Seeing the invisible: from imagined to virtual urban landscapes

Urban ecosystems consist of infrastructure features working together to provide services for inhabitants. Infrastructure functions akin to an ecosystem, having dynamic relationships and interdependencies. However, with age, urban infrastructure can deteriorate and stop functioning. Additional pressures on infrastructure include urbanizing populations and a changing climate that exposes vulnerabilities. To manage the urban infrastructure ecosystem in a modernizing world, urban planners need to integrate a coordinated management plan for these co-located and dependent infrastructure features. To implement such a management practice, an improved method for communicating how these infrastructure features interact is needed. This study aims to define urban infrastructure as a system, identify the systematic barriers preventing implementation of a more coordinated management model, and develop a virtual reality tool to provide visualization of the spatial system dynamics of urban infrastructure. Data was collected from a stakeholder workshop that highlighted a lack of appreciation for the system dynamics of urban infrastructure. An urban ecology VR model was created to highlight the interconnectedness of infrastructure features. VR proved to be useful for communicating spatial information to urban stakeholders about the complexities of infrastructure ecology and the interactions between infrastructure features.https://doi.org/10.1016/j.cities.2019.102559Published versio

An Integrated EPLOS Database as a Tool Supporting TSL Companies

The paper presents the conceptual design of a database for the European Portal of Logistics Services (EPLOS) and its application. The database contains the data on logistics companies, the infrastructure for road, railway, inland, and air transport, as well as the data on the nodal elements of logistics infrastructure (warehouse facilities, seaports, transhipment terminals). Complete and verified information is the fundamental condition for rational decisions about the realization of logistics processes on a meso- and macroeconomic scale. Authors present the relations in the making of the EPLOS database, its assumed scope, and the potential benefits for the TSL market from accessing the EPLOS database

A Methodology for Assessing Eco-efficiency in Logistics Networks

Recent literature on sustainable logistics networks points to two important questions: (i) How to spot the preferred solution(s) balancing environmental and business concerns? (ii) How to improve the understanding of the trade-offs between these two dimensions? We posit that a complete exploration of the efficient frontier and trade-offs between profitability and environmental impacts are particularly suitable to answer these two questions. In order to deal with the exponential number of basic efficient points in the frontier, we propose a formulation that performs in exponential time for the number of objective functions only. We illustrate our findings by designing a complex recycling logistics network in Germany.Eco-efficiency;Environmental impacts;Profitability;Recycling logistics network

Applications of Virtual Reality

Information Technology is growing rapidly. With the birth of high-resolution graphics, high-speed computing and user interaction devices Virtual Reality has emerged as a major new technology in the mid 90es, last century. Virtual Reality technology is currently used in a broad range of applications. The best known are games, movies, simulations, therapy. From a manufacturing standpoint, there are some attractive applications including training, education, collaborative work and learning. This book provides an up-to-date discussion of the current research in Virtual Reality and its applications. It describes the current Virtual Reality state-of-the-art and points out many areas where there is still work to be done. We have chosen certain areas to cover in this book, which we believe will have potential significant impact on Virtual Reality and its applications. This book provides a definitive resource for wide variety of people including academicians, designers, developers, educators, engineers, practitioners, researchers, and graduate students

Understanding Hidden Memories of Recurrent Neural Networks

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017

Design of monitoring applications and prediction of key industrial metrics: IIoT + AI

The global industry has suffered deep changes in the last years because of the successful development and integration of new technologies. Industry 4.0 has emerged as a new standard for achieving efficiency and improving processes. Among the technologies used in Industry 4.0, Internet of Things applied to industry (IIoT) enable real-time, intelligent, and autonomous access, collection, analysis, communications, and exchange of process, product and/or service information, within the industrial environment, so as to optimize overall production value. Because of its importance, in this project, a methodology for extracting, analyzing and using the data gathered by IIoT devices is proposed in order to extract meaningful information and to predict industrial key metrics with Artificial Intelligence. In addition, for the complete validation of the proposed methodology, a practical implementation of all the mentioned aspects is carried out by developing a study of the industrial process in the wastewater treatment field using the data collected by an Industrial Internet of Things infrastructure and modelling key time series metrics, such as total organic carbon (TOC) and carbon removal performance (CRP) by using Machine Learning models XGBOOST Regressor, Multi-Layer Perceptron (MLP) Regressor and Support Vector Regressor (SVR) to implement a dashboard with an operational panel and a decision-making panel that helps anticipate possible deviations in the performance of the industrial process

Production optimization using discrete simulation

Mestrado APNOR e Universidade de S. PetersburgoProduction and manufacturing setups involving lean solutions and customer driven “pull” logic (e.g. kanban systems) are more and more common. Usually, these systems allow companies to increase efficiency, quality levels, work force motivation and general productivity. Although these systems are not too difficult to plan and operate, in complex situations, even small adjustments can produce some unforeseen effects. In this scenario, discrete simulation can provide the tools to model the underlying systems and test the desired changes before implementation. In this work we modelled typical pull production systems with more or less complexity using a commercial discrete simulation software (SIMIO). Once the modelling phase was completed, different adjustments in the number of Kanban cards in the system were tested and evaluated, in order to optimize the system. Also, the final simulation model was built generic enough to be used in classroom environment to familiarize students with pull production concepts.As configurações de produção e fabricação envolvendo soluções lean e a lógica pull orientada ao cliente (por exemplo, sistemas kanban) são cada vez mais comuns. Normalmente, estes sistemas permitem que as empresas aumentem a eficiência, os níveis de qualidade, a motivação da força de trabalho e a produtividade geral. Embora esses sistemas não sejam muito difíceis de planear e operar, em situações complexas, mesmo pequenos ajustes podem produzir alguns efeitos imprevistos. Nesse cenário, a simulação discreta pode fornecer as ferramentas para modelar os sistemas subjacentes e testar as alterações desejadas antes da implementação. Neste trabalho modelamos sistemas típicos de produção puxada com maior ou menor complexidade usando um software comercial para simulação discreta (SIMIO). Uma vez concluída a fase de modelação, foram testados e avaliados diferentes ajustes no número de cartões kanban no sistema, a fim de otimizar o sistema. Além disso, o modelo de simulação final foi construído de forma suficientemente genérica para ser usado em ambiente de sala de aula para familiarizar os alunos com conceitos de produção puxada (pull).Организация производства и технологическая наладка с применением концепций «бережливого» и «вытягивающего» производства, ориентированных на нужды потребителя (например, система канбан) получают все более широкое распространение. Обычно, данные системы позволяют компаниям повышать эффективность, уровень качества, мотивацию сотрудников и производительность в целом. И хотя реализация данных подходов не является слишком трудоемкой, в сложных ситуациях даже малейшие корректировки могут привести к непредвиденным последствиям. В таком случае, дискретное моделирование может предоставить инструменты для создания базовых моделей и их тестирования, до внесения изменений в реальную систему. В данной работе было смоделировано типичное, более-менее сложное вытягивающее производство с применением коммерческого программного средства дискретного имитационного моделирования (SIMIO). После создания симуляции было протестировано и оценено использование разного количества канбан карт в системе с целью ее оптимизации. Также, финальная симуляция была создана достаточно общей, чтобы ее можно было использовать во время аудиторных занятий для ознакомления студентов с концепцией вытягивающего производства

Using Social Media Websites to Support Scenario-Based Design of Assistive Technology

