PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management

While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. Diverse and heterogeneous datasets are being generated from various sources, which could be utilized across the complete policies lifecycle (i.e. modelling, creation, evaluation and optimization) to realize efficient policy management. To this end, in this paper we present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques in order to make the data ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders.

Introducing an Edge-Native Deep Learning Platform for Exergames

The recent advancements in the areas of computer vision and deep learning with the development of convolutional neural networks and the profusion of highly accurate general purpose pre-trained models, create new opportunities for the interaction of humans with systems and facilitate the development of advanced features for all types of platforms and applications. Research, consumer and industrial applications increasingly integrate deep learning frameworks into their operational flow, and as a result of the availability of high performance hardware (Computer Boards, GPUs, TPUs) also for individual consumers and home use, this functionality has been moved closer to the end-users, at the edge of the network. In this work, we exploit the aforementioned approaches and tools for the development of an edge-native platform for exergames, which includes innovative gameplay and features for the users. A prototype game was created using the platform that was deployed in the real-world scenario of a rehabilitation center. The proposed approach provides advanced user experience based on the automated, real-time pose and gesture detection, and in parallel maintains low-cost to enable wide adoption in multiple applications across domains and usage scenarios.

Joint Multi-object Detection and Segmentation from an Untrimmed Video

In this paper, we present a novel method for jointly detecting and segmenting multiple objects from an untrimmed video. Unlike most existing video object segmentation methods that can only handle a trimmed video in which all video frames contain the target objects, we address a more practical and difficult problem, i.e., joint multi-object detection and segmentation from an untrimmed video where the target objects do not always appear per frame. In particular, our method consists of two modules, i.e., object decision module and object segmentation module. The object decision module is used to detect the objects and decide which target objects need to be separated out from video. As there are usually two or more target objects and they do not always appear in the whole video, we introduce the data association into object decision module to identify their correspondences among frames. The object segmentation module aims to separate the target objects identified by object decision module. In order to extensively evaluate the proposed method, we introduce a new dataset named UNVOSeg dataset, in which \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.2\%$$\end{document} of the video frames do not contain objects. Experimental results on four datasets demonstrate that our method outperforms most of the state-of-the-art approaches.

Protocol Deployment for Employing Honeypot-as-a-Service

The YAKSHA project aims at reinforcing EU-ASEAN cooperation and building partnerships in cybersecurity domain by developing a solution tailored to specific national needs leveraging EU know-how and local knowledge. YAKSHA enhances cybersecurity readiness levels for its end-users, helps better prevent cyber-attacks, reduces cyber-risks and better governs the whole cybersecurity process. The EU-ASEAN cooperation also helps to mitigate some of the weaknesses identified in the cybersecurity ecosystem. In this paper, we consider the protocol deployment process for employing honeypot-as-a-service, with focus on the Internet of Things (IoT) use case.

An Advanced Deep Learning Model for Short-Term Forecasting U.S. Natural Gas Price and Movement

Natural gas constitutes one of the most actively traded energy commodity with a significant impact on many financial activities of the world. The accurate natural gas price prediction and the direction of price changes are considered essential since these forecasts are utilized in energy sustainability planning, commodity trading and decision making, covering both the supply and demand side of natural gas market. In this research, a new deep learning prediction model is proposed for short-term forecasting natural gas price and movement. The proposed forecasting model exploits the ability of convolutional layers for providing a deep insight in natural gas data and the efficiency of LSTM layers for learning short-term and long-term dependencies. Additionally, a significant advantage of the proposed model is its abilities to predict the price of natural gas on the following day (regression) and also to predict if the price on the next day will increase, decrease or stay stable (classification) with respect to today’s price. The conducted series of experiments demonstrated that the proposed model considerably outperforms state-of-the-art deep learning and machine learning models.

