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With the prevalence of voice devices in our daily life, speech data is accumulated at an unprecedented speed. The vast amount of speech data form an invaluable database for security surveillance and financial risk management. However, the speeches collected from different sources are not necessarily annotated with regard to the speaker identity, making the task of retrieving all the speech records for a given identity extremely challenging. In this paper, we propose a scalable system for Identity-Oriented Speech Retrieval (IO-SR), which seamlessly integrates speaker modeling and deep indexing techniques. Given a speech snippet and a speech database, IO-SR efficiently retrieves all speech snippets that are uttered by the same speaker as the given one. Evaluations on an industrial dataset containing millions of speech snippets show that our system achieves superior performance compared with the state-of-the-arts.

With the rise of Intelligent Virtual Assistants (IVAs), there is a necessary rise in human effort to identify conversations containing misunderstood user inputs. These conversations uncover error in natural language understanding and help prioritize and expedite improvements to the IVA. As human reviewer time is valuable and manual analysis is time consuming, prioritizing the conversations where misunderstanding has likely occurred reduces costs and speeds improvement. In addition, less conversations reviewed by humans mean less user data is exposed, increasing privacy. We present a scalable system for automated conversation review that can identify potential miscommunications. Our system provides IVA designers with suggested actions to fix errors in IVA understanding, prioritizes areas of language model repair, and automates the review of conversations where desired.Verint - Next IT builds IVAs on behalf of other companies and organizations, and therefore analyzes large volumes of conversational data. Our review system has been in production for over three years and saves our company roughly $1.5 million in annotation costs yearly, as well as shortened the refinement cycle of production IVAs. In this paper, the system design is discussed and performance in identifying errors in IVA understanding is compared to that of human reviewers.

Designing a scalable system to capture CO₂ from the air and convert it into valuable chemicals, fuels, and materials could be transformational for mitigating climate change. Climate models predict that negative greenhouse gas emissions will be required by the year 2050 in order to stay below a 2 °C change in global temperature. The processes of CO₂ capture, CO₂ conversion, and finally product separation all require significant energy inputs; devising a system that simultaneously minimizes the energy required for all steps is an important challenge. To date, a variety of prototype or pilot-level CO₂ capture and/or conversion systems have been designed and built targeting the individual objectives of either capture or conversion. One approach has focused on CO₂ removal from the atmosphere and storage of pure pressurized CO₂. Other efforts have concentrated on CO₂ conversion processes, such as electrochemical reduction or fermentation. Only a few concepts or analyses have been developed for complete end-to-end processes that perform both CO₂ capture and transformation.

Route planning in transportation networks is typically performed as a single optimization at trip departure. In this paper, we consider the impact of within-trip replanning on the performance of the overall network in a fully-algorithmic route selection scenario. An experimental study of three real road networks using synthetic demand demonstrates in over 200 trials the effects of replanning with respect to the replanning rate and the adoption rate of replanning. Overall network travel times are reduced by up to 48.49% from a baseline where all drivers are assigned a single route, demonstrating the profound effect of dynamic within-trip replanning. These observations are part of our work exploring a system-optimal route planning strategy that is robust to network size and conditions.

Cell-based transcriptional reporters are invaluable in high-throughput compound and CRISPR screens for identifying compounds or genes that can impact a pathway of interest. However, many transcriptional reporters have weak activities and transient responses. This can result in overlooking therapeutic targets and compounds that are difficult to detect, necessitating the resource-consuming process of running multiple screens at various timepoints. Here, we present RADAR, a digitizer circuit for amplifying reporter activity and retaining memory of pathway activation. Reporting on the AP-1 pathway, our circuit identifies compounds with known activity against PKC-related pathways and shows an enhanced dynamic range with improved sensitivity compared to a classical reporter in compound screens. In the first genome-wide pooled CRISPR screen for the AP-1 pathway, RADAR identifies canonical genes from the MAPK and PKC pathways, as well as non-canonical regulators. Thus, our scalable system highlights the benefit and versatility of using genetic circuits in large-scale cell-based screening., Cell-based transcriptional reporters are an invaluable part of highthroughput screening, but many such reporters have weak or transient responses. Here, the authors describe a digitizer circuit for amplifying reporter activity, increasing sensitivity, and retaining memory of pathway activation.

