Identification of smartphone brand and model via forensic video analysis

Recording videos on smartphones and other mobile devices, given their enormous popularity, is currently very common. The portability of these devices facilitates their use for recording videos in a wide variety of situations, including while witnessing criminal activities. These videos can be later used as evidence in legal proceedings. Therefore, the forensic analysis of videos taken with mobile device videos is important, and could serve for legal and also investigative purposes. It is necessary, however, to use techniques that are quite specific to this type of devices, given some peculiar features of their cameras. In this paper, we will address the issue of video source acquisition identification by presenting a technique based on sensor noise and wavelet transform extraction from video key frames. These frames are extracted using an efficient algorithm that takes their content into account, improving the selection of frames to be analyzed over past proposals. The scheme presented consists of four stages: (1) Key frames extraction, (2) sensor pattern noise extraction, (3) feature extraction, and (4) classifier training and prediction. We also present experimental results that support the validity of the techniques used and show promising results.     

Smartphones or laptops in the collaborative classroom? A study of video-based learning in higher education

This paper explores how the use of smartphones vs. laptops influences students’ engagement, behaviour and experience watching academic videos in a collaborative classroom. Experiments were run in authentic teaching sessions with a total of 483 first-year higher education students. The methodology applied is a quasi-experimental design with post-test-only, being the independent variable, the device used to visualise the academic videos. Results indicate that the use of laptops has provided better results in terms of student’s engagement with the videos, their collaborative behaviour and satisfaction with the device. Hence, the findings of this research suggest that the type of mobile device used in activities that consider the use of videos in a collaborative class need to be carefully chosen to maximise the student’s comfortability – and in consequence, their engagement with the video-based learning activity and their positive behaviour and experience within the collaborative context.     

A methodology for engineering collaborative and ad-hoc mobile applications using SyD middleware

Today’s web applications are more collaborative and utilize standard and ubiquitous Internet protocols. We have earlier developed System on Mobile Devices (SyD) middleware to rapidly develop and deploy collaborative applications over heterogeneous and possibly mobile devices hosting web objects. In this paper, we present the software engineering methodology for developing SyD-enabled web applications and illustrate it through a case study on two representative applications: (i) a calendar of meeting application, which is a collaborative application and (ii) a travel application which is an ad-hoc collaborative application. SyD-enabled web objects allow us to create a collaborative application rapidly with limited coding effort. In this case study, the modular software architecture allowed us to hide the inherent heterogeneity among devices, data stores, and networks by presenting a uniform and persistent object view of mobile objects interacting through XML/SOAP requests and responses. The performance results we obtained show that the application scales well as we increase the group size and adapts well within the constraints of mobile devices.     

CPA-VoD: Cloud and Peer-Assisted Video on Demand System for Mobile Devices

With the rapid development of WiFi and 3G/4G, people tend to view videos on mobile devices. These devices are ubiquitous but have small memory to cache videos. As a result, in contrast to traditional computers, these devices aggravate the network pressure of content providers. Previous studies use CDN to solve this problem. But its static leasing mechanism in which the rental space cannot be dynamically adjusted makes the operational cost soar and incompatible with the dynamically video delivery. In our study, based on a thorough analysis of user behavior from Tencent Video, a popular Chinese on-line video share platform, we identify two key user behaviors. Firstly, lots of users in the same region tend to watch the same video. Secondly, the popularity distribution of videos conforms with the Pareto principle, i.e., the top 20% popular videos own 80% of all video traffic. To turn these observations into silver bullet, we propose and implement a novel cloud- and peer-assisted video on demand system (CPA-VoD). In the system, we group users in the same region as a peer swarm, and in the same peer swarm, users can provide videos to other users by sharing their cached videos. Besides, we cache the 10% most popular videos in cloud servers to further alleviate the network pressure. We choose cloud servers to cache videos because the rental space can be dynamically adjusted. According to the evaluation on a real dataset from Tencent Video, CPA-VoD alleviates the network pressure and the operation cost excellently, while only 20.9% traffic is serviced by the content provider.     

数据驱动的移动应用用户接受度建模与预测

应用市场(appmarket)已经成为互联网环境下软件应用开发和交付的一种主流模式.相对于传统模式,应用市场模式下,软件的交付周期更短,用户的反馈更快,最终用户和开发者之间的联系更加紧密和直接.为应对激烈的竞争和动态演变的用户需求,移动应用开发者必须以快速迭代的方式不断更新应用,修复错误缺陷,完善应用质量,提升用户体验.因此,如何正确和综合理解用户对软件的接受程度(简称用户接受度),是应用市场模式下软件开发需考量的重要因素.近年来兴起的软件解析学(softwareanalytics)关注大数据分析技术在软件行业中的具体应用,对软件生命周期中大规模、多种类的相关数据进行挖掘和分析,被认为是帮助开发者提取有效信息、作出正确决策的有效途径.从软件解析学的角度,首先论证了为移动应用构建综合的用户接受度指标模型的必要性和可行性,并从用户评价数据、操作数据、交互行为数据这3个维度给出基本的用户接受度指标.在此基础上,使用大规模真实数据集,在目标用户群体预测、用户规模预测和更新效果预测等典型的用户接受度指标预测问题中,结合具体指标,提取移动应用生命周期不同阶段的重要特征,以协同过滤、回归融合、概率模...     

