Engaging Undergraduate Students in Transportation Studies through Simulating Transportation for Realistic Engineering Education and Training (STREET)

The practice of transportation engineering and planning has evolved substantially over the past several decades. A new paradigm for transportation engineering education is required to better engage students and deliver knowledge. Simulation tools have been used by transportation professionals to evaluate and analyze the potential impact of design or control strategy changes. Conveying complex transportation concepts can be effectively achieved by exploring them through simulation. Simulation is particularly valuable in transportation education because most transportation policies and strategies in the real world take years to implement with a prohibitively high cost. Transportation simulation allows learners to apply different control strategies in a risk-free environment and to expose themselves to transportation engineering methodologies that are currently in practice. Despite the advantages, simulation, however, has not been widely adopted in the education of transportation engineering. Using simulation in undergraduate transportation courses is sporadic and reported efforts have been focused on the upper-level technical elective courses. A suite of web-based simulation modules was developed and incorporated in the undergraduate transportation courses at University of Minnesota. The STREET (Simulating Transportation for Realistic Engineering Education and Training) research project was recently awarded by NSF (National Science Foundation) to develop web-based simulation modules to improve instruction in transportation engineering courses and evaluate their effectiveness. Our ultimate goal is to become the epicenter for developing simulation-based teaching materials, an active textbook, which offers an interactive learning environment to undergraduate students. With the hand-on nature of simulation, we hope to improve student understanding of critical concepts in transportation engineering and student motivation toward transportation engineering, and improve student retention in the field. We also would like to disseminate the results and teaching materials to other colleges to integrate the simulation modules in their curricula.Transportation Education and Training, Transportation Simulation, Roadway Geometry Design

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

DeepSQLi: Deep Semantic Learning for Testing SQL Injection

Security is unarguably the most serious concern for Web applications, to which SQL injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi vulnerabilities is of ultimate importance, yet is unfortunately far from trivial to implement. This is because the existence of a huge, or potentially infinite, number of variants and semantic possibilities of SQL leading to SQLi attacks on various Web applications. In this paper, we propose a deep natural language processing based tool, dubbed DeepSQLi, to generate test cases for detecting SQLi vulnerabilities. Through adopting deep learning based neural language model and sequence of words prediction, DeepSQLi is equipped with the ability to learn the semantic knowledge embedded in SQLi attacks, allowing it to translate user inputs (or a test case) into a new test case, which is semantically related and potentially more sophisticated. Experiments are conducted to compare DeepSQLi with SQLmap, a state-of-the-art SQLi testing automation tool, on six real-world Web applications that are of different scales, characteristics and domains. Empirical results demonstrate the effectiveness and the remarkable superiority of DeepSQLi over SQLmap, such that more SQLi vulnerabilities can be identified by using a less number of test cases, whilst running much faster

Polish grid infrastructure for science and research

Structure, functionality, parameters and organization of the computing Grid in Poland is described, mainly from the perspective of high-energy particle physics community, currently its largest consumer and developer. It represents distributed Tier-2 in the worldwide Grid infrastructure. It also provides services and resources for data-intensive applications in other sciences.Comment: Proceeedings of IEEE Eurocon 2007, Warsaw, Poland, 9-12 Sep. 2007, p.44

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

The estimation of muscular forces is useful in several areas such as biomedical or rehabilitation engineering. As muscular forces cannot be measured in vivo non-invasively they must be estimated by using indirect measurements such as surface electromyography (sEMG) signals or by means of inverse dynamic (ID) analyses. This paper proposes an approach to estimate muscular forces based on both of them. The main idea is to tune a gain matrix so as to compute muscular forces from sEMG signals. To do so, a curve fitting process based on least-squares is carried out. The input is the sEMG signal filtered using singular spectrum analysis technique. The output corresponds to the muscular force estimated by the ID analysis of the recorded task, a dumbbell weightlifting. Once the model parameters are tuned, it is possible to obtain an estimation of muscular forces based on sEMG signal. This procedure might be used to predict muscular forces in vivo outside the space limitations of the gait analysis laboratory.Postprint (published version

Automatic differentiation in machine learning: a survey

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names "dynamic computational graphs" and "differentiable programming". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms "autodiff", "automatic differentiation", and "symbolic differentiation" as these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 339)

This bibliography lists 105 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance