Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressed Sensing Framework for Multi-channel ECG Signals

Electrocardiogram (ECG) signals are the manifestation of underlying electrical phenomena of heart, which are responsible for its various functionalities. ECG is used as an important non-invasive tool by the cardiologists to diagnose and assess a wide range of cardiac ailments. With advancements in wireless body area network (WBAN) technologies, significant research has been done in recent decades to develop low-cost personalized remote health monitoring systems for next-generation of e-healthcare solutions. With ever increasing number of cardiovascular patients, WBAN-enabled ECG telemonitoring has generated significant interest among the biomedical community. Ambulatory ECG enables remote monitoring of vital heart parameters and allows early medical interventions in case of lifethreatening heart diseases. However, existing ECG monitoring systems still suffer from various challenges, such as limited autonomy, bulkiness, limited functionalities, etc. In recent years, compressed sensing (CS) has emerged as a promising framework to address these challenges. Low-complex and highly energy-efficient data reduction procedure of CS makes it an attractive choice over traditional wavelet-based techniques for embedded on-node ECG data compression in resource-constrained telemonitoring applications. The main objective of the present dissertation is to explore the CS framework for multichannel ECG (MECG) signals. CS-based signal processing involves two operations: 1) CS encoding, where the data is linearly projected over a lower dimensional subspace using a sensing matrix to get the compressed measurements of the signal, 2) Signal reconstruction (decoding), where the original signal is reconstructed in a sparse domain using incomplete signal measurements. In the present thesis, there are three major contributions. First work focuses on efficient MECG signal encoding using an eigenspace CS approach. Second and third works contribute towards faithful signal reconstruction when low signal measurements are available at low data rates (or at high compression ratio).
GF + MMT = GLF - From Language to Semantics through LF

These days, vast amounts of knowledge are available online, most of it in written form. Search engines help us access this knowledge, but aggregating, relating and reasoning with it is still a predominantly human effort. One of the key challenges for automated reasoning based on natural-language texts is the need to extract meaning (semantics) from texts. Natural language understanding (NLU) systems describe the conversion from a set of natural language utterances to terms in a particular logic. Tools for the co-development of grammar and target logic are currently largely missing. We will describe the Grammatical Logical Framework (GLF), a combination of two existing frameworks, in which large parts of a symbolic, rule-based NLU system can be developed and implemented: the Grammatical Framework (GF) and MMT. GF is a tool for syntactic analysis, generation, and translation with complex natural language grammars and MMT can be used to specify logical systems and to represent knowledge in them. Combining these tools is possible, because they are based on compatible logical frameworks: Martin-Lof type theory and LF. The flexibility of logical frameworks is needed, as NLU research has not settled on a particular target logic for meaning representation. Instead, new logics are developed all the time to handle various language phenomena. GLF allows users to develop the logic and the language parsing components in parallel, and to connect them for experimentation with the entire pipeline.
Foundational Process Relations in Bio-Ontologies

This paper aims to provide rigor to the process relations in the OBO relations ontology. We present a formal axiomatization of these relations in first order logic as a definitional extension of the Process Specification Language (PSL) Ontology. In particular, we formalize the process parthood, process specialization, process instantiation and instance level parthood relations of OBO. We also present a formal characterization of the models of our axiomatization and provide representation theorems showing the correctness of our characterization.
A Multi-Actor Framework Centered around an Assistive Mobile Robot for Elderly People Living Alone

In a social and economic context characterized by a constantly aging population, the research for new technologies able to assist elderly people is getting more and more attention. In this extended abstract we illustrate the main components of the European project MoveCare, a multi-actor framework designed to assist pre-frail elders living alone. The proposed system is centered around an assistive mobile robot that provides the user with a set of functionalities to support cognitive and social stimulation, assistance, and transparent monitoring.
Soliciting reader contributions to software tutorials

Online software tutorials help a wide range of users acquire skills with complex software, but are not always easy to follow. For example, a tutorial might target users with a high skill level, or it might contain errors and omissions. Prior work has shown that user contributions, such as user comments, can add value to a tutorial. Building on this prior work, we investigate an approach to soliciting structured tutorial enhancements from tutorial readers. We illustrate this approach through a prototype called Antorial, and evaluate its impact on reader contributions through a multi-session evaluation with 13 participants. Our findings suggest that structuring tutorial contributions has positive impacts on both the number and type of reader contributions. Our findings also point to design considerations for systems that aim to support community-based tutorial refinement, and suggest promising directions for future research. Thesis Advisor Author Andrea Bunt Patrick Dubois iii Table of
Dynamic Cloud Service Management for Scalable Internet of Things Applications