A Framework to Support the 5G Densification

5G networks are shaping a new ecosystem necessitating various transformations of the existing network infrastructures combined with the use of network softwarization and programmability, so as to satisfy the needs of all the involved stakeholders (telecom/service providers, infrastructure owners, tenants, vertical industries, end-users, etc.), while a wide range of issues have to be addressed spanning from technology to business domains. The 5G-PPP project 5G-PHOS [1] proposes a novel framework to allow telecom operators and service providers to overcome 5G densification issues while supporting the stringent 5G requirements in a flexible and cost efficient manner to allow for commercialization. This paper aims at providing indicative architectural instantiations of the 5G-PHOS solution, depicting the way the technology supports the 5G requirements and the stakeholders’ needs along with the functionalities and the deployment feasibility of an ambitious 5G fronthaul/backhaul network solution.

Preservation of the Exchange Principle via Lattice Operations on (S,N)– Implications

In this paper, we investigate a special case of an open problem that is related to the exchange principle, a property of fuzzy implications. We focus on the cases of (S,N)– implications and the preservation of the exchange principle via lattice operations. We present and prove some sufficient conditions such that the exchange principle is preserved under the join and meet operations if we use (S,N)– implications.

Cross-Domain Authorship Attribution Using Pre-trained Language Models

Authorship attribution attempts to identify the authors behind texts and has important applications mainly in cyber-security, digital humanities and social media analytics. An especially challenging but very realistic scenario is cross-domain attribution where texts of known authorship (training set) differ from texts of disputed authorship (test set) in topic or genre. In this paper, we modify a successful authorship verification approach based on a multi-headed neural network language model and combine it with pre-trained language models. Based on experiments on a controlled corpus covering several text genres where topic and genre is specifically controlled, we demonstrate that the proposed approach achieves very promising results. We also demonstrate the crucial effect of the normalization corpus in cross-domain attribution.

Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems

As the requirements for automatic speech recognition are continually increasing, the demand for accuracy and efficiency is also of particular interest. In this paper, we present most of the well-known Automated Speech Recognition systems (ASR), and we benchmark three of them, namely the IBM Watson, Google, and Wit, using the WER, Hper, and Rper error metrics. The experimental results show that Google’s automatic speech recognition performs better among the three systems. We intend to extend the benchmarking both to include most of the available Automated Speech Recognition systems and increase our test data.

Using Multimodal Contextual Process Information for the Supervised Detection of Connector Lock Events

The field of sound event detection is a growing sector which has mainly focused on the identification of sound classes from daily life situations. In most cases these sound detection models are trained on publicly available sound databases, up to now, however, they do not include acoustic data from manufacturing environments. Within manufacturing industries, acoustic data can be exploited in order to evaluate the correct execution of assembling processes. As an example, in this paper the correct plugging of connectors is analyzed on the basis of multimodal contextual process information. The latter are the connector’s acoustic properties and visual information recorded in form of video files while executing connector locking processes.

For the first time optical microphones are used for the acquisition and analysis of connector sound data in order to differentiate connector locking sounds from each other respectively from background noise and sound events with similar acoustic properties. Therefore, different types of feature representations as well as neural network architectures are investigated for this specific task.

The results from the proposed analysis show, that multimodal approaches clearly outperform unimodal neural network architectures for the task of connector locking validation by reaching maximal accuracy levels close to 85\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}. Since in many cases there are no additional validation methods applied for the detection of correctly locked connectors in manufacturing industries, it is concluded that the proposed connector lock event detection framework is a significant improvement for the qualitative validation of plugging operations.

Ontological Foundations of Modelling Security Policies for Logical Analytics

Modelling of knowledge and actions in AI has advanced over the years but it is still a challenging topic due to the infamous frame problem, the inadequate formalization and the lack of automation. Some problems in cyber security such as logical vulnerability, risk assessment, policy validation etc. still require formal approach. In this paper we present the foundations of a new formal framework to address these challenges. Our approach is based on three-level formalisation: ontological, logical and analytical levels. Here we are presenting the first two levels which allow to model the security policies and provide a practical solution to the frame problem by efficient utilization of parameters as side effects. Key concepts are the situations, actions, events and rules. Our framework has potential use for analysis of a wide range of transactional systems within the financial, commercial and business domains and further work will include analytical level where we can perform vulnerability analysis of the model.

