This paper presents a multi-agent simulation for fleet applications with a primary focus on electric vehicles. The overall system design is characterized by a distributed and decentralized structure. Basic idea is to use an evolutionary algorithm in combination with a Monte Carlo method to optimize behavioral patterns of agents. Within an iterative process, agent plans are executed, scored and modified - if necessary. In doing so, a set of activity schedules is created that is compatible with given constraints, such as range restrictions of electric cars or space limitations at charging stations. In so doing agents compete with each other for limited resources within a single iteration step. The proposed model makes use of a map-based approach that takes time variant traffic conditions into account. With help of speed profiles and a longitudinal vehicle dynamic model, travel time and energy consumption calculations are carried out. As the fleet and the charging infrastructure configuration are input parameters of the proposed model, it is possible to perform case studies for several electrification scenarios. In total two different scenarios are explored. First the proposed simulation model is validated with help of measured fleet data. Second the suitability of electric cars for a taxi use case is assessed. Within the first simulation study, the daily mileage is reflected with an average variation of 7.8 % and waiting times with an average deviation of 0.05 %. The second electrification scenario provides transparent indications for choosing both an appropriate electric vehicle concept and charging infrastructure configuration.
Los modelos de simulación de calidad de agua, son herramientas ambientales que permiten interpretar y predecir la respuesta de un cuerpo de agua a las cargas contaminantes externas. El programa de simulación de calidad de agua (WASP versión 7.41) se utilizó para simular y evaluar la relación entre los nutrientes externos y la calidad de agua, en la Laguna de Ayarza, Santa Rosa, Guatemala. El modelo toma en cuenta dos ciclos de nutrientes (N y P), por medio de variables de calidad de agua: temperatura, nitrato (NO 3 ), amonio (NH 4 ), nitrógeno total (TN),fosfato (PO 4 ), fósforo total (TP), y oxígeno disuelto (OD). El modelo se construyó tomando en cuenta la morfología del lago y las condiciones climáticas. El lago se dividió en siete segmentos, tomando en cuenta los flujos y los parámetros fisicoquímicos para cada uno. Se determinó el coeficiente de dispersión del lago y se calibró utilizando los datos de octubre 2010 a febrero 2011. El post-procesamiento se realizó por medio del software GNUPLOT. Los resultados de la modelación muestran que los valores de fósforo en todo el lago, presentan niveles de eutrofización, los valores de nitrógeno presentan niveles oligotróficos e indican que el lago soporta carga contaminanterelativamente alta.
We present a high-resolution interactive and collaborative data visualization framework capable of supporting multiple user interaction on a large screen display. The data visualization framework takes advantage of SAGE2 collaborative visualization workspace. SAGE2 is designed to take multiple displays and use them as one high-resolution multiuser workspace. SAGE2 users are able to access and manipulate this workspace through a modern web browser. Our data visualization framework, ParaSAGE, supports scientific data visualization by extending ParaViewWeb into a SAGE2 desktop application where a subset of ParaView scientific visualization functionalities is accessible to SAGE2 users. We also developed a SAGE2 desktop application based on D3.js library to support information data visualization efforts.
A significant number of communities of bodybuilding drugs and Anabolic Androgenic Steroid users exist online (the term steroid forum on google returns over 140,000 results). However, very little is known about them as most research has traditionally focused on addiction recovery online communities. This work aims to provide an insight into understanding the on-line communities of bodybuilding drug and AAS users from multiple perspectives through the lens of harm reduction. In order to do so, we embedded ourselves in various online communities for several months, conducted 32 interviews and conducted a co-design session with bodybuilding drugs and AAS users. The results indicate that these online communities are central to the drug use of many individuals, and that harm is a significant issue for the majority of these on-line communities. Based on this, we present and discuss an initial conceptual model to demonstrate how researchers can approach these communities, build rapport, and conduct collaboratory research and co-design with them.
This article presents a collaborative game based on tangible interaction, called ITCol (Tangible Interaction for Collaboration). It has been developed to tackle a specific educational need in the context of a post-graduate course at a School of Computer Science. The purpose of the application is, through a detective game, to help adult students experience and experiment with collaborative work. ITCol proposes an interaction mode through tangible objects placed on a horizontal tabletop. In this article, we focus on describing how the use of this type of tangible interaction helps students to experience collaboration, considering characteristics such as individual responsibility, positive interdependence, developer interaction, among others. Additionally, the advances achieved in the evaluation process are described, as well as the initial results that have been obtained.