Despite mechanical stimulation having profound effects on plant growth and development and modulating responses to many other stimuli, including to gravity, much of the molecular machinery triggering plant mechanical responses remains unknown. This gap in our knowledge arises in part from difficulties in applying reproducible, long-term touch stimulation to plants. We describe the design and implementation of the Automated Botanical Contact Device (ABCD) that applies intermittent, controlled, and highly reproducible mechanical stimulation by drawing a plastic sheet across experimental plants. The device uses a computer numerical control platform and continuously monitors plant growth and development using automated computer vision and image analysis. The system is designed around an open-source architecture to help promote the generation of comparable datasets between laboratories. The ABCD also offers a scalable system that could be deployed in the controlled environment setting, such as a greenhouse, to manipulate plant growth and development through controlled, repetitive mechanostimulation.

Since 2001, the Pegasus Workflow Management System has evolved into a robust and scalable system that automates the execution of a number of complex applications running on a variety of heterogeneous, distributed high-throughput, and high-performance computing environments. Pegasus was built on the principle of separation between the workflow description and workflow execution, providing the ability to port and adapt the workflow based on the target execution environment. Through its user-driven research and development, it has adapted to the needs of a number of scientific communities, utilizing and developing novel algorithms and software engineering solutions. This paper describes the evolution of Pegasus over time and provides motivations behind the design decisions. It concludes with selected lessons learned.

Human mesenchymal stem cells (hMSCs) are effective in treating disorders resulting from an inflammatory or heightened immune response. The hMSCs derived from induced pluripotent stem cells (ihMSCs) share the characteristics of tissue derived hMSCs but lack challenges associated with limited tissue sources and donor variation. To meet the expected future demand for ihMSCs, there is a need to develop scalable methods for their production at clinical yields while retaining immunomodulatory efficacy. Herein, we describe a platform for the scalable expansion and rapid harvest of ihMSCs with robust immunomodulatory activity using degradable gelatin methacryloyl (GelMA) microcarriers. GelMA microcarriers were rapidly and reproducibly fabricated using a custom microfluidic step emulsification device at relatively low cost. Using vertical wheel bioreactors, 8.8 to 16.3‐fold expansion of ihMSCs was achieved over 8 days. Complete recovery by 5‐minute digestion of the microcarriers with standard cell dissociation reagents resulted in >95% viability. The ihMSCs matched or exceeded immunomodulatory potential in vitro when compared with ihMSCs expanded on monolayers. This is the first description of a robust, scalable, and cost‐effective method for generation of immunomodulatory ihMSCs, representing a significant contribution to their translational potential., A scalable system for the generation and rapid harvest of immunomodulatory induced pluripotent stem cell‐derived mesenchymal stem cells..

In a country like India, with excessive population density in all big cities, motorcycles have become dominant modes of transport. It is observed that most motorcyclists avoid wearing helmets despite it being an indispensable safety equipment, whose use can significantly reduce the risk of severe head and brain injuries during accidents. Due to violations of most of the traffic and safety rules, motorcycle accidents have been skyrocketing in the recent years. Hence, it’s the need of the hour to build an effective and scalable system capable of automatic helmet detection by analyzing the surveillance camera’s traffic videos. Although several theoretical deep learning-based models have been proposed to detect helmets for the traffic surveillance aspect, an optimal solution for the industry application is less discussed. This paper demonstrates a novel implementation of the Faster R-CNN and SSD framework for accurate helmet detection in real-time low-quality surveillance videos. The experimental results claim that there is a trade-off between accuracy and execution speed. We also present a comprehensive comparative analysis of the two algorithms and determine the best real-time use case scenarios for each of them.

Reliable systems have been the subject of much research in the past years, with societal dependency on computer systems becoming more and more apparent. With more and more organizations embracing DevOps culture, there is a persistent need to understand how these systems are built and what their trade-offs are. This paper discusses and benchmarks the components of a modern fault tolerant and easily scalable system, designed to maximize up-time. The paper also describes the techniques used in the development of such a system. The system architecture described is implemented through several services deployed for a new critical single sign-on system deployed at CERN (The European Laboratory for Particle Physics). A case study of several critical components of the system is also performed, identifying several trade-offs, issues and future research directions.