SDN-Enabled IoT Anomaly Detection Using Ensemble Learning

Internet of Things (IoT) devices are inherently vulnerable due to insecure design, implementation, and configuration. Aggressive behavior change, due to increased attacker’s sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices is a making challenge. To detect intensive attacks and increase device uptime, we propose a novel ensemble learning model for IoT anomaly detection using software-defined networks (SDN). We use a deep auto-encoder to extract handy features for stacking into an ensemble learning model. The learned model is deployed in the SDN controller to detect anomalies or dynamic attacks in IoT by addressing the class imbalance problem. We validate the model with real-time testbed and benchmark datasets. The initial results show that our model has a better and more reliable performance than the competing models showcased in the relevant related work.

On the Learnability of Concepts

Word Embeddings are used widely in multiple Natural Language Processing (NLP) applications. They are coordinates associated with each word in a dictionary, inferred from statistical properties of these words in a large corpus. In this paper we introduce the notion of “concept” as a list of words that have shared semantic content. We use this notion to analyse the learnability of certain concepts, defined as the capability of a classifier to recognise unseen members of a concept after training on a random subset of it. We first use this method to measure the learnability of concepts on pretrained word embeddings. We then develop a statistical analysis of concept learnability, based on hypothesis testing and ROC curves, in order to compare the relative merits of various embedding algorithms using a fixed corpora and hyper parameters. We find that all embedding methods capture the semantic content of those word lists, but fastText performs better than the others.

An Intelligent Cloud-Based Platform for Effective Monitoring of Patients with Psychotic Disorders

The therapy of patients with psychotic disorders (i.e., bipolar disorder and schizophrenia) could benefit from the constant monitoring of their physiological and motor parameters. In this paper, we present an innovative and advanced cloud based platform that facilitates the effective monitoring of such patients. A commodity smartwatch is used for biosignal and motion data collection at a 24/7 basis. The paper describes the technical details of the implemented application both on the smartwatch and the cloud server side. Technical challenges regarding the upload, the storage and the battery constraints of the smartwatch are also discussed, along with the initial results regarding data visualization and processing.

Innovative Deep Neural Network Fusion for Pairwise Translation Evaluation

A language independent deep learning (DL) architecture for machine translation (MT) evaluation is presented. This DL architecture aims at the best choice between two MT (S1, S2) outputs, based on the reference translation (Sr) and the annotation score. The outputs were generated from a statistical machine translation (SMT) system and a neural machine translation (NMT) system. The model applied in two language pairs: English - Greek (EN-EL) and English - Italian (EN-IT). In this paper, a variety of experiments with different parameter configurations is presented. Moreover, linguistic features, embeddings representation and natural language processing (NLP) metrics (BLEU, METEOR, TER, WER) were tested. The best score was achieved when the proposed model used source segments (SSE) information and the NLP metrics set. Classification accuracy has increased up to 5% (compared to previous related work) and reached quite satisfactory results for the Kendall τ score.

AI Based Real-Time Signal Reconstruction for Wind Farm with SCADA Sensor Failure

Supervisory Control and Data Acquisition (SCADA) systems used in wind turbines for monitoring the health and performance of a wind farm can suffer from data loss due to sensor failure, transmission link breakdown or network congestion. Sensory data is used for important control decisions and such data loss can make the failures harder to detect. This work proposes various solutions to reconstruct the lost information of important SCADA parameters using Linear and non-linear Artificial Intelligence (AI) algorithms. It comprises of three major contributions; (1) signal reconstruction from other available SCADA parameters, (2) comparison of linear and non-linear AI models, and (3) generalization of the AI algorithms between turbines. Experimental results demonstrate the effectiveness of the developed methodologies for reconstruction of the lost information for valuable planning decisions.