Minimizing the impact of network convergence events has been an active area of research and innovation since it usually occurs unexpectedly and triggers (often jolting) alarms in network operations centers. Examination of router logs, network management system information, and router configuration reviews are the standard tools for assessment and often do not lead to satisfactory conclusions. We introduce Route Convergence Visualizer providing the network operator with a fast and simple tool to understand what exactly happened and who was impacted during a network convergence event. The main novel ingredients include (a) effectiveness of information delivery regarding convergence events, (b) programmability of the tool, and (c) application of the tool. At last, we will enlighten an indispensable collaboration between network operators and software engineers for SDN solutions which require contributions from two disparate engineering disciplines, networking and application development.
The sophistication of novel strains of polymorphic viruses, such as Stuxnet, has increased over the last decade. Traditional tools such as anti-virus, firewalls, intrusion detection/prevention systems, etc. may be incapable of detecting such strains. As a result, new methods need to be introduced in order to detect this family of malware. Combining dynamic malware analysis techniques with machine learning tools can prove useful in the progression of developing an effective and efficient classifier. This paper explores the use of dynamic analysis of malware and machine learning to create a classifier for polymorphic virus detection.
This research is aligned with the author's Dissertation titled ‘Virtual World Consumer Behavior’-2016. It tests a model which looks at the connection between Virtual Worlds (VW) user attributes and VW purchases intentions. Results of this study are aimed at improving understanding of VW users so stakeholders and engineers can improve game system attributes overall leading to VW profits. This study ties together Theory of Reasoned Action (TRA), Flow Theory and a new component ‘Desire for Uniqueness’ to explain how specific user attributes guide VW purchase intentions through VW shopping attitudes and subjective norms (SN). The study collects data via online surveys from volunteer VW users then analyzes the data with Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to validate the measurement and structural model. Results show positive relationships between these user attributes to their VW shopping attitudes and VW shopping SNs which lead to positive purchase intentions. These results imply TRA, Flow Theory and Desire for Uniqueness can explain VW consumer behavior through some specific user attributes; scales from previous studies are adaptable to this model; and VW businesses, developers and researchers would benefit by using this knowledge to prioritize efforts by looking at specific attributes as areas of opportunity.
This paper describes a case study, carried out in an educational setting, whose purpose includes the analysis of the impact that role-playing games (RPGs), with the mediation of the immersive virtual environment Second Life, causes in the oral practice of linguistic and discursive communicative sub-competences in English. It is discussed an interdisciplinary participation of the communicative approach of languages, the technologies involved in the development of this study, RPGs and task-based learning. The study outlines the phases that make up the experience, the categories of analysis addressed from the triangulation of data collected, and finally the conclusions and future research.
Currently, E-mail is one of the most important methods of communication. However, the increasing of spam e-mails causes traffic congestion, decreasing productivity, phishing, which has become a serious problem for our society. And the number of spam e-mail is increasing every year. Therefore, spam e-mail filtering is an important, meaningful and challenging topic. The aim of this research is to find an effective solution to filter possible spam e-mails. And as we know, in recent days, there are many techniques that spammers use to avoid spam-detection such as obfuscation techniques. In this case, the following proposed approach uses email content only to build keyword corpus, together with some text processing to handle obfuscation technique. The algorithm was evaluated using the CSDMC2010 SPAM corpus dataset that contained 4327 emails in the training dataset and 4292 emails in the testing dataset. The experimental results show that the proposed algorithm has 92.8% accuracy.
This work introduces a novel approach to dynamic online machine to machine argumentation, which does not require human intervention. The proposed model is a hybrid between weighted, Dung style argumentation frameworks, and competitive facility placement Voronoi games and delivers the outcome in graphic form.
The reliability of today's evermore complex production systems gains in importance for competitive business environments. Failures and downtimes of machines lead to production losses and related high costs. Hence, effective and efficient maintenance is regarded as a strategic competitive advantage. In order to ensure the availability of spare parts and maintenance personnel while operating at reasonable costs, the coordination and planning of the different actors in a spare parts supply chain has become more important. Therefore, this research will study the characteristics of spare parts supply chains, empirically analyze various instances of real life spare parts supply chains and investigate how coordination in current spare parts supply chains can be improved. The main contributions of this research will be a novel classification system for differentiating classes of spare parts supply chains based on their observed coordination characteristics and correspondingly providing guidance for improving coordination deficits in these classes.