Social group formation with mobile cloud services

Social networks have become quite popular these days, and the creation of social groups of people with common interests results in sharing and collaborative relationships between the members. Besides, cloud computing and mobile computing domains have advanced rapidly and are the promising technologies for the near future. Mobile technologies are recently drawing their attention to the cloud computing due to the increasing demand of the applications for processing power, storage space and energy. To bring the benefits from these different developments together, we tried to build an Android application, CroudSTag, which aids in forming social groups of common interest, from the mobile devices. The application obtains a set of pictures/videos from a storage cloud, uses cloud services like the video processing and the face recognition from multiple cloud providers to identify the people from the media content, and aids in forming social groups on facebook, a well-known social network. The application is explained along with detailed architectural and technological choices. The performance analysis of the application shows that the social groups can be formed with significant ease and reasonable performance latencies from the mobile devices.     

利用HTTP协议进行移动视频分发的应用研究

在过去的十年中,基于互联网的视频分发服务已经成为现代日常生活的一部分。3G蜂窝网覆盖范围的扩展以及大量高性能的移动设备的出现,加剧了对于大数据量可扩展移动视频分发业务的需求。采用第三屏幕作为视频主屏幕的方式,解决移动数据分发生态系统中的效率低下问题以及移动数据分发基础设施的可扩展问题已成为研究热点之一。文中在研究当前移动内容分发系统基本策略的基础上,提出了一种基于HTTP协议的改进的移动视频分发方法,有效地提高了移动视频分发系统的服务质量和可扩展性。     

Personal mobile services

Ubiquitous information access through mobile devices has become a typical practice in everyday life. The mobile service paradigm shifts the role of mobile devices from consumers to providers, opening up new opportunities for a multitude of collaborative services and applications ranging from sharing personal information to collaborative participatory sensing. Although many basic principles of the standard Web service approach continue to apply, the inherent constraints of mobile devices and broadband wireless access render the deployment of the standard architecture in mobile environments inefficient. This paper introduced personal services, a user-centric paradigm that enables service-oriented interactions among mobile devices that are controlled via user-specified authorization policies. Personal services exploit the user’s contact list (ranging from phonebook to social lists) in order to publish and discover Web services while placing users in full control of their own personal data and privacy. Experimental validation demonstrates the ability of personal services to foster a new generation of collaborative mobile services. Performance evaluation results show that the publication and discovery through contact lists are efficient and that service announcements and discovery requests can reach a huge number of users in a few seconds. Results also support a conclusion that resources-constrained devices can collaborate to carry out functionalities beyond the ability of their resources limitations.     

基于 Phonegap 的移动课件应用的设计与实现

随着移动互联网的快速发展,在移动设备上观看讲座、培训课件等视频将成为空闲时间学习的重要途径,而课件视频容量太大,下载耗时太长无疑造成了课件资源获取、存储上的困难,给移动学习带来了很多不便.本文提出了只使用课件音频、课件 ppt 图片以及图片时间配置文件来取代视频播放的解决方案,并详细介绍了基于 Phonegap 的移动混合方式开发的原理与好处以及应用系统的关键模块的详细设计与实现.     

Collaborative caching for efficient dissemination of personalized video streams in resource constrained environments

The ever increasing deployment of broadband networks and simultaneous proliferation of low-cost video capturing and multimedia-enabled mobile devices such as smart cellular phones, netbook computers, and tablet computers have triggered a wave of novel mobile multimedia applications making video streaming on mobile devices increasingly popular and commonplace. Networked environments consisting of mobile devices tend to be highly heterogeneous in terms of client-side and system-wide resource constraints, clients’ queries for information, geospatial distribution, and dynamic trajectories of the mobile clients, and client-side and server-side privacy and security requirements. Invariably, the video streams need to be personalized to provide a resource-constrained mobile device with video content that is most relevant to the client’s request while simultaneously satisfying the client-side and system-wide resource constraints, privacy and security requirements and the constraints imposed by the geospatial distribution and dynamic trajectories of the mobile clients relative to the server(s). In this paper, we present the design and implementation of a distributed system, consisting of several geographically distributed video personalization servers and proxy caches, for efficient dissemination of personalized video in a resource-constrained mobile environment. With the objective of optimizing cache performance, a novel cache replacement policy and multi-stage client request aggregation strategy, both of which are specifically tailored for personalized video content, are proposed. A novel Latency-Biased Collaborative Caching (LBCC) protocol based on counting Bloom filters is designed for further enhancing the scalability and efficiency of disseminating personalized video content. The benefits and costs associated with collaborative caching for disseminating personalized video content to resource-constrained and geographically distributed clients are analyzed and experimentally verified. The impact of different levels of collaboration among the caches and the advantages of using multiple video personalization servers with varying degrees of mirrored content on the efficiency of personalized video delivery are also studied. Experimental results demonstrate that the proposed collaborative caching scheme, coupled with the proposed personalization-aware cache replacement and client request aggregation strategies, provides a means for efficient dissemination of personalized video streams in resource-constrained environments.     