Dynamic Resource Allocation and Computation Offloading for Edge Computing System

In this work, we propose a dynamic optimization scheme for an edge computing system with multiple users, where the radio and computational resources, and offloading decisions, can be dynamically allocated with the variation of computation demands, radio channels and the computation resources. Specifically, with the objective to minimize the energy consumption of the considered system, we propose a joint computation offloading, radio and computational resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, the main problem is divided into several sub-problems at each time slot and are addressed separately. The simulation results demonstrate the effectiveness of the proposed scheme.

Boosted Ensemble Learning for Anomaly Detection in 5G RAN

The emerging 5G networks promises more throughput, faster, and more reliable services, but as the network complexity and dynamics increases, it becomes more difficult to troubleshoot the systems. Vendors are spending a lot of time and effort on early anomaly detection in their development cycle and majority of the time is spent on manually analyzing system logs. While main research in anomaly detection uses performance metrics, anomaly detection using functional behaviour is still lacking in depth analysis. In this paper we show how a boosted ensemble of Long Short Term Memory classifiers can detect anomalies in the 5G Radio Access Network system logs. Acquiring system logs from a live 5G network is difficult due to confidentiality issues, live network disturbance, and problems to repeat scenarios. Therefore, we perform our evaluation on logs from a 5G test bed that simulate realistic traffic in a city. Our ensemble learns the functional behaviour of an application by training on logs from normal execution time. It can then detect deviations from normal behaviour and also be retrained on false positive cases found during validation. Anomaly detection in RAN shows that our ensemble called BoostLog, outperforms a single LSTM classifier and further testing on HDFS logs confirms that BoostLog also can be used in other domains. Instead of using domain experts to manually analyse system logs, BoostLog can be used by less experienced trouble shooters to automatically detect anomalies faster and more reliable.

On the Reusability of Sentiment Analysis Datasets in Applications with Dissimilar Contexts

The main goal of this paper is to evaluate the usability of several algorithms on various sentiment-labeled datasets. The process of creating good semantic vector representations for textual data is considered a very demanding task for the research community. The first and most important step of a Natural Language Processing (NLP) system, is text preprocessing, which greatly affects the overall accuracy of the classification algorithms. In this work, two vector space models are created, and a study consisting of a variety of algorithms, is performed on them. The work is based on the IMDb dataset which contains movie reviews along with their associated labels (positive or negative). The goal is to obtain the model with the highest accuracy and the best generalization. To measure how well these models generalize in other domains, several datasets, which are further analyzed later, are used.

Recommender systems for IoT enabled quantified-self applications

As an emerging trend in big data science, applications based onthe Quantified-Self (QS)engage individuals in the self-tracking of any kind of biological, physical, behavioral, or environmental information as individuals or groups. There are new needs and opportunities for recommender systems to develop new models/approaches to support QS application users.Recommender systemscan help to more easily identify relevant artifacts for users and thus improve user experiences. Currently recommender systems are widely and effectively used in the e-commerce domain (e.g., online music services, online bookstores). Next-generation QS applications could include more recommender tools for assisting the users of QS systems based on their personal self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. In this paper, we propose three new recommendation approaches for QS applications:Virtual Coach,Virtual Nurse, andVirtual Sleep Regulatorwhich help QS users to improve their health conditions.Virtual Coachworks like a real fitness coach to recommend personalized work-out plans whereasVirtual Nurseconsiders the medical history and health targets of a user to recommend a suitable physical activity plan.Virtual Sleep Regulatoris specifically designed for insomnia (a kind of sleep disorder) patients to improve their sleep quality with the help of recommended physical activity and sleep plans. We explain how these proposed recommender technologies can be applied on the basis of the collected QS data to create qualitative recommendations for user needs. We present example recommendation results ofVirtual Sleep Regulatoron the basis of the dataset from a real world QS application.

