Atta Badii

A Context-Awareness Framework for Intelligent Networked Embedded Systems

This paper describes the Context Awareness Framework developed for the HYDRA Middleware Project. The HYDRA project aims to develop middleware to support intelligent networked embedded system based on a service-oriented architecture, deployable on both new and existing networks of distributed wireless and wired devices. A well developed Context Awareness Framework (CAF) is crucial for the success of such intelligent networked systems. Changing requirements and dynamic environments are drivers for context-aware applications. The automatic execution of system adaptations leads to a successful and effective information and communication system that provides information and functionality that is relevant and at the right level of complexity with respect to the current situation. This paper elaborates on the various issues involved in the design and development of a Context Awareness Framework for an intelligent networked embedded system.

Robust background model for pixel based people counting using a single uncalibrated camera

Several pixel-based people counting methods have been developed over the years. Among these the product of scale-weighted pixel sums and a linear correlation coefficient is a popular people counting approach. However most approaches have paid little attention to resolving the true background and instead take all foreground pixels into account. With large crowds moving at varying speeds and with the presence of other moving objects such as vehicles this approach is prone to problems. In this paper we present a method which concentrates on determining the true-foreground, i.e. human-image pixels only. To do this we have proposed, implemented and comparatively evaluated a human detection layer to make people counting more robust in the presence of noise and lack of empty background sequences. We show the effect of combining human detection with a pixel-map based algorithm to i) count only human-classified pixels and ii) prevent foreground pixels belonging to humans from being absorbed into the background model. We evaluate the performance of this approach on the PETS 2009 dataset using various configurations of the proposed methods. Our evaluation demonstrates that the basic benchmark method we implemented can achieve an accuracy of up to 87% on sequence “S1.L1 13–57 View 001” and our proposed approach can achieve up to 82% on sequence “S1.L3 14–33 View 001” where the crowd stops and the benchmark accuracy falls to 64%.

Towards Semantic Resolution of Security in Ambient Environments

Driven by new network and middleware technologies such as mobile broadband, near-field communication, and context awareness the so-called ambient lifestyle will foster innovative use cases in different domains. In the EU project Hydra high-level security, trust and privacy concerns such as loss of control, profiling and surveillance are considered at the outset. At the end of this project the Hydra middleware development platform will have been designed so as to enable developers to realise secure ambient scenarios. This paper gives a short introduction to the Hydra project and its approach to ensure security by design. Based on the results of a focus group analysis of the user domain “building automation” typical threats are evaluated and their risks are assessed. Then, specific security requirements with respect to security, privacy, and trust are derived in order to incorporate them into the Hydra Security Meta-Model. How concepts such as context, semantic resolution of security, and virtualisation support the overall Hydra approach will be introduced and illustrated on the basis of a technical building automation scenario.

Visual context identification for privacy-respecting video analytics

With the growing need for privacy-aware and privacy-respecting CCTV systems, it becomes crucial to develop workflows and architectures that can support and enhance privacy protection. Recent advances in image processing enable the automation of many surveillance tasks, increasing the risks of privacy infringements. Fortunately, image processing and pattern recognition techniques can also be used for automatically evaluating the context in which video surveillance takes place, and can therefore be employed for applying context-specific privacy rules. This paper describes how Bag-of-Visual-Words algorithms as well as human tracking and gait analysis cane used for recognizing specific sub-contexts that necessitate the application of particular privacy protection rules in usage contexts such as ambient assisted living, public or workspace surveillance We explain how the data of a multi-modal surveillance system should be handled in order to avoid unnecessary processing of sensitive information through Image Quality Descriptors that will support visual classifications by computing reliability measures relating to the image quality such as noise or problems with respect to ambient conditions.