In recent years, NoSQL databases have solved the problem of managing big data, on the other hand, RDF data has become increasingly used. SPARQL is the language used for querying RDF data. In this paper, we will present a new proposal for a system for mapping SPARQL queries to queries from NoSQL languages like Cassandra QL, MongoDB QL, etc. The idea of this technique is based on the principle of engineering metamodels, of which we first proposed the SPARQL metamodel. And secondly, we have proposed metamodels of NoSQL languages. The third part consists of carrying out a transformation between these two metamodels, the source metamodel of SPARQL and the target metamodel of NoSQL languages.

We present AS-CDG, a novel automatic scalable system for data-driven coverage-directed generation. The goal of AS-CDG is to find the test templates that maximize the probability of hitting uncovered events. The system contains two phases, one for a coarse-grained search that finds relevant parameters and the other for a fine-grained search for the settings of these parameters. To overcome the lack of evidence in the search, we replace the real target with an approximated target induced by neighboring events, for which we have evidence. Usage results on real-life units of high-end processors illustrate the ability of the proposed system to automatically find the desired test-templates and hit the previously uncovered target events.

We propose a granular architecture as a potential impact protector and signal filter. The system consists of monodispersed granular chains decorated with side beads fixed (welded) on sidewalls. Numerical results from usual monodispersed chains and simple tapered chains are employed as reference. An appropriate material combination and an optimal radii ratio between the side and axial granules show that more than $90%$ of the momentum amplitude of solitary pulses can be attenuated using a small and scalable system composed of five axial granules. Considering sinusoidal signals, the findings suggest that regular chains (monodispersed and tapered) are low-pass filters with cutoff frequencies as functions of the driven amplitude, restitutional losses, and tapering. The granular chain with fixed decoration filters all the input signals.

Inactivated Foot-and-Mouth Disease (FMD) vaccine has proven to be effective in the control of the disease. However, its production has some disadvantages, including the costly biosafety facilities required for the production of huge amounts of growing live virus, the need of an exhaustive purification process to eliminate non-structural proteins of the virus in the final formulations in order to differentiate infected from vaccinated animals and variable local regulatory restrictions to produce and commercialize the vaccine. Thus, a novel vaccine against FMD that overcome these restrictions is desirable. Although many developments have been made in this regard, most of them failed in terms of efficacy or when considering their transferability to the industry. We have previously reported the use of transient gene expression in mammalian cells to produce FMD virus-like particles (VLPs) as a novel vaccine for FMD and demonstrated the immunogenicity of the recombinant structures in animal models. Here, we report the optimization of the production system by assaying different DNA:polyethylenimine concentrations, cell densities, and direct and indirect protocols of transfection. Also, we evaluated the reproducibility and scalability of the technology to produce high yields of recombinant VLPs in a cost-effective and scalable system compatible with industrial tech-transfer of an effective and safe vaccine. Estación Experimental Agropecuaria Bariloche Fil: Mignaqui, Ana Clara. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Ferella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Virología e Innovaciones Tecnológicas; Argentina Fil: Cass, Brian. Human Health Therapeutics Research Center, National Research Council Canada; Canadá Fil Mukankurayija, Larissa. Human Health Therapeutics Research Center, National Research Council Canada; Canadá Fil: L’Abbé, Denis. Human Health Therapeutics Research Center, National Research Council Canada; Canadá Fil: Bisson, Louis. Human Health Therapeutics Research Center, National Research Council Canada; Canada Fil: Sánchez, Cintia. Biogénesis-Bagó; Argentina Fil: Scian, Romina. Biogénesis-Bagó; Argentina Fil: Cardillo, Sabrina Beatriz. Biogénesis-Bagó; Argentina Fil: Durocher, Yves. Human Health Therapeutics Research Center, National Research Council Canada; Canadá Fil: Wigdorovitz, Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Virología e Innovaciones Tecnológicas; Argentina

This article discusses the design and implementation of a scalable system architecture that integrates wireless sensor networks (WSNs) into the Internet of Things (IoT) and exploits cloud services to autonomously configure wireless sensor nodes to measure and transmit sensed data only at periods when the environment changes more often. The implementation relies on software-defined networking (SDN) features to simplify WSN management and exploits the power of existing cloud computing platforms to execute a reinforcement learning algorithm that makes decisions based on the environment's evolution.

