INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND APPLICATIONS
(ICCCA 2020)
05 - 07 February 2020
Dr. A.P.J. Abdul Kalam Technical University, Lucknow
Colabration with
Madan Mohan Malaviya University of Technology, Gorakhpur

Keynote/Invited Speakers of ICCCA-2020

Speaker : Prof. Radim Burget
Signal Processing Laboratory. Department of Telecommunications, Brno University of Technology. Brno, Czech Republic, European Union
  • Title: Towards Artificial Super Intelligence
  • Abstarct: This presentation is focused on artificial intelligence (AI) and information processing. First, gives a summary of the development in the field from its very beginning, it briefly mentions the most important achievements of humanity in AI and as well as it summarises the most important successes in 2018 and what was changed in that year. The lecture gives a brief introduction to so-called deep neural networks and what they are, including several animations and demonstrations showing AI capabilities. The presentation is aimed at managers & decision-makers as well as to experts and engineers working in the field. Decision-makers and managers will learn where AI is standing in 2019, where this area is expected to be going in the next years and what today capabilities of AI are. Some parts of the presentation will be aimed at experts actively working with deep neural networks and will provide tips and tricks. These tips and tricks will be inspired by the latest findings from 2018 and can help you with just little changes to achieve state-of-the-art performance. Audience will also learn how is current AI compared to human intelligence, what is hype around AI and what is not, whether we can ever reach artificial super intelligence (ASI) and whether we are close to achieve ASI or not.

Prof. Carlos M. Travieso-Gonzalez
Head of Signals and Communications Department Institute for Technological Development and Innovation in Communications (IDeTIC) University of Las Palmas de Gran Canaria (ULPGC), Spain.
  • Title:E-Health tools on emotional detection
  • Abstarct: The physiological signals also known biosignals, most common and used for biomedical and biometric identification, are the electrocardiogram (ECG) and electroencephalogram (EEG). ECG measures the electrical activity of the heart and EEG measures the electrical activity of the brain. There are other very rarely used signals that we consider studying as part of this work. For example, the electromyogram (EMG) which is a record of the electrical activity produced by the muscles and nerves and the galvanic skin response (GSR)or skin conductance, which is an indication of psychological or physiological arousal such as fear, anger or other feelings. The detection of the degree of emotion through physiological signals is a very poorly studied area that can offer a new and efficient system, which deals with using the combination of several physiological signals as a method of identifying the degree of emotion. The objective of this proposal is to analyze the physiological signals that show people's emotions, quantify it and perform an automatic detection, which can become an innovative and robust tool that shows the degree of emotion. To implement the system, digital image processing techniques and artificial intelligence methods will be applied to obtain an objective low-cost emotion measurement system using physiological signals.

Prof. Rossi Setchi
Director of Research Centre in AI, Robotics and Human-Machine Systems (IROHMS) Deputy Head of School, Director of Research, Cardiff School of Engineering, Cardiff University, United Kingdom.
  • Title: The Future of Human-Machine Systems: Human-Like AI and Human-Centred Robotics
  • Abstarct: This talk will focus on the main principles of human-like computing and human-centred robotics that will provide machines and robots with human-like perceptual, reasoning and learning abilities, and enable safe and intuitive interactions and co-existence of man and machines. The main research challenge is the need for more acceptable, understandable and explainable AI and robotic solutions. Professor Setchi will discuss the limitations of the current AI and statistical Machine Learning and the need for more advanced human-like AI, which learns from humans using a small number of examples. She will explain how computational semantics and advances in smart sensing can be used to create human-like AI and improve productivity, creativity, situational awareness, intuitive interaction and reduce the likelihood of human confusion. She will illustrate her talk with examples from several multidisciplinary projects, which investigate real-world problems in the context of human-centred and context-aware computing.

Prof. Sabah A. Jassim
Professor of Mathematics and Computation School of Computing, University of Buckingham, Buckingham MK18 1EG, United Kingdom.
  • Title: Topological Data Analysis for Machine learning in Computer Vision.
  • Abstarct: The availability of sophisticated image processing tools enables a variety of image manipulation without leaving visual traces. These tools are frequently exploited to tamper with image content including malicious morphing attacks on face biometrics, hiding secrets communication by steganography, and creating fake images/videos. Image analysis aim to automatically or semi-automatically distinguish different classes of images depending on the objective(s) of the specific application. Traditional image analysis schemes rely on extracting texture feature vectors (TFV) and training a variety of classifiers on these vectors or their histograms. Here we adopt the newly emerging Topological Data Analysis (TDA) paradigm that exploits changes to spatial distribution of (TFV’s). TDA and its computational tool of Persistent Homology will be shown to be highly effective in detecting a variety of barely visible tiny changes in image texture primitives as a result of malicious tampering or naturally occurring abnormalities. We present a variety of topologically sensitive TFVs that can be used to deal with such image analysis applications. We shall demonstrate applicability of TDA image analysis schemes to detect face morphing and fake video attacks, detecting decease-caused distortion of tissue scans, and as a Steganalysis tool to detect hidden secret transactions.

Prof. Ahmed Abdelgawad
Professor of Computing Engineering. Central Michigan University ET 130A Mount Pleasant, MI 48849, USA.
  • Title: All What You Need to Know About Internet of Things (IoT)
  • Abstarct: Internet of Things (IoT) is the network of physical objects or “things” embedded with electronics, software, sensors, and network connectivity. It enables the objects to collect, share, and analyze data. The IoT has become an integral part of our daily lives through applications such as public safety, intelligent tracking in transportation, industrial wireless automation, personal health monitoring, and health care for the aged community. IoT is one of the latest technologies that will change our lifestyle in the coming years. Experts estimate that as of now, there are 23 billion connected devices, and by 2020 it would reach 30 billion devices. This talk aims to introduce the design and implementation of IoT signal processing systems. The foundations of IoT will be discussed throughout real applications. Challenges and constraints for future research in IoT will be discussed. In addition, research opportunities and collaboration will be offered for the attendees.

Prof. Hao Ying
IEEE Fellow, Professor, Department of Electrical and Computer Engineering Wayne State University, Detroit, Michigan 48202, USA
  • Title: Comparison of Machine Learning Techniques to Identify Sepsis Patients in the Emergency Department
  • Abstarct: Background: Sepsis is a major health concern with substantial impact on health care. Early identification of patients with sepsis is critical to provide timely care for patients. There were reports of applying machine learning techniques for sepsis detection in the literature. These techniques produced models with varying degree of transparency. They can be transparent models such as Decision Trees, semi-transparent models such as Naïve Bayes Network, and black-box models such as Support Vector Machine (SVM) and Neural Networks. Objectives: The objective of this study is to compare the performance of several machine learning techniques to identify emergency department septic patients. In the comparison, we also included the results of the novel rule-point sepsis alert system that we developed previously. Methods: This study uses a random sample of 912 sepsis and 975 non-sepsis patients admitted to the emergency department of the Detroit Medical Center. Each case was adjudicated individually. Our rulebased model is a point-based system which assigns different weighted points to each rule. The patients were divided into 14 groups based on age and other patient variables, and each group has its own set of rules. Thresholds of the variables in each rule were optimized by a genetic algorithm (GA) to maximize sepsis detection performance. We used MATLAB Classification Learner that generated the result of 23 different machine learning models. We also used MATLAB’s Neural Network Toolbox to train and build different sizes of Neural Network models with one hidden-layer. Results: The 23 machine learning models result in a variety of performances. The Fine Gaussian SVM showed the highest sensitivity with 95.8% but with 77.7% specificity and 80.1% positive predictive value (PPV). The Coarse k-Nearest Neighborhood (KNN) had the highest specificity with 94.5% but sensitivity was at 84.9%. For balanced results with over 90% sensitivity, specificity and PPV, the Medium Gaussian SVM and Medium KNN models were at the top. The Neural Network models also varied in performances. The best performed network had 141 neurons (93.24% Sensitivity, 96.13% Specificity and 97.18% PPV), followed by 36 neurons network (94.0% Sensitivity, 96.05 Specificity and 96.41 PPV). The rule-point system resulted in 90.9% sensitivity, 90.9% specificity and 90.3% PPV. Conclusion: Among the machine learning classifiers, the Neural Network model achieved the highest detection performance. One major drawback of this model is its poor transparency and ability to explain the results to the domain experts. We found no apparent relationship between the number of neurons in the hidden layer and detection accuracy of the network. The GA-optimized rule-point sepsis alert system can provide comparable performance to the NN and SVM models

Prof. Aleksandar Poleksic
Department of Computer Science University of Northern Iowa, USA
  • Title: Integrating biological knowledge to better predict relationships in a biological system
  • Abstarct: A key problem in biological research is to discover and quantify the relationships (associations or interactions) between different entities in a complex biological system. Recent years have seen the development of computational techniques for predicting such relationships based upon the data from a specific pair of domains (e.g. predicting whether any given drug is likely to treat any given disease). However, in spite of advanced computational techniques available, the progress in achieving prediction accuracy necessary for biological discoveries has been dismal at best. We demonstrate that a better understanding of a biological system as a whole is necessary to overcome the poor methods’ performance inherit to data in any two given domains. In one approach, a biological system can be viewed as a network, consisting of different domains (such as genes, diseases, drugs, symptoms, etc.). The individual relationships between entities in any two domains (e.g. drug-treats-disease) in such a network can be predicted not only directly, but also transitively (e.g. drug-upregulates-gene-downregulates-disease). We demonstrate that a simple voting mechanism applied to different types of transitive predictions can significantly increase the accuracy obtained by the current, state-of-the-art methodologies.

Prof. Cesare Alippi
Professor (Information Processing Systems) Professor with the Politecnico di Milano, Milano, Italy and Università della Svizzera Italiana, Lugano, Switzerland.
  • Title: Neural Graph Processing: an embedding-based approach
  • Abstarct: Many fields, like physics, neuroscience, chemistry, and sociology, investigate phenomena by processing multivariate measurements advantageously represented as a sequence of attributed graphs. Graphs come in different forms, with variable attributes, topology, and ordering, making it difficult to perform a mathematical analysis in the graph space. Within this framework, we are interested in processing graph datastreams to solve applications e.g., detect structural changes in the graph sequence, a situation associated with time variance, faults, anomalies or events of interest as well as design sophisticated processing like those requested by predictors. On the change detection front, theoretic results show that, under mild hypotheses, the confidence level of an event detected in the graph domain can be associated with another confidence level in an embedding space; this enables the identification of events in the graph domain by investigating embedded data. The opposite holds. However, evaluation of distances between graphs and identification of an appropriate embedding for the problem at hand are far from being trivial tasks with deep adversarial learning approaches and constant curvature manifold transformation showing to be appropriate transformations able to solve the problem. Deep autoregressive predictive models can then be designed to operate directly on graphs, hence providing the building blocks for other future sophisticated neural processing.

Prof. Tarek El-Ghazawi
ECE Professor and Director Institute for Massively Parallel Applications and Computing Technology (IMPACT) The George Washington University, USA
  • Title: Rebooting Computing- The Search for Post-Moore’s Law Breakthroughs
  • Abstarct: The field of high-performance computing (HPC) or supercomputing refers to the building and using computing systems that are orders of magnitude faster than our common systems. The top supercomputer, Summit, can perform 148,600 trillion calculations in one second (148.6 PF on LINPAC). The top two supercomputers are now in the USA followed by two Chinese supercomputers. Many countries are racing to break the record and build an ExaFLOP supercomputer that can perform more than one million trillion (quintillion) calculations per second. In fact the USA is planning two supercomputers in 2021 one of which, when fully operational (Frontier), will perform at 1.5 EF. Scientists however are concerned that we are reaching many physical limits and we need new innovative ideas to make it to the next generation of computing. This talk will consider where we stand and where we are going with the current state of supercomputing with emphasis on future processors, and some of the ideas that scientists are looking at to re-invent computing. A comparative understanding of Nuromorphic and Brain-Inspired Computing, Quantum Computing and innovative computing paradigms will be provided along with an assessment of progress so far and the road ahead. Further, I cover some of our own progress on Nanophotnonic PostMoore’s law processing efforts.

Prof. Jean-Pierre Leburton
ECE Professor and Director Gregory Stillman Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA.
  • Title: 2D Nano-Electronic Materials for Bio-sensing
  • Abstarct: The last two decades have experienced rapid technological developments in the search of cheap and high accuracy devices for fast bio-molecular identification. In the realm of DNA and protein sequencing, there has been an increasing interest in the use of nanopores in solid-state materials because of their distinct advantage over biological pores in terms of flexibility in pore design and mechanical strength. Two-dimensional (2D) solid state materials such as graphene and Molybdenum di-sulphide (MoS2) in particular have attracted attention because of their atomically thin layered structure and electrically active characteristics, predisposing them to offer single base resolution and simultaneously multiple modalities of detecting biomolecular translocation. 2D nanopore devices promise seamless integration with semiconductor electronics and are poised to revolutionize a variety of technologies such as genomics, point-of-care diagnostics and digital data storage to name a few. The past year has witnessed a flurry of activity to experimentally realize nanopore Field Effect Transistors (FETs) and understand the fundamental sensing mechanism in such devices. Currently, the dominant consensus from theoretical calculations has involved the electrostatic modulation of the FET current due to the translocating biomolecules. In this talk, we review and provide insights into this sensing principle by modeling the electron flow through 2D material nanopore FETs. We describe a method to systematically characterize nanopores FETs by contrasting the changes in the FET behavior before-and-after nanopore drilling and DNA translocation. We outline measurable predictions of high-resolution FET based sensing of DNA-protein complexes and damaged DNA. We compare these FET signals to the corresponding ionic current signals calculated from all-atom Molecular dynamics simulations. Further, we also outline possible techniques to improve the detection SNR by augmenting pore and device design with statistical signal processing algorithms. Finally, we propose a scalable device design of nanopore FETs to detect and identify translocations of single-biomolecules in a massively parallel scheme.

Prof. Dr. Peter Peer
Vice-Dean Faculty of Computer & Information Science Computer Vision Lab University of Ljubljana, Slovenia, European Union.
  • Title: Deep Sclera Segmentation and Recognition
  • Abstarct: In this talk we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around convolutional neural networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets comes with a rich set of annotations, such as a per-pixel markup of various eye parts (including the sclera vasculature), identity, gaze-direction and gender labels. We conduct rigorous experiments on SBVPI with competing techniques from the literature and show that the combination of the proposed segmentation and descriptor-computation models results in highly competitive recognition performance.

Prof. Ronald Tetzlaff
Chair of Fundamentals of Electrical Engineering Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering Technische Universität , Dresden, Germany.
  • Title: Coming Soon
  • Abstarct: Coming Soon
Prof. Allon Guez
Dept. of Electrical and Computer Engineering Drexel University, USA
  • Title: On Medical Automation - Drexel Group Projects
  • Abstarct:

    Abstarct: Abstract: The talk will report on current Drexel group work in developing several medical devices and systems. The talk will cover: • Wearables for fall detection and injury mitigation;
    • EEG based brain state recognition system;
    • EEG based preference and recommendation system and,
    • Medium reaction model for micro-robotic systems in soft (brain like) tissue.

Prof. Stephen Pistorius
Professor & Associate Head: Medical Physics (Physics & Astronomy) Professor (Radiology) Vice Director & Graduate Chair: Biomedical Engineering Graduate Program University of Manitoba, Canada.
  • Title: Towards Medical Imaging without images; Advanced Image Reconstruction and Machine learning in PET and Microwave Imaging
  • Abstarct: Cancer mortality is higher in remote regions in Canada and in developing countries where access to early detection is limited. Mammography, the standard for breast cancer screening, uses ionizing radiation, requires breast compression, has a high false-positive rate, and requires a well-established human and capital infrastructure. Positron Emission Tomography (PET), an important functional imaging modality, also uses ionizing radiation, which scatters when it interacts with tissue, depositing dose, but traditionally not providing any value from an imaging perspective. Breast Microwave Imaging (BMI) and Scatter Enhanced PET potentially have improved sensitivity and specificity when compared to traditional mammographic x-ray and PET reconstruction techniques. Image reconstruction typically uses algebraic methods; iterative Delay and Sum (for microwave imaging) or Maximum Likelihood Expectation maximization (MLEM) approaches (for PET), but there are technical and computational constraints which limit their applicability. This presentation is a tour through time and place; starting with experiments that show the benefits of improved reconstruction techniques for microwave radar imaging for breast cancer detection, scatter imaging for PET and finishing with a demonstration as to how machine learning and artificial intelligence can be used to reconstruct images, and to detect breast lesions even when they may not be visible.

Prof. Viera Rozinajova
Institute of Informatics, Information Systems and Software Engineering (FIIT) Vice-dean - Faculty of Informatics and Information Technologies Slovak University of Technology, Bratislava, European Union
  • Title: Towards more effective Smart Grid using Data Analytics
  • Abstarct: Energy belongs for a long time to the most important aspects of our lives. There are several new phenomenons in current smart grid environment: the energy is produced not only in traditional way, but it is supplied also by distributed renewable energy sources. Thus the consumers become also prosumers. Another aspect concerns the anticipated massive usage of electric vehicles, which will require bigger load – we have to prepare the grid also for this type of load. In this talk we will offer an insight into the contemporary approaches to ensuring more effective operation of smart grid. It is based on the analysis of huge datasets, generated by smart meters. Since traditional ways of data processing are no longer sufficient, an urgent need for novel approaches occurs. In order to manage smart grid effectively, we need smart technologies to manage power load forecasting, trading, load balancing and grid optimization. Our research team has developed successful solutions to address these challenges, making thus possible to achieve huge energy and financial savings.

Prof. Pierre Maréchal
Institut de Mathématiques de Toulouse Université Paul Sabatier Toulouse, France.
  • Title: Blind deblurring of barcodes via Kullback-Leibler divergence
  • Abstarct: : Barcode encoding schemes impose symbolic constraints which fix certain segments of the image. The maximum entropy on the mean method enables the use of a prior probability distribution. We exploit these complementary features to develop an entropic method for blind barcode deblurring. We assess our results via both standard bar code reading software as well as smart phones.

Prof. Nong Ye
Director, Information and Systems Assurance Laboratory Ira A. Fulton School of Engineering Arizona State University, USA.
  • Title:Multivariate and Univariate Analysis of Engineering Student Data to Identify Engineering Retention Characteristics
  • Abstarct: In many fields, relations of variables hold for only certain values of variables, or there are different relations for different values of variables. The Partial-Value Association Discovery (PVAD) algorithm discovers variable relations/associations that exist in partial ranges of variable values from large amounts of data in a computationally efficient way. This lecture shows how the PVAD algorithm is used in two applications. The first application uses the PVAD algorithm to analyze engineering student data. Partial-value data associations of engineering student characteristics are examined to identify engineering retention characteristics. The second application uses the PVAD algorithm to analyze network flow data from computer networks. Partial-value data associations of network flow characteristics are put together detect network anomalies for cyber security.

Prof. Gordon B. Agnew
Dept. of Electrical and Computer Engineering University of Waterloo, Canada.
  • Title:Tracing How the Evolution of Cryptographic Systems is Tied to the Evolution of Computing
  • Abstarct: In this talk we will examine the dramatic impact that advances in computing has impacted the evolution of cryptographic systems. One of the most significant areas is in Public Key Cryptographic systems. In the mid 1970's when the first public disclosure of Public Key Cryptography was made, computational complexity on personal computers made their use impractical. In 1985, the suggestion of using Elliptic Curves as a method of Public Key Cryptography as put forward. At the time, it was thought that ECC was impractical due to the computational complexity at that time. Today, Elliptic Curve Cryptography is used in many applications on smart phones. We will trace the history how improvements in computing power had a direct impact on cryptographic systems.

Prof. Paulo M. Mendes
Director, Center for MicroElectroMechanical Systems (CMEMS) University of Minho, Portugal, European Union.
  • Title: Probe for optimized complete tumor resection: from electromagnetics to 3D multimodal image fusion
  • Abstarct: Cancer is a worldwide leading cause of death, being brain in the top 10 of cancer mortality, in the group of highest per-patient initial cost, and in the list of devastating prognosis and least available solutions. Tumor degree of resection fully correlates with life expectation, but using only surgeon´s impression, complete resection rates may not be as good as desirable. Electromagnetic properties of human tissues show a characteristic behavior that may be used for tumor detection, enabling intraoperative tumor resection monitoring. Such probe will require specific frequency range and on-chip micromachined features for signal transmission, routing, and processing, and on-tip detection. Furthermore, all those sensing devices and tools must be integrated with standard clinical practice used for tumor resection. This talk will discuss the required electromagnetics theory, microfabrication techniques, imaging techniques, and integration technology to allow the development and integration of a probe for intraoperative tumor resection monitoring, featuring a multimodal image fusion, to guide surgeons in the reduction of the residual tumor sizes.

Professor Jason Crain
IBM Research United Kingdom & Visiting Professor, University of Oxford United Kingdom
  • Title: Next generation materials simulation - applications of electronic course graining to complex systems
  • Abstarct: Atoms and molecules adapt to their environment through a hierarchy of electronic responses. These fundamental, many-body phenomena give rise to emergent behaviour across the physical and life sciences. However, their incorporation in predictive simulations of large systems faces significant challenges. We introduce here a new class of molecular model employing embedded quantum oscillators as a coarse-grained representation of the collective electronic responses. This representation generates all manifestations of long-range interactions. The resulting level of completeness in physical description enables isolated molecule properties to define model parameters, thereby eliminating fitting to condensed phase data. Thus, the framework provides a physical and intuitive basis for predictive, next-generation simulation which can be implemented efficiently on massively parallel computers. Path integral methods are introduced as a practical solution to the coarse-grained version of the many-body electronic problem which avoids artificial truncation of the interaction terms. Combined with molecular dynamics to evolve nuclei on the model electronic surface, through on-the-fly force computation, the method can simulate large soft matter systems at finite temperature. Example applications to specific systems will be presented

Prof. Sokrates T. Pantelides
University Distinguished Professor of Physics and Engineering William A. and Nancy F. McMinn Professor of Physics and Professor of Electrical Engineering, Vanderbilt University, USA. Distinguished Visiting Scientist, Oak Ridge National Laboratory, USA
  • Title: Probing nanoscale materials by combining computation and microscopies
  • Abstarct: The advent of high-performance computers and advanced algorithms enable quantum calculations in nanostructures with high accuracy, probing atomic configurations and electronic, magnetic, optical, and mechanical properties. Atomic-resolution microscopies such as scanning transmission electron microscopy and scanning tunneling microscopy provide complementary experimental observations. This talk will describe a number of examples, primarily in two-dimensional materials, where a combination of computation and microscopies lead to the discovery of new materials, e.g., fusion of bilayers into a new monolayer with novel stoichiometry1, 2 and intrinsically patterned 2D materials3, 4; new nanostructures, e.g., atomically precise graphene origami5 and monolayer amorphous carbon6; and new phenomena, e.g., unconventional ferroelectricity7. Collaborators, coauthors of cited papers, are acknowledged. The theory work was supported by the U.S. Department of Energy, grant No. DE-FG02-09ER46554. 1. J. Lin, S. Zuluaga, P. Yu, Z. Liu, S. T. Pantelides and K. Suenaga, "Novel Pd2Se3 Two-Dimensional Phase Driven by Interlayer Fusion in Layered PdSe2", Physical Review Letters 119, (2017). 2. S. Zuluaga, J. Lin, K. Suenaga and S. T. Pantelides, "Two-dimensional PdSe2-Pd2Se3 junctions can serve as nanowires", 2D Materials 5, 035025 (2018). 3. X. Lin, J. C. Lu, Y. Shao, Y.-Y. Zhang, X. Wu, J. B. Pan, L. Gao, S. Y. Zhu, K. Qian, Y.-F. Zhang, D.-L. Bao, L. F. Li, Y. Q. Wang, Z. L. Liu, J. T. Sun, T. Lei, C. Liu, J. O. Wang, K. Ibrahim, D. N. Leonard, W. Zhou, H. M. Guo, Y. L. Wang, S.-X. Du, S. T. Pantelides, and H,-J. Gao, "Intrinsically patterned two-dimensional materials for selective adsorption of molecules and nanoclusters", Nature Materials. 16, 5 (2017). 4. Z.-L. Liu, B. Lei, Z.-L. Zhu, L. Tao, J. Qi, D.-L. Bao, X. Wu, L. Huang, Y.-Y. Zhang and X. Lin, "Spontaneous Formation of 1D Pattern in Monolayer VSe2 with Dispersive Adsorption of Pt Atoms for HER Catalysis", Nano Letters (2019). 5. H. Chen, X.-L. Zhang, Y.-Y. Zhang, D. Wang, D.-L. Bao, Y. Que, W. Xiao, S. Du, M. Ouyang and S. T. Pantelides, "Atomically precise, custom-design origami graphene nanostructures", Science 365, 1036-1040 (2019). 6. C.-T. Toh, H. Zhang, J. Lin, A. S. Mayorov, Y.-P. Wang, C. Orofeo, D. B. Ferry, H. Anderson, Z. Guo, N. Kakenov, H. R. Sims, K. Suenaga, S. T. Pantelides, and B. Öyilmaz, "Synthesis and properties of free-standing monolayer amorphous carbon", Nature, in press (2019). 7. J. B. Brehm. S. M. Neumayer, L. Tao, A. O’Hara, M. Chyasnavichus, M. A. Susner, M. A. McGuire, S. V. Kalinin, S. Jesse, P. Ganesh, S. T. Pantelides, P. Maksymovych, and N. Balke, "Tunable quadruple-well ferroelectric van der Waals crystals", Nature Materials, in press (2019).

Prof. Ali Ghrayeb
Dept. of Electrical and Computer Engineering Texas A&M University's campus in Qatar
  • Title:Leveraging UAVs in Cellular Network Planning: Challenges and Potential
  • Abstarct: Reducing the cost of deploying cellular networks is of great interest to all concerned parties, including service providers and users. Such cost can be associated with operation (OPEX) or capital (CAPEX). Towards achieving this goal, the wireless industry is moving towards zero-touch cellular networks, i.e., zero human intervention. The need for having unsupervised (i.e., automated) cellular networks is aligned with the vision of having a dynamic cellular architecture, enabled by the use of mobile equipment (e.g., unmanned aerial vehicle base stations), which gives the architecture flexibility to adapt quickly and frequently to service demands. To this end, the concept of self-organizing network (SON) has been established and added to the list of 5G/6G key enabling technologies that aim at automating the processes of planning, configuration, management, and healing of cellular networks. Among these processes, radio access network (RAN) planning has received special intention, since it decides on the required radio resources and the equipment to deploy, which directly affects CAPEX. Motivated by the above, we present in this talk a framework that aims at developing an unsupervised planning process that provides the essential planning parameters of cellular networks, including the minimum number of required base stations (BSs), their positions, coverage, and antenna radiation patterns, while taking into consideration the inter-cell interference and satisfying capacity, coverage and transmit power constraints. We make use of the statistical machine learning (SML) theory to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users’ positions, are learned or inferred using Gibbs sampling theory and Bayes’ theory. The inference process involves linking the observations and the planning parameters through a probabilistic model (i.e., problem formulation) which yields a Dirichlet process. Through several numerical examples, we show that the performance of the proposed framework is superior to two existing main planning approaches, including the k-mean based approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design.

Prof. Vaidy Sunderam
Samuel Candler Dobbs Professor of Computer Science Chair, Department of Mathematics and Computer Science, Director, Computational and Life Sciences Strategic Initiative, Emory University, Atlanta, USA.
  • Title: Trends and Issues in Data Driven Dynamic Systems
  • Abstarct: : Data driven systems are rapidly increasing in popularity and deployment, especially in spatio-temporal domains. Numerous smart devices collect and report observations, for individual and collective value, but with variable reliability and potential loss of privacy. We present several research contributions aimed at addressing: (1) task assignment in crowdsourcing systems with privacy protection whereby participants may be optimally assigned tasks in a geospatial setting without compromising their true locations; (2) truth discovery whereby reports or observations from multiple entities can be fused to improve the veracity of specific events; and (3) tensor factorization methods for extracting patterns from spatiotemporal data that can be used for a variety of applications. Models, issues, approaches, and preliminary results will be presented.

Prof. Khan M. Iftekharuddin
Associate Dean for Research and Graduate Programs, Director, Vision Lab. Batten College of Engineering and Technology Old Dominion University, USA
  • Title: Quantitative Image Analysis for Brian Tumor Segmentation
  • Abstarct: This talk will discuss an integrated quantitative image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation, respectively. We will demonstrate efficacy of intensity, and multiresolution texture features such as fractal dimension (FD) and multifractional Brownian motion (mBm) for robust tumor and abnormal tissue segmentation in brain MRI. We introduce several machine learning and deep learning methods for this purpose. Finally, we evaluate our method using in large scale public and private datasets.

Prof. Doan B Hoang
School of Electrical and Data Engineering Faculty of Engineering and Information Technology. University of Technology Sydney, Australia.
  • Title: Cloud Security – quantitative metrics, software-defined policy driven interaction, and trust-based big data processing framework.
  • Abstarct: The demand for security of cyber systems is ever-increasing as these critical infrastructures are constantly adapting to emerging sophisticated applications and interconnecting a vast number of IOT devices. This leads to a large attack surface a cyber-system has to cover to ensure its security. Cloud computing, in particular, has been truly adopted as a large-scale distributed computing paradigm. The rapid growth of both public and private cloud systems provides cloud users with various options in deploying computational and storage resources, especially for big data applications. These cloud systems expose unavoidably the vulnerability on data security and privacy due to their outsourced nature. Security and privacy have thus become a great concern in cloud computing in which users risk the leakage of their private data. However, existing cloud security models do not equip themselves with sufficient measures to protect cloud systems proactively as well as reactively. In this talk, we articulate several forward-looking security models and techniques for protecting cloud systems: quantitative metrics for measuring security risks, software-defined policy-driven interaction for detecting and predicting security breaches, and techniques for protecting cloud data

Prof. Jont Allen
Dept. of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, USA
  • Title: Coming Soon
  • Abstarct: Coming Soon
Prof. Theda Daniels-Race
M. B. Voorhies Distinguished Professor, School of Electrical Engineering and Computer Science and the LSU Center for Computation and Technology (CCT), Louisiana State University, USA
  • Title: Developments in Hybrid Electronic Materials at the Nanoscale for Next-Generation Applications
  • Abstarct: As “small has hit the wall” (Moore’s Law) semiconductor based industries struggle to keep pace with consumer demands for smaller, faster, and ever more affordable electronic devices. By the same token, researchers operating under the broadly defined umbrella of nanoelectronics seek to challenge traditional design paradigms and fabrication practices as these are used to create solid-state devices. Thus, motivated by both scientific curiosity and societal needs, my research focus is in the area of HEMs or hybrid electronic materials. In this talk, I will provide an overview of my work in progress as principal investigator for the Applied Hybrid Electronic Materials & Structures (AHEMS) Laboratory, as I have established in the Division of Electrical and Computer Engineering at Louisiana State University. With an eye toward future nanoelectronic materials and devices, my team works to develop ways to deposit and characterize HEMs, as well as designs innovative yet low-cost apparatus and techniques, in order to explore these materials and their nanoscale properties. By taking a fundamental or “bottom up” approach to nanoelectronics, we investigate the unique physics and potential functions of HEMs as we look toward developments “beyond the transistor” in the next-generation of computing and related applications

Prof. Hamid Vakilzadian
Department of ECE, University of Nebraska-Lincoln, USA
  • Title: Coming Soon
  • Abstarct: Coming Soon
Prof. Alan Sprague
Electrical and Computer Engineering University of Alabama at Birmingham, Birmingham, United Kingdom.
  • Title: Tying Ethics & Teamwork Training for Computing Students - Toward Ethics-in-Action Apps
  • Abstarct: Most Computer Science and Engineering curricula in the US require ethics training and teamwork skills. Indeed, the Accrediting Board for Engineering and Technology mandates that ethics and teamwork be course objectives in at least one course in these majors. However, these subjects are often insufficiently covered because departmental faculty may not have had much ethics or teamwork training. Instead of providing training on specific teamwork skills, it’s often assumed that requiring students to do team projects will automatically result in their acquiring effective teamwork skills. This is not always true. As Lingard (2010) states there’s evidence that negative experiences in team projects can lead students to fear and avoid team work. Computer Science and Engineering students can take standalone courses, which deal with ethics, not necessarily teamwork. The “ethics as a separate subject” approach may/not help students understand the ethical and societal implications of technologies they are working on. Institutions are increasingly recognizing the importance of embedding ethics in the STEM curriculum (Grosz et al., 2019) to provide students with tools for moral reasoning and decision-making and to consider the societal implications of technological projects they engage in. At Harvard University, Professors Grosz (CS) and Simmons (Philosophy) designed a ground-breaking model “EthiCS” which pairs CS faculty with advanced Philosophy graduate students to find ethics-rich topics, with no clear right or wrong answers, and work on those, in each of 13 CS courses. At the University of Alabama at Birmingham, we, a team composed of one Computer Science professor (Sprague) and one practical ethicist (Diaz-Sprague) have developed an Ethics and Teamwork Minimodule to be embedded in CS Capstone courses and Electrical & Computing Engineering courses. The minimodule consists of 4 class periods (2 weeks). The first two class periods offer a refresher on ethical principles and tips on moral reasoning, based on segments of video lectures by Michael Sandel: “Justice: What’s the Right Thing to Do” and Frans de Waal: “On Animal Morality.” These are followed by guided discussions in which students participate as teams. In the second week, students lead demonstrations of teamwork skills. Outside of class each team of students selects and practices a teambuilding game. On days 3 and 4 they lead the class in playing the game. There are peer evaluations of games and voting for the best overall presentation. Students take a comprehensive quiz on day 4. We use the VALUE rubric on Teamwork on day 1 and on day 4 to assess progress on student attitudes towards teamwork. We observed three team failures in a CS Capstone course in fall 2016, before the minimodule on Ethics & Teamwork was implemented. By spring 2017, which had the first version of the minimodule implemented, the CS Capstone course had only one team failure. In Electrical and Computing Engineering ECE485 there were no team failures in either fall 2018 and fall 2019. Feedback from students regarding the usefulness of the minimodule to their class was positive. Although other factors may have been at play, we tentatively conclude that embedding a minimodule on Ethics and Teamwork in advanced undergraduate CS or Computing or Engineering students reduces teamwork failures, energizes the class environment, and has the potential for making difficult to teach and often neglected topics such as ethics and teamwork, more manageable, more time-efficient, and sometimes even entertaining and enjoyable. Since the Capstone course requires students work on teams on a software engineering design project and EE485 is a prerequisite for the design course for Engineering students, we encourage students to develop some type of moral guidance tool. This may be tips for de-escalating conflict, increase positivity, random acts of kindness or be a repository of time-tested proverbs, aphorisms, or moral codes derived from many religions or cultures. Currently we are running an Art, Essay of App Design Challenge or Ethics-in-Action contest, open to all interested students, regardless of institution. See https://www.uab.edu/engineering/ece/news/127-ethics-in-action

Prof. Martin Fabian
Dept. of Electrical engineering Chalmers University of Technology, Gothenburg, Sweden
  • Title: The Supervisory Control Theory; a Classical AI Approach Comes of Age
  • Abstarct: : The Supervisory Control Theory (SCT) takes a control-theoretic approach to systems modeled by finite-state machines (automata), which is why it can be said to belong to the realm of what has lately come to be called "classical AI". The SCT community has now worked on problems within this context for more than 30 years, in many different research directions, but the main issue being the ability to handle the computational complexity. Recent breakthroughs in this research have now made the SCT an approach useful for industrially interesting problems. This talk will present some of the latest development in the SCT coupled to real industrial applications. New algorithms and a tool, Supremica, implementing them will be presented. One industrial example will be the verification of the lane change algorithm for an autonomous car, were a serious bug was found. This was an MSc project that eventually led to the financing of several industrial PhD students. Another example will be the development of new control systems for Dutch water locks. This was a project financed by the Rijkswaterstaat, the Dutch Ministry of Infrastructure and Water Management, which led to new algorithms for automatic generation of coordination of large numbers of sensors and actuators according to given requirement specifications. In addition, a brief introduction to relevant parts of automata theory and the SCT will be given since it is assumed that the audience is not very knowledgeable in the SCT. Supremica is available for download, free for education and research, at www.supremica.org.

Prof. Fred Choobineh
Blackman Distinguished Professor of College of Engineering. Dept. of Electrical and Computer Engineering, University of Nebraska-Lincoln, USA.
  • Title: Stochastic systems evaluation and selection criteria and their computational challenges
  • Abstarct: Stochastic systems are generally evaluated and compared using the expected value of one or more performance metric of interest. The use of expected value is prevalent in engineering applications and its use implicitly implies that the designers and/or system operators are risk neutral. However, designers and system operators rightfully are concerned about the system’s risk exposure and therefore should use an evaluation metric that is sensitive to risk. In this talk we review some potential risk-sensitive evaluation criteria and discuss their computational and statistical challenges. We conclude the talk by highlighting a few engineering research areas where risk-sensitive evaluation criteria have been used.

Prof. Suresh Subramaniam
Professor and Chair Department of Electrical and Computer Engineering George Washington University, Washington DC, USA
  • Title: The Evolution of Data Center Network Architectures
  • Abstarct: Our society is becoming increasingly dependent on analyzing huge amounts of data generated in a large variety of ways. Analyzing the “big data” in the cloud requires a tremendous amount of computing and storage resources, and data centers have emerged as the workhorses of the cloud. Large data centers already consist of tens of thousands of servers, and are expected to scale to hundreds of thousands or even millions of servers with total throughputs on the order of several Tbps. Data centers are extremely power-hungry, and already account for over 2% of worldwide energy consumption. Designing data center networks that are both scalable and energy-efficient is very challenging. To address this challenge, the architecture of the data center network has evolved from the conventional multi-layer architecture to modern approaches that marry electronics and optics. This talk takes a look at this evolution and discusses emerging alternatives.

Prof. Anna Soffía Hauksdóttir
Professor, Department of Electrical and Computer Engineering Engineering and Physical Sciences University of Iceland Reykjavík, Iceland
  • Title: The Ups and Downs of Closed Form System Response
  • Abstarct: Closed form expressions for solutions to MIMO systems are typically derived by means of Laplace transforms. Central to these expressions is the matrix exponential $e^{At}$ and they usually include the eigenvalues and the coefficients of the characteristic equation. In this paper, we derive such expressions by focusing, instead, on the fundamental solution of the underlying differential equation. The advantage of this is twofold. First, it simplifies the derivation of such expressions. Second, since we present an effective procedure for the evaluation of the fundamental solution and its derivatives, it can be used as a basis for procedures to evaluate these expressions both numerically and symbolically. These expressions lead to formulae for evaluating the response directly at any time when the input is impulse, polynomial or harmonic. For more general inputs these formulae can be used to derive time stepping procedures based on piecewise polynomial approximations. However, the expressions typically become numerically unstable when the systems reach a size of 15-20. Thus we also derive analogous matrix formulae that result in numerically stable expressions for the same cases, when they are e.g. evaluated by Taylor series approximations. From an educational viewpoint we see it to be valuable to relate in this way direct expressions for responses of small systems presented in textbooks to the numerical procedures used in routines for large systems.

Prof. Israel Koren
Dept. of Electrical and Computer Engineering University of Massachusetts, USA
  • Title: Detecting and counteracting benign faults and malicious attacks in cyber physical systems
  • Abstarct: The use of cyber-physical system (CPS) is rapidly expanding and many of their applications require a highly reliable and secure implementation as they control critical infrastructures or even life-critical devices. Unfortunately, current techniques for achieving high reliability and security incur high overheads. In particular, integrating countermeasures against security attacks is problematic as security threats are often not well defined, evolve continuously, and as a result, many CPSs often remain vulnerable. We propose to exploits the physical plant state information to enhance both reliability and security. Our approach, which monitors the controlled plant state trajectory, allows for tunable fault-tolerance as well as detection of malicious attacks, and it achieves these at a low overhead. The plant state space consists of safe and marginal state subspaces. In the safe subspace the CPS will continue its safe operation even if the worst case control signal is applied. In contrast, any erroneous control applied when the plant state is marginal, may lead to a catastrophic system failure. Such an erroneous control output may be due to either a benign fault or a malicious security attack. As most of the time the plant will be deep within its safe subspace, we can avoid using expensive redundancy techniques and thus, reduce the computational load while still guaranteeing safe operation. When a marginal state of the plant is detected, it will signal the potential presence of a "natural" fault or malicious attack. Our scheme will counter this by switching to a critical mode involving higher levels of redundancy to combat natural failures as well as alternative mechanisms to defeat malicious attacks. A major challenge in our approach is to determine, in real-time, whether the current state of the physical plant is deep within its safe sub-space or is marginal. We have used various machine learning techniques for classifying the state and our results indicate that with a reasonable number of entries in a lookup table and with a short execution time, the required classification can be performed efficiently.

Prof. Jason O'Kane
Director, Center for Computational Robotics, Department of Computer Science and Engineering University of South Carolina, USA.
  • Title: Coverage Planning for Mobile Robots with Constrained Motion and Limited Sensing
  • Abstarct: Autonomous mobile robots must operate effectively in spite of movement and sensing capabilities that are often incomplete and unreliable. One recurring example of this confluence of challenges is the coverage problem, in which the robot must move across every point in a region of interest. Coverage algorithms have important applications in environmental monitoring, cleaning, humanitarian demining, painting, and exploration. Known algorithms for such problems generally cannot account directly for realistic limitations on the robot's sensors and actuators. This talk will present our work that overcomes these limitations, leading to results that include both hardness results and practical coverage algorithms for terrestrial, aquatic, and aerial robots.

Prof. Xenofon Koutsoukos
Associate Chair, Department of Electrical Engineering and Computer Science Vanderbilt University, USA.
  • Title:System Science of Security and Resilience of Cyber-Physical Systems
  • Abstarct: The exponential growth of information and communication technologies have caused a profound shift in the way humans engineer systems leading to the emergence of closed loop systems involving strong integration and coordination of physical and cyber components, often referred to as cyber-physical systems (CPS). Complex CPS abound in modern society and it is not surprising that many of these systems are safety and mission critical that makes them a target for attacks. The talk will present principles and methods for designing and analysing resilient CPS architectures that deliver required service in the face of compromised components. A fundamental challenge is to understand the basic tenets of CPS resilience and how they can be used in developing resilient architectures. The proposed approach integrates redundancy, diversity, and hardening methods for designing either passive resilience methods that are inherently robust against attacks and active resilience methods that allow responding to attacks. In addition, we will describe a modelling and simulation integration platform for experimentation and evaluation of resilient CPS using smart transportation systems as the application domain. Evaluation of resilience is based on attacker-defender games using simulations of sufficient fidelity.

Prof. Peter Puschner
Institute of Computer Engineering Technische Universitaet Wien Vienna, Austria
  • Title: Temporal Control in Multi-Component Systems
  • Abstarct: The talk addresses the problem of providing timing guarantees in systems that consist of multiple interacting components. Without precaution, resource competition for the communication network and interference caused by incoming communication requests undermine the temporal predictability of such systems. We therefore propose to use controlled, time-triggered communication to restrict the interference between interacting components at the network level. For the realization of components, on the other hand, we provide two strategies to hide respectively avoid disturbances caused by incoming communication. The combination of these two strategies allows engineers to build multi-component systems that are fully time-predictable, i.e., they provide time-predictability and temporal control both at the system level and the component level.

Prof. Ilangko Balasingham
Department of Electronic Systems Norwegian University of Science and Technology (NTNU) Trondheim, Norway.
  • Title: Internet of smart implants – micro-nan scale devices with artificial intelligence connected to 5G networks
  • Abstarct: Wireless body area networks enable ingestible, implantable, insertable and on-body sensors and actuators to be connected to wireless networks in a seamless, reliable, secure manner interfacing with the Internet. The networks enable remote health status monitoring, diagnosis, and treatment delivery. My talk will present the latest results on wireless communication technologies for implants including radio frequency and human body galvanic communication to address next generation leadless pacemakers and robotic capsule endoscopes. I will show how machine learning/deep learning methods are used for improved diagnosis and drug delivery. I will briefly introduce an emerging scientific field called molecular communication technology and will give some examples on how to signal and communicate with human cells aiming to connect them to the Internet. The talk will highlight some of the novel applications and ongoing research projects funded by the EU.

Prof. Raquel Diaz-Sprague
Electrical and Computing Engineering, University of Alabama at Birmingham, Birmingham Alabama 35205, USA
  • Title: Tying Ethics & Teamwork Training for Computing Students - Toward Ethics-in-Action Apps
  • Abstarct: Most Computer Science and Engineering curricula in the US require ethics training and teamwork skills. Indeed, the Accrediting Board for Engineering and Technology mandates that ethics and teamwork be course objectives in at least one course in these majors. However, these subjects are often insufficiently covered because departmental faculty may not have had much ethics or teamwork training. Instead of providing training on specific teamwork skills, it’s often assumed that requiring students to do team projects will automatically result in their acquiring effective teamwork skills. This is not always true. As Lingard (2010) states there’s evidence that negative experiences in team projects can lead students to fear and avoid team work. Computer Science and Engineering students can take standalone courses, which deal with ethics, not necessarily teamwork. The “ethics as a separate subject” approach may/not help students understand the ethical and societal implications of technologies they are working on. Institutions are increasingly recognizing the importance of embedding ethics in the STEM curriculum (Grosz et al., 2019) to provide students with tools for moral reasoning and decision-making and to consider the societal implications of technological projects they engage in. At Harvard University, Professors Grosz (CS) and Simmons (Philosophy) designed a ground-breaking model “EthiCS” which pairs CS faculty with advanced Philosophy graduate students to find ethics-rich topics, with no clear right or wrong answers, and work on those, in each of 13 CS courses. At the University of Alabama at Birmingham, we, a team composed of one Computer Science professor (Sprague) and one practical ethicist (Diaz-Sprague) have developed an Ethics and Teamwork Minimodule to be embedded in CS Capstone courses and Electrical & Computing Engineering courses. The minimodule consists of 4 class periods (2 weeks). The first two class periods offer a refresher on ethical principles and tips on moral reasoning, based on segments of video lectures by Michael Sandel: “Justice: What’s the Right Thing to Do” and Frans de Waal: “On Animal Morality.” These are followed by guided discussions in which students participate as teams. In the second week, students lead demonstrations of teamwork skills. Outside of class each team of students selects and practices a teambuilding game. On days 3 and 4 they lead the class in playing the game. There are peer evaluations of games and voting for the best overall presentation. Students take a comprehensive quiz on day 4. We use the VALUE rubric on Teamwork on day 1 and on day 4 to assess progress on student attitudes towards teamwork. We observed three team failures in a CS Capstone course in fall 2016, before the minimodule on Ethics & Teamwork was implemented. By spring 2017, which had the first version of the minimodule implemented, the CS Capstone course had only one team failure. In Electrical and Computing Engineering ECE485 there were no team failures in either fall 2018 and fall 2019. Feedback from students regarding the usefulness of the minimodule to their class was positive. Although other factors may have been at play, we tentatively conclude that embedding a minimodule on Ethics and Teamwork in advanced undergraduate CS or Computing or Engineering students reduces teamwork failures, energizes the class environment, and has the potential for making difficult to teach and often neglected topics such as ethics and teamwork, more manageable, more time-efficient, and sometimes even entertaining and enjoyable. Since the Capstone course requires students work on teams on a software engineering design project and EE485 is a prerequisite for the design course for Engineering students, we encourage students to develop some type of moral guidance tool. This may be tips for de-escalating conflict, increase positivity, random acts of kindness or be a repository of time-tested proverbs, aphorisms, or moral codes derived from many religions or cultures. Currently we are running an Art, Essay of App Design Challenge or Ethics-in-Action contest, open to all interested students, regardless of institution. See https://www.uab.edu/engineering/ece/news/127-ethics-in-action

Prof John T Sheridan
FSPIE, FOSA, FIMA Vice Principal for Research, Innovation and Impact UCD College of Engineering and Architecture Professor of Optical Engineering, UCD School of Electrical and Electronic Engineering University College Dublin, Ireland
  • Title: Coming Soon
  • Abstarct: Coming Soon