Change detection based on graph cuts

In this paper we propose a moving object detection system based on Graph Cut. Our method relies on motion modelling using an optical flow algorithm and a classical background subtraction module based on Mixture of Gaussians. The main contribution in our approach is the fusion of the two mask models, as well as the particular cost function used by the Graph Cut algorithm. The experiments as performed on CDnet 2014 benchmark showed that our system has very good results in scenarios such as Bad Weather or PTZ, but is less robust in detecting small changes in the scene.

ATD: A Multiplatform for Semiautomatic 3-D Detection of Kidneys and Their Pathology in Real Time

This research presents a novel multifunctional platform focusing on the clinical diagnosis of kidneys and their pathology (tumors, stones and cysts), using a “templates”-based technique. As a first step, specialist clinicians train the system by accurately annotating the kidneys and their abnormalities creating “3-D golden standard models.” Then, medical technicians experimentally adjust rules and parameters (stored as “templates”) for the integrated “automatic recognition framework” to achieve results which are closest to those of the clinicians. These parameters can later be used by nonexperts to achieve increased automation in the identification process. The systems functionality was tested on 20 MRI datasets (552 images), while the “automatic 3-D models” created were validated against the “3-D golden standard models.” Results are promising as they yield an average accuracy of 97.2% in successfully identifying kidneys and 96.1% of their abnormalities thus outperforming existing methods both in accuracy and in processing time needed.

CORBYS cognitive control architecture for robotic follower

In this paper the novel generic cognitive robot control architecture CORBYS is presented. The objective of the CORBYS architecture is the integration of high-level cognitive modules to support robot functioning in dynamic environments including interacting with humans. This paper presents the preliminary integration of the CORBYS architecture to support a robotic follower. Experimental results on high-level empowerment-based trajectory planning have demonstrated the effectiveness of ROS-based communication between distributed modules developed in a multi-site research environment as typical for distributed collaborative projects such as CORBYS.

The Automatic Identification and Prioritisation of Criminal Networks from Police Crime Data

The identification of criminal networks is not a routine exploratory process within the current practice of the law enforcement authorities; rather it is triggered by specific evidence of criminal activity being investigated. A network is identified when a criminal comes to notice and any associates who could also be potentially implicated would need to be identified if only to be eliminated from the enquiries as suspects or witnesses as well as to prevent and/or detect crime. However, an identified network may not be the one causing most harm in a given area.. This paper identifies a methodology to identify all of the criminal networks that are present within a Law Enforcement Area, and, prioritises those that are causing most harm to the community. Each crime is allocated a score based on its crime type and how recently the crime was committed; the network score, which can be used as decision support to help prioritise it for law enforcement purposes, is the sum of the individual crime scores.

Analysing the factors responsible for effectiveness of implementation and integration of enterprise resource planning systems in the printing industry

Purpose – This papers purpose is to examine the impact of ERP adoption in one of the most traditional sectors, namely the printing industry, within a case study of five typical large‐scale printing organisations set in the developing economy context of Pakistan.Design/methodology/approach – The research methodology and the analysis motivated an interpretive approach. The inductive reasoning approach has been used, since the aim was to find something new for the printing industry and to try to discover the advantages that ERP could bring about for the industry. In this research study, semi‐structured interviews were conducted along with a number of visits to the targeted printing firms selected as being prototypical of the sector in this business environment. The research questions focused on identifying and exploring the benefits related to ERP for the printing industry, and why it was needed.Findings – The research shows that all the functional categories identified in the literature for ERP can be intr...

Factors mediating the routinisation of e-learning within a traditional university education environment

Technology-enhanced or Computer Aided Learning (e-learning) can be institutionally integrated and supported by learning management systems or Virtual Learning Environments (VLEs) to offer efficiency gains, effectiveness and scalability of the e-leaning paradigm. However this can only be achieved through integration of pedagogically intelligent approaches and lesson preparation tools environment and VLE that is well accepted by both the students and teachers. The aim of this study is to consider how effective and efficient e-learning is in a web platform environment. The paper concludes that blended learning approaches offer the most flexible and scalable route to e-learning.