Teaching Construction in the Virtual University: the WINDS project

This paper introduces some of the Information Technology solutions adopted in Web based INtelligent Design Support (WINDS) to support education in A/E/C design. The WINDS project WINDS is an EC-funded project in the 5th Framework, Information Society Technologies programme, Flexible University key action. WINDS is divided into two actions: ·The research technology action is going to implement a learning environment integrating an intelligent tutoring system, a computer instruction management system and a set of co-operative supporting tools. ·The development action is going to build a large knowledge base supporting Architecture and Civil Engineering Design Courses and to experiment a comprehensive Virtual School of Architecture and Engineering Design. During the third year of the project, more than 400 students all over Europe will attend the Virtual School. During the next three years the WINDS project will span a total effort of about 150 man-years from 28 partners of 10 European countries. The missions of the WINDS project are: Advanced Methodologies in Design Education. WINDS drives a breakdown with conventional models in design education, i.e. classroom or distance education. WINDS implements a problem oriented knowledge transfer methodology following Roger Schank's Goal Based Scenario (GBS) pedagogical methodology. GBS encourages the learning of both skills and cases, and fosters creative problem solving. Multidisciplinary Design Education. Design requires creative synthesis and open-end problem definition at the intersection of several disciplines. WINDS experiments a valuable integration of multidisciplinary design knowledge and expertise to produce a high level standard of education. Innovative Representation, Delivery and Access to Construction Education. WINDS delivers individual education customisation by allowing the learner access through the Internet to a wide range of on-line courses and structured learning objects by means of personally tailored learning strategies. WINDS promotes the 3W paradigm: learn What you need, Where you want, When you require. Construction Practice. Construction industry is a repository of ""best practices"" and knowledge that the WINDS will profit. WINDS system benefits the ISO10303 and IFC standards to acquire knowledge of the construction process directly in digital format. On the other hand, WINDS reengineers the knowledge in up-to-date courses, educational services, which the industries can use to provide just-in-time rather than in-advance learning. WINDS IT Solutions The missions of the WINDS project state many challenging requirements both in knowledge and system architecture. Many of the solutions adopted in these fields are innovative; others are evolution of existing technologies. This paper focuses on the integration of this set of state-of-the-art technologies in an advanced and functionally sound Computer Aided Instruction system for A/E/C Design. In particular the paper deals with the following aspects: Standard Learning Technology Architecture The WINDS system relies on the in progress IEEE 1484.1 Learning Technology Standard Architecture. According to this standard the system consists of two data stores, the Knowledge Library and the Record Database, and four process: System Coach, Delivery, Evaluation and the Learner. WINDS implements the Knowledge Library into a three-tier architecture: 1.Learning Objects: ·Learning Units are collections of text and multimedia data. ·Models are represented in either IFC or STEP formats. ·Cases are sets of Learning Units and Models. Cases are noteworthy stories, which describes solutions, integrate technical detail, contain relevant design failures etc. 2.Indexes refer to the process in which the identification of relevant topics in design cases and learning units takes place. Indexing process creates structures of Learning Objects for course management, profile planning procedures and reasoning processes. 3.Courses are taxonomies of either Learning Units or a design task and Course Units. Knowledge Representation WINDS demonstrates that it is possible and valuable to integrate a widespread design expertise so that it can be effectively used to produce a high level standard of education. To this aim WINDS gathers area knowledge, design skills and expertise under the umbrellas of common knowledge representation structures and unambiguous semantics. Cases are one of the most valuable means for the representation of design expertise. A Case is a set of Learning Units and Product Models. Cases are noteworthy stories, which describe solutions, integrate technical details, contain relevant design failures, etc. Knowledge Integration Indexes are a medium among different kind of knowledge: they implement networks for navigation and access to disparate documents: HTML, video, images, CAD and product models (STEP or IFC). Concept indexes link learning topics to learning objects and group them into competencies. Index relationships are the base of the WINDS reasoning processes, and provide the foundation for system coaching functions, which proactively suggest strategies, solutions, examples and avoids students' design deadlock. Knowledge Distribution To support the data stores and the process among the partners in 10 countries efficiently, WINDS implements an object oriented client/server as COM objects. Behind the DCOM components there is the Dynamic Kernel, which dynamically embodies and maintains data stores and process. Components of the Knowledge Library can reside on several servers across the Internet. This provides for distributed transactions, e.g. a change in one Learning Object affects the Knowledge Library spread across several servers in different countries. Learning objects implemented as COM objects can wrap ownership data. Clear and univocal definition of ownerships rights enables Universities, in collaboration with telecommunication and publisher companies, to act as "education brokers". Brokerage in education and training is an innovative paradigm to provide just-in-time and personally customised value added learning knowledg

