Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mental Rotation Tutors: A Flexible, Computer-Based Tutoring Model for Intelligent Problem Selection

The present research focuses on the development of an intelligent, computer-based tutoring model for selecting problems in domains where multiple skills are needed to solve a problem and the reasons for errors are not easily diagnosed. In this paper we report on the development and evaluation of the Mental Rotation Tutors and the intelligent models driving the problem selection engine or “domain reasoner”. The domain reasoner evaluated each student based upon seven different core skills and chose the next problem based upon the student's level of proficiency in all seven areas. Two versions of the tutor were developed. The first versions targeted improving the student's ability to infer what combination of rotations were required to go from one view to another. The second version targeted improving the student's ability to apply a provided set of rotations to an object and report the final orientation. The results of two successive experiments demonstrated that students with low spatial ability derived the most measurable benefit from interacting with the tutors. The tutors also successfully diagnosed students' skill levels and provided problems that were appropriate to each student's current level of proficiency.
On the Benefits of Representing Music Objects as Fuzzy Sets

Over the past few years, fuzzy set theory has become a well-established mathematical theory that has been applied in many practical applications. In this paper, we elaborate on the use of fuzzy sets in the context of music information retrieval. First, we demonstrate that music objects like artists and songs can straight- forwardly be represented as fuzzy sets, and then we explain why it is beneficial to represent them in this way. In particular, we illus- trate the potential of this fuzzy approach by describing how we re- lied on it to build the popular "Multi Tag Search" demonstration on http://playground.last. fm.
Studies Regarding As(V) Adsorption from Underground Water by Fe-XAD8-DEHPA Impregnated Resin. Equilibrium Sorption and Fixed-Bed Column Tests

The characteristics of arsenic adsorption onto Fe-XAD8-DEHPA resin were studied on the laboratory scale using aqueous solutions and natural underground waters. Amberlite XAD8 resin was impregnated with di(2-ethylhexyl) phosphoric acid (DEHPA) via the dry method of impregnation. Fe(III) ions were loaded onto the impregnated resin by exploiting the high affinity of arsenic towards iron. The studies were conducted by both in contact and continuous modes. Kinetics data revealed that the removal of arsenic by Fe-XAD8-DEHPA resin is a pseudo-second-order reaction. The equilibrium data were modelled with Freundlich Langmuir and Dubinin Radushkevich (D-R) isotherms and it was found that the Freundlich model give the poorest correlation coefficient. The maximum adsorption capacity obtained from the Langmuir isotherm is 22.6 µg As(V)/g of Fe-XAD8-DEHPA resin. The mean free energy of adsorption was found in this study to be 7.2 kJ/mol and the ΔG° value negative (−9.2 kJ/mol). This indicates that the sorption process is exothermal, spontaneous and physical in nature. The studied Fe-XAD8-DEHPA resin showed excellent arsenic removal performance by sorption, both from synthetic solution and the natural water sample, and could be regenerated simply by using aqueous NaOH or HCl solutions.
Characterization of Three Venom Peptides from the Spitting Spider Scytodes thoracica

We present the solution-state NMR structures and preliminary functional characterizations of three venom peptides identified from the spitting spider Scytodes thoracica. Despite little sequence identity to other venom peptides, structural characterization reveals that these peptides contain an inhibitor cystine knot motif common to many venom peptides. These are the first structures for any peptide or protein from spiders of the Scytodidae family. Many venom peptides target neuronal ion channels or receptors. However, we have not been able to determine the target of these Scytodes peptides so we can only state with certainty the channels and receptors that they do not target.
Question–answer games

We propose strategic games wherein the strategies consist of players asking each other questions and answering those questions. We study simplifications of such games wherein two players simultaneously ask each other a question that the opponent is then obliged to answer. The motivation for our research is to model conversation including the dynamics of questions and answers, to provide new links between game theory and dynamic logics of information, and to exploit the dynamic/strategic structure that, we think, lies implicitly inside epistemic models for epistemic languages, and to make that structure an explicit subject of logical study. Our main contributions are: the notion of a two-person question-answer game with information goals, the existence and computation of equilibria for these games, the correspondence with Bayesian games and their equilibria, and a connection between logic and game theory namely the existence of equilibria for positive goal formulae.
Topology prediction and convergence for networks on mobile vehicles

Topology prediction and convergence is a new technique to improve the quality of service (QoS) in mobile wireless networks; it is an algorithm that predicts the topology of the networks depending on network surrounding environment. The predicted topology will be used to update the routing tables in each node in the network. The number of routing control packets will be reduced significantly; because routing tablepsilas updates depend on prediction not on control packets. So the throughput of the network will be improved. This technique will improve the QoS for mobile wireless network not only by reducing the number of packet overhead but it will also reduce the delay time to obtain a route form source to destination, because routes are always available. Performance assessment was conducting by simulation using MATLAP. The result showed improvement in both network throughput and delay time.
Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover

We present approximation algorithms for two problems: Stochastic Boolean Function Evaluation (SBFE) and Stochastic Submodular Set Cover (SSSC). Our results for SBFE problems are obtained by reducing them to SSSC problems through the construction of appropriate utility functions. We give a new algorithm for the SSSC problem that we call Adaptive Dual Greedy. We use this algorithm to obtain a 3-approximation algorithm solving the SBFE problem for linear threshold formulas. We also get a 3-approximation algorithm for the closely related Stochastic Min-Knapsack problem, and a 2-approximation for a natural special case of that problem. In addition, we prove a new approximation bound for a previous algorithm for the SSSC problem, Adaptive Greedy. We consider an approach to approximating SBFE problems using existing techniques, which we call the Q-value approach. This approach easily yields a new result for evaluation of CDNF formulas, and we apply variants of it to simultaneous evaluation problems and a ranking problem. However, we show that the Q-value approach provably cannot be used to obtain a sublinear approximation factor for the SBFE problem for linear threshold formulas or read-once DNF.
A Proposal for Weak-Memory Local Reasoning

Program logics are formal systems for specifying and reasoning about software programs. Most program logics make the strong assumption that all threads agree on the value of shared memory at all times. This assumption can be unsound though for programs with races, like many concurrent data structures. Verification of these difficult programs must take into account the weaker models of memory provided by the architectures on which they execute. In this paper, we describe progress toward a program logic for local reasoning about racy concurrent programs executing on a weak, x86-like memory model.
Consensus under communication delays

This paper deals with the consensus problem under communication network inducing delays. It is well-known that introducing a delay leads in general to a reduction of the performance or to instability due to the fact that time-delay systems are infinite dimensional. For instance, the set of initial conditions of a time-delay system is not a vector but a function taken in an interval. Therefore, investigating the effect of time-delays in the consensus problem is an important issue. In the present paper, we assume that each agent receives instantaneously its own state information but receives the state information from its neighbors after a constant delay. Two stability criteria are provided based on the frequency approach and on Lyapunov-Krasovskii techniques given in terms of LMI. An analytic expression of the consensus equilibrium which depends on the delay and on the initial conditions taken in an interval is derived. The efficiency of the method is tested for different network communication schemes.
Comparison of two approaches to approximated reasoning

A comparison is made of two approaches to approximate reasoning: Mamdani's interpolation method and the implication method. Both approaches are variants of Zadeh's compositional rule of inference. It is shown that the approaches are not equivalent. A correspondence between the approaches is established via the inverse of the implied fuzzy relation. The interpolation method has the lowest time-complexity, provided the minimum operator is chosen as t-norm. Otherwise, the time-complexity of both methods is the same. It is more efficient to first compile a set of fuzzy rules into a fuzzy relation, instead of aggregating inference results for each fuzzy rule separately.
Applied template metaprogramming in siunits: the library of unit-based computation

While scientific programmers typically make heavy use of a programming language’s native numeric data types, such practice has been a common source of errors: it obscures the diverse intentions (e.g., distances, masses, energies, momenta, etc.) that any such purely numeric value could represent. Limitations in programming language expressiveness and compiler technology have historically made it difficult to address commensuration in a programming context. However, SIUNITS provides, in C++, a convenient means of computing with numeric values that have attached units. SIUNITS applies compile-time type checking, thus avoiding run-time overhead. Via heavy use of template metaprogramming, SIUNITS provides data types corresponding to all base and derived dimensions specified by le Système international d’Unités (SI), the recognized international standard for describing measurable quantities and their units. Thus, for example, an object resulting from the product of two LENGTH objects is automatically of type AREA. As an important additional feature, SIUNITS permits arbitrary combinations of several provided measurement views, and even allows knowledgeable users to construct their own views.
Source Retrieval for Plagiarism Detection from Large Web Corpora: Recent Approaches

This paper overviews the five source retrieval approaches that have been submitted to the seventh international competition on plagiarism detection at PAN 2015. We compare the performances of these five approaches to the 14 methods submitted in the two previous years (eight from PAN 2013 and six from PAN 2014). For the third year in a row, we invited software submissions instead of run submissions, such that cross-year evaluations are possible. This year’s stand-alone source retrieval overview can thus to some extent also be used as a reference to the different ideas presented in the last three years—the text alignment subtask will be depicted in another individual overview. Linda Cappellato and Nicola Ferro and Gareth Jones and Eric San Juan (eds.): CLEF 2015 Labs and Workshops, Notebook Papers, 8-11 September, Toulouse, France. CEUR Workshop Proceedings. ISSN 1613-0073, http://ceur-ws.org/Vol-1391/, 2015.
Mitigating phishing by a new ID-based chameleon hash without key exposure

