Analysing Petri Nets in a Calculus of Context-aware Ambients

This paper proposes an approach to analysing and verifying Petri nets using a Calculus of Context-aware Ambients (CCA). We propose an algorithm that transforms a Petri net into a CCA process. This demonstrates that any system that can be specified in Petri nets can also be specified in CCA. Besides, the system can be analysed and verified using the CCA verification tools. We illustrate the practicality of our approach using a case study of the dining cryptographers problem

Future wireless applications for a networked city: services for visitors and residents

Future wireless networks will offer near-ubiquitous high-bandwidth communications to mobile users. In addition, the accurate position of users will be known, either through network services or via additional sensing devices such as GPS. These characteristics of future mobile environments will enable the development of location-aware and, more generally, context-sensitive applications. In an attempt to explore the system, application, and user issues associated with the development and deployment of such applications, we began to develop the Lancaster GUIDE system in early 1997, finishing the first phase of the project in 1999. In its entirety, GUIDE comprises a citywide wireless network based on 802.11, a context-sensitive tour guide application with, crucially, significant content, and a set of supporting distributed systems services. Uniquely in the field, GUIDE has been evaluated using members of the general public, and we have gained significant experience in the design of usable context-sensitive applications. We focus on the applications and supporting infrastructure that will form part of GUIDE II, the successor to the GUIDE system. These developments are designed to expand GUIDE outside the tour guide domain, and to provide applications and services for residents of the city of Lancaster, offering a vision of the future mobile environments that will emerge once ubiquitous high-bandwidth coverage is available in most cities

Living Innovation Laboratory Model Design and Implementation

Living Innovation Laboratory (LIL) is an open and recyclable way for multidisciplinary researchers to remote control resources and co-develop user centered projects. In the past few years, there were several papers about LIL published and trying to discuss and define the model and architecture of LIL. People all acknowledge about the three characteristics of LIL: user centered, co-creation, and context aware, which make it distinguished from test platform and other innovation approaches. Its existing model consists of five phases: initialization, preparation, formation, development, and evaluation. Goal Net is a goal-oriented methodology to formularize a progress. In this thesis, Goal Net is adopted to subtract a detailed and systemic methodology for LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps. Big data, crowd sourcing, crowd funding and crowd testing take place in suitable steps to realize UUI, MCC and PCA throughout the innovation process in LIL 2.0. It would become a guideline for any company or organization to develop a project in the form of an LIL 2.0 project. To prove the feasibility of LIL Goal Net Model, it was applied to two real cases. One project is a Kinect game and the other one is an Internet product. They were both transformed to LIL 2.0 successfully, based on LIL goal net based methodology. The two projects were evaluated by phenomenography, which was a qualitative research method to study human experiences and their relations in hope of finding the better way to improve human experiences. Through phenomenographic study, the positive evaluation results showed that the new generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf

Object detection via a multi-region & semantic segmentation-aware CNN model

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.Comment: Extended technical report -- short version to appear at ICCV 201

Learning 3D Human Pose from Structure and Motion

3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose

Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)

This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems. This technique allows us to analyze the operational processes of software systems under real-life conditions at multiple levels of granularity. The work can be positioned in-between reverse engineering and process mining. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life (software) event logs demonstrate the feasibility and usefulness of the approach and show the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis. This Technical Report version includes the guarantee proofs for the proposed discovery algorithm