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Online judges (OJ) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This meta-information could be extremely valuable to help users quickly find what types of problems they need most. To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures. Initially, these topics were used to support programming competitions and experienced learners. However, with OJs being increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors. In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus. After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%. We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs.

Predicting student performance as early as possible and analysing to which extent initial student behaviour could lead to failure or success is critical in introductory programming (CS1) courses, for allowing prompt intervention in a move towards alleviating their high failure rate. However, in CS1 performance prediction, there is a serious lack of studies that interpret the predictive model’s decisions. In this sense, we designed a long-term study using very fine-grained log-data of 2056 students, collected from the first two weeks of CS1 courses. We extract features that measure how students deal with deadlines, how they fix errors, how much time they spend programming, and so forth. Subsequently, we construct a predictive model that achieved cutting-edge results with area under the curve (AUC) of.89, and an average accuracy of 81.3%. To allow an effective intervention and to facilitate human-AI collaboration towards prescriptive analytics, we, for the first time, to the best of our knowledge, go a step further than the prediction itself and leverage this field by proposing an approach to explaining our predictive model decisions individually and collectively using a game-theory based framework (SHAP), (Lundberg et al., 2020) that allows interpreting our black-box non-linear model linearly. In other words, we explain the feature effects, clearly by visualising and analysing individual predictions, the overall importance of features, and identification of typical prediction paths. This method can be further applied to other emerging competitive models, as the CS1 prediction field progresses, ensuring transparency of the process for key stakeholders: administrators, teachers, and students.

The specification of quality of service (QoS) requirements in traditional networks is limited by the high administrative cost of these environments. Nevertheless, newer network paradigms, as software-defined networks (SDNs), simplify and relaxes the management of networks. In this sense, SDN can provide a simple/effective way to develop QoS provisioning. In this paper, we propose a QoS provision architecture exploiting the capabilities of SDN. Our approach allows the specification of classes of service and also negotiates the QoS requirements between applications and the SDN network controller. The SDN controller, in turn, monitors the network and adjusts its performance through resource reservation and traffic prioritization. We developed a proof-of-concept of our proposal and, our experimental results show that the additional routines present low overhead, whereas- -for a given test application- we observe a reduction of up to 47% in transfer times.

Facing the changes on the generation profile of the current energy systems, new technologies are required to follow such developments. The Lithium-ion battery energy storage systems are a choice for ensure the frequency stability of the system. Hence, this paper studying the charge and discharge behavior of these devices when connected to microgrid in order to execute the primary frequency regulation service.

The aim of this study was to compare the ratio of activation of the vastus lateralis (VL) and biceps femoris (BF) muscles between healthy (CG) and anterior cruciate ligament reconstructed (ACL-R) subjects during gait. Nineteen subjects, ten from GC and nine from ACL-R, participated in the study. VL and BF activity was captured and the VL/BF ratio was obtained by dividing each point on the curve of the vastus lateralis and biceps femoris. This ratio was compared between the two groups using an unpaired t test (a = 0.05). Differences between the two groups were found (p

Personal computers and mobile devices connected in network provide a great potential to build distributed applications which can explore a wide range of available idle resources. They are called Peer-to-Peer Applications (P2P) and play an important role in the evolution of the Internet. Whereas P2P transactions already dominate the worldwide bandwidth it mostly consists of file sharing, leaving out memory, storage and computing power. Amassing the computational resources scattered in the internet requires a compelling collaboration strategy. This paper presents a proposal of a p2p sharing resources mechanism, as well as a policy of negotiation based on barter economics. This model has the purpose of increasing the efficiency of P2P network and reducing its number of parasite members.

This paper evaluates how well a significant number of broadcasting protocols for MANETs behave when under a realistic scenario of momentary failures and topology changes, which is represented by an omission fault model. Reliable protocols which use additional mechanisms to ensure higher delivery rates beyond best- effort guarantees - have been proposed, but they exhibit unacceptably high end-to-end delays. As a result of the study conducted, a new mechanism that helps to enhance the reliability of deterministic broadcasting protocols is proposed. The mechanism allows for scalability, and is capable of ensuring good delivery rates (in spite of failures) while maintaining lower end-to-end delays. Simulation results demonstrate the efficacy of the mechanism.