Chameleon signatures were introduced by Krawczyk and Ra- bin to provide a non-transferable signature scheme. However, the non- transferability property requires the willingness of the recipient in conse- quentially exposing a secret key and therefore invalidating all signatures issued to the same recipient's public key. This notion has been extended by Chen et al. to allow a "key-exposure" freeness scheme. However, it was concluded that to achieve this key-exposure freeness, one would re- quire a technique called "identity customization". Therefore, the notion of identity-based chameleon hash function becomes redundant since the identity is always needed in the construction of chameleon hash func- tions themselves. In this paper, in contrast to the previous construction, we construct an identity-based chameleon hash without key exposure without requiring any identity customization. More importantly, using the framework proposed by Susilo and Mu, we extended our scheme to mitigate phishing. Furthermore, our scheme can be easily extended to multi-party scenario, where a phishing scenario can be mitigate in a mailing list scenario, which is more practical.
XML MINING : FROM TREES TO STRINGS ?

XML is becoming this few years the standard of data exchange in the Web and a new data description language. Consequently, in a Data Mining context, optimizing storage and access time to XML documents is becoming a new challenge. Indeed, for mining XML documents we have to parse them in order to obtain a tree data structure in RAM memory. This tree structure is more flexible and have a beter time access and navigation than the textual format. Moreover, this tree representation of XML documents presents more semantic-richness than the textual one. Thus, for an eficient XML mining task, the XML mapping stage will conserve the semantic of data (hierarchie of concepts wthin the XML document) and generate a compact data structure. In this paper we present an overview of XML mapping propositions and we discuss advantages and drawbacks for an efficient mining. Then, we note relevent concepts to consider in the XML mapping for an efficient mining.
The Equivalent Differentiated Services Model

It is now quite agreed that the IP layer should provide more sophisticated services than the simple best-effort service to meet the application's quality of service requirements. Different proposals for improving the IP stack have been proposed but their deployment on experimental network shows different scaling problems. In this paper we present a new differentiated service scheme called ``Equivalent Differentiated Services''. The main features of the EDS scheme is that it allows to differentiate an arbitrary number of traffic classes without giving an absolute better service to any. EDS is simple, robust and scalable and provides no absolute guarantees in delay or loss rate to individual flows or aggregates. EDS acts as a network layer protocol and needs some adaptation done by an end-to-end transport layer. We present the EDS model, then we examine the implementation, simulation and validation issues.
A Novel NoC Architecture Framework for 3 D Chip MPSoC Implementations

This paper presents a framework for high-level exploration and RTL design of an optimized Network-on-Chip (NoC) architecture for 3D chips. The RTL is derived from the high-level exploration methodology in a semi-automated way. FPGA implementation figures are given for various implementation parameters of the Network Interface Element, demonstrating the performance/area trade-off of 3D NoC architectures. Additionally, power consumption measurements for 2D and 3D Network Interface Elements are provided for FPGA prototype implementation. Keywords-3D Chips, NoC, FPGA
Prognostic scores in a gastroenterology intensive care unit.

BACKGROUND Several prognostic systems have been developed and validated in general Intensive Care Units (ICUs). No assessment of these scores was performed in specialized Gastroenterology Intensive Care Units (GICUs). AIM To assess the prognostic accuracy of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II and Sequential Organ Failure Assessment (SOFA) scores systems to predict mortality in a GICU. METHODS Retrospective study of 300 consecutively admissions in a GICU. Demographics, indication for admission, APACHE II, SAPS II and SOFA scores and survival at GICU discharge were recorded. Discrimination was evaluated using receiver operations characteristic (ROC) curves and area under a ROC curve (AUC). Calibration was estimated using the Hosmer-Lemeshow goodness-of-fit test. RESULTS Overall GICU mortality was 5.3%. APACHE II, SAPS II and SOFA mean scores of nonsurvivors (21.9, 46.2 and 9.3, respectively) were found to be significantly higher than those of survivors (11.9, 26.7 and 2.2, respectively) (p < 0.001). Discrimination was excellent for all the prognostic systems, with AUC = 0.900, 0.903 and 0.965 for APACHE II, SAPS II and SOFA, respectively. Similarly, APACHE II, SAPS II and SOFA scores achieved good calibration, with p = 0.671, 0.928 and 0.775, respectively. Among the three scores, SOFA showed the best performance, with overall correctness of prediction of 94.0%, while it was 86.2% for APACHE II and 82.7% for SAPS II. CONCLUSIONS in GICU, APACHE II, SAPS II and SOFA scores have excellent prognostic accuracy and, among the three scores, SOFA has the greatest overall correctness of prediction.
Volatility forecasting and liquidity: Evidence from individual stocks

