Data structures (Computer science)

Ordinal classification refers to an important category of real world problems,
in which the attributes of the instances to be classified and the classes are
linearly ordered. Many applications of machine learning frequently involve
situations exhibiting an order among the different categories represented by
the class attribute. In ordinal classification the class value is converted into a
numeric quantity and regression algorithms are applied to the transformed
data. The data is later translated back into a discrete class value in a postprocessing
step. This thesis is devoted to an empirical study of ordinal and
non-ordinal classification algorithms for intrusion detection in WLANs. We
used ordinal classification in conjunction with nine classifiers for the
experiments in this thesis. All classifiers are parts of the WEKA machinelearning
workbench. The results indicate that most of the classifiers give
similar or better results with ordinal classification compared to non-ordinal
classification.

A variety of classifiers for solving classification problems is available from
the domain of machine learning. Commonly used classifiers include support vector
machines, decision trees and neural networks. These classifiers can be configured
by modifying internal parameters. The large number of available classifiers and
the different configuration possibilities result in a large number of combinatiorrs of
classifier and configuration settings, leaving the practitioner with the problem of
evaluating the performance of different classifiers. This problem can be solved by
using performance metrics. However, the large number of available metrics causes
difficulty in deciding which metrics to use and when comparing classifiers on the
basis of multiple metrics. This paper uses the statistical method of factor analysis
in order to investigate the relationships between several performance metrics and
introduces the concept of relative performance which has the potential to case the
process of comparing several classifiers. The relative performance metric is also
used to evaluate different support vector machine classifiers and to determine if the
default settings in the Weka data mining tool are reasonable.

This research addresses the need for increased interoperability between the varied access control systems in use today, and for a secure means of providing access to remote physical devices over untrusted networks. The Universal Physical Access Control System (UPACS) is an encryption-enabled security protocol that provides a standard customizable device control mechanism that can be used to control the behavior of a wide variety of physical devices, and provide users the ability to securely access those physical devices over untrusted networks.

The Google Android mobile phone platform is one of the dominant smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications (Apps). The popularity of Android platform draws attention of many developers which also attracts the attention of cybercriminals to develop different kinds of malware to be inserted into the Google Android Market or other third party markets as safe applications. In this thesis, we propose to combine permission, API (Application Program Interface) calls and function calls to build a Heuristic-­Based framework for the detection of malicious Android Apps. In our design, the permission is extracted from each App’s profile information and the APIs are extracted from the packed App file by using packages and classes to represent API calls. By using permissions, API calls and function calls as features to characterize each of Apps, we can develop a classifier by data mining techniques to identify whether an App is potentially malicious or not. An inherent advantage of our method is that it does not need to involve any dynamic tracking of the system calls but only uses simple static analysis to find system functions from each App. In addition, Our Method can be generalized to all mobile applications due to the fact that APIs and function calls are always present for mobile Apps. Experiments on real-­world Apps with more than 1200 malwares and 1200 benign samples validate the algorithm performance.
Research paper published based on the work reported in this thesis:
Naser Peiravian, Xingquan Zhu, Machine Learning for Android Malware Detection
Using Permission and API Calls, in Proc. of the 25th IEEE International Conference on
Tools with Artificial Intelligence (ICTAI) – Washington D.C, November 4-­6, 2013.

Object segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based approach to background modeling for motion-based object segmentation in video sequences. In particular, we show how Probabilistic Neural Network (PNN) architecture can be extended to form an unsupervised Bayesian classifier for the domain of video object segmentation. The constructed Background Modeling Neural Network (BNN) is capable of efficiently handling segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed neural network serve as an exclusive model of the background and are temporally updated to reflect the observed background statistics. The proposed approach is designed to enable an efficient, highly-parallelized hardware implementation. Such a system would be able to achieve real-time segmentation of high-resolution image sequences.

Web services have become increasingly important over the past decades. Versatility and platform independence are just some of their advantages. On the other hand, grid computing enables the efficient distribution of computing resources. Together, they provide a great source of computing power that can be particularly leveraged by mobile devices. Mobile computing enables information creation, processing, storage and communication without location constraints [63], not only improving business' operational efficiency [63] but actually changing a way of life. However, the convenience of anytime and anywhere communication is counterbalanced by small screens, limited computing power and battery life. Despite these limitations, mobile devices can extend grid functionality by bringing to the mix not only mobile access but sensing capabilities as well, gathering information from their surroundings through built in mechanisms, such as microphone, camera, GPS and even accelerometers. Prior work has already demonstrated the possibility of enabling Web Services Resource Framework (WSRF) access to grid resources from mobile device clients in the WSRF-ME project [39], where a representative Nokia S60 Smartphone application was created on a framework, which extends the JSR-172 functionality to achieve WSRF compliance. In light of today's mobile phone market diversity, this thesis extends the solution proposed by WSRF-ME to non-Java ME phones and to Android devices in particular. Android-based device numbers have grown considerably over the past couple of years despite its recent creation and reduced availability of mature software tools.

The increasing availability of Web services and grid computing has made easier the access and reuse of different types of services. Web services provide network accessible interfaces to application functionality in a platform-independent manner. Developments in grid computing have led to the efficient distribution of computing resources and power through the use of stateful web services. At the same time, mobile devices as a platform of computing have become a ubiquitous, inexpensive, and powerful computing resource. Concepts such as cloud computing has pushed the trend towards using grid concepts in the internet domain and are ideally suited for internet-supported mobile devices. Currently, there are a few complete implementations that leverage mobile devices as a member of a grid or virtual organization. This thesis presents a framework that enables the use of mobile devices to access stateful Web services on a Globus-based grid. To illustrate the presented framework, a user-friendly mobile application has been created that utilizes the framework libraries do to demonstrate the various functionalities that are accessible from any mobile device that supports Java ME.

The field of Video Transcoding has been evolving throughout the past ten years. The need for transcoding of video files has greatly increased because of the new upcoming standards which are incompatible with old ones. This thesis takes the method of using machine learning for video transcoding mode decisions and discusses ways to improve the process of generating the algorithm for implementation in different video transcoders. The transcoding methods used decrease the complexity in the mode decision inside the video encoder. Also methods which automate and improve results are discussed and implemented in two different sets of transcoders: H.263 to VP6 , and MPEG-2 to H.264. Both of these transcoders have shown a complexity loss of almost 50%. Video transcoding is important because the quantity of video standards have been increasing while devices usually can only decode one specific codec.

H.264/AVC encoder complexity is mainly due to variable size in Intra and Inter frames. This makes H.264/AVC very difficult to implement, especially for real time applications and mobile devices. The current technological challenge is to conserve the compression capacity and quality that H.264 offers but reduce the encoding time and, therefore, the processing complexity. This thesis applies machine learning technique for video encoding mode decisions and investigates ways to improve the process of generating more general low complexity H.264/AVC video encoders. The proposed H.264 encoding method decreases the complexity in the mode decision inside the Inter frames. Results show, at least, a 150% average reduction of complexity and, at most, 0.6 average increases in PSNR for different kinds of videos and formats.