-匿名位置隐私保护算法已经不能满足连续查询下用户位置隐私需求。针对用户轨迹隐私保护提出新的保护方法,该方法采用不可信第三方中心匿名器,用户获取自己的真实位置后首先在客户端进行模糊处理,然后提交给第三方匿名器,第三方匿名器根据用户的隐私需求结合用户某时刻的真实位置信息生成虚假用户,然后根据历史数据生成虚假轨迹。为了进一步提高虚假轨迹与用户真实轨迹的相似性,该算法提出了虚假轨迹生成的两个约束条件：虚假轨迹距用户真实轨迹的距离约束和相似性约束。经大量实验证明,该算法与传统的不同时刻K-匿名算法相比,不仅可以满足连续查询的用户轨迹隐私保护而且可以满足基于快照的LBS用户位置隐私保护。