A central goal of systems neuroscience is to simultaneously measure the activities of all achievable neurons in the brain at millisecond resolution in freely moving animals. This paper describes a protocol converter which is part of a measurement acquisition system for multichannel real time recording of brain signals. In practice, in such techniques, a primary consideration of reliability leads to great necessity towards increasing the sampling rate of these signals while simultaneously increasing the resolution of A/D conversion to 24 bits or even to the unprecedented 32 bits per sample. In fact, this was the guiding principle for our team in the present study. By increasing the temporal and amplitude resolution, it is supposed that we get enabled to discover or recognize and identify new signal components which have previously been masked at a “low” temporal and amplitude resolution, and these new signal components, in the future, are likely to contribute to a deeper understanding of the workings of the brain., Electrical engineering; Neuroscience; Electrical system; Electrical system planning; Systems neuroscience; I2S protocol; TCP/IP protocol; Protocol converter; Scalable system

We present ID'em, a novel tagging and localization method that employs an array of Inductive Sensors to 'image' patterns of electrically conductive dots that are embedded underneath the surfaces of materials that cover the environments that we inhabit. ID'em addresses drawbacks found with existing tagging/localization technologies, while drawing on some of their attributes and strengths, thus creating a cost-effective, scalable system that is robust enough to be deployed pervasively. We present a detailed description of the system, applications that leverage ID'em's unique affordances, and address ID'em's strengths and limitations. With ID'em, we envision a future where the materials that we use to cover and build our everyday environments come imbued with information that can provide valuable context for rich, diverse interactions and capabilities.

We describe a high-throughput computing system for running jobs on public and private computing clouds using the HTCondor job scheduler and the cloudscheduler VM provisioning service. The distributed cloud computing system is designed to simultaneously use dedicated and opportunistic cloud resources at local and remote locations. It has been used for large scale production particle physics workloads for many years using thousands of cores on three continents. Cloudscheduler has been modernized to take advantage of new software designs, improved operating system capabilities and support packages. The result is a more resilient and scalable system, with expanded capabilities.

Building reliable systems for neural architecture search requires careful design consideration due to the high computational demands coupled with the necessity of fault-tolerance. In this domain, it is not uncommon for applications to crash due to GPU memory exhaustion, which makes fault-tolerance and even more important attribute of a distributed neural architecture search system. We propose an RPC-based system that is robust to node failures and provides elastic compute abilities, allowing the system to add or remove computational resources as needed. The system is demonstrated on the task of neural architecture search for image classification using the CIFAR-10 dataset. Our system achieves near linear scaling and is robust to multiple GPU node failures, allowing the failed nodes to restart and rejoin.

Inspecting and assessing the quality of traffic infrastructure (such as the state of the signboards or road markings) is challenging for humans due to (i) the massive length of roads that countries will have and (ii) the regular frequency at which this needs to be done. In this paper, we demonstrate a scalable system that uses computer vision for automatic inspection of road infrastructure from a simple video captured from a moving vehicle. We validated our method on 1500 kms of roads captured in and around the city of Hyderabad, India. Qualitative and quantitative results demonstrate the feasibility, scalability and effectiveness of our solution.

We present a design for a novel mobile sensing system (AMSense) that uses vehicles as mobile sensing nodes in a network to capture spatiotemporal properties of pedestrians and cyclists (active modes) in urban environments. In this dynamic, multi-sensor approach, real-time data, algorithms, and models are fused to estimate presence, positions and movements of active modes with information generated by a fleet of mobile sensing platforms. AMSense offers a number of advantages over the traditional methods using stationary sensor systems or more recently crowd-sourced data from mobile and wearable devices, as it represents a scalable system that provides answers to spatiotemporal resolution, intrusiveness, and dynamic network conditions. In this paper, we motivate the need and show the potential of such a sensing paradigm, which supports a host of new research and application development, and illustrate this with a practical urban sensing example. We propose a first design, elaborate on a variety of requirements along with functional challenges, and outline the research to be performed with the generated data.

Autism is at the moment, a common disorder. Prevalence of Autism Spectrum Disorder (ASD) is reported to be 1 in every 88 individuals. Early diagnosis of ASD has a significant impact to the livelihood of autistic children and their parents, or their caregivers. In this paper, we have developed an unsupervised online learning model for ASD classification. The proposed approach is a hybrid approach, consisting, the temporal coherency deep networks approach, and, the self-organizing dual memory approach. The primary objective of the research is, to have a scalable system that can achieve online learning, and, is able to avoid the catastrophic forgetting phenomena in neural networks. We have evaluated our approach using an ASD specific dataset, and obtained promising results that are well inclined in supporting the overall objective of the research.

The article presents the structure, principle of operation and measurement properties of the developed control system intended for e-management of detection and measurement of disturbances in the low-frequency bandwidth of the PLC (3 kHz-148 kHz). As a detector, Rogowski’s coil made in high-density printed technology (PCB HDI) was used. The results of testing the parameters of both particular components and the completed system in transient as well as in steady state are presented. Based on the obtained measurement results, appropriate practical conclusions are formulated.

Graph databases such as chemical databases, protein databases, and RNA motif databases, are simply a collection of graphs. Querying a graph database involves the computation of a subgraph isomorphism problem (which is NP-complete) for each graph in the database. Therefore, an index is required to filter out false positives and reduce the number of subgraph isomorphisms to compute.In this demo, we introduce SQUID, a scalable system for querying, updating and indexing dynamic graph databases, i.e., databases changing over time, and showcase it on chemical databases. The tool uses a graph coarsening-based index that is able to answer both subgraph and supergraph queries. It also allows the database to be changed with an automatic index update. Also, it displays information found in the graph database in a concise manner that is easier to understand.

Пояснювальна записка містить 54 сторінок, включає 35 рисунків, 2 таблиці, 7 посилань. Метою дипломної роботи є створення інформаційної системи бронювання (залізничних) квитків. Створено клієнтський додаток за допомогою мови програмування Java в середовищі розробки IntelliJ IDEA. Додаток працює з нереляційною розподіленою базою MongoDB. Розроблено програмний продукт бронювання (залізничних) квитків з функціями бронювання квитків з вибором місця для пасажира, авторизації в системі, з наданням різних рівнів доступу, відміною бронювання квитків, бронювання квитків, перегляд вільних для бронювання квитків, користувачів системи, заброньованих квитків, реєстрацією. The explanatory note contains 54 pages, including 35 illustrations, 2 tables, 7 links. The purpose of the thesis is to create an information system for reservation (rail) tickets. The client application was created using the Java programming language in the IntelliJ IDEA development environment. The application works with a non-relational distributed MongoDB database. A software product was developed for booking (rail) tickets with functions provided by choosing a place for a passenger, authorizing the system, providing different levels of access, refusing to book tickets, booking tickets, viewing free tickets, user accounts. Целью дипломной работы является создание информационной системы бронирования (железнодорожных) билетов. Создан клиентское приложение с помощью языка программирования Java в среде разработки IntelliJ IDEA. Приложение работает с нереляционные распределенной базой MongoDB. Разработан программный продукт бронирования (железнодорожных) билетов с функциями бронирования билетов с выбором места для пассажира, авторизации в системе, с предоставлением различных уровней доступа, отменой бронирования билетов, бронирование билетов, просмотр свободных для бронирования билетов, пользователей системы, забронированных билетов, регистрации.

Industry 4.0 (I4.0) is characterized by a decentralized intelligence using service-based information technology. With the existing technologies in industrial solutions, the implementation of partly automated collaborative work of decentralized devices is difficult to achieve. Current industrial solutions fulfill manufacturer specific requirements that are not transferable to other industrial challenges. These customized solutions do not allow a plug-and-produce approach or a dynamically scalable system network. This work presents an approach for this through the use of a proposed service-based Industry 4.0 middleware. An example of its use is introduced in the form of the collaborative work of cranes, which handle the mentioned challenges. The proposed middleware is based on the service-oriented architecture with an evaluation of the I4.0 middleware using a demonstrator of collaborative work between cranes.

Here, we describe a highly-effective catalytic method for the synthesis of heteroarene-containing unnatural amino acids., A robust system for the preparation of β-heteroaryl α-amino acid derivatives has been developed using photoredox catalysis. This system operates via regiospecific activation of halogenated pyridines (or other heterocycles) and conjugate addition to dehydroalanine derivatives to deliver a wide range of unnatural amino acids. This process was conducted with good efficiency on large scale, the application of these conditions to amino ketone synthesis is shown, and a simple protocol is given for the preparation of enantioenriched amino acid synthesis, from a number of radical precursors.

We propose a new intelligent, non-intrusive, scalable system to assess real-time dependency, considering the social environment, and the affects of people who are losing their autonomy. A monitoring interface can issue alerts autonomously in case of emergency. The system stays connected to the AGGIR grid’s algorithm so that the GIR index can be automatically modified in real-time and transmitted to care services, prescribers and caregivers.

Els documents demogràfics permeten estudiar un gran conjunt de comportaments en diferents àmbits com ara l'evolució social i econòmica del passat. Gràcies a les tècniques informàtiques de visió per computador es pretenen construir xarxes socials històriques aprofitant les dades demogràfiques de les que es disposen. Aquest treball es centra en la implementació d'una arquitectura Big Data on es puguin emmagatzemar les diferents dades demogràfiques, garantint un sistema escalable per tal de poder anar ampliant amb tants nodes com sigui necessari tant l'espai com la potència. Per aconseguir-ho, s'han escollit algunes de les tecnologies Big Data més utilitzades actualment i s'ha n tractat dades reals de diverses poblacions catalanes. Demographic documents allow us to study a large group of behaviors in different areas such as the social and economic evolution of the past. Thanks to computer based computer vision techniques, it is intended to build historical social networks taking advantage of demographic data available. This work focuses on the implementation of a Big Data architecture where the different demographic data can be stored, guaranteeing a scalable system in order to be able to expand with as many nodes as it is necessary both space and power. To achieve this, some of the most widely used Big Data technologies have been chosen and real data from different Catalan populations have been dealt with. Los documentos demográficos permiten estudiar un gran conjunto de comportamientos en diferentes ámbitos como la evolución social y económica del pasado. Gracias a las técnicas informáticas de visión por computador se pretenden construir redes sociales históricas aprovechando los datos demográficos de las que se disponen. Este trabajo se centra en la implementación de una arquitectura Big Data donde se puedan almacenar los diferentes datos demográficos, garantizando un sistema escalable para poder ir ampliando con tantos nodos como sea necesario tanto el espacio como la potencia. Para conseguirlo, se han escogido algunas de las tecnologías Big Data más utilizadas actualmente y se han tratado datos reales de diversas poblaciones catalanas.

As the online resources start to take a significant part in every aspect of our lives students from the Plovdiv University „Paisii Hilendarski” have developed a strategy for implementing video materials in the education process in the field of physics. There are many different advantages of this method from which both students and tutors can benefit in comparison with the standalone conventional educational approaches. The basic idea of the project we are working on is to develop a sustainable and scalable system of online resources which will help the better understanding of basic principles and subjects in the physics education. Even more important is the fact that they will be created by students for students. Many scientists and researchers can confirm the benefits of using such resources – it provides better time efficiency, the language used is more comprehensive for students. However the accuracy is constantly monitored by experienced specialists in the matter being explained. In this article, we share our experience with the preparation of the online didactic video materials and evaluation of their effectiveness. We believe that the topic of the article is up to date and the methodology described in it is applicable to other subjects. This article is dedicated to physics teachers and researchers in the physics education.

MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include machine learning, and spatial processing. In meantime, huge volumes of spatio-temporal trajectory data are coming from different sources over sometime, raised the demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in trajectory data processing. This work describes Summit; a full-fledged MapReduce framework with native support for trajectory data.

Strong empathic bonding between members of a team can elevate team performance tremendously but it is not clear how such bonding within human-machine teams may impact upon mission success. Prior work using self-reporting surveys and end-of-task metrics do not capture how such bonding may evolve over time and impact upon task fulfillment. Furthermore, sensor-based measures do not scale easily to facilitate the need to collect substantial data for measuring potentially subtle effects. We introduce TEAMMATE, a system designed to provide insights into the emotional dynamics humans may form for machine teammates that could critically impact upon the design of human machine teams Refereed/Peer-reviewed

It has been observed that the autonomous vehicles (also called self-driving car, driverless car) are attracting people in this modern world. Their demands are increasing in the area of transportation and traveling facility. This paper describes how cloud and big data technology is related with self-driving car concept. This paper also presents the solution for storage of huge amount of data that is generated by sensor network of autonomous vehicles by implementing HDFS (Hadoop Distributed File System) an efficient storage mechanism of Hadoop platform. This paper describes how Apache Spark tool of Hadoop ecosystem family is used to process algorithms and maintain software's in self-driving car. The proposed model provides fault-tolerant, highly availability of data and widely scalable system for the self-driving car.