Indiana University-Purdue University Indianapolis (IUPUI)Having representative users, who have the targeted disability, in accessibility studies is vital to the validity of research findings. Although it is a widely accepted tenet in the HCI community, many barriers and difficulties make it very resource-demanding for accessibility researchers to recruit representative users. As a result, researchers recruit non-representative users, who do not have the targeted disability, instead of representative users in accessibility studies. Although such an approach has been widely justified, evidence showed that findings derived from non-representative users could be biased and even misleading. To address this problem, researchers have come up with different solutions such as building pools of users to recruit from. But still, the data is not widely available and needs a lot of effort and resource to build and maintain. On the other hand, online social media websites have become popular in the last decade. Many online communities have emerged that allow online users to discuss health-related subjects, exchange useful information, or provide emotional support. A large amount of data accumulated in such online communities have gained attention from researchers in the healthcare domain. And many researches have been done based on data from social media websites to better understand health problems to improve the wellbeing of people. Despite the increasing popularity, the value of data from social media websites for accessibility research remains untapped. Hence, my work aims to create methods that could extract valuable information from data collected on social media websites for accessibility practitioners to support their design process. First, I investigate methods that enable researchers to effectively collect representative data from social media websites. More specifically, I look into machine learning approaches that could allow researchers to automatically identify online users who have disabilities (representative users). Second, I investigate methods that could extract useful information from user-generated free-text using techniques drawn from the information extraction domain. Last, I explore how such information should be visualized and presented for designers to support the scenario-based design process in accessibility studies

Exploring Societal Computing based on the Example of Privacy

Data privacy when using online systems like Facebook and Amazon has become an increasingly popular topic in the last few years. This thesis will consist of the following four projects that aim to address the issues of privacy and software engineering. First, only a little is known about how users and developers perceive privacy and which concrete measures would mitigate their privacy concerns. To investigate privacy requirements, we conducted an online survey with closed and open questions and collected 408 valid responses. Our results show that users often reduce privacy to security, with data sharing and data breaches being their biggest concerns. Users are more concerned about the content of their documents and their personal data such as location than about their interaction data. Unlike users, developers clearly prefer technical measures like data anonymization and think that privacy laws and policies are less effective. We also observed interesting differences between people from different geographies. For example, people from Europe are more concerned about data breaches than people from North America. People from Asia/Pacific and Europe believe that content and metadata are more critical for privacy than people from North America. Our results contribute to developing a user-driven privacy framework that is based on empirical evidence in addition to the legal, technical, and commercial perspectives. Second, a related challenge to above, is to make privacy more understandable in complex systems that may have a variety of user interface options, which may change often. As social network platforms have evolved, the ability for users to control how and with whom information is being shared introduces challenges concerning the configuration and comprehension of privacy settings. To address these concerns, our crowd sourced approach simplifies the understanding of privacy settings by using data collected from 512 users over a 17 month period to generate visualizations that allow users to compare their personal settings to an arbitrary subset of individuals of their choosing. To validate our approach we conducted an online survey with closed and open questions and collected 59 valid responses after which we conducted follow-up interviews with 10 respondents. Our results showed that 70% of respondents found visualizations using crowd sourced data useful for understanding privacy settings, and 80% preferred a crowd sourced tool for configuring their privacy settings over current privacy controls. Third, as software evolves over time, this might introduce bugs that breach users' privacy. Further, there might be system-wide policy changes that could change users' settings to be more or less private than before. We present a novel technique that can be used by end-users for detecting changes in privacy, i.e., regression testing for privacy. Using a social approach for detecting privacy bugs, we present two prototype tools. Our evaluation shows the feasibility and utility of our approach for detecting privacy bugs. We highlight two interesting case studies on the bugs that were discovered using our tools. To the best of our knowledge, this is the first technique that leverages regression testing for detecting privacy bugs from an end-user perspective. Fourth, approaches to addressing these privacy concerns typically require substantial extra computational resources, which might be beneficial where privacy is concerned, but may have significant negative impact with respect to Green Computing and sustainability, another major societal concern. Spending more computation time results in spending more energy and other resources that make the software system less sustainable. Ideally, what we would like are techniques for designing software systems that address these privacy concerns but which are also sustainable - systems where privacy could be achieved "for free", i.e., without having to spend extra computational effort. We describe how privacy can indeed be achieved for free an accidental and beneficial side effect of doing some existing computation - in web applications and online systems that have access to user data. We show the feasibility, sustainability, and utility of our approach and what types of privacy threats it can mitigate. Finally, we generalize the problem of privacy and its tradeoffs. As Social Computing has increasingly captivated the general public, it has become a popular research area for computer scientists. Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. We highlight some of the relevant research topics and open problems that we foresee in Societal Computing. We feel that these topics, and Societal Computing in general, need to gain prominence as they will provide useful avenues of research leading to increasing benefits for society as a whole