A Smart Infotainment System Equiped with Emotional Intelligence

Bedside infotainment technology has been gaining popularity, helping providers to address patient needs and improve hospitalization experience. Such systems have the potential to become valuable tools for medical and nursing personnel, with the integration of patient monitoring features, bolstering efficiency and coordination. Extending their utility beyond conventional monitoring, the incorporation of affective computing capabilities would allow for early detection of potentially dangerous situations, as an individual's emotional state has a direct effect on their health, cognitive status, behaviour and quality of life. Furthermore, the addition of a serious games module would provide additional value for patients with cognitive decline or mobility issues. This work presents a novel bedside infotainment system, equipped with the aforementioned capabilities and designed to address the needs of patients in long-term care facilities, such as recreation and rehabilitation centres.

CrowdHEALTH: Big Data Analytics and Holistic Health Records

The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies.

An Enhanced Device-Transparent Real-Time Teleconsultation Environment for Radiologists

This paper describes a novel web-based platform promoting real-time advanced teleconsultation services on medical imaging. Principles of heterogeneous workflow management systems and state-of-the-art technologies such as the microservices architectural pattern, peer-to-peer networking, and the single-page application concept are combined to build a scalable and extensible platform to aid collaboration among geographically distributed healthcare professionals. The real-time communication capabilities are based on the webRTC protocol to enable direct communication among clients. This paper discusses the conceptual and technical details of the system, emphasizing on its innovative elements.

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.

Automated integration of wireless biosignal collection devices for patient-centred decision-making in point-of-care systems

The proper acquisition of biosignals data from various biosensor devices and their remote accessibility are still issues that prevent the wide adoption of point-of-care systems in the routine of monitoring chronic patients. This Letter presents an advanced framework for enabling patient monitoring that utilises a cloud computing infrastructure for data management and analysis. The framework introduces also a local mechanism for uniform biosignals collection from wearables and biosignal sensors, and decision support modules, in order to enable prompt and essential decisions. A prototype smartphone application and the related cloud modules have been implemented for demonstrating the value of the proposed framework. Initial results regarding the performance of the system and the effectiveness in data management and decision-making have been quite encouraging.

A modelling and simulation framework for compound medical applications in regional healthcare networks

Regional healthcare information networks have already started to grow across Europe, in order to cover local healthcare provision needs, especially in isolated regions, where there is often no availability of central general hospitals. The paper discusses a modelling and simulation framework for the design of regional healthcare information networks running compound medical and QoS-sensitive applications. The proposed framework decomposes the compound medical applications into combinations of elementary traffic profiles, assesses appropriate values to the traffic parameters of the assigned models and defines suitable simulation scenarios. The simulation results are analysed and finally lead to reliable bandwidth estimations of the links of the healthcare information network under design. The proposed framework has been thoroughly validated through its application for the design of a healthcare network in the islands of the North Aegean Sea, running actual compound medical applications in the context of a national research project.

An overview of platforms for cloud based development

This paper provides an overview of the state of the art technologies for software development in cloud environments. The surveyed systems cover the whole spectrum of cloud-based development including integrated programming environments, code repositories, software modeling, composition and documentation tools, and application management and orchestration. In this work we evaluate the existing cloud development ecosystem based on a wide number of characteristics like applicability (e.g. programming and database technologies supported), productivity enhancement (e.g. editor capabilities, debugging tools), support for collaboration (e.g. repository functionality, version control) and post-development application hosting and we compare the surveyed systems. The conducted survey proves that software engineering in the cloud era has made its initial steps showing potential to provide concrete implementation and execution environments for cloud-based applications. However, a number of important challenges need to be addressed for this approach to be viable. These challenges are discussed in the article, while a conclusion is drawn that although several steps have been made, a compact and reliable solution does not yet exist.

Real-time medical collaboration services over the web

The gradual shift in modern medical practice, from working alone clinical doctors to MDTs (Multi-Disciplinary Teams), raises the need of online real-time collaboration among geographically distributed medical personnel. The paper presents a Web-based platform, featuring an efficient medical data management and exchange, for hosting real-time collaborative services. The presented work leverages state-of-the-art features of the web (technologies and APIs) to support client-side medical data processing. Moreover, to address the typical bandwidth bottleneck and known scalability issues of centralized data sharing, an indirect RPC (Remote Process Call) scheme is introduced through object synchronization over the WebRTC paradigm.