A web-based pervasive recommendation system for mobile tourist guides

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user’s location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of ‘context-aware rating’, whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.     

Imperceptible visible watermarking based on postcamera histogram operation

A real-world scene captured via digital devices, such as a digital still camera, video recorder and mobile device, is a common behavior in recent decades. With the increasing availability, reproduction and sharing of media, the intellectual property of digital media is incapable of guaranty. To claim the ownership of digital camera media, the imperceptible visible watermarking (IVW) mechanism was designed based on the observation that most camera devices contain the postcamera histogram operation. The IVW approach can achieve advantages both the content readability of invisible watermarking methodology and the visual ownership identification of visible watermarking methodology. The computational complexity of IVW is low and can be effectively applied to almost any of the digital electronic devices when capturing the real-world scene without additional instruments. The following results and analysis demonstrate the novel scheme is effective and applicable for versatile images and videos captured.     

Perses: A framework for the continuous evaluation of the QoS of distributed mobile applications

The increasing capabilities of mobile devices have led to the emergence of new paradigms exploiting them. These paradigms foster the onload and distribution of functionalities on mobile devices, allowing the development of distributed mobile applications. This distribution reduces the latency and the data traffic overhead and improves privacy. As in any other mobile application, their success largely depends on the quality of service (QoS) they offer. Nevertheless, the evaluation of distributed mobile applications is particularly complex due to the number, heterogeneity, and interactions between the devices involved. Current techniques allow developers to assess the quality of a single device, but they are not designed for highly heterogeneous, distributed, and collaborative environments. This paper presents a framework called Perses, which allows the creation of virtual scenarios with multiple heterogeneous mobile devices to launch end-to-end tests to evaluate not only each device but also the interactions among them. The framework was evaluated against a real deployment, showing that the behavior and the quality attributes measured are similar to those of the real deployment, allowing developers to evaluate these applications before launching them. Finally, Perses was integrated into a DevOps methodology to automate its execution and further facilitate its adoption by software companies.     

一种基于用户播放行为序列的个性化视频推荐策略

本文针对在线视频服务网站的个性化推荐问题,提出了一种基于用户播放行为序列的个性化推荐策略.该策略通过深度神经网络词向量模型分析用户播放视频行为数据,将视频映射成等维度的特征向量,提取视频的语义特征.聚类用户播放历史视频的特征向量,建模用户兴趣分布矩阵.结合用户兴趣偏好和用户观看历史序列生成推荐列表.在大规模的视频服务系统中进行了离线实验,相比随机算法、基于物品的协同过滤和基于用户的协同过滤传统推荐策略,本方法在用户观看视频的Top-N推荐精确率方面平均分别获得22.3%、30.7%和934%的相对提升,在召回率指标上分别获得52.8%、41%和1065%的相对提升.进一步地与矩阵分解算法SVD++、基于双向LSTM模型和注意力机制的Bi-LSTM+Attention算法和基于用户行为序列的深度兴趣网络DIN比较,Top-N推荐精确率和召回率也得到了明显提升.该推荐策略不仅获得了较高的精确率和召回率,还尝试解决传统推荐面临大规模工业数据集时的数据要求严苛、数据稀疏和数据噪声等问题.     