We evaluate the accuracy of four historical volatility measures and option-implied volatility in predicting future volatility using 10-year daily data for 92 stocks. In addition, we examine the effect of liquidity (proxied by option volume and stock market capitalization) on forecast accuracy. Our results suggest that forecast accuracy using implied volatility compared to historical measures is in general inferior, but for larger firms and higher option trading volume firms implied volatility forecasts are more accurate than historically based forecasts. Among the four historical measures, Parkinson's extreme value method and adjusted mean absolute difference perform best.
Poster Abstract : MIRRORS — An Integrated Framework for Capturing Real World Behaviour for Models of Ad Hoc Networks

The simulation models used in mobile ad hoc network research have been criticised for lack of realism. While credited with ease of understanding and implementation, they are often based on theoretical models, rather than real world observations. Criticisms have centred on radio propagation or mobility models. In this work, we take an integrated approach to modelling the real world that underlies a mobile ad hoc network. The aim is to deepen understanding of these networks. While pointing out the correlations between the space, radio propagation and mobility models, we use mobility as a focal point to propose a new framework, MIRRORS: Mobility Integration of Radio Requirements of Real-world Simulations, that aims to capture real world behaviour across different views and scenarios in the real world. We give the formulation of a specific model within the framework and present simulation results that reflect topology properties of the networks synthesised. Compared with the existing models studied, our model presents a wider spectrum of variation in the metrics examined, due to the model encapsulating more detailed dynamics. While the common approach is to focus on performance evaluation of existing protocols using these models, we discuss protocol design opportunities across layers in view of the simulation results.
Network QoS for Grid Systems

Grid users may wish to have fine-grained control of quality of service (QoS) guarantees in a network in order to allow timely data transfer in a distributed application environment. We present a discussion of the issues and problems involved, with some critical analysis. We propose possible solutions by making reference to and analysing existing work. Also, we describe the mechanisms being proposed as part of a work-in-progress (being conducted by the authors) that uses a peer-to-peer approach to micro-manage network capacity allocations at the edge of the network, at end-sites, in a multi-domain scenario. Scheduling controllers at the end-sites are employed, which are subject to local administrative controls and have flexibility in resource allocation based on user requests for network capacity. We highlight the issues in scaling such systems to large numbers of users and the issues concerning the interfaces available to applications and end-users for accessing such services.
An efficient Computer Aided Decision Support System for breast cancer diagnosis using Echo State Network classifier

The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested system produces high classification accuracy of 98% as well as high sensitivity and specificity rates. We compared the performance of ESN with Support Vector Machine (SVM) and other classifiers and results generated indicate that ESN can compete with benchmark classifier and in some cases beat them. The high rate of Sensitivity and Specificity also signifies the power of ESN classifier to detect positive and negative case correctly.
FOUR-DIMENSIONAL SAR IMAGING SCHEME BASED ON COMPRESSIVE SENSING

The observation data obtained from 4-D synthetic aperture radar system is sparse and non-uniform in the baseline-time plane. Hence, the imaging results acquired by traditional Fourier- based methods are limited by high sidelobes. Considering the sparse structure of actual target space in high frequency radar application, a novel 4-D imaging scheme based on compressive sensing is proposed in this paper. Firstly, the azimuth-slant range image is acquired by traditional pulse compression. Then, the basis matrix and the measurement matrix are constructed based on the sparse distribution of the radar positions and the signal form after the azimuth-slant range compression. Moreover, a weighted matrix related to the supporting fleld of the target is introduced to the cost function. Finally, the elevation-velocity image is reconstructed with this new cost function. Simulation results conflrm the efiectiveness of the proposed method.
A Distributed Algorithm for Large-Scale Generalized Matching

Generalized matching problems arise in a number of applications, including computational advertising, recommender systems, and trade markets. Consider, for example, the problem of recommending multimedia items (e.g., DVDs) to users such that (1) users are recommended items that they are likely to be interested in, (2) every user gets neither too few nor too many recommendations, and (3) only items available in stock are recommended to users. State-of-the-art matching algorithms fail at coping with large real-world instances, which may involve millions of users and items. We propose the first distributed algorithm for computing near-optimal solutions to large-scale generalized matching problems like the one above. Our algorithm is designed to run on a small cluster of commodity nodes (or in a MapReduce environment), has strong approximation guarantees, and requires only a poly-logarithmic number of passes over the input. In particular, we propose a novel distributed algorithm to approximately solve mixed packing-covering linear programs, which include but are not limited to generalized matching problems. Experiments on real-world and synthetic data suggest that a practical variant of our algorithm scales to very large problem sizes and can be orders of magnitude faster than alternative approaches.