Estimation of Robot Position and Orientation Using a Stationary Fisheye Camera

A core problem in robotics is the determination of the location and pose of a mobile robot in its environment. The localization is a basic operation, which must be successfully carried out in complex environments using imprecise and/or contaminated data and is essential for a broad range of mobile robot tasks, since the robot behavior depends on its position. In this work, we propose the use of a stationary fisheye camera for real time robot localization in indoor environments. We employ a model for the formation of the image by the fisheye camera, which can be used for accelerating the segmentation of the robot's top surface, as well as for calculating the robot's true position in the real world frame of reference. The proposed algorithm for robot localization exploits the calibrated fisheye camera model and the known dimensions of the robot, whereas it does not depend on any information from the robot's sensors and does not require visual landmarks in the indoor environment. Furthermore, the pose (orientation) of the robot is determined using a triangular shape placed on top of the robot's flat top surface, using Hu's moment invariants, appropriately modified using the calibrated fisheye camera model. Initial results are presented from video sequences and are compared to the ground truth position, obtained by the robot's sensors. The dependence of the average positional error with the distance from the camera is also measured.

Hair removal on dermoscopy images

Digital Dermoscopy is a tool commonly used by dermatologists for assisting the diagnosis of skin lesions. The presence of hair in such dermoscopic images frequently occludes significant diagnostic information and reduces their value. In this work we propose algorithms that successfully identify and remove hair from the dermoscopic images. The proposed algorithms consist of two parts; the first deals with the identification of hair, while the second part concerns the image restoration using interpolation. For the evaluation of the algorithms we used ground truth images with synthetic hair and compared the results with the commonly used in the literature DullRazor tool. According to the experimental results the proposed hair removal algorithms can be used successfully in the detection and removal of both dark and light colored hair.

An advanced image analysis tool for the quantification and characterization of breast cancer in microscopy images

The paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images. The proposed tool utilizes adaptive thresholding and a Support Vector Machines classifier. The segmentation results are enhanced through a Majority Voting and a Watershed technique, while an object labeling algorithm has been developed for the fast and accurate validation of the recognized cells. Expert pathologists evaluated the tool and the reported results are satisfying and reproducible.

Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy

The interest in image dermoscopy has been significantly increased recently and skin lesion images are nowadays routinely acquired for a number of skin disorders. An important finding in the assessment of a skin lesion severity is the existence of dark dots and globules, which are hard to locate and count using existing image software tools. In this work we present a novel methodology for detecting/segmenting and count dark dots and globules from dermoscopy images. Segmentation is performed using a multi-resolution approach based on inverse non-linear diffusion. Subsequently, a number of features are extracted from the segmented dots/globules and their diagnostic value in automatic classification of dermoscopy images of skin lesions into melanoma and non-malignant nevus is evaluated. The proposed algorithm is applied to a number of images with skin lesions with known histo-pathology. Results show that the proposed algorithm is very effective in automatically segmenting dark dots and globules. Furthermore, it was found that the features extracted from the segmented dots/globules can enhance the performance of classification algorithms that discriminate between malignant and benign skin lesions, when they are combined with other region-based descriptors.

Intelligent Health Monitoring Based on Pervasive Technologies and Cloud Computing

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising.

Automated skin lesion assessment using mobile technologies and cloud platforms

This paper presents a smart phone based system for storing digital images of skin areas depicting regions of interest (lesions) and performing self-assessment of these skin lesions within these areas. The system consists of a mobile application that can acquire and identify moles in skin images and classify them according their severity into melanoma, nevus and benign lesions. The proposed system includes also a cloud infrastructure exploiting computational and storage resources. This cloud-based architecture provides interoperability and support of various mobile environments as well as flexibility in enhancing the classification model. Initial evaluation results are quite promising and indicate that the application can be used for the task of skin lesions initial assessment.

A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces

Three-dimensional (3D) medical imaging has been incorporated in routine clinical practice, since the required infrastructure has become increasingly affordable. New algorithms and applications are needed to serve the additional image processing and analysis functions in 3D space. In this work we propose a system for semi-automatic modeling and segmentation of elongated salient and anatomical objects in 3D medical images. The proposed methodology is based on a novel mathematical formalization of a well-known class of geometric primitives, namely generalized cylinders (GCs), which exhibits advantages over the existing parametric definition. Since the anatomical objects have to be modeled by their intersection with the transverse image planes, the proposed methodology includes also a new seeded region growing (SRG) segmentation algorithm for ellipse detection in 2D images, based on a priori shape knowledge. Finally, the resulting GC model is used to initialize an active surface (AS) segmentation method, in order to accurately delineate the required object. In this work we present the proposed algorithms in detail, along with the evaluation of the accuracy of the model-based segmentation by experts. Results show that elongated objects like the aorta and the trachea may be segmented with sensitivity between 90% and 95%. The proposed SRG-ellipse detector requires minimal user-initialization and its executions requires only few seconds for each image slice on an average laptop. The evolution of the AS requires less than one second per iteration for a typical CT image. Comparisons are provided with state of the art semi-automatic medical image processing software, which validate the merit of the proposed work.

Certification and Security Issues in Biomedical Grid Portals

User authentication and data security are very important aspects for the deployment and proper function of biomedical grid portals, since both sensitive data issues and controlled access to grid resources must be addressed. This chapter discusses certification and security issues in biomedical grid portals and presents the security infrastructure of GRISSOM (Grids for In Silico Systems biology and Medicine) platform. The platform consists of a web-based portal and a Web Service that enables statistical analysis of microarray cDNA data with the use of EGEE Grid infrastructure. The security infrastructure addresses user authentication and access issues, data encryption, Grid secure access and Web Service Security. The appendix of the chapter contains code snapshots on how to implement secure authentication in Web Services and create user SSL certificates on demand.

Advanced characterization of microscopic kidney biopsies utilizing image analysis techniques

Correct annotation and identification of salient regions in Kidney biopsy images can provide an estimation of pathogenesis in obstructive nephropathy. This paper presents a tool for the automatic or manual segmentation of such regions along with methodology for their characterization in terms of the exhibited pathology. The proposed implementation is based on custom code written in Java and the utilization of open source tools (i.e. RapidMiner, ImageJ). The corresponding implementation details along with the initial evaluation of the proposed integrated system are also presented in the paper.

Cancer cells detection and pathology quantification utilizing image analysis techniques

This paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images utilizing adaptive thresholding and a Support Vector Machines classifier. The segmentation results are also enhanced through a Majority Voting and a Watershed technique. The proposed tool was evaluated by experts on breast cancer images and the reported results were accurate and reproducible.

Video segmentation of moving humans for assistive environments

In this paper, we present two recently proposed efficient methods for human segmentation from video in indoor environments: the illumination sensitive background method and the self-organizing background subtraction (SOBS) method. Both methods maintain multiple background models. The SOBS method has been modified in this work for gray-scale frames, in order to decrease processing times. The video data are acquired indoors from a fixed fish-eye camera in the living environment. The paper presents the algorithmic implementation and modifications details, while results are also presented for a small number of video sequences.

A collaborative biomedical image mining framework: application on the image analysis of microscopic kidney biopsies

The analysis and characterization of biomedical image data is a complex procedure involving several processing phases, like data acquisition, preprocessing, segmentation, feature extraction and classification. The proper combination and parameterization of the utilized methods are heavily relying on the given image data set and experiment type. They may thus necessitate advanced image processing and classification knowledge and skills from the side of the biomedical expert. In this work, an application, exploiting web services and applying ontological modeling, is presented, to enable the intelligent creation of image mining workflows. The described tool can be directly integrated to the RapidMiner, Taverna or similar workflow management platforms. A case study dealing with the creation of a sample workflow for the analysis of kidney biopsy microscopy images is presented to demonstrate the functionality of the proposed framework.