The authors propose an extension to an existing structure, the gamma filter, replacing the real pole on the tap-to-tap transfer function with a pair of complex conjugate poles and a zero. The new structure is, like the gamma filter, an IIR filter with restricted feedback whose stability is trivial to check. While the gamma filter decouples the memory depth from the filter order for low-pass signals, the proposed structure decouples the memory depth and the central frequency from the filter order for band-pass signals. The learning equations of the model parameters are presented and shown to introduce an additive O(p) complexity to the backpropagation algorithm, where p is the filter order. The error surface for a linear filter is investigated in a system identification context, and the presence of local minima is confirmed. The performance of the proposed model was found to be better than that of the time-delay neural net in a nonlinear system identification context. >

Proves the equivalence between the Gamma and Laguerre filters. Applying the optimal conditions for Gamma filters, which are easy to obtain, the author arrives at the optimal conditions for Laguerre filters. Curiously these conditions are the same as those of a truncated Laguerre series approximation, which corresponds to the usage of an impulse as the input of the Laguerre filter. The author illustrates these results with an example. The author also investigates the relative merits of both structures in an adaptive filter setup. >

This paper presents a partial sub-optimal solution to the following problem: given a finite-dimensional linear subspace /spl Gscr/ for the transfer function of an analog or digital adaptive filter and a class /spl Pscr/ of power spectral densities of its input signal, find one basis of /spl Gscr/ for which the numerical condition of its correlation matrix, weighted by any power spectral density of /spl Pscr/, is as small as possible.

We present some results concerning the asymptotic distribution of the eigenvalues of a class of block-Toeplitz matrices, generated by the correlation of the internal signals of the so-called generalized orthonormal basis function model. Special cases of this family of models are the FIR model, the Laguerre model, and the "two-parameter" Kautz model. Our results are a generalization of a well-known result of Szego, and are illustrated with computational examples.

Summary form only given. The porphyrin iron complexes are important components of nature involved in various biological processes. We report on the studies of the effect of pH and ionic strength on the equilibrium of different forms of the Fe(lII) complex of mesotetrakis(4-N-methyl-pyridiniomyl) porphyrin Fe(II)TmPyP or FeTMP in aqueous solutions by linear optical absorption, the Z-scan technique and EPR. The experimental results of Z-scan and electroparamagnetic resonance (EPR) are compared with semi-empirical quantum chemical calculations (ZINDO).

The author presents theoretical results concerning a generalization of the transversal filter, called the generalized feedforward filter, where the unit delay of the tapped delay line is replaced by a general transfer function. A novel method of computing the partial derivatives of a performance function with respect to the tap-to-tap transfer function parameters in generalized feedforward filters is presented. Its application to the gamma filter results in an analytic method of computing the best value of mu (a parameter that controls the memory depth of the filter) and in an explanation of the peculiar structure of the squared error performance function. The theoretical results are illustrated by an example. >

In this paper we investigate the time delay propagation rates in a Vehicular Ad-Hoc Network, where vehicular connectivity is supported by both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) protocols. In our vision, seamless connectivity issues in a VANET with nearby network infrastructure, can be fixed by an opportunistic choice of a vehicular protocol between V2V and V2I. Such a decision is taken by each vehicle whenever it needs to transmit messages. Our technique — called as Vehicle-to-X — represents a handoff procedure between V2V and V2I, and vice versa, in order to keep vehicles connected independent of mobility issues and traffic scenarios. We investigate the time delay as a performance metric for protocol switching, and present the time propagation rates which occur when vehicles are transmitting warning messages, via V2V or V2I. Simulation results show how the simultaneous usage of pre-existing network infrastructure, together with inter-vehicular communications, provides low delays; while traditional opportunistic vehicular communications increases the transmission time delays and does not guarantee seamless connectivity to vehicles.