pinecone/core-2020-05-10-deduplication huggingface dataset:
Automatic Discovery of Complementary Learning Resources

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.Students in a learning experience can be seen as a community working simultaneously (and in some cases collaboratively) in a set of activities. During these working sessions, students carry out numerous actions that affect their learning. But those actions happening outside a class or the Learning Management System cannot be easily observed. This paper presents a technique to widen the observability of these actions. The set of documents browsed by the students in a course was recorded during a period of eight weeks. These documents are then processed and the set with highest similarity with the course notes are selected and recommended back to all the students. The main problem is that this user community visits thousands of documents and only a small percent of them are suitable for recommendation. Using a combination of lexican analysis and information retrieval techniques, a fully automatic procedure to analyze these documents, classify them and select the most relevant ones is presented. The approach has been validated with an empirical study in an undergraduate engineering course with more than one hundred students. The recommended resources were rated as "relevant to the course" by the seven instructors with teaching duties in the course.Work partially funded by the Learn3 project, “Plan Nacional de I+D+I TIN2008-05163/TSI”, the Acción Integrada Ref. DE2009-0051, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650) and TELMA Project (Plan Avanza TSI-020110-2009-85)
Usage Pattern Recognition in Student Activities

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.This paper presents an approach of collecting contextualized attention metadata combined from inside as well as outside a LMS and analyzing them to create feedback about the student activities for the teaching staff. Two types of analyses were run on the collected data: first, key actions were extracted to identify usage patterns and tendencies throughout the whole course and then usage statistics and patterns were identified for some key actions in more detail. Results of both analyses were visualized and presented to the teaching staff for evaluation.The research leading to these results has received funding
from the European Community’s Seventh Framework Programme (FP7/2007-
2013) under grant agreement no 231396 (ROLE project). Work was also partially
funded by the Learn3 project (TIN2008-05163/TSI), the eMadrid project
(S2009/TIC-1650), and the Acción Integrada DE2009-0051
Orchestration and Feedback in Lab Sessions: Improvements in Quick Feedback Provision

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011The benefits of formative assessment and the provision of quick feedback to the learners are unquestionable. A lot of effort needs however to be devoted by the teacher in order to assess the learner's status and providing quality and valuable feedback when actually implementing these techniques in real-world situations during active learning activities. The aim of this work is to offer teachers a tool that facilitates the quick provision of relevant feedback to the students, implemented as backchannel notifications in a lab session for ensuring scalability. Although being a work-in-progress, the aforementioned tool has been partially evaluated in a real course on programming multimedia applications.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" and the Madrid regional project “eMadrid: Investigación y Desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” (S2009/TIC-1650)
Orchestrating Learning Activities in 3D Virtual Worlds: IMS-LD in Open Wonderland

Proceedings of: 6th European Conference on Technology Enhanced Learning Towards Ubiquitous Learning (EC-TEL 2011). Palermo, Italy, 20-23 September, 2011.Immersive environments such as virtual worlds and virtual reality platforms are being increasingly used for educational purposes. Possibilities of these environments are huge, but also are the technical challenges that have to be overcome in order for these platforms to become more usable. One of the main problems that educators have to face when designing an educational experience for virtual worlds has to do with the orchestration of the learning sequence. In this paper, we present a novel approach to support the deployment and execution of Units of Learning (UoLs), described with an Educational Modeling Language such as IMS-LD [1], into a virtual world, with the aim to facilitate the creation of well defined learning sequences for these platforms.This research has been partially supported by the following projects: The Spanish CDTI project “España Virtual” funded by the Ingenio 2010 programme, subcontracted by Deimos Space; The Spanish project “Learn3: Towards Learning of the Third Kind” (TIN2008-05163/TSI) funded by the Spanish “Plan Nacional de I+D+i” of the Ministry of Research and Innovation; The project “eMadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” (S2009/TIC-1650) funded by the Government of the Region of Madrid.Publicad
Towards the Prediction of User Actions on Exercises with Hints Based on Survey Results

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.The actions a user performs on exercises depending on the different hinting techniques applied, can be used to adapt future exercises. In this paper, we propose a survey for users in order to know their different actions depending on different conditions. The analysis of preliminary results for some questions of the model shows that there is a correlation between some survey questions and the real student actions, but there is a case in which there is not such correlation. For the cases where that correlation exists, this correlation leads to think that some prediction of users actions based on survey results is possible.Work partially funded by the Learn3 project TIN2008-05163/TSI
within the Spanish “Plan Nacional de I+D+I”, and the Madrid regional community
project eMadrid S2009/TIC-1650
QuizMap: Open social student modeling and adaptive navigation support with TreeMaps

In this paper, we present a novel approach to integrate social adaptive navigation support for self-assessment questions with an open student model using QuizMap, a TreeMap-based interface. By exposing student model in contrast to student peers and the whole class, QuizMap attempts to provide social guidance and increase student performance. The paper explains the nature of the QuizMap approach and its implementation in the context of self-assessment questions for Java programming. It also presents the design of a semester-long classroom study that we ran to evaluate QuizMap and reports the evaluation results. © 2011 Springer-Verlag Berlin Heidelberg
On the Semantics of Bipolarity and Fuzziness

This paper analyzes the relationship between fuzziness and bipolarity, notions which were devised to address different kinds of uncertainty: linguistic imprecision, in the former, and knowledge relevance and character or polarity, in the latter. Although different types of fuzziness and bipolarity have been defined, these relations are not always clear. This paper proposes the use of four-valued extensions to provide a formal method to rigorously define and compare the semantics and logical structure of diverse combinations of fuzziness and bipolarity types. As a result, this paper claims that these notions and their different types are independent and not semantically equivalent despite its possible formal equivalence
Learning valued relations from data

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are in many real-world applications often expressed in a graded manner. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and valued relations are considered, and it unifies existing approaches because different types of valued relations can be modeled, including symmetric and reciprocal relations. This framework establishes in this way important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated on a case study in document retrieval
Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons

Species-environment relationships are used for evaluating the current status of target species and the potential impact of natural or anthropogenic changes of their habitat. Recent researches reported that the results are strongly affected by the quality of a data set used. The present study attempted to apply pairwise comparisons to modelling fish habitat preference with Takagi-Sugeno-type fuzzy habitat preference models (FHPMs) optimized by a genetic algorithm (GA). The model was compared with the result obtained from the FHPM optimized based on mean squared error (MSE). Three independent data sets were used for training and testing of these models. The FHPMs based on pairwise comparison produced variable habitat preference curves from 20 different initial conditions in the GA. This could be partially ascribed to the optimization process and the regulations assigned. This case study demonstrates applicability and limitations of pairwise comparison-based optimization in an FHPM. Future research should focus on a more flexible learning process to make a good use of the advantages of pairwise comparisons
Retrieval of 3D polygonal objects based on multiresolution signatures

In this paper we present a method for retrieving 3D polygonal objects by using two sets of multiresolution signatures. Both sets are based on the progressive elimination of object's details by iterative processing of the 3D meshes. The first set, with five parameters, is based on mesh smoothing. This mainly affects an object's surface. The second set, with three parameters, is based on difference volumes after successive mesh erosions and dilations. Characteristic feature vectors are constructed by combining the features at three mesh resolutions of each object. In addition to being invariant to mesh resolution, the feature vectors are invariant to translation, rotation and size of the objects. The method was tested on a set of 40 complex objects with mesh resolutions different from those used in constructing the feature vectors. By using all eight features, the average ranking rate obtained was 1.075: 37 objects were ranked first and only 3 objects were ranked second. Additional tests were carried out to determine the significance of individual features and all combinations. The same ranking rate of 1.075 can be obtained by using some combinations of only three features. © 2011 Springer-Verlag
Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation

A key question in machine perception is how to adaptively build upon existing capabilities so as to permit novel functionalities. Implicit in this are the notions of anomaly detection and learning transfer. A perceptual system must firstly determine at what point the existing learned model ceases to apply, and secondly, what aspects of the existing model can be brought to bear on the newlydefined learning domain. Anomalies must thus be distinguished from mere outliers, i.e. cases in which the learned model has failed to produce a clear response; it is also necessary to distinguish novel (but meaningful) input from misclassification error within the existing models.We thus apply a methodology of anomaly detection based on comparing the outputs of strong and weak classifiers [8] to the problem of detecting the rule-incongruence involved in the transition from singles to doubles tennis videos. We then demonstrate how the detected anomalies can be used to transfer learning from one (initially known) rule-governed structure to another. Our ultimate aim, building on existing annotation technology, is to construct an adaptive system for court-based sport video annotation
Lectures on LHC Physics

With the discovery of the Higgs boson the LHC experiments have closed the
most important gap in our understanding of fundamental interactions. We now
know that the interactions between elementary particles can be described by
quantum field theory, more specifically by a renormalizable gauge theory. This
theory is valid to arbitrarily high energy scales and do not require an
ultraviolet completion. In these notes I cover three aspects to help understand
LHC results in the Higgs sector and in searches for physics beyond the Standard
Model: many facets of Higgs physics, QCD as it is relevant for LHC
measurements, and standard phenomenological background knowledge. The lectures
should put young graduate students into a position to really follow advanced
writeups and first research papers. In that sense they can serve as a starting
point for a research project in LHC physics. With this new, significantly
expanded version I am confident that also some more senior colleagues will find
them useful and interesting.Comment: Lecture notes, comments very welcome! Published as Lecture Notes in
  Physics, Vol 844, Springer 2012, ISBN 978-3-642-24039-3; v6: signifcantly
  improved and expanded version, in particular in the Higgs secto
Face analysis using curve edge maps

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking
Leakage-resilient coin tossing

The ability to collectively toss a common coin among n parties
in the presence of faults is an important primitive in the arsenal of
randomized distributed protocols. In the case of dishonest majority, it
was shown to be impossible to achieve less than 1
r bias in O(r) rounds
(Cleve STOC ’86). In the case of honest majority, in contrast, unconditionally
secure O(1)-round protocols for generating common unbiased
coins follow from general completeness theorems on multi-party secure
protocols in the secure channels model (e.g., BGW, CCD STOC ’88).
However, in the O(1)-round protocols with honest majority, parties
generate and hold secret values which are assumed to be perfectly hidden
from malicious parties: an assumption which is crucial to proving the
resulting common coin is unbiased. This assumption unfortunately does
not seem to hold in practice, as attackers can launch side-channel attacks
on the local state of honest parties and leak information on their secrets.
In this work, we present an O(1)-round protocol for collectively generating
an unbiased common coin, in the presence of leakage on the local
state of the honest parties. We tolerate t ≤ ( 1
3
−  )n computationallyunbounded
Byzantine faults and in addition a Ω(1)-fraction leakage on
each (honest) party’s secret state. Our results hold in the memory leakage
model (of Akavia, Goldwasser, Vaikuntanathan ’08) adapted to the
distributed setting.
Additional contributions of our work are the tools we introduce to
achieve the collective coin toss: a procedure for disjoint committee election,
and leakage-resilient verifiable secret sharing.Proceedings 25th International Symposium, DISC 2011, Rome, Italy, September 20-22, 2011.National Defense Science and Engineering Graduate FellowshipNational Science Foundation (U.S.) (CCF-1018064
Leaf segmentation and tracking using probabilistic parametric active contours

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset
Towards Joint Tardos Decoding: The 'Don~Quixote' Algorithm

International audience'Don Quixote' is a new accusation process for Tardos traitor tracing codes which is, as far as we know, the first practical implementation of joint decoding. The first key idea is to iteratively prune the list of potential colluders to keep the computational effort tractable while going from single, to pair,\ldots to $t$-subset joint decoding. At the same time, we include users accused in previous iterations as side-information to build a more discriminative test. The second idea, coming from the field of mismatched decoders and compound channels, is to use a linear decoder based on the worst case perceived collusion channel. The decoder is tested under two accusation policies: to catch one colluders and to catch as many colluders as possible. The probability of false positive is controlled thanks to a rare event estimator. We describe a fast implementation supporting millions of users and compare our results with two recent fingerprinting codes
Solving the Resource Allocation Problem in a Multimodal Container Terminal as a Network Flow Problem

International audienceContinuously increasing global container trade and pressure from a limited number of large shipping companies are enforcing the need for efficient container terminals. By using internal material handling resources efficiently, transfer times and operating costs are reduced. We focus our study on container terminals using straddle carriers for transportation and storage operations. We assume that straddle carriers are shared among maritime and inland transport modes (truck, train, barge). The problem is thus to decide how many resources to allocate to each transport mode in order to minimize vehicle (vessel, truck, train, barge) delays. We present a mixed integer linear programming model, based on a network flow representation, to solve this allocation problem. The modular structure of the model enables us to represent different container terminals, transport modes and service strategies. We present parts of our model and exemplary applications for a terminal at the Grand Port Maritime de Marseille in France
A notion of event quality for contextualized planning and decision support systems

This paper presents an approach for contextualizing an event-based decision support system for scheduling patient assessments in a hospital. To cope with unexpected delays, patient coordinators often pursue a worst case scenario when
scheduling patient assessments, leading to an underutilization of human resources and equipment when the procedure went without complications. We present a context-based decision support system for patient planning that helps the patient coordinator with taking well-informed rescheduling decisions and anticipating changes in other patients' schedules. The system uses information and events produced by medical equipment. As these events can be non-deterministic, we demonstrate how our domain specific context model can be used to contextualize events to enhance their quality and ascertain their meaning.status: publishe
Robust Specification of Real Time Components

International audienceSpecification theories for real-time systems allow to reason about in-terfaces and their implementation models, using a set of operators that includes satisfaction, refinement, logical and parallel composition. To make such theories applicable throughout the entire design process from an abstract specification to an implementation, we need to be able to reason about possibility to effectively implement the theoretical specifications on physical systems. In the literature, this implementation problem has already been linked to the robustness problem for Timed Automata, where small perturbations in the timings of the models are intro-duced. We address the problem of robust implementations in timed specification theories. Our contributions include the analysis of robust timed games and the study of robustness with respect to the operators of the theory
Secure two-party computation over a Z-Channel

In secure two-party computation, two mutually distrusting parties are interested in jointly computing a function, while preserving the privacy of their respective inputs. However, when communicating over a clear channel, security against computationally unbounded adversaries is impossible. Thus is the importance of noisy channels, over which we can build Oblivious Transfer (OT), a fundamental primitive in cryptography and the basic building block for any secure multi-party computation. The noisy channels commonly used in current constructions are mostly derived from the Binary Symmetric Channel (BSC), which is modified to extend the capabilities of an attacker. Still, these constructions are based on very strong assumptions, in particular on the error probability, which makes them hard to implement. In this paper, we provide a protocol achieving oblivious transfer over a Z-channel, a natural channel model in various contexts, ranging from optical to covert communication. The protocol proves to be particularly efficient for a large range of error probabilities p (e.g., for 0.17 ≤ p ≤ 0.29 when a security parameter ε = 10− 9 is chosen), where it requires a limited amount of data to be sent through the channel. Our construction also proves to offer security against unfair adversaries, who are able to select the channel probability within a fixed range. We provide coding schemes that can further increase the efficiency of the protocol for probabilities distant from the range mentioned above, and also allow the use of a Z-channel with an error probability greater than 0.5. The flexibility and the efficiency of the construction make an actual implementation of oblivious transfer a more realistic prospect