Developing communication skills in higher education students could be a challenge to professors due to the time needed to provide formative feedback. This work presents RAP, a scalable system to provide automatic feedback to entry-level students to develop basic oral presentation skills. The system improves the state-of-the-art by analyzing posture, gaze, volume, filled pauses and the slides of the presenters through data captured by very low-cost sensors. The system also provides an off-line feedback report with multimodal recordings of their performance. An initial evaluation of the system indicates that the system's feedback highly agrees with human feedback and that students considered that feedback useful to develop their oral presentation skills.

Megakaryocytes (MKs) are the precursors of platelets (PLTs) and may be used for PLT production in vivo or in vitro, as well as a source for PLT-derived growth factors. Induced pluripotent stem cells represent an unlimited cell source for the in vitro production of MKs. This study aimed at developing an effective, xeno-free and scalable system to produce high numbers of MKs. In particular, microcarrier beads-assisted stirred bioreactors were evaluated as a means of improving MK yields. This method resulted in the production of 18.7 × 107 MKs per 50 ml medium. Laminin-coated microcarriers increased MK production per iPSC by up to 10-fold. MKs obtained in this system showed typical features of mature MKs and were able to produce PLTs in vitro and in vivo. To increase safety, MKs produced in the bioreactors were irradiated; a procedure that did not affect their capability to form proPLTs and PTLs after transfusion. In vitro generated MKs represent a promising alternative to donor PLTs and open the possibility for the development of innovative MK-based cell therapies.

We present a perfusion culture system with miniature bioreactors and peristaltic pumps. The bioreactors are designed for perfusion, live-cell imaging studies, easy incorporation of microfabricated scaffolds, and convenience of operation in standard cell culture techniques. By combining with miniature peristaltic pumps—one for each bioreactor to avoid cross-contamination and to maintain desired flow rate in each—we have made a culture system that facilitates perfusion culture inside standard incubators. This scalable system can support multiple parallel perfusion experiments. The major components are fabricated by three-dimensional printing using VeroWhite, which we show to be amenable to ex vivo cell culture. Furthermore, the components of the system can be reused, thus making it economical. We validate the system and illustrate its versatility by culturing primary rat hepatocytes, live imaging the growth of mouse fibroblasts (NIH 3T3) on microfabricated ring-scaffolds inserted into the bioreactor, performing perfusion culture of breast cancer cells (MCF7), and high-magnification imaging of hepatocarcinoma cells (HuH7).

Resumen El sistema de monitorización remota Infrasmart diseñado por Tecnalia es usado como base para el desarrollo de supervisión de infraestructuras civiles tanto existentes como de nueva construcción en donde el despliegue de la instalación debe de ser lo menos intrusiva posible. Para conseguir su objetivo el sistema se basa en módulos de adquisición distribuidos con capacidad de comunicación inalámbrica de larga distancia y auto-alimentación orientada a la eliminación del cableado propio de este tipo de instalaciones. A su vez el concentrador de datos gestiona la recepción de la información de dichos módulos y la evacuación de los mismos a un servidor de datos seguro orientado a la supervisión remota de la infraestructura. Actualmente la capacidad de medida de los nodos de adquisición abarca tanto medidas cuasi-estáticas, (sensores de temperatura e hilo vibrante), como medidas dinámicas accionadas por evento, (acelerómetros). En adición existe la capacidad de integrar nuevos sensores conforme a nuevas especificaciones dando como resultado un sistema abierto y escalable en función de los parámetros de interés de la infraestructura a monitorizar. Abstract The Infrasmart remote monitoring system designed by Tecnalia is used as a basis for the development of supervision of both existing and new construction civil infrastructures where the deployment of the installation should be as unobtrusive as possible. To achieve its objective, the system is based on distributed acquisition modules with long-distance wireless communication capability and self-feeding aimed at eliminating the wiring of these types of installations. At the same time, the data concentrator manages the reception of the information of said modules and the evacuation thereof to a secure data server oriented to the remote supervision of the infrastructure. Currently, the measurement capacity of the acquisition nodes includes both quasi-static measures (temperature sensors and vibrating wire), and dynamic measurements driven by events (accelerometers). In addition, there is the ability to integrate new sensors according to new specifications resulting in an open and scalable system depending on the parameters of interest of the infrastructure to be monitored.

The problem of effective organization of Students service center (SSC) activities is considered. In this paper is proposed combine agents interaction and queuing system model for creation real time control of SSC load balancing. The developed combined model allows to minimize the number of required personnel resources and their idle time and to create adaptive, modular, well scalable system.