APPLET: a privacy-preserving framework for location-aware recommender system

Location-aware recommender systems that use location-based ratings to produce recommendations have recently experienced a rapid development and draw significant attention from the research community. However, current work mainly focused on high-quality recommendations while underestimating privacy issues, which can lead to problems of privacy. Such problems are more prominent when service providers, who have limited computational and storage resources, leverage on cloud platforms to fit in with the tremendous number of service requirements and users. In this paper, we propose a novel framework, namely APPLET, for protecting user privacy information, including locations and recommendation results, within a cloud environment. Through this framework, all historical ratings are stored and calculated in ciphertext, allowing us to securely compute the similarities of venues through Paillier encryption, and predict the recommendation results based on Paillier, commutative, and comparable encryption. We also theoretically prove that user information is private and will not be leaked during a recommendation. Finally, empirical results over a real-world dataset demonstrate that our framework can efficiently recommend POIs with a high degree of accuracy in a privacy-preserving manner.     

A management framework for systems users

Many business managers today are in need of practical guidance in determining which information systems management practices are appropriate for their particular organization. Of central importance is the management of the information systems development process. A field research study was conducted at five major U.S. corporations to determine those information systems development management practices that had proved successful. The research method consisted of in-depth interviews with managers and technical people at each company. In addition, the reputation of each company as a successful manager of the information systems function was validated using a set of organizational performance criteria developed by a panel of prominent information systems professionals. The results of the research are presented in the form of a planning and control framework of successful management practices. The framework describes and classifies those practices common to all companies and clearly differentiates those practices which varied significantly among the companies studied. The framework is designed to enable management to readily identify those information system development practices that have proved successful and to select from among them those most appropriate to the local situation. It can also serve as a guide for self-study on the part of the individual manager.     

A maximum likelihood framework for determining moving edges

The determination of moving edges in an image sequence is discussed. An approach is proposed that relies on modeling principles and likely hypothesis testing techniques. A spatiotemporal edge in an image sequence is modeled as a surface patch in a 3-D spatiotemporal space. A likelihood ratio test enables its detection as well as simultaneous estimation of its related attributes. It is shown that the computation of this test leads to convolving the image sequence with a set of predetermined masks. The emphasis is on a restricted but widely relevant and useful case of surface patch, namely the planar one. In addition, an implementation of the procedure whose computation cost is merely equivalent to a spatial gradient operator is presented. This method can be of interest for motion-analysis schemes, not only for supplying spatiotemporal segmentation, but also for extracting local motion information. Moreover, it can cope with occlusion contours and important displacement magnitude. Experiments have been carried out with both synthetic and real images     

Mobility profiler: A framework for discovering mobility profiles of cell phone users

Mobility path information of cell phone users play a crucial role in a wide range of cell phone applications, including context based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. However, there is a disconnect between the low level location data logs available from the cell phones and the high level mobility path information required to support these cell phone applications. In this paper, we present formal definitions to capture the cell phone users’ mobility patterns and profiles, and provide a complete framework, Mobility Profiler, for discovering mobile cell phone user profiles starting from cell based location data. We use real-world cell phone log data (of over 350 K h of coverage) to demonstrate our framework and perform experiments for discovering frequent mobility patterns and profiles. Our analysis of mobility profiles of cell phone users expose a significant long tail in a user’s location-time distribution: A total of 15% of a cell phone user’s time is spent on average in locations that each appears with less than 1% of total time.     

A framework for unsupervised mesh based segmentation of moving objects

Multimedia analysis usually deals with a large amount of video data with a significant number of moving objects. Often it is necessary to reduce the amount of data and to represent the video in terms of moving objects and events. Event analysis can be built on the detection of moving objects. In order to automatically process a variety of video content in different domain, largely unsupervised moving object segmentation algorithms are needed. We propose a fully unsupervised system for moving object segmentation that does not require any restriction on the video content. Our approach to extract moving objects relies on a mesh-based combination of results from colour segmentation (Mean Shift) and motion segmentation by feature point tracking (KLT tracker). The proposed algorithm has been evaluated using precision and recall measures for comparing moving objects and their colour segmented regions with manually labelled ground truth data. Results show that the algorithm is comparable to other state-of-the-art algorithms. The extracted information is used in a search and retrieval tool. For that purpose a moving object representation in MPEG-7 is implemented. It facilitates high performance indexing and retrieval of moving objects and events in large video databases, such as the search for similar moving objects occurring in a certain period.     

