Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

Bayesian networks for enhancement of requirements engineering: a literature review

Requirements analysis is the software engineering stage that is closest to the users’ world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.     

Stability of Local On‐Ramp Metering Control Laws

A framework for stability analysis of local on‐ramp metering control strategies based on the cell transmission model is presented. Within this framework, it is possible to formulate Lyapunov and input‐state stability results for on‐ramp metering control strategies in an open section of highway with on‐ramps. Using this analysis, recommendations for the design of on‐ramp metering control laws set points are derived. Two examples on the use of such analysis are presented. One deals with the stability analysis of a local on‐ramp metering control law and the other with the design of a disturbance observer that, used in combination with the local on‐ramp metering control law, provides a more robust response to traffic regulation. Simulation results are included that confirm the possibility of using this framework to test the impact of local on‐ramp metering control strategies.     

On location observability notions for switching systems

The focus of this paper is on the analysis of initial discrete state distinguishability notions for switching systems, in a discrete time setting. Moreover, the relationship between initial discrete state distinguishability and the problem of reconstructing the current discrete state is addressed.     

Selfish Bin Packing

Following recent interest in the study of computer science problems in a game theoretic setting, we consider the well known bin packing problem where the items are controlled by selfish agents. Each agent is charged with a cost according to the fraction of the used bin space its item requires. That is, the cost of the bin is split among the agents, proportionally to their sizes. Thus, the selfish agents prefer their items to be packed in a bin that is as full as possible. The social goal is to minimize the number of the bins used. The social cost in this case is therefore the number of bins used in the packing.     

LPS: A Language Prototyping System Using Modular Monadic Semantics

This paper describes LPS, a Language Prototyping System that facilitates the modular development of interpreters from semantic building blocks. The system is based on the integration of ideas from Modular Monadic Semantics and Generic Programming.To define a new programming language, the abstract syntax is described as the fixpoint of non-recursive pattern functors. For each functor an algebra is defined whose carrier is the computational monad obtained from the application of several monad transformers to a base monad. The interpreter is automatically generated by a catamorphism or, in some special cases, a monadic catamorphism.The system has been implemented as a domain-specific language embedded in Haskell and we have also implemented an interactive framework for language testing.     

Robot arm reaching through neural inversions and reinforcement learning

We present a neural method that computes the inverse kinematics of any kind of robot manipulators, both redundant and non-redundant. Inverse kinematics solutions are obtained through the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics. The inversion provides difference vectors in the joint space from difference vectors in the workspace. Our differential inverse kinematics (DIV) approach can be viewed as a neural network implementation of the Jacobian transpose method for arm kinematic control that does not require previous knowledge of the arm forward kinematics. Redundancy can be exploited to obtain a special inverse kinematic solution that meets a particular constraint (e.g. joint limit avoidance) by inverting an additional neural network The usefulness of our DIV approach is further illustrated with sensor-based multilink manipulators that learn collision-free reaching motions in unknown environments. For this task, the neural controller has two modules: a reinforcement-based action generator (AG) and a DIV module that computes goal vectors in the joint space. The actions given by the AG are interpreted with regard to those goal vectors.     

Utilizing national public-key infrastructure in mobile payment systems

Payments are the locomotive behind any business domain. It has been predicted that mobile payments will become one of the most successful mobile services, and the security of payments is an important requirement. However, it is difficult to strongly authenticate mobile users remotely and provide an adequate level of non-repudiation of transactions. In this article, we argue that a nationwide public-key infrastructure supported by governmental bodies can be used in a mobile payment system. Not only does it provide strong security, but it also makes the system open to any mobile user, merchant, or financial service provider. Two payment protocols are described: one for virtual point-of-sale payments, and one for vending machine payments. We argue that such a system can be implemented using open development platforms, and its performance is adequate for enabling swift transactions. A prototype of a system which accepts virtual point-of-sale payments is implemented, and its performance and usability are evaluated.     

Influence of temperature on the liver circadian clock in the ruin lizard Podarcis sicula

Reptiles represent an interesting animal model to investigate the influence of temperature on molecular circadian clocks. The ruin lizard Podarcis sicula lives in a continental climate and it is subjected to wide range of environmental temperatures during the course of the year. As consequence, ruin lizard daily activity pattern includes either the hibernation or periods of inactivity determined by hypothermia. Here we showed the rhythmic expression of two clock genes, lPer2 and lClock, in the liver of active lizards exposed to summer photo-thermoperiodic conditions. Interestingly, the exposition of lizards to hypothermic conditions, typical of winter season, induced a strong dampening of clock genes mRNA rhythmicity with a coincident decrease of levels. We also examined the qualitative and quantitative distribution of lPER2 and lCLOCK protein in different cellular compartments during the 24-h cycle. In the liver of active lizards both proteins showed a rhythmic expression profile in all cellular compartments. After 3 days at 6 degrees C, some temporal fluctuations of the lCLOCK and lPER2 are still detectable, although, with some marked modifications in respect to the values detected in the liver of active lizards. Besides demonstrating the influence of low temperature on the lizard liver circadian oscillators, present results could provide new essential information for comparative studies on the influence of temperature on the circadian system across vertebrate classes.