An indoor localization system in a multiblock workspace

As service robots and other ubiquitous technology have evolved, an increasing need for the autonomous navigation of mobile objects has arisen. In a large number of localization schemes, the absolute-position estimation method, which relies on navigation beacons or landmarks, has been widely used as it has the advantages of being economical and accurate. However, only a few of these schemes have expanded their application to complicated workspaces, or those that have many rooms or blocks. As the navigation of mobile objects in complicated workspaces is vital for ubiquitous technology, multiblock navigation is necessary. This article presents methodologies and techniques for the multiblock navigation of the indoor localization system with active beacon sensors. This new indoor localization system design includes ultrasonic attenuation compensation, dilution-of-precision analysis, and a fault detection and isolation algorithm using redundant measurements. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

基于J2ME和J2EE的城市公交查询系统的设计与实现

J2ME和J2EE技术是目前Java两大主要技术,并在应用中取得了成功。本文基于J2ME和J2EE技术实现了一个移动手机版的城市公交查询系统。其中,J2ME技术拥有移动手机客户端的开发,而J2EE技术则用于后台服务器和手机客户端的交互,以及后台服务器信息的发布。本系统实现了三种查询功能:路线查询、站点查询和站站查询。通过本系统将极大地方便手机用户对公交信息的查询。     

CPFinder: Finding an unknown caller's profession from anonymized mobile phone data

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.     

Design of a 2-DOF-PID controller using an improved sine–cosine algorithm for load frequency control of a three-area system with nonlinearities

This paper proposes an improved sine–cosine algorithm (ISCA) based 2-DOF-PID controller for load frequency control. A three-area test system is built for study, while some physical constraints (nonlinearities) are considered for the investigation of a realistic power system. The proposed method is used as the parameter optimizer of the LFC controller in different scenarios. The 2-DOF-PID controllers are used because of their capability of fast disturbance rejection without significant increase of overshoot in set-point tracking. The 2-DOF-PID controllers’ efficacy is observed by examining the responses with the outcomes obtained with PID and FOPID controllers. The simulation results with the suggested scheme are correlated with some of the existing algorithms, such as SCA, SSA, ALO, and PSO in three different scenarios, i.e., a disturbance in two areas, in three areas, and in the presence of physical constraints. In addition, the study is extended to a four-area power system. Statistical analysis is performed using the Wilcoxon Sign Rank Test (WSRT) on 20 independent runs. This confirms the supremacy of the proposed method.     

Predicting Mobile Cross-Platform Adaptation Using a Hybrid Sem–ANN Approach

Owing to constant changes in user needs, new technologies have been introduced to keep pace by building sustainable applications. Researchers and practitioners are keen to understand the factors that create an attractive user interface. Although the use of cross-platform applications and services is increasing, limited research has examined and evaluated cross-platforms for developing mobile applications for different operating systems. This study evaluates cross-platform features, identifying the main factors that help to create an attractive user adaptation when building sustainable applications for both Android and iOS. Flutter and React Native were selected so end-users could test their features using the cross-platform usability assessment model. Usability, satisfaction, and navigation were tested to measure the cross-platform adaptation and end-user experience. The data were analyzed using hybrid structural equation modeling (SEM) and artificial neural network (ANN) approaches. The study results show that usability and navigation both have a positive effect on adaptation on Flutter and React Native, while satisfaction only has an effect on Flutter. The navigation variable was also the most significant predictor of adaptation for both models. This study has several implications and makes contributions to the research field, to developers, and to end-users.     

The shadow approach: An orphan detection protocol for mobile agents

Orphan detection in distributed systems is a well-researched field for which many solutions exist. These solutions exploit well defined parent-child relationships given in distributed systems. But they are not applicable in mobile agent systems, since no similar natural relationship between agents exist. Thus new protocols have to be developed. In this paper one such protocol for controlling mobile mobile agents and for orphan detection is presented. The shadow approach presented in this paper uses the idea of a placeholder (shadow) which is assigned by the agent system to each new agent. This defines an artificial relationship between agents and shadow. The shadow records the location of all dependent agents. Removing the root shadow implies that all dependent agents are declared orphan and are eventually be terminated. We introduce agent proxies that create a path from shadow to every agent. In an extension of the basic protocol we additionally allow the shadow to be mobile.The shadow approach can be used for termination of groups of agents even if the exact location of each single agent is not known.     

移动应用中OTP认证机制的安全性分析

一次性口令(简称OTP)认证机制可以实现一次一密,具备更高的安全性,同时,其实现简单、成本低、无需第三方公证,中分适合于受限的移动应用,但难以抵御小数攻击以及没有实现双向认证,其安全隐患主要在于参与一次性口令生成的随机数以及口令认证信息均以明文方式传送,因此,可以采用椭圆曲线密码体制ECC对随机数及认证信息进行加密.     

The influence of design aesthetics in usability testing: Effects on user performance and perceived usability

This article examined the effects of product aesthetics on several outcome variables in usability tests. Employing a computer simulation of a mobile phone, 60 adolescents (14-17 yrs) were asked to complete a number of typical tasks of mobile phone users. Two functionally identical mobile phones were manipulated with regard to their visual appearance (highly appealing vs not appealing) to determine the influence of appearance on perceived usability, performance measures and perceived attractiveness. The results showed that participants using the highly appealing phone rated their appliance as being more usable than participants operating the unappealing model. Furthermore, the visual appearance of the phone had a positive effect on performance, leading to reduced task completion times for the attractive model. The study discusses the implications for the use of adolescents in ergonomic research.     

MANET网中基于簇结构的认证服务方案

针对移动Ad Hoc网络无中心的特性及一般分布式认证中单跳认证节点少于门限值、CA节点不可靠等问题,采用基于多层簇的体系结构和 Feldman可验证秘密共享技术,提出一种基于簇结构的认证服务方案,结果证明该方案可实现各级簇头和簇成员的分布式认证,解决单跳内认证节点少于门限值问题,防止对系统私钥的重构等外部攻击及内部被攻破节点对分布式认证的干扰。