Improving Search through A3C Reinforcement Learning based Conversational Agent

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.Comment: 17 pages, 7 figure

On the Collaboration of an Automatic Path-Planner and a Human User for Path-Finding in Virtual Industrial Scenes

This paper describes a global interactive framework enabling an automatic path-planner and a user to collaborate for finding a path in cluttered virtual environments. First, a collaborative architecture including the user and the planner is described. Then, for real time purpose, a motion planner divided into different steps is presented. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using these pre-computed data, a second algorithm finds a collision free path in real time. Once the path is found, an haptic artificial guidance on the path is provided to the user. The user can then influence the planner by not following the path and automatically order a new path research. The performances are measured on tests based on assembly simulation in CAD scenes

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc

Independent Evaluation of the Jim Joseph Foundation's Education Initiative Final Report

The Jim Joseph Foundation created the Education Initiative to increase the number of educators and educational leaders who are prepared to design and implement high-quality Jewish education programs. The Jim Joseph Foundation granted $45 million to three premier Jewish higher education institutions (each institution received$15 million) and challenged them to plan and implement programs that used new content and teaching approaches to increase the number of highly qualified Jewish educators serving the field. The three grantees were Hebrew Union College–Jewish Institute of Religion (HUC-JIR), the Jewish Theological Seminary (JTS), and Yeshiva University (YU). The grant covered program operation costs as well as other costs associated with institutional capacity building. The majority of the funds (75 percent) targeted program planning and operation. The grantees designed and piloted six new master's degree and doctoral degree programs or concentrations;1 eight new certificate, leadership, and professional development programs;2 two new induction programs;3 and four new seminars within the degree programs. 4 The Education Initiative also supported financial assistance for students in eight other advanced degree programs. 5 The grantees piloted innovative teaching models and expanded their use of educational technology in the degree and professional development programs. According to the theory of change that drives the Jim Joseph Foundation's Education Initiative, five types of activities must take place if higher education institutions are to successfully enhance the Jewish education workforce. These activities include (1) improved marketing and recruitment of talented individuals into ongoing education programs, (2) a richer menu of programs requiring different commitments of time to complete and offering varying content, (3) induction programs to support program participants' transition to new employment settings, (4) well-planned and comprehensive strategies for financial sustainability, and (5) interinstitutional collaboration. As shown in Exhibit 1, the five types of activities are divided into two primary categories. The first category (boxes outlined in green) addresses the delivery of programs that provide educators and educational leaders with research-based and theory-based knowledge and vetted instructional tools. The second category (boxes outlined in orange) is not programmatic; rather, it involves sharing knowledge, building staff capabilities, enhancing management structures, and providing technological and financial support to enable the development of quality programming that is sustainable after the grant ends

INTELLIGENTE TRANSPORT SYSTEMEN ITS EN VERKEERSVEILIGHEID

This report discusses Intelligent Transport Systems (ITS). This generic term is used for a broad range of information-, control- and electronic technology that can be integrated in the road infrastructure and the vehicles themselves, saving lives, time and money bymonitoring and managing traffic flows, reducing conges-tion, avoiding accidents, etc. Because this report was written in the scope of the Policy Research Centre Mobility & Public Works, track Traffic Safety, it focuses on ITS systems from the traffic safety point of view. Within the whole range of ITS systems, two categories can be distinguished: autonomous and cooperative systems. Autonomous systems are all forms of ITS which operate by itself, and do not depend on the cooperation with other vehicles or supporting infrastructure. Example applications are blind spot detection using radar, electronic stability control, dynamic traffic management using variable road signs, emergency call, etc. Cooperative systems are ITS systems based on communication and cooperation, both between vehicles as between vehicles and infrastructure. Example applications are alerting vehicles approaching a traffic jam, exchanging data regarding hazardous road conditions, extended electronic brake light, etc. In some cases, autonomous systems can evolve to autonomous cooperative systems. ISA (Intelligent Speed Adaptation) is an example of this: the dynamic aspect as well as communication with infrastructure (eg Traffic lights, Variable Message Sign (VMS)...) can provide additional road safety. This is the clear link between the two parts of this report. The many ITS applications are an indicator of the high expectations from the government, the academic world and the industry regarding the possibilities made possible by both categories of ITS systems. Therefore, the comprehensive discussion of both of them is the core of this report. The first part of the report covering the autonomous systems treats two aspects: 1. Overview of European projects related to mobility and in particular to road safety 2. Overview for guidelines for the evaluation of ITS projects. Out of the wide range of diverse (autonomous) ITS applications a selection is made; this selection is focused on E Safety Forum and PreVENT. Especially the PreVent research project is interesting because ITS-applications have led to a number of concrete demonstration vehicles that showed - in protected and unprotected surroundings- that these ITS-applications are already technically useful or could be developed into useful products. The component “guidelines for the evaluation of ITS projects” outlines that the government has to have specific evaluation tools if the government has the ambition of using ITS-applications for road safety. Two projects -guidelines for the evaluation of ITS projects- are examined; a third evaluation method is only mentioned because this description shows that a specific targeting of the government can be desirable : 1. TRACE describes the guidelines for the evaluation of ITS projects which are useful for the evaluation of specific ITS-applications. 2. FITS contains Finnish guidelines for the evaluation of ITS project; FIS is an adaptation of methods used for evaluation of transport projects. 3. The third evaluation method for the evaluation of ITS projects is developed in an ongoing European research project, eImpact. eImpact is important because, a specific consultation of stake holders shows that the social importance of some techniques is underestimated. These preliminary results show that an appropriate guiding role for the government could be important. In the second part of this document the cooperative systems are discussed in depth. These systems enable a large number of applications with an important social relevance, both on the level of the environment, mobility and traffic safety. Cooperative systems make it possible to warn drivers in time to avoid collisions (e.g. when approaching the tail of a traffic jam, or when a ghost driver is detected). Hazardous road conditions can be automatically communicated to other drivers (e.g. after the detection of black ice or an oil trail by the ESP). Navigation systems can receive detailed real-time up-dates about the current traffic situation and can take this into account when calculating their routes. When a traffic distortion occurs, traffic centers can immediately take action and can actively influence the way that the traffic will be diverted. Drivers can be notified well in advance about approaching emergency vehicles, and can be directed to yield way in a uniform manner. This is just a small selection from the large number of applications that are made possible because of cooperative ITS systems, but it is very obvious that these systems can make a significant positive contribution to traffic safety. In literature it is estimated that the decrease of accidents with injuries of fatalities will be between 20% and 50% . It is not suprising that ITS systems receive a lot of attention for the moment. On an international level, a number of standards are being established regarding this topic. The International Telecommunications Uniont (ITU), Institute for Electrical and Electronics Engineers (IEEE), International Organization for Standardization (ISO), Association of Radio Industries and Business (ARIB) and European committee for standardization (CEN) are currently defining standards that describe different aspects of ITS systems. One of the names that is mostly mentioned in literature is the ISO TC204/WG16 Communications Architecture for Land Mobile environment (CALM) standard. It describes a framework that enables transparent (both for the application and the user) continuous communication through different communication media. Besides the innumerable standardization activities, there is a great number of active research projects. On European level, the most important are the i2010 Intelligent Car Initiative, the eSafety Forum, and the COMeSafety, the CVIS, the SAFESPOT, the COOPERS and the SEVECOM project. The i2010 Intelligent Car Initiative is an European initiative with the goal to halve the number of traffic casualties by 2010. The eSafety Forum is an initiative of the European Commission, industry and other stakeholders and targets the acceleration of development and deployment of safety-related ITS systems. The COMeSafety project supports the eSafety Forum on the field of vehicle-to-vehicle and vehicle-to-infrastructure communication. In the CVIS project, attention is given to both technical and non-technical issues, with the main goal to develop the first free and open reference implementation of the CALM architecture. The SAFEST project investigates which data is important for safety applications, and with which algorithmsthis data can be extracted from vehicles and infrastructure. The COOPERS project mainly targets communication between vehicles and dedicated roadside infrastructure. Finally, the SEVECOM project researches security and privacy issues. Besides the European projects, research is also conducted in the United States of America (CICAS and VII projects) and in Japan (AHSRA, VICS, Smartway, internetITS). Besides standardization bodies and governmental organizations, also the industry has a considerable interest in ITS systems. In the scope of their ITS activities, a number of companies are united in national and international organizations. On an international level, the best known names are the Car 2 Car Communication Consortium, and Ertico. The C2C CC unites the large European car manufacturers, and focuses on the development of an open standard for vehicle-to-vehicle and vehicle-to-infrastructure communications based on the already well established IEEE 802.11 WLAN standard. Ertico is an European multi-sector, public/private partnership with the intended purpose of the development and introduction of ITS systems. On a national level, FlandersDrive and The Telematics Cluster / ITS Belgium are the best known organizations. Despite the worldwide activities regarding (cooperative) ITS systems, there still is no consensus about the wireless technology to be used in such systems. This can be put down to the fact that a large number of suitable technologies exist or are under development. Each technology has its specific advantages and disadvantages, but no single technology is the ideal solution for every ITS application. However, the different candidates can be classified in three distinct categories. The first group contains solutions for Dedicated Short Range Communication (DSRC), such as the WAVE technology. The second group is made up of several cellular communication networks providing coverage over wide areas. Examples are GPRS (data communication using the GSM network), UMTS (faster then GPRS), WiMAX (even faster then UMTS) and MBWA (similar to WiMAX). The third group consists of digital data broadcast technologies such as RDS (via the current FM radio transmissions, slow), DAB and DMB (via current digital radio transmissions, quicker) and DVB-H (via future digital television transmissions for mobiledevices, quickest). The previous makes it clear that ITS systems are a hot topic right now, and they receive a lot of attention from the academic world, the standardization bodies and the industry. Therefore, it seems like that it is just a matter of time before ITS systems will find their way into the daily live. Due to the large number of suitable technologies for the implementation of cooperative ITS systems, it is very hard to define which role the government has to play in these developments, and which are the next steps to take. These issues were addressed in reports produced by the i2010 Intelligent Car Initiative and the CVIS project. Their state of the art overview revealed that until now, no country has successfully deployed a fully operational ITS system yet. Seven EU countries are the furthest and are already in the deployment phase: Sweden, Germany, the Netherlands, the United Kingdom, Finland, Spain and France. These countries are trailed by eight countries which are in the promotion phase: Denmark, Greece, Italy, Austria, Belgium,Norway, the Czech Republic and Poland. Finally, the last ten countries find themselves in the start-up phase: Estonia, Lithuania, Latvia, Slovenia, Slovakia, Hungary, Portugal, Switzerland, Ireland and Luxembourg. These European reports produced by the i2010 Intelligent Car Initiative and the CVIS project have defined a few policy recommendations which are very relevant for the Belgian and Flemish government. The most important recommendations for the Flemish government are: • Support awareness: research revealed that civilians consider ITS applications useful, but they are not really willing to pay for this technology. Therefore, it is important to convince the general public of the usefulness and the importance of ITS systems. • Fill the gaps: Belgium is situated in the promotion phase. This means that it should focus at identifying the missing stakeholders, and coordinating national and regional ITS activities. Here it is important that the research activities are coordinated in a national and international context to allow transfer of knowledge from one study to the next, as well as the results to be comparable. • Develop a vision: in the scope of ITS systems policies have to be defined regarding a large number of issues. For instance there is the question if ITS users should be educated, meaning that the use of ITS systems should be the subject of the drivers license exam. How will the regulations be for the technical inspection of vehicles equipped with ITS technology? Will ITS systems be deployed on a voluntary base, or will they e.g. be obliged in every new car? Will the services be offered by private companies, by the public authorities, or by a combination of them? Which technology will be used to implement ITS systems? These are just a few of the many questions where the government will have to develop a point of view for. • Policy coordination: ITS systems are a policy subject on an international, national and regional level. It is very important that these policy organizations can collaborate in a coordinated manner. • Iterative approach to policy development: developing policies for this complex matter is not a simple task. This asks for an iterative approach, where policy decisions are continuously refined and adjusted