Grading multiple choice exams with low-cost andportable computer-vision techniques

Although technology for automatic grading of multiple choice exams has existed for several decades, it is not yet as widely available or affordable as it should be. The main reasons preventing this adoption are the cost and the complexity of the setup procedures. In this paper, Eyegrade, a system for automatic grading of multiple choice exams is presented. While most current solutions are based on expensive scanners, Eyegrade offers a truly low-cost solution requiring only a regular off-the-shelf webcam. Additionally, Eyegrade performs both mark recognition as well as optical character recognition of handwritten student identification numbers, which avoids the use of bubbles in the answer sheet. When compared with similar webcam-based systems, the user interface in Eyegrade has been designed to provide a more efficient and error-free data collection procedure. The tool has been validated with a set of experiments that show the ease of use (both setup and operation), the reduction in grading time, and an increase in the reliability of the results when compared with conventional, more expensive systems.This work was partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01” and the “Emadrid: Investigación y desarrollo de tecnologias para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650).Publicad

A Distributed Collaborative System for Flexible Learning Content Production and Management

Authoring learning content is an area under pressure due to conflicting requirements. Adaptive, templatebased, highly interactive, multimedia-rich content is desired for current learning environments. At the same time, authors need a system supporting collaboration, easy re-purposing, and continuous updates with a lower adoption barrier to keep the production process simple, specially for high enrollment learning scenarios. Other areas such as software development have adopted effective methodologies to cope with a similar increase in complexity. In this paper an authoring system is presented to support a community of authors in the creation of learning content. A set of pre-defined production rules and templates are offered. Following the single source approach, authors create documents that are then automatically processed to obtain various derived resources. The toolkit allows for simple continuous updates, the re-use and re-purpose of course material, as well as the adaptation of resources to different target groups and scenarios. The toolkit has been validated by analyzing its use over a three year period in two high enrollment engineering courses. The results show effective support and simplification of the production process as well as its sustainability over time.Work partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01”, and the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650).Publicad

Lostrego: a distributed stream-based infrastructure for the real-time gathering and analysis of heterogeneous educational data

The quick technological evolution of the last decades has also reached learning environments, where the use of networked computing devices such as laptops, smartphones, tablets, IoT devices, servers, etc. is continuously growing. In particular, those computerized learning environments have the potential to track the activity of teachers and students in them, which enables the development of innovative applications that enrich the learning process by analyzing the collected data. The majority of related work in this field has been centered on batch gathering and analysis of the data. However, in order to integrate more reactive applications, there is a need for an infrastructure that enables the real-time collection and analysis of data in learning environments. Such an infrastructure should be scalable and flexible enough to cope with heterogeneous data coming from different types of learning settings. This paper presents Lostrego, a stream-based, modular, scalable and flexible distributed infrastructure that allows the gathering and analysis of educational data from heterogeneous data sources in a real-time fashion. Lostrego applications are composed by interconnected services that can be reused in different courses. The results of the evaluation of Lostrego in two editions of a computer programming course with 233 students and 384,702 gathered events are also reported.This work was partially funded by: the Spanish Competitiveness and Economy Ministry through projects “RESET UC3M: Reformulando Ecosistemas Escalables Educativos” (TIN 2014 53199 C3 1 R) and “Hermes Smartdriver. Conducción eficiente y procesamiento semántico de la información” (TIN2013 46801 C4 2 R); and by the Community of Madrid through its regional project “eMadrid” (S2013/ICE 2715)

Hashing and canonicalizing notation 3 Graphs

This paper presents a hash and a canonicalization algorithm for Notation 3 (N3) and Resource Description Framework (RDF) graphs. The hash algorithm produces, given a graph, a hash value such that the same value would be obtained from any other equivalent graph. Contrary to previous related work, it is well-suited for graphs with blank nodes, variables and subgraphs. The canonicalization algorithm outputs a canonical serialization of a given graph (i.e. a canonical representative of the set of all the graphs that are equivalent to it). Potential applications of these algorithms include, among others, checking graphs for identity, computing differences between graphs and graph synchronization. The former could be especially useful for crawlers that gather RDF/N3 data from the Web, to avoid processing several times graphs that are equivalent. Both algorithms have been evaluated on a big dataset, with more than 29 million triples and several millions of subgraphs and variables.Publicad

Social noise: generating random numbers from Twitter streams

Due to the multiple applications of random numbers in computer systems (cryptography, online gambling, computer simulation, etc.) it is important to have mechanisms to generate these numbers. True Random Number Generators (TRNGs) are commonly used for this purpose. TRNGs rely on non-deterministic sources to generate randomness. Physical processes (like noise in semiconductors, quantum phenomenon, etc.) play this role in state of the art TRNGs. In this paper, we depart from previous work and explore the possibility of defining social TRNGs using the stream of public messages of the microblogging service Twitter as randomness source. Thus, we define two TRNGs based on Twitter stream information and evaluate them using the National Institute of Standards and Technology (NIST) statistical test suite. The results of the evaluation confirm the feasibility of the proposed approach.This work has been partially funded by the Spanish Ministerio de Economa y Competitividad through the project HERMES-SMARTDRIVER (TIN2013-46801- C4-2-R)

Some notes on justified representation

Proceedings of 10th Multidisciplinary Workshop on Advances in Preference Handling (MPREF) in conjunction with IJCAI 2016, New York City, USA, July 9thMulti-winner voting systems are often applied to scenarios in which it is desirable that the set of winners represents the different opinions or preferences of the agents involved in the election. Because of that, the development of axioms that capture the idea of representation and the study of multi-winner voting rules with such axioms is of great interest. In the context of approval-based committee voting, Aziz et al. proposed in 2015 at the AAAI Conference two axioms related to the concept of repre sentation. These axioms are called justified representation (JR) and extended justified representation (EJR). In this paper we present new results related to these axioms. First of all, we close an issue that was left open by Aziz et al. regarding the maximum number of seats for which the Reweighted Approval Voting satisfies JR. Second, we discuss a problem in the definition of EJR: a set of candidates can provide perfect representation for a given election and fail to provide EJR. We propose an alternative axiom which we have called proportional justified representation (PJR). We prove that PJR remedies that problem, while providing precisely the same results as EJR for all the voting systems that Aziz et al. analyzed in their paper.This work was supported in part by the Spanish Ministerio de Economa y Competitividad (project HERMES-SMARTDRIVER TIN2013-46801-C4-2-R) and by the Autonomous Community of Madrid (project e-Madrid S2013/ICE-2715)

Ztreamy: a middleware for publishing semantic streams on the web

In order to make the semantic sensor Web a reality, middleware for efficiently publishing semantically-annotated data streams on the Web is needed. Such middleware should be designed to allow third parties to reuse and mash-up data coming from streams. These third parties should even be able to publish their own value-added streams derived from other streams and static data. In this work we present Ztreamy, a scalable middleware platform for the distribution of semantic data streams through HTTP. The platform provides an API for both publishing and consuming streams, as well as built-in filtering services based on data semantics. A key contribution of our proposal with respect to other related systems in the state of the art is its scalability. Our experiments with Ztreamy show that a single server is able, in some configurations, to publish a real-time stream to up to 40.000 simultaneous clients with delivery delays of just a few seconds, largely outperforming other systems in the state of the art.Publicad

Detection of barriers to mobility in the smart city using Twitter

We present a system that analyzes data extracted from the microbloging site Twitter to detect the occurrence of events and obstacles that can affect pedestrian mobility, with a special focus on people with impaired mobility. First, the system extracts tweets that match certain prede ned terms. Then, it obtains location information from them by using the location provided by Twitter when available, as well as searching the text of the tweet for locations. Finally, it applies natural language processing techniques to con rm that an actual event that affects mobility is reported and extract its properties (which urban element is affected and how). We also present some empirical results that validate the feasibility of our approach.This work was supported in part by the Analytics Using Sensor Data for FLATCity Project (Ministerio de Ciencia, innovación y Universidades/ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI), under Grant TIN2016-77158-C4-1-R, and in part by the European Regional Development Fund (ERDF)

Estimating the stress for drivers and passengers using deep learning

Proceedings of JARCA 2016: XVIII JARCA Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence: El Toyo, Almería (Spain), 23-29 June, 2016The number of vehicles in circulation has become a problem both for safety and for the citizens health. Public transport is a solution to reduce its impact on the environment. One of the keys to encourag e users to use it is to improve comfort. On the other hand, numerous studies highlight that drivers are more likely to suffer physical and psychological illnesses due to the sedentary nature of this work and workload . In this paper, we propose a model to p redict the stress level on drivers and passengers. The solution is based on deep learning algorithms. The proposal employs the Heart Rate Variability (HRV) and telemetry from the vehicle in order to anticipate the incoming stress . It has been validated in a real environment on distinct routes. The results show that it predict s the stress by 86 % on drivers and 92% on passengers. This algorithm could be used to develop driving assistants that recommend actions to smooth driving, reducing the work load and the passenger stress.The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte

Patterns for distributed real-time stream processing

In recent years, big data systems have become an active area of research and development. Stream processing is one of the potential application scenarios of big data systems where the goal is to process a continuous, high velocity flow of information items. High frequency trading (HFT) in stock markets or trending topic detection in Twitter are some examples of stream processing applications. In some cases (like, for instance, in HFT), these applications have end-to-end quality-of-service requirements and may benefit from the usage of real-time techniques. Taking this into account, the present article analyzes, from the point of view of real-time systems, a set of patterns that can be used when implementing a stream processing application. For each pattern, we discuss its advantages and disadvantages, as well as its impact in application performance, measured as response time, maximum input frequency and changes in utilization demands due to the pattern.This work been partially supported by Distributed Java Infrastructure for Real-Time Big Data (CAS14/00118). It has been also partially funded by eMadrid (S2013/ICE-2715), HERMES-MARTDRIVER (TIN2013-46801-C4-2-R) and AUDACity (TIN2016-77158-C4-1-R); and also by European Union's 7th Framework Program under Grant Agreement FP7-IC6-318763. We are also in debt with our anonymous reviewers that improved the quality of the article