The Internet of Things (IoT) is growing rapidly and official reports suggest that soon it will become one of the key technologies of the Internet infrastructure. Cloud services are already integrated with IoT, providing solutions for a large number of applications. Various Cloud IoT platforms are available for IoT users, both as open source and commercial solutions. One of the important features of a cloud IoT platform is the management of services and the deployment time required to set up a platform dedicated to a specific IoT use case. This paper presents a cloud IoT platform deployment mechanism that aims to improve the deployment time between an IoT network and a cloud IoT platform. The approach is based on a platform descriptor which describes the IoT application requirements. Therefore, software-based functionalities automate the provisioning of the platform to accommodate scalability in cloud IoT applications. The experimentation and evaluation of the proposed mechanism is conducted over a real data-center laboratory. We are comparing our mechanism with a Unix bash shell scripting deployment method and the results achieve over 50% improvement in both platform deployment time, as well as the time required to update the platform.
An experimental digital learning environment with universal accessibility

Abstract Today, digital media are an integral part of most people’s daily routine. Therefore, tablets, smartphones and the internet have become increasingly important at school, as well. At the same time, digitisation offers great potential for teaching highly heterogeneous learning groups individually and comprehensively. However, the use of digital media at German schools is still in its infancy. In order to create a basis for the successful implementation of digital media in teaching, it is necessary to generate profound knowledge about the effectiveness of digital learning environments regarding heterogeneous learning groups. Within this framework, we develop and evaluate a digital learning environment for lower secondary classrooms (age group 13–14) in this project. The aim of the study is to determine the effects of the use of tablet computers in different teaching phases on the students’ learning outcome. In order to analyse the effectiveness of the tablets in the classroom, we assess the expertise knowledge of the students before and after the teaching unit as well as the students’ attitude towards the teaching unit and towards working with tablet computers. With the help of a screen-capture-software, it is possible to retrace the students’ action steps while working with the tablets. Additionally, the lessons are filmed to compare the work behaviour of the learners with and without tablets. Furthermore, the cognitive abilities and the self-concept of the students are determined. This paper focuses on the development and design of the study, as the evaluation has not yet been completed.
An Efficient Contention-Window Based Reporting for Internet of Things Features in Cognitive Radio Networks

Internet of things (IoT) is a new challenging paradigm for connecting heterogeneous networks. However, an explosive increase in the number of IoT cognitive users requires a mass of sensing reporting; thus, it increases complexity of the system. Moreover, bandwidth utilization, reporting time, and communication overhead arise. To realize spectrum sensing, how to collect sensing results by reducing the communication overhead and the reporting time is a problem of major concern in future wireless networks. On the other hand, cognitive radio is a promising technology to access the spectrum opportunistically. In this paper, we propose a contention-window based reporting approach with a sequential fusion mechanism. The proposed reporting scheme reduces the reporting time and the communication overhead by collecting sensing results from the secondary users with the highest reliability at a fusion center by utilizing Dempster-Shafer evidence theory. The fusion center broadcasts the sensing results once a global decision requirement is satisfied. Through simulations, we evaluate the proposed scheme in terms of percentage of the number of reporting secondary users, error probability, percentage of reporting, and spectral efficiency. As a result, it is shown that the proposed scheme is more effective than a conventional order-less sequential reporting scheme.
Content Based Image Retrieval using Median Optimized Cross-Channel Local Ternary Pattern

— Accurately finding images from a huge color image dataset is a highly complex task for human perspective. Different CBIR techniques are developed for describing the image content based on extracted low level features. The Proposed method extracts cross-channel chromatic-textural information using HSV color space components. This method extracts not only texture statistical information but also captures color information. Median Optimized Local Ternary Pattern is less susceptible to noise and can capture information about the macrostructure. It is extremely robust to random pixel corruption and image noise, including salt and noise, Gaussian Blur, Gaussian noise. Results of experiment are based on Dataset of corel 1k, corel 10K, MIT Vistex, Stex dataset shows that the proposed system much better compared to existing techniques. It achieves high precision and greatly reduces the processing time and it performs better with regard to average recall and precision.
Hierarchical Structure Learning in a Bayesian Network for the Analysis of Purchasing Behavior

: In the original Bayesian network model, the hierarchical structure of the variables is not assumed. When modeling the relation between the sales of products in a retail industry, it is better to consider a hierarchical structure of items (e.g., first, second, and third classifications). To apply a Bayesian network to such data, we have to focus on one hierarchy only in order to acquire a Bayesian network model. However, focusing only on the first or second classification provides a high-level view, but makes it difficult to understand customers’ purchasing behavior in detail. On the other hand, focusing on the third classification results in a considerable number of nodes and a complicated network structure. Thus, capturing the overall relationship between products is not straightforward. Therefore, we propose a hierarchical Bayesian network model and a new learning method based on max-min hill-climbing learning algorithm. In the proposed method, we focus on the hierarchical structure of products, which enables us to construct a lower layer that considers the causal relationships in the upper layer. Furthermore, we investigate a case study using actual hierarchical data on consumer purchases, and demonstrate the proposed model using a simulation analysis.
Hardware / Software Architectural and Technological Exploration for Energy-Efficient and Reliable Biomedical Devices

NOWADAYS, the ubiquity of smart appliances in our everyday lives is increasingly strengthening the links between humans and machines. Beyond making our lives easier and more convenient, smart devices are now playing an important role in personalized healthcare delivery. This technological breakthrough is particularly relevant in a world where population aging and unhealthy habits have made non-communicable diseases the first leading cause of death worldwide according to international public health organizations. In this context, smart health monitoring systems termed Wireless Body Sensor Nodes (WBSNs), represent a paradigm shift in the healthcare landscape by greatly lowering the cost of long-term monitoring of chronic diseases such as cardiovascular disorders, as well as improving patients’ lifestyles. WBSNs are able to autonomously acquire biological signals with different modalities and embed on-node Digital Signal Processing (DSP) capabilities to deliver clinically-accurate health diagnoses in real-time, even outside of a hospital environment. Energy efficiency and reliability are fundamental requirements for WBSNs, since they must operate for extended periods of time, while relying on compact batteries to reduce patients’ discomfort. These constraints, in turn, impose carefully designed hardware and software architectures for hosting the execution of complex biomedical applications. In this thesis, I develop and explore novel solutions at the architectural and technological level of the integrated circuit design domain, to enhance the energy efficiency and reliability of current WBSNs. Firstly, following a top-down approach driven by the characteristics of biomedical algorithms, I perform an architectural exploration of a heterogeneous and reconfigurable computing platform devoted to bio-signal analysis. By interfacing a shared Coarse-Grained Reconfigurable Array (CGRA) accelerator, this domain-specific platform can achieve higher performance and energy savings, beyond the capabilities offered by a baseline multi-processor system. More precisely, I propose three architectural versions of the CGRA, each contributing differently to the maximization of the application parallelization. The proposed Single, Multi and Interleaved-Datapath CGRA designs allow the developed platform to achieve substantial energy savings of up to 37 %, when executing complex biomedical applications, with respect to a multi-core-only platform. Secondly, I investigate how the modeling of technology reliability issues in logic and memory components can be exploited to adequately adjust the frequency and supply voltage of a circuit, with the aim of optimizing its computing performance and energy efficiency. To this end, I propose a novel framework for workload-dependent Bias Temperature In-
Meta-modeling of Big Data management layer

Nowadays, the magnitude of data generated daily through the technological environment has increased enormously. This massive amount of heterogeneous data led to the emergence of a large number of big data systems and technologies that share similar architectures but with different implementations. In essence, the common architecture is composed of many components: Data sources, Ingestion, Hadoop Storage, Hadoop Platform management, Visualization, Monitoring, and Security Layers. In our way for a unified abstract implementation, we proposed, in previous works, meta-models for data sources, ingestion, storage, and visualization layers. We also relied on our previous comparatives studies to define key concepts of management layer in Big Data. Thus, we shall present in this paper our meta-model for management layer in Big Data by applying techniques related to Model Driven Engineering. The main goal of this universal meta-modeling is to enable Big Data distribution providers to offer standard and unified solutions for a Big Data system.
A Computational Model Based on Emotion Energy

Emotional intelligence plays an important role in the communication between people and computers or robots. Based on psychodynamics, in this paper, the concept of emotional energy is proposed and the emotional energy is quantified as a unified parameter to measure the emotional state. On this basis, the energy distribution description space of the emotional states is established, which is the foundation for further describing the changing state of emotional state. The process of emotional change is regarded as a stochastic process in this paper, and Markov chain is used to describe the spontaneous transfer process of emotional state. The results show that the model can simulate the process of emotional state changes to some extent. Index Term—artifical emotion, psychological energy, emotion model
Bursty Event Detection in Twitter Streams

Social media, in recent years, have become an invaluable source of information for both public and private organizations to enhance the comprehension of people interests and the onset of new events. Twitter, especially, allows a fast spread of news and events happening real time that can contribute to situation awareness during emergency situations, but also to understand trending topics of a period. The article proposes an online algorithm that incrementally groups tweet streams into clusters. The approach summarizes the examined tweets into the cluster centroid by maintaining a number of textual and temporal features that allow the method to effectively discover groups of interest on particular themes. Experiments on messages posted by users addressing different issues, and a comparison with state-of-the-art approaches show that the method is capable to detect discussions regarding topics of interest, but also to distinguish bursty events revealed by a sudden spreading of attention on messages published by users.
Network Security System and Implementation Scheme of Information Infrastructure in Geological Prospecting Industry

This paper analyzed the construction requirements of the network security of information infrastructure in the geological prospecting industry, and proposed a network security system of information infrastructure for the geological prospecting industry, including physical security, network communication security, computing environment security, regional border security and security management. With the weak infrastructure of the industry units and the lack of operation management system, the overall solution for the network security construction of information infrastructure for the geological prospecting industry was proposed. Based on the constructed information security system and the overall solution, a typical network security implementation scheme of information infrastructure for the geological prospecting industry units is proposed.
Enhanced Grid-Based Visual Analysis of Retinal Layer Thickness with Optical Coherence Tomography

Optical coherence tomography enables high-resolution 3D imaging of retinal layers in the human eye. The thickness of the layers is commonly assessed to understand a variety of retinal and systemic disorders. Yet, the thickness data are complex and currently need to be considerably reduced prior to further processing and analysis. This leads to a loss of information on localized variations in thickness, which is important for early detection of certain retinal diseases. We propose an enhanced grid-based reduction and exploration of retinal thickness data. Alternative grids are computed, their representation quality is rated, and best fitting grids for given thickness data are suggested. Selected grids are then visualized, adapted, and compared at different levels of granularity. A visual analysis tool bundles all computational, visual, and interactive means in a flexible user interface. We demonstrate the utility of our tool in a complementary analysis procedure, which eases the evaluation of ophthalmic study data. Ophthalmologists successfully applied our solution to study localized variations in thickness of retinal layers in patients with diabetes mellitus.
Deep Boltzmann machine algorithm for accurate medical image analysis for classification of cancerous region

In this research work, a deep learning algorithm is applied to the medical domain to deliver a better healthcare system. For this, a deep learning framework for classification the region of interest pattern of complex hyperspectral medical images is proposed. The performance of computer-aided diagnosis by verifying the region in hyperspectral image by pre and post-cancerous region classification is enhanced. For this a deep Boltzmann machine (DBM) architecture of the bipartite structure as an unsupervised generative model was developed. The performance of DBM is compared with deep convolutional neural network architecture. For implementation, a three-layer unsupervised network with a backpropagation structure is used. From the presented dataset, image patches are collected and classified into two classes, namely non-informative and discriminative classes as labelled classes. The spatial information is used for classification and spectral-spatial representation of class labels is formed. In the labelled classes, the accuracy, false-positive predictions, sensitivity are obtained for the proposed fully-connected network. By the proposed cognitive computation technique an accuracy of 95.5% with 93.5% sensitivity was obtained. From the obtained classification, accuracy and success rate DBM provide a better classification of complex images compared to traditional convolution network.
Extraction of CIM-Based Distribution Grid Topology Information for Observability

In order to implement fault-detection and diagnosis applications in Low Voltage (LV) grids, the data from customer connections needs to be processed jointly with measurements from the distribution grid by other Distribution System Operator (DSO) systems and in addition correlated to the LV grid topology. In practical DSO systems, the LV grid topology data is included in asset management databases and may use the Common Information Model (CIM) as data model. This grid topology information plays an important role in fault-detection and diagnosis. Thus, this paper presents an architecture and a concrete implementation to extract relevant grid topology information for use in fault detection and diagnosis from a CIM based asset management database. The approach is demonstrated and validated via CIM-based grid topology model from a real medium-sized distribution grid operator.