This paper presents a possible solution to a fundamental limitation facing all blockchain-based systems; scalability. We propose a temporal rolling blockchain which solves the problem of its current exponential growth, instead replacing it with a constant fixed-size blockchain. We conduct a thorough analysis of related work and present a formal analysis of the new rolling blockchain, comparing the results to a traditional blockchain model to demonstrate that the deletion of data from the blockchain does not impact on the security of the proposed blockchain model before concluding our work and presenting future work to be conducted.
Reading the news is a favorite hobby for many people anywhere in the world. With the popularity of the Internet and social media, users are constantly provided, or even bombarded, with the latest news around the world. With numerous sources of news, it has become a real challenge for users to follow the news that they are interested. Previous work used user profile to recommend personalized news; and used RSS feeds and latest tweets to provide popular, trendy news. In this work we combine these two methods with three enhancements. First, to personalize news recommendation we used a hybrid approach, which involved the analyses of click through, user tweets, and user Twitter friends list to build user profile, this method significantly improves the accuracy of user profile. Second, to address the importance of temporal dynamics, we add a unique new feature of location preference to the news recommendation system. Third, we allow users to choose the ratio of popular news vs. trendy news they desire. The resulting system is then evaluated based on user satisfaction and accuracy. The results show that the average user satisfaction increases from 8.6 to 9.4 when location preference is added, while the accuracy of the recommendation system is around 92-95%. We believe that the proposed system is a successful example of incorporating temporal dynamics to recommendation systems; the combination of using hybrid user profile, popularity, trends and location would have significant impact on other recommendation systems in the future.
When people want to collaborate with someone, they have to use a common language. For the non-native speaker of the language, to keep up with the conversation and talk equally with the native speaker is not always easy. We propose the speech speed awareness system in this paper. The system warns when the speech rate becomes too fast for the non-native speaker, reminding the native speaker that not all the conversation participants are native, and preventing the non-native speaker from being left behind of the conversation without the effort of asking a repeat many times to help them collaborate more efficiently.
Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.
Networked organizations must grapple with a constant trade-off between ease of workflow for their employees, and devoting time and resources to computer security. In a group of collaborators whose workflows can differ substantially, creating broad and cohesive awareness around security can be difficult, especially for spaces like news institutions, where continuous collaboration must be carried out under continuous threat of cyberattack. Using a sensemaking framework, we analyzed interviews with two levels of organizational actors, lower-level reporters and higher-level supervising editors. Fragmented sensemaking, in which individuals maintain their own discrete and disconnected approaches to the complex situation of computer security, was pervasive. Storytelling as a sensemaking strategy, however, was found in both levels. In particular, while personal stories were shared by all partici-pants, higher-level editors on average shared more second-hand narratives they'd heard about other organizations.Noting that editors described how such second-hand stories shaped their security decisions, we conclude with recommendations for integrating storytelling methods into robust training modules for computer security in collaborative working environments.
Imagine you are an operating room nurse. Could training with virtual human teammates empower you to speak up to a bullying teammate? Could virtual teammates change the way you speak as to reduce errors? How about learn new patient safety policies or efficiently transfer care?In this talk, we will explore the emerging area of using virtual humans to subtly influence healthcare teams' teamwork and communication skills. This application of virtual humans could have significant patient safety impact as teamwork and communication is the top reason for adverse events in critical care areas, such as the emergency room, intensive care unit, and operating room.
In this paper we analyze the characteristics of collaborative systems and the characteristics of shared economy supporting systems. Uberization as present in many applications: Airbnb, Uber, BlablaCar, AMAP, circular economy, etc. needs a cooperative system support. We examine this approach from the point of view of ICT (Information and Communication Technologies) and, more specifically, HMI (Human Machine Interaction) and CSCW (Computer Supported Cooperative Work) and indicate what must be added to collaborative systems to support uberization. This paper also shows how to identify appropriate collaborative models and how to add new uberization services to obtain an uberization supporting platform. A case of design of a collaborative application for Carbon Free Parcel Distribution is also presented, and corresponding intermediation algorithms are discussed.
Discovery Lab Global (DLG) is a not-for-profit Entrepreneurial STEM Lab and Concepts Accelerator for strategic workforce development that focuses primarily on bridging the last 2 years of high school first 2 years of college. It builds on the insights and lessons-learned gained in the 10-year operation of the original Air Force Research Laboratory's (AFRL) Discovery Lab workforce development program (2005-2015). AFRL Discovery Lab ended in 2015 when the founding director retired to start up Discovery Lab Global as a not-for-profit STEM and strategic workforce development initiative. The presentation will focus on the crucial role that collaboration technologies will play in scaling up from a 100-student program into a 1,000-plus student program with global reach.
