On the Feasibility of Transfer-learning Code Smells using Deep Learning

Context: A substantial amount of work has been done to detect smells in source code using metrics-based and heuristics-based methods. Machine learning methods have been recently applied to detect source code smells; however, the current practices are considered far from mature. Objective: First, explore the feasibility of applying deep learning models to detect smells without extensive feature engineering, just by feeding the source code in tokenized form. Second, investigate the possibility of applying transfer-learning in the context of deep learning models for smell detection. Method: We use existing metric-based state-of-the-art methods for detecting three implementation smells and one design smell in C# code. Using these results as the annotated gold standard, we train smell detection models on three different deep learning architectures. These architectures use Convolution Neural Networks (CNNs) of one or two dimensions, or Recurrent Neural Networks (RNNs) as their principal hidden layers. For the first objective of our study, we perform training and evaluation on C# samples, whereas for the second objective, we train the models from C# code and evaluate the models over Java code samples. We perform the experiments with various combinations of hyper-parameters for each model. Results: We find it feasible to detect smells using deep learning methods. Our comparative experiments find that there is no clearly superior method between CNN-1D and CNN-2D. We also observe that performance of the deep learning models is smell-specific. Our transfer-learning experiments show that transfer-learning is definitely feasible for implementation smells with performance comparable to that of direct-learning. This work opens up a new paradigm to detect code smells by transfer-learning especially for the programming languages where the comprehensive code smell detection tools are not available

A computational model for real-time calculation of electric field due to transcranial magnetic stimulation in clinics

The aim of this paper is to propose an approach for an accurate and fast (real-time) computation of the electric field induced inside the whole brain volume during a transcranial magnetic stimulation (TMS) procedure. The numerical solution implements the admittance method for a discretized realistic brain model derived from Magnetic Resonance Imaging (MRI). Results are in a good agreement with those obtained using commercial codes and require much less computational time. An integration of the developed codewith neuronavigation toolswill permit real-time evaluation of the stimulated brain regions during the TMSdelivery, thus improving the efficacy of clinical applications

Evolution and Modern Approaches for Thermal Analysis of Electrical Machines

In this paper, the authors present an extended survey on the evolution and the modern approaches in the thermal analysis of electrical machines. The improvements and the new techniques proposed in the last decade are analyzed in depth and compared in order to highlight the qualities and defects of each. In particular, thermal analysis based on lumped-parameter thermal network, finite-element analysis, and computational fluid dynamics are considered in this paper. In addition, an overview of the problems linked to the thermal parameter determination and computation is proposed and discussed. Taking into account the aims of this paper, a detailed list of books and papers is reported in the references to help researchers interested in these topics

Evaluation of electric and magnetic fields distribution and SAR induced in 3D models of water containers by radiofrequency radiation using FDTD and FEM simulation techniques

In this study, two software packages using different numerical techniques FEKO 6.3 with Finite-Element Method (FEM) and XFDTD 7 with Finite Difference Time Domain Method (FDTD) were used to assess exposure of 3D models of square, rectangular, and pyramidal shaped water containers to electromagnetic waves at 300, 900, and 2400 MHz frequencies. Using the FEM simulation technique, the peak electric field of 25, 4.5, and 2 V/m at 300 MHz and 15.75, 1.5, and 1.75 V/m at 900 MHz were observed in pyramidal, rectangular, and square shaped 3D container models, respectively. The FDTD simulation method confirmed a peak electric field of 12.782, 10.907, and 10.625 V/m at 2400 MHz in the pyramidal, square, and rectangular shaped 3D models, respectively. The study demonstrated an exceptionally high level of electric field in the water in the two identical pyramid shaped 3D models analyzed using the two different simulation techniques. Both FEM and FDTD simulation techniques indicated variations in the distribution of electric, magnetic fields, and specific absorption rate of water stored inside the 3D container models. The study successfully demonstrated that shape and dimensions of 3D models significantly influence the electric and magnetic fields inside packaged materials; thus, specific absorption rates in the stored water vary according to the shape and dimensions of the packaging materials.Comment: 22 pages, 30 figures and 2 table