Convergence agent model for developing u-healthcare systems

Ubiquitous networking, the interlinking of computers, consumer electronics, automobiles, home appliances, medical equipment, transportation systems, animals and plants through an overlapping of electronic spaces and physical spaces, is expected to provide an environment that enhances our lives through improved convenience, efficiency and safety in all areas.Today’s healthcare industry emphasizes safety, efficiency, patient-oriented approach, timeliness and balance. u-healthcare makes it possible to safely deliver appropriate services from any location at any time. To explain the emergence of u-healthcare, one must invoke, first of all, progress in IT and medical technology, and then the desire and willingness on the part of health care institutions to adopt the new service concept and increased health care demand. Digitalization of information, introduction of broadband communication and leaps made in healthcare technology in recent years have provided the technological capacity necessary for the achievement of u-healthcare.Based on recently reached theoretical results, integrating u-healthcare environments in virtual organizations, we proposed a methodology for the design and implementation of u-healthcare, linking the distributed mobile agents with medical entities into a convergence and collaborative environment. Despite the challenges in implementing and deploying u-healthcare, the advantages of ubiquitous healthcare are enabled by our smart model for the soft computing endowed ubiquitous. The strength of our approach is that it relates to u-healthcare a system which consists of its dual character emerging from the synergetic interaction between ubiquitous computing techniques and the mobile devices in convergence manner. We have developed a set of initial healthcare agent services on the mobile collaboration framework. We evaluate the functionality and effectiveness of our convergence mobile agent, whether the platform can manage services based on the model and whether the results of u-healthcare services discovery could satisfy user’s requirements with example cases.     

基于普适计算的虚拟场景自适应网络发布研究

针对移动设备访问三维虚拟场景受到网络带宽和终端计算能力的制约,使得在线利用虚拟现实进行移动学习效率不高,基于此提出了基于普适计算的自适应优化选择技术.对三维实体进行本体语义描述,建立相应的优化模型.在此基础上通过WURFL对手持移动设备进行采样获取设备参数,对参数进行分析从而建立普适规则,利用普适计算进行自适应优化选择.测试结果表明,经过自适应选择后手持移动设备荻取三维虚拟场景资源的速度和效率得到明显提高.     

Videolization: knowledge graph based automated video generation from web content

Web content nowadays can also be accessed through new generation of Internet connected TVs. However, these products failed to change users’ behavior when consuming online content. Users still prefer personal computers to access Web content. Certainly, most of the online content is still designed to be accessed by personal computers or mobile devices. In order to overcome the usability problem of Web content consumption on TVs, this paper presents a knowledge graph based video generation system that automatically converts textual Web content into videos using semantic Web and computer graphics based technologies. As a use case, Wikipedia articles are automatically converted into videos. The effectiveness of the proposed system is validated empirically via opinion surveys. Fifty percent of survey users indicated that they found generated videos enjoyable and 42 % of them indicated that they would like to use our system to consume Web content on their TVs.

     

Mobile collaborative medical display system

Because of recent advances in wireless communication technologies, the world of mobile computing is flourishing with a variety of applications. In this study, we present an integrated architecture for a personal digital assistant (PDA)-based mobile medical display system that supports collaborative work between remote users. We aim to develop a system that enables users in different regions to share a working environment for collaborative visualization with the potential for exploring huge medical datasets. Our system consists of three major components: mobile client, gateway, and parallel rendering server. The mobile client serves as a front end and enables users to choose the visualization and control parameters interactively and cooperatively. The gateway handles requests and responses between mobile clients and the rendering server for efficient communication. Through the gateway, it is possible to share working environments between users, allowing them to work together in computer supported cooperative work (CSCW) mode. Finally, the parallel rendering server is responsible for performing heavy visualization tasks. Our experience indicates that some features currently available to our mobile clients for collaborative scientific visualization are limited due to the poor performance of mobile devices and the low bandwidth of wireless connections. However, as mobile devices and wireless network systems are experiencing considerable elevation in their capabilities, we believe that our methodology will be utilized effectively in building quite responsive, useful mobile collaborative medical systems in the very near future.     

Supporting device discovery and spontaneous interaction with spatial references

The RELATE interaction model is designed to support spontaneous interaction of mobile users with devices and services in their environment. The model is based on spatial references that capture the spatial relationship of a user’s device with other co-located devices. Spatial references are obtained by relative position sensing and integrated in the mobile user interface to spatially visualize the arrangement of discovered devices, and to provide direct access for interaction across devices. In this paper we discuss two prototype systems demonstrating the utility of the model in collaborative and mobile settings, and present a study on usability of spatial list and map representations for device selection.     

Energy consumption of data mining algorithms on mobile phones: Evaluation and prediction

The pervasive availability of increasingly powerful mobile computing devices like PDAs, smartphones and wearable sensors, is widening their use in complex applications such as collaborative analysis, information sharing, and data mining in a mobile context. Energy characterization plays a critical role in determining the requirements of data-intensive applications that can be efficiently executed over mobile devices. This paper presents an experimental study of the energy consumption behavior of representative data mining algorithms running on mobile devices. Our study reveals that, although data mining algorithms are compute- and memory-intensive, by appropriate tuning of a few parameters associated to data (e.g., data set size, number of attributes, size of produced results) those algorithms can be efficiently executed on mobile devices by saving energy and, thus, prolonging devices lifetime. Based on the outcome of this study we also proposed a machine learning approach to predict energy consumption of mobile data-intensive algorithms. Results show that a considerable accuracy is achieved when the predictor is trained with specific-algorithm features.