Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

Spatially Aware Computing for Natural Interaction

Spatial information refers to the location of an object in a physical or digital world. Besides, it also includes the relative position of an object related to other objects around it. In this dissertation, three systems are designed and developed. All of them apply spatial information in different fields. The ultimate goal is to increase the user friendliness and efficiency in those applications by utilizing spatial information. The first system is a novel Web page data extraction application, which takes advantage of 2D spatial information to discover structured records from a Web page. The extracted information is useful to re-organize the layout of a Web page to fit mobile browsing. The second application utilizes the 3D spatial information of a mobile device within a large paper-based workspace to implement interactive paper that combines the merits of paper documents and mobile devices. This application can overlay digital information on top of a paper document based on the location of a mobile device within a workspace. The third application further integrates 3D space information with sound detection to realize an automatic camera management system. This application automatically controls multiple cameras in a conference room, and creates an engaging video by intelligently switching camera shots among meeting participants based on their activities. Evaluations have been made on all three applications, and the results are promising. In summary, this dissertation comprehensively explores the usage of spatial information in various applications to improve the usability

Big Data Research in Italy: A Perspective

The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains

UbiqLog: a generic mobile phone based life-log framework

Smart phones are conquering the mobile phone market; they are not just phones they also act as media players, gaming consoles, personal calendars, storage, etc. They are portable computers with fewer computing capabilities than personal computers. However unlike personal computers users can carry their smartphone with them at all times. The ubiquity of mobile phones and their computing capabilities provide an opportunity of using them as a life logging device. Life-logs (personal e-memories) are used to record users' daily life events and assist them in memory augmentation. In a more technical sense, life-logs sense and store users' contextual information from their environment through sensors, which are core components of life-logs. Spatio-temporal aggregation of sensor information can be mapped to users' life events. We propose UbiqLog, a lightweight, configurable and extendable life-log framework that uses mobile phone as a device for life logging. The proposed framework extends previous research in this field, which investigated mobile phones as life-log tool through continuous sensing. Its openness in terms of sensor configuration allows developers to create exible, multipurpose life-log tools. In addition to that this framework contains a data model and an architecture, which can be used as reference model for further life-log development, including its extension to other devices, such as ebook readers, T.V.s, etc

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Digital Forensic Tools & Cloud-Based Machine Learning for Analyzing Crime Data

Digital forensics is a branch of forensic science in which we can recreate past events using forensic tools for legal measure. Also, the increase in the availability of mobile devices has led to their use in criminal activities. Moreover, the rate at which data is being generated has been on the increase which has led to big data problems. With cloud computing, data can now be stored, processed and analyzed as they are generated. This thesis documents consists of three studies related to data analysis. The first study involves analyzing data from an android smartphone while making a comparison between two forensic tools; Paraben E3: DS and Autopsy. At the end of the study, it was concluded that most of the activities performed on a rooted android device can be found in its internal memory. In the second study, the Snapchat application was analyzed on a rooted Android device to see how well it handles privacy issues. The result of the study shows that some of the predefined activities performed on the Snapchat application as well as user information can be retrieved using Paraben E3: DS forensic tool. The third study, machine learning services on Microsoft Azure and IBM Watson were used in performing predictive analysis to uncover their performance. At the end of the experiments, the Azure machine learning studio was seen to be more user friendly and builds models faster compared to the SSPS Modeler in the IBM Watson Studio. This research is important as data needs to be analyzed in order to generate insights that can aid organizations or police departments in making the best decisions when analyzing crime data