pinecone/core-2020-05-10-deduplication huggingface dataset:
Estimation and prediction of the vehicle's motion basedon visual odometry and Kalman filter

Proceeding of: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012The movement of the vehicle is an useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by different methods, for instance, by using a GPS or by means of the visual odometry. However, there are some situations where both methods do not work correctly. For example, there are areas in urban environments where the signal of the GPS is not available, as tunnels or streets with high buildings. On the other hand, the algorithms of computer vision are affected by outdoor environments, and the main source of difficulties is the variation in the ligthing conditions. A method to estimate and predict the movement of the vehicle based on visual odometry and Kalman filter is explained in this paper. The Kalman filter allows both filtering and prediction of vehicle motion, using the results from the visual odometry estimation.This work was also supported by Spanish Government through the CICYT projects FEDORA (Grant TRA2010-20255-C03-01), Driver Distraction Detector System (Grant TRA2011-29454-C03-02) and by CAM through the projects SEGVAUTO-II.Publicad
DSP embedded smart surveillance sensor with robust SWAD-based tracker

Smart video analytics algorithms can be embedded within surveillance sensors for fast in-camera processing. This paper presents a DSP embedded video analytics system for object and people tracking, using a PTZ camera. The tracking algorithm is based on adaptive template matching and it employs a novel Sum of Weighted Absolute Differences. The video analytics is implemented on the DSP board DM6437 EVM and it automatically controls the PTZ camera, to keep the target central to the field of view. The EVM is connected to the network and the tracking algorithm can be remotely activated, so that the PTZ enhanced with the DSP embedded video analytics becomes a smart surveillance sensor. The system runs in real-time and simulation results demonstrate that the described SWAD outperforms other template matching measures in terms of efficiency and accuracy
Aspect oriented business process modeling with precedence

Complexity is a major concern which is aimed to be overcome by people through modeling. One way of reducing complexity is separation of concerns, e.g. separation of business process from applications. One sort of concerns are cross-cutting concerns i.e. concerns which are scattered and tangled through one of several models. In business process management, examples of such concerns are security and privacy policies. To deal with these cross-cutting concerns, the aspect orientated approach was introduced in the software development area and recently also in the business process management area. The work presented in this paper elaborates on aspect oriented process modelling. It extends earlier work by defining a mechanism for capturing multiple concerns and specifying a precedence order according to which they should be handled in a process. A formal syntax of the notation is presented precisely capturing the extended concepts and mechanisms. Finally, the relevant of the approach is demonstrated through a case study
Using NMF for analyzing war logs

We investigate a semi-automated identification of technical problems occurred by armed forces weapon systems during mission of war. The proposed methodology is based on a semantic analysis of textual information in reports from soldiers (war logs). Latent semantic indexing (LSI) with non-negative matrix factorization (NMF) as technique from multivariate analysis and linear algebra is used to extract hidden semantic textual patterns from the reports. NMF factorizes the term-by-war log matrix - that consists of weighted term frequencies into two non-negative matrices. This enables natural parts-based representation of the report information and it leads to an easy evaluation by human experts because human brain also uses parts-based representation. For an improved research and technology planning, the identified technical problems are a valuable source of information. A case study extracts technical problems from military logs of the Afghanistan war. Results are compared to a manual analysis written by journalists of 'Der Spiegel'
Improved emergency management by a loosely coupled logistic system

We investigate a robust and intelligent logistic system for emergency management where existing commercial logistic systems are loosely coupled with logistic systems of emergency management organizations and armed forces. This system is used to supply the population in case of a disaster where a high impact of environmental conditions on logistics can be seen. Very important are robustness as the ability of a logistic system to remain effective under these conditions and intelligent behavior for automated ad-hoc decisions facing unforeseen events. Scenario technique, roadmapping, as well as surveys are used as qualitative methodologies to identify current weaknesses in emergency management logistics and to forecast future development of loosely coupled logistic systems. Text mining and web mining analysis as quantitative methodologies are used to improve forecasting. As a result, options are proposed for governmental organizations and companies to enable such a loosely coupled logistic system within the next 20 years
Location and Routing Models for Emergency Response Plans with Priorities

In emergency planning, consideration of emergency priorities is a necessity. This paper presents new formulations of the facility location problem (FLP) and vehicle routing problem with time windows (VRPTW) with considerations of priority. Our models ensure that higher priority locations are considered before the lower priority ones, for both facility and routing decisions. The FLP is solved using an MIP solver, while a tabu search based metaheuristic is developed for the solution of the VRPTW. Under a set of possible emergency scenarios with limited emergency resources, our models were able to serve higher priority locations better than the much utilized Maximal Coverage Location Problem (MCLP) model. We also present preliminary work and results for an integrated location-routing analysis which improves service results further.Singapore-MIT Alliance for Research and Technology (Center for Future Mobility
Real-time visualization of a sparse parametric mixture model for BTF rendering

Bidirectional Texture Functions (BTF) allow high quality visualization of real world materials exhibiting complex appearance and details that can not be faithfully represented using simpler analytical or parametric representations. Accurate representations of such materials require huge amounts of data, hindering real time rendering. BTFs compress the raw original data, constituting a compromise between visual quality and rendering time. This paper presents an implementation of a state of the art BTF representation on the GPU, allowing interactive high fidelity visualization of complex geometric models textured with multiple BTFs. Scalability with respect to the geometric complexity, amount of lights and number of BTFs is also studied.Fundação para a Ciência e Tecnologi
Software components as invariant-typed arrows

Keynote talk at CBSOFT, Natal, September 2012nvariants are constraints on software components which restrict their behavior in some desirable way, but whose maintenance entails some kind of proof obligation discharge. Such constraints may act not only over the input and output domains, as in a purely functional setting, but also over the underlying state space, as in the case of reactive components. This talk introduces an approach for reasoning about invariants which is both compositional and calculational: compositional because it is based on rules which break the complexity of such proof obligations across the structures involved; calculational because such rules are de- rived thanks to an algebra of invariants encoded in the language of binary relations. A main tool of this approach is the pointfree transform of the predicate calculus, which opens the possibility of changing the underly- ing mathematical space so as to enable agile algebraic calculation. The development of a theory of invariant preservation requires a broad, but uniform view of computational processes embodied in software components able to take into account data persistence and continued interaction. Such is the plan for this talk: we first introduce such processes as arrows, and then invariants as their types
Model-based programming environments for spreadsheets

Although spreadsheets can be seen as a flexible programming environment, they lack some of the concepts of regular programming languages, such as structured data types. This can lead the user to edit the spreadsheet in a wrong way and perhaps cause corrupt or redundant data. We devised a method for extraction of a relational model from a spreadsheet and the subsequent embedding of the model back into the spreadsheet to create a model-based spreadsheet programming environment. The extraction algorithm is specific for spreadsheets since it considers particularities such as layout and column arrangement. The extracted model is used to generate formulas and visual elements that are then embedded in the spreadsheet helping the user to edit data in a correct way. We present preliminary experimental results from applying our approach to a sample of spreadsheets from the EUSES Spreadsheet Corpus.ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-010048.  FCT grant SFRH/BPD/73358/2010
Personalizing and Improving Tag-Based Search in Folksonomies

International audienceRecently, the approaches that combine semantic web ontologies and web 2.0 technologies have constituted a significant research field. We present in this paper an original approach concerning a technology that has recognized a great popularity in these recent years, we talk about folksonomies. Our aim in this contribution is propose new technique for the Social Semantic Web technologies in order to see how we can overcome the problem of tags' ambiguity automatically in folksonomies even when we choose representing these latter with ontologies. We'll also illustrate how we can enrich any folksonomy by a set of pertinent data to improve and facilitate the resources' retrieval in these systems; all this with tackling another problem, we speak about spelling variations. Among the powerful technologies of Web 2.0, we find folksonomies, this term has recently appeared on the net to describe a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content. Ontologies which constitute the backbone of semantic web contribute significantly in solving the problems of semantics during the definition and the search of information. However even with the strong points of folksonomies and ontologies; their combination together still suffers from some problems. As examples we can cite the problem of tags' ambiguity and spelling variations (or Synonymy) in folksonomies. Our goal in this contribution is to show how we can exploit the power of social interactions between the folksonomy's members in order to extract the meaning of terms and overcome the problems of tags' ambiguity and spelling variations. Also we will try to show how we can use the principle of rules-based systems with ontologies for helping our system to enhance automatically the folksonomy by relevant facts can increase the data available within our system wit
Cell nuclei detection using globally optimal active contours with shape prior

Cell nuclei detection in fluorescent microscopic images is an important and time consuming task for a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make this a challenging task for automated detection of individual nuclei using image analysis. This paper proposes a novel and robust detection method based on the active contour framework. The method exploits prior knowledge of the nucleus shape in order to better detect individual nuclei. The method is formulated as the optimization of a convex energy function. The proposed method shows accurate detection results even for clusters of nuclei where state of the art methods fail
Modeling and Model Predictive Control of Nonlinear Hydraulic System

This paper deals with modeling and control of a hydraulic three tank system. A process of creating a computer model in MATLAB / Simulink environment is described and optimal PID and model predictive controllers are proposed. Modeling starts with creation of an initial mathematical model based on first principles approach. Further, the initial model is modified to obtain better correspondence with real-time system and parameters of the modified system are identified from measurements. The real time system contains nonlinearities which cannot be neglected and therefore are identified and included in the final mathematical model. Resulting model is used for control design. As the real-time system has long time constants, usage of Simulink model dramatically speeds up design process. Optimal PID and MPC controllers are proposed and compared. Described techniques are not limited to one particular modeling problem but can be used as an illustrative example for modeling of many technological processes
Trust in e-Voting systems : a case study

Completed research paperThe act of  voting is one of the most representative of Democracy, 
being  widely  recognized  as  a  fundamental  right  of  citizens.  The  method  of 
voting has been the subject of many studies and improvements over time. The 
introduction of electronic voting or e-voting demands the fulfillment of several 
requirements in order to maintain the security levels of the paper ballot method 
and the degree of trust people place in the voting process. The ability to meet 
those  requirements  has  been  called  into  question  by  several  authors.  This 
exploratory research aims to identify what factors influence voters’ confidence 
in e-voting systems. A case study was conducted in an organization where such 
a  system  has  been  used  in  several  elections.  A  total  of  51  e-voters  were 
interviewed.  The  factors  that  were  found  are  presented  and  discussed,  and 
proposals for future work are suggested.Fundação para a Ciência e a Tecnologia (FCT
Choosing the right medium for municipal eParticipation based on stakeholder expectations

Published version of a chapter in the book: Electronic Participation. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-33250-0_3This paper examines the expectations and communication needs of relevant stakeholder groups for municipal eParticipation in a small Norwegian municipality. We identified relevant stakeholder groups with the municipality, and asked them about their communication preferences through a combined Delphi study and survey approach. The findings show that information about local issues, information about issues relevant for the individual stakeholder, and dialogue on business’ needs and employment are the three most important communication needs. E-mail and the municipal web site are the two preferred modes of communication, with social media ranking third. For dialogue and participation, a face to face meeting is the preferred mode of communication. Our findings show that effective municipal communication requires a number of different media, depending on what is being communicated. We conclude by outlining a framework for media choice in eParticipation
Enhancing Orchestration of Lab Sessions by Means of Awareness Mechanisms

Proceedings of: 7th European Conference of Technology Enhanced Learning: 21st Century Learning for 21st Century Skills (EC-TEL 2012), Saarbrücken (Germany), 18-21 September, 2012Orchestrating learning is a quite complex task. In fact, it has been identified as one of the grand challenges in Technology Enhanced Learning (TEL) by the Stellar Network of Excellence. The objective of this article is to provide teachers and students with a tool to help them in their effort of orchestrating learning, that makes use of awareness artefacts. Using this powerful mechanism in lab sessions, we propose four different aspects of orchestration as the target for improvement: the management of the resources in the learning environment; the interventions of the teacher and provision of formative feedback; the collection of evidences for summative assessment; and the re-design of the activity, adjusting some parameters for future enactments. The proposal has been tested in a real course of Multimedia Applications with junior students (3rd course), measuring the benefits for the orchestration.This research has been partially supported by the project
“Learn3: Towards Learning of the Third Kind” (TIN2008-05163/TSI) of the Spanish
“Plan Nacional de I+D+i", the Madrid regional project “eMadrid: Investigación y
Desarrollo de tecnologías para el e-learning en la Comunidad de Madrid”
(S2009/TIC-1650) and the “EEE project” (TIN2011-28308-C03-01) of the Spanish
“Plan Nacional de I+D+i"
Motivational Social Visualizations for Personalized E-Learning

A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate resources, and how to engage them into using these resources and benefiting from them. Personalized and social learning have been suggested as potential methods for addressing these problems. Our work presented in this paper attempts to combine the ideas of personalized and social learning. We introduce Progressor + , an innovative Web-based interface that helps students find the most relevant resources in a large collection of self-assessment questions and programming examples. We also present the results of a classroom study of the Progressor +  in an undergraduate class. The data revealed the motivational impact of the personalized social guidance provided by the system in the target context. The interface encouraged students to explore more educational resources and motivated them to do some work ahead of the course schedule. The increase in diversity of explored content resulted in improving students’ problem solving success. A deeper analysis of the social guidance mechanism revealed that it is based on the leading behavior of the strong students, who discovered the most relevant resources and created trails for weaker students to follow. The study results also demonstrate that students were more engaged with the system: they spent more time in working with self-assessment questions and annotated examples, attempted more questions, and achieved higher success rates in answering them
Key Action Extraction for Learning Analytics

Proceedings of: 7th European Conference on Technology Enhanced Learning (EC-TEL 2012): 21st Century Learning for 21st Century Skills. Saarbrücken, Germany, September 18-21, 2012.Analogous to keywords describing the important and relevant content of a document we extract key actions from learners' usage data assuming that they represent important and relevant parts of their learning behaviour. These key actions enable the teachers to better understand the dynamics in their classes and the problems that occur while learning. Based on these insights, teachers can intervene directly as well as improve the quality of their learning material and learning design. We test our approach on usage data collected in a large introductory C programming course at a university and discuss the results based on the feedback of the teachers.Work partially funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 231396 (ROLE project), the Learn3 project (TIN2008-05163/TSI), the eMadrid project (S2009/TIC-1650), and the Acci´on Integrada DE2009-0051.Publicad
Adaptation “in the Wild”: Ontology-Based Personalization of Open-Corpus Learning Material

Teacher and students can use WWW as a limitless source of learning material for nearly any subject. Yet, such abundance of content comes with the problem of finding the right piece at the right time. Conventional adaptive educational systems cannot support personalized access to open-corpus learning material as they rely on manually constructed content models. This paper presents an approach to this problem that does not require intervention from a human expert. The approach has been implemented in an adaptive system that recommends students supplementary reading material and adaptively annotates it. The results of the evaluation experiment have demonstrated several significant effects of using the system on students’ learning
Energy Awareness Displays - Prototype for personalised energy consumption feedback

Börner, D., Storm, J., Kalz, M., & Specht, M. (2012). Energy Awareness Displays - Prototype for personalised energy consumption feedback. In A. Ravencroft, S. Lindstaedt, C. D. Kloos, & D. Hernández-Leo (Eds.), 21st Century Learning for 21st Century Skills - 7th European Conference on Technology Enhanced Learning (EC-TEL 2012), LNCS 7563 (pp. 471-476). September, 18-21, 2012, Saarbrücken, Germany: Springer.The paper presents the “Energy Awareness Displays” project that makes hidden energy consumption data visible and accessible for people working in office buildings. Besides raising awareness on the topic and introducing relevant conservation strategies, the main goal is to provide dynamic situated feedback when taking individual consumption actions at the workplace. Therefore a supporting infrastructure as well as two example applications to access and explore the consumption information have been implemented and evaluated. The paper presents and discusses the approach, the developed infrastructure and applications, as well as the evaluation results
Text Recognition in Videos using a Recurrent Connectionist Approach

International audienceMost OCR (Optical Character Recognition) systems developed to recognize texts embedded in multimedia documents segment the text into characters before recognizing them. In this paper, we propose a novel approach able to avoid any explicit character segmentation. Using a multi-scale scanning scheme, texts extracted from videos are first represented by sequences of learnt features. Obtained representations are then used to feed a connectionist recurrent model specifically designed to take into account dependencies between successive learnt features and to recognize texts. The proposed video OCR evaluated on a database of TV news videos achieves very high recognition rates. Experiments also demonstrate that, for our recognition task, learnt feature representations perform better than hand-crafted features
Learning Deep Belief Networks from Non-Stationary Streams

18.10.13 KB. Ok to add author version to spiral from LNCS; embargo period expired. SpringerDeep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams. © 2012 Springer-Verlag