Hidden-Markov Program Algebra with iteration

We use Hidden Markov Models to motivate a quantitative compositional semantics for noninterference-based security with iteration, including a refinement- or "implements" relation that compares two programs with respect to their information leakage; and we propose a program algebra for source-level reasoning about such programs, in particular as a means of establishing that an "implementation" program leaks no more than its "specification" program. This joins two themes: we extend our earlier work, having iteration but only qualitative, by making it quantitative; and we extend our earlier quantitative work by including iteration. We advocate stepwise refinement and source-level program algebra, both as conceptual reasoning tools and as targets for automated assistance. A selection of algebraic laws is given to support this view in the case of quantitative noninterference; and it is demonstrated on a simple iterated password-guessing attack

Association of p60c-src with endosomal membranes in mammalian fibroblasts.

We have examined the subcellular localization of p60c-src in mammalian fibroblasts. Analysis of indirect immunofluorescence by three-dimensional optical sectioning microscopy revealed a granular cytoplasmic staining that co-localized with the microtubule organizing center. Immunofluorescence experiments with antibodies against a number of membrane markers demonstrated a striking co-localization between p60c-src and the cation-dependent mannose-6-phosphate receptor (CI-MPR), a marker that identifies endosomes. Both p60c-src and the CI-MPR were found to cluster at the spindle poles throughout mitosis. In addition, treatment of interphase and mitotic cells with brefeldin A resulted in a clustering of p60c-src and CI-MPR at a peri-centriolar position. Biochemical fractionation of cellular membranes showed that a major proportion of p60c-src co-enriched with endocytic membranes. Treatment of membranes containing HRP to alter their apparent density also altered the density of p60c-src-containing membranes. Similar density shift experiments with total cellular membranes revealed that the majority of membrane-associated p60c-src in the cell is associated with endosomes, while very little is associated with plasma membranes. These results support a role for p60c-src in the regulation of endosomal membranes and protein trafficking

Extraction and Classification of Diving Clips from Continuous Video Footage

Due to recent advances in technology, the recording and analysis of video data has become an increasingly common component of athlete training programmes. Today it is incredibly easy and affordable to set up a fixed camera and record athletes in a wide range of sports, such as diving, gymnastics, golf, tennis, etc. However, the manual analysis of the obtained footage is a time-consuming task which involves isolating actions of interest and categorizing them using domain-specific knowledge. In order to automate this kind of task, three challenging sub-problems are often encountered: 1) temporally cropping events/actions of interest from continuous video; 2) tracking the object of interest; and 3) classifying the events/actions of interest. Most previous work has focused on solving just one of the above sub-problems in isolation. In contrast, this paper provides a complete solution to the overall action monitoring task in the context of a challenging real-world exemplar. Specifically, we address the problem of diving classification. This is a challenging problem since the person (diver) of interest typically occupies fewer than 1% of the pixels in each frame. The model is required to learn the temporal boundaries of a dive, even though other divers and bystanders may be in view. Finally, the model must be sensitive to subtle changes in body pose over a large number of frames to determine the classification code. We provide effective solutions to each of the sub-problems which combine to provide a highly functional solution to the task as a whole. The techniques proposed can be easily generalized to video footage recorded from other sports.Comment: To appear at CVsports 201

High-Spatial Resolution Laser Doppler Blood Flow Imaging

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.A full-field laser Doppler blood flow imaging (LDI) system based on an FPGA (Field Programmable Gate Array) coupled with a high-speed CMOS (Complementary Metal-Oxide-Semiconductor) camera chip has been developed which provides blood flow images with flexible frame rates and spatial resolution. When a high spatial resolution is required, 1280x1024-pixel blood flow images were obtained by processing up to 2048 samples at 0.2 frames per second (fps). Alternatively, a maximum of 15.5fps was achieved by reducing the spatial resolution and sampling points to 256x256 pixels and 128 samples respectively. This system was applied to a high-spatial resolution flow imaging application in which a mixture of water and polystyrene microspheres was pumped through a micropipette (diameter = 250m) with controlled velocities, and the resulting flow was imaged and processed. The performance was demonstrated by the resulting flow images which are of size 1280×1024 pixels and obtained by processing 2048 samples at each pixel

Fracture Behavior of Alumina/Monazite Multilayer Laminates

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65994/1/j.1151-2916.2000.tb01278.x.pd

Pose is all you need: The pose only group activity recognition system (POGARS)

We introduce a novel deep learning based group activity recognition approach called the Pose Only Group Activity Recognition System (POGARS), designed to use only tracked poses of people to predict the performed group activity. In contrast to existing approaches for group activity recognition, POGARS uses 1D CNNs to learn spatiotemporal dynamics of individuals involved in a group activity and forgo learning features from pixel data. The proposed model uses a spatial and temporal attention mechanism to infer person-wise importance and multi-task learning for simultaneously performing group and individual action classification. Experimental results confirm that POGARS achieves highly competitive results compared to state-of-the-art methods on a widely used public volleyball dataset despite only using tracked pose as input. Further our experiments show by using pose only as input, POGARS has better generalization capabilities compared to methods that use RGB as input.Comment: 12 pages, 7 figure

Multi-Purpose Designs in Lightweight Cryptography

The purpose of this thesis is to explore a number of techniques used in lightweight cryptography design and their applications in the hardware designs of two lightweight permutations called sLiSCP and sLiSCP-light. Most of current methods in lightweight cryptography are optimized around one functionality and is only useful for applications that require their specific design. We aimed to provide a design that can provide multiple functionalities. In this thesis, we focus and show the hash function and authenticated encryption of our design. We implemented two lightweight permutations designs of sLiSCP and sLiSCP-light in VHDL. During the verification of sLiSCP cipher, we discovered additional area that could be saved if we tweaked the design slightly. This would lead us to consider the design of sLiSCP-light which helps dramatically reduce area. Results of our designs of sLiSCP and sLiSCP-light satisfied the lightweight requirements, including hardware area, power, and throughput, for applications such as passive RFID tags. Lastly, we did tests on the randomness of Simeck and Simon Feistel structures. We wanted to observe the pseudorandom nature of structures similar to Simeck and Simon so we performed exhaustive tests on small instances of these structures to trace any trends in their behavior. We confirmed that Simon and Simeck were very consistent and provided acceptable pseudorandom results. For larger sizes, we expect similar results from Simon and Simeck

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga