First Responders' Localization and Health Monitoring During Rescue Operations

Currently, first responders’ coordination and decision-making during res-cue, firefighting or police operations is performed via radio/GSM channels with some support of video streaming. In unknown premises, officers have no global situational awareness on operation status, which reduces coordination efficiency and increases decision making mistakes. This paper pro-poses a solution enabling the situational awareness by introducing an integrated operation workflow for actors localization and health monitoring. The solution will provide global situational awareness to both coordinators and actors, thereby increasing efficiency of coordination, reducing mistakes in decision making and diminishing risks of unexpected situations to appear. This will result in faster operation progress, lower number of human casualties and financial losses and, the most important, saved human lives in calamity situations

Deadline constrained video analysis via in-transit computational environments

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g. OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g. latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.TS

Responsible Composition and Optimization of Integration Processes under Correctness Preserving Guarantees

Enterprise Application Integration deals with the problem of connecting heterogeneous applications, and is the centerpiece of current on-premise, cloud and device integration scenarios. For integration scenarios, structurally correct composition of patterns into processes and improvements of integration processes are crucial. In order to achieve this, we formalize compositions of integration patterns based on their characteristics, and describe optimization strategies that help to reduce the model complexity, and improve the process execution efficiency using design time techniques. Using the formalism of timed DB-nets - a refinement of Petri nets - we model integration logic features such as control- and data flow, transactional data storage, compensation and exception handling, and time aspects that are present in reoccurring solutions as separate integration patterns. We then propose a realization of optimization strategies using graph rewriting, and prove that the optimizations we consider preserve both structural and functional correctness. We evaluate the improvements on a real-world catalog of pattern compositions, containing over 900 integration processes, and illustrate the correctness properties in case studies based on two of these processes.Comment: 37 page

Responsible composition and optimization of integration processes under correctness preserving guarantees

Enterprise Application Integration deals with the problem of connecting heterogeneous applications, and is the centerpiece of current on-premise, cloud and device integration scenarios. For integration scenarios, structurally correct composition of patterns into processes and improvements of integration processes are crucial. In order to achieve this, we formalize compositions of integration patterns based on their characteristics, and describe optimization strategies that help to reduce the model complexity, and improve the process execution efficiency using design time techniques. Using the formalism of timed DB-nets — a refinement of Petri nets — we model integration logic features such as control- and data flow, transactional data storage, compensation and exception handling, and time aspects that are present in reoccurring solutions as separate integration patterns. We then propose a realization of optimization strategies using graph rewriting, and prove that the optimizations we consider preserve both structural and functional correctness. We evaluate the improvements on a real-world catalog of pattern compositions, containing over 900 integration processes, and illustrate the correctness properties in case studies based on two of these processes

Application of Cloud-Based Geospatial Technologies to Flowering Phenology and Environmental Education

Cloud-based geospatial technologies are rapidly improving the flow of information from the environment to end-users. Cloud-based photo storage websites were used to create and manage species and spatial-temporal metadata in a digital photographic inventory of plant flowering observations collected at Lake Issaqueena, SC from January, 2012 to December, 2014. Statistical analysis of species and temporal metadata revealed significant (p \u3c 0.05) inter-annual shifts in flowering time among several species during and after extreme high monthly temperature in March, 2012 and extreme high monthly total precipitation in July and August, 2013. An interactive ArcGIS Online map with sampling locations of flowering plants was developed and published. The interactive ArcGIS Online map enables web-based knowledge discovery of flowering phenology by allowing users to filter map contents, view plant pictures, navigate to additional plant information in the USDA PLANTS Database, and render spatial-temporal flowering patterns using the heat map view and time settings. The conceptual workflow for managing, integrating, and mapping plant flowering observations has numerous potential applications in species monitoring, allowing for higher volume and quality data to be collected and shared openly. A Cloud-based ESRI Story Map was developed for teaching Soil Forming Factors: Topography in undergraduate soil science education. Student evaluation of the ESRI Story Map was positive, and responses indicate students broadly preferred the ESRI Story Map as a stand-alone teaching module or as supplemental to PowerPoint slides. Teaching with ESRI Story Maps is very different than GIS education, and is well suited for fostering critical and spatial thinking because students do not need to possess prior skills in GIS software, allowing them to spend more time learning the topic at hand in interactive teaching modules. Teaching with ESRI Story Maps has enormous potential in soil science and other environmental disciplines, but more research is needed to develop specific teaching objectives and exercises using ESRI Story Maps

Orchestration of music emotion recognition services - automating deployment, scaling and management

Every day, thousands of new songs are created and distributed over the internet. These ever-increasing databases introduced the need for automatic search and organization methods, that allow users to better filter and browse such collections. However, fundamental research in the MER field is very academic, with the typical work presenting results in the form classification metrics – how good the approach worked in the tested datasets and providing access to the data and methods. In order to overcome this problem, we built and deployed a platform to orchestrate a distributed, resilient, and scalable, music emotion recognition (MER) application using Kubernetes that can be easily expanded in the future. The solution developed is based on a proof of concept that explored the usage of containers and microservices in MER but had some gaps. We reengineered and expanded it, proposing a properly orchestrated, containerbased solution, and adopting a DevOps development culture with continuous integration (CI) and continuous delivery (CD) that in an automated way, makes it easy for the different teams to focus on developing new blocks separately. At the application level, instead of analyzing the audio signal recurring to only three audio features, the system now combines a large number of audio and lyric (text) features, explores different parts of audio (vocals, accompaniment) in segments (e.g., 30-second segments instead of the full song) and uses properly trained machine learning (ML) classifiers, a contribution by Tiago António. At the orchestration level, it uses Kubernetes with Calico as the networking plugin, providing networking for the containers and pods and Rook with Ceph for the persistent block and file storage. To allow external traffic into the cluster, will use HAproxy as an external ingress controller on an external node, with BIRD providing BGP peering with Calico, allowing the communication between the pods and the external node. ArgoCD was selected as the continuous delivery tool, constantly syncing with a git repository, and thus maintaining the state of the cluster manifests up to date, which allows totally abstracting developers from the infrastructure. A monitoring stack combining Prometheus, Alertmanager and Grafana allows the constant monitoring of running iv applications and cluster status, collecting metrics that can help to understand the state of operations. The administration of the cluster can be carried out in a simplified way using Portainer. The continuous implementation pipelines run on GitHub Actions, integrating software and security tests and automatically build new versions of the containers based on tag releases and publish them on DockerHub. This implementation is fully cloud native and backed only by open source software

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903