A location-aware multicasting protocol for Bluetooth Location Networks

Bluetooth Location Network (BLN) is a Bluetooth radio network that is composed of some mobile Bluetooth devices and static Bluetooth units, and is established at the system initialization to form a spontaneous network topology. In a BLN, a multicast service is defined as the periodical delivering of messages from a Service Server to a set of mobile devices which are the multicast members predefined by the Service Server. Several multicast protocols have been proposed for the Ad-Hoc networks, but they create an inefficient multicast tree for the BLN due to the existing differences in the radio characteristics between Ad-Hoc and Bluetooth radio networks. The present paper analyzes these differences and proposes a novel multicasting protocol for constructing an efficient multicast tree in a BLN. The proposed protocol constructs a multicast tree with good features which include the shortest path, a higher degree of path sharing, and fewer forwarding nodes. Simulation results reveal that the proposed multicast protocol outperforms the existing multicast protocols in the BLN.     

LACO: A location-aware cooperative query system for securely personalized services

Location-aware computing technology becomes promising for pervasive personalization services which run anytime, anywhere, and on any device. These services should be based on contextual queries that are provided in a fast and flexible manner. To do so, cooperative query answering may be useful to support query relaxation and to provide both approximate matches as well as exact matches. To facilitate query relaxation, a knowledge representation framework has been widely adopted which accommodates semantic relationships or distance metrics to represent similarities among data values. However, research shows few legacy cooperative query mechanisms that consider location-awareness. Hence, the purpose of this paper is to propose a securely personalized location-aware cooperative query that supports conceptual distance metric among data values, while considering privacy concerns around user context awareness. To show the feasibility of the methodology proposed in this paper, we have implemented a prototype system, LACO, in the area of site search in an actual large-scale shopping mall.     

A system for detection of moving caption text in videos: a news use case

Extraction of news text captions aims at a digital understanding of what is happening in a specific region during a certain period that helps in better communication between different nations because we can easily translate the plain text from one language to another. Moving text captions causes blurry effects that are a significant cause of text quality impairments in the news channels. Most of the existing text caption detection models do not address this problem in a way that captures the different dynamic motion of captions, gathers a full news story among several frames in the sequence, resolves the blurring effect of text motion, offers a language-independent model, or provides it as an end-to-end solution for the community to use. We process the frames coming in sequence and extract edge features using either the Hough transform or our color-based technique. We verify text existence using a Convolutional Neural Network (CNN) text detection pre-trained model. We analyze the caption motion status using hybrid pre-trained Recurrent Neural Network (RNN) of Long Short-Term Memory (LSTM) type model and correlation-based model. In case the motion is determined to be horizontal rotation, there are two problems. First, it means that text keeps rotating with no stop resulting in a high blurring effect that affects the text quality and consequently resulting in low character recognition accuracy. Second, there are successive news stories which are separated by the channel logo or long spaces. We managed to solve the first problem by deblurring the text image using either Bicubic Spline Interpolation (BSI) technique or the Denoising Autoencoder Neural Network (DANN). We solved the second problem using a Point Feature Matching (PFM) technique to match the existing channel logo with the channels’ logo database (ground truth). We evaluate our framework using Abbyy® SDK as a standalone tool used for text recognition supporting different languages.

     

A design framework of a smartphone user interface for elderly users

Universal Access in the Information Society - Most smartphones currently in the market have complicated user interfaces (UIs) that elderly users find difficult to use. Such difficulties often lead...     

Georoy: A location-aware enhancement to Viceroy peer-to-peer algorithm

The success of experiences such as Seattle and Houston Wireless has attracted the attention on the so called wireless mesh community networks. These are wireless multihop networks spontaneously deployed by users willing to share communication resources. Due to the community spirit characterizing such networks, it is likely that users will be willing to share other resources besides communication resources, such as data, images, music, movies, disk quotas for distributed backup, and so on. To support resource exchange in these wireless mesh community networks, algorithms for efficient retrieval of information are required. In this paper we introduce Georoy, an algorithm for the efficient retrieval of the information on resource location based on the Viceroy peer-to-peer algorithm. Differently from Viceroy, Georoy exploits the capability of setting and managing a direct mapping between the resource ID and the node which maintains information about its location so as to speed up the search process. Simulation results show that Georoy enables efficient and scalable search of resources and can be successfully used in wireless mesh community networks.     

Rover: scalable location-aware computing

All the components necessary for realizing location-aware computing are available in the marketplace today. What has hindered the widespread deployment of location-based systems is the lack of an integration architecture that scales with user populations. The authors have completed the initial implementation of Rover, a system designed to achieve this sort of integration and to automatically tailor information and services to a mobile user's location. Their studies have validated Rover's underlying software architecture, which achieves system scalability through high-resolution, application-specific resource scheduling at the servers and network. The authors believe that this technology will greatly enhance the user experience in many places, including museums, amusement and theme parks, shopping malls, game fields, offices, and business centers. They designed the system specifically to scale to large user populations and expect its benefits to increase with them.     

Mining location-aware discriminative blocklets for recognizing landmark architectures

Building recognition is an important and still challenging problem in computer vision and pattern recognition field. In this work, we propose a location-aware building recognition model by introducing blocklets, i.e., spatial adjacent blocks associated with their relative positions. First, by evenly partitioning each building image into blocks, we construct a spatial pyramid to describe the spatial relations of blocks. Second, we obtain blocklets by extracting spatial adjacent blocks. And we can cast the building recognition as matching between blocklets from different buildings. Third, towards an efficient matching, a hierarchical sparse coding method is proposed to represent each blocklet by a linear combination of basis blocklets. Furthermore, towards an effective matching, an LDA Jieping (In: Proceedings of ICML 1087–1093 2007)-like scheme is adopted to select the blocklets with high discrimination. Finally, we carry out the architecture recognition based on the selected highly discriminative blocklets. Experimental results on four datasets demonstrate our model is robust to occlusions and large change in backgrounds.     

User needs for location-aware mobile services

Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.     

A soft computing based location-aware access control for smart buildings

The evolution of wireless communications and pervasive computing is transforming current physical spaces into real smart environments. These emerging scenarios are expected to be composed by a potentially huge amount of heterogeneous smart objects which can be remotely accessed by users via their mobile devices anytime, anywhere. In this paper, we propose a distributed location-aware access control mechanism and its application in the smart building context. Our approach is based on an access control engine embedded into smart objects, which are responsible to make authorization decisions by considering both user location data and access credentials. User location data are estimated using a novel indoor localization system based on magnetic field data sent by user through her personal phone. This localization system implements a combination of soft computing techniques over the data collected by smartphones. Therefore, our location-aware access control mechanism does not require any intermediate entity, providing the benefits of a decentralized approach for smart environments. From the results obtained, we can consider our proposal as a promising approach to tackle the challenging security requirements of typical pervasive environments.     

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

Multimedia Tools and Applications - The progressive growth of today’s digital world has made news spread exponentially faster on social media platforms like Twitter, Facebook, and Weibo....     

支持多数不规则用户的隐私保护联邦学习框架

针对联邦学习存在处理大多数不规则用户易引起聚合效率降低,以及采用明文通信导致参数隐私泄露的问题,基于设计的安全除法协议构建针对不规则用户鲁棒的隐私保护联邦学习框架.该框架通过将模型相关计算外包给两台边缘服务器以减小采用同态加密产生的高额计算开销,不仅允许模型及其相关信息以密文形式在边缘服务器上进行密文聚合,还支持用户在...     

Design iterations for a location-aware event planner

We present the user-centered design and testing process of a mobile, location-aware event planner. Using questionnaires, interviews, and discussions with potential users, we investigated the ways individuals plan social events, such as business meetings, dinners and gatherings, and perform the attendant communication tasks. We catalogued the contextually dependent ways in which people plan their meetings and informal social events and devised a wide range of conceptual sketches to address our potential users professed needs. We developed a preliminary PDA-based prototype based on clear conceptual entities (people, places, locations and events) and designed to support short interaction sequences and large maps. Its evaluation with a group of nine participants provided qualitative feedback and prompted an interface redesign. This development process taught us a number of interesting lessons applicable to this category of location-aware applications, which we report at the end of the article. The set of design recommendations can assist designers in the development of location aware systems.     

A two-tier test approach to developing location-aware mobile learning systems for natural science courses

The advancement of wireless and mobile technologies has enabled students to learn in an environment that combines learning resources from both the real world and the digital world. Although such an approach has been recognized as being innovative and important, several problems have been revealed in practical learning activities. One major problem is owing to the lack of proper learning strategies or tools for assisting the students to acquire knowledge in such a complex learning scenario. Students might feel excited or engaged when using the mobile devices to learn in the real context; nevertheless, their learning achievements could be disappointing. To deal with this problem, this study presents a mobile learning system that employs Radio Frequency Identification (RFID) technology to detect and examine real-world learning behaviors of students. This study also utilizes each student’s responses from a two-tier test (i.e., multiple-choice questions in a two-level format) to provide personalized learning guidance (called two-tier test guiding, T³G). The experimental results from a natural science course of an elementary school show that this innovative approach is able to improve the learning achievements of students as well as enhance their learning motivation.