Retraction of “Developing an integrated display of health data for aging in place”

The following article from Human Factors and Ergonomics in Manufacturing & Service Industries, “Developing an Integrated Display of Health Data for Aging in Place,” by Yang Gong and Arpita Chandra, published online on October 20, 2011 in Wiley Online Library ( www.onlinelibrary.wiley.com ), has been retracted by agreement between the authors, the journal Editors‐in‐Chief, Waldemar Karwowski and Gavriel Salvendy, and Publisher Wiley Periodicals, Inc. The retraction has been agreed as these articles were mistakenly published after being rejected.     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

An industrial study of applying input space partitioning to test financial calculation engines

This paper presents results from an industrial study that applied input space partitioning and semi-automated requirements modeling to large-scale industrial software, specifically financial calculation engines. Calculation engines are used in financial service applications such as banking, mortgage, insurance, and trading to compute complex, multi-conditional formulas to make high risk financial decisions. They form the heart of financial applications, and can cause severe economic harm if incorrect. Controllability and observability of these calculation engines are low, so robust and sophisticated test methods are needed to ensure the results are valid. However, the industry norm is to use pure human-based, requirements-driven test design, usually with very little automation. The Federal Home Loan Mortgage Corporation (FHLMC), commonly known as Freddie Mac, concerned that these test design techniques may lead to ineffective and inefficient testing, partnered with a university to use high quality, sophisticated test design on several ongoing projects. The goal was to determine if such test design can be cost-effective on this type of critical software. In this study, input space partitioning, along with automation, were applied with the help of several special-purpose tools to validate the effectiveness of input space partitioning. Results showed that these techniques were far more effective (finding more software faults) and more efficient (requiring fewer tests and less labor), and the managers reported that the testing cycle was reduced from five human days to 0.5. This study convinced upper management to begin infusing this approach into other software development projects.     

Memetic cooperative coevolution of Elman recurrent neural networks

Cooperative coevolution decomposes an optimisation problem into subcomponents and collectively solves them using evolutionary algorithms. Memetic algorithms provides enhancement to evolutionary algorithms with local search. Recently, the incorporation of local search into a memetic cooperative coevolution method has shown to be efficient for training feedforward networks on pattern classification problems. This paper applies the memetic cooperative coevolution method for training recurrent neural networks on grammatical inference problems. The results show that the proposed method achieves better performance in terms of optimisation time and robustness.     

Low intensity visual data improvement using DWT and LWT based regularizer

Image Completion plays a vital role in compressed sensing, machine learning, and computer vision applications. The Rank Minimization algorithms are used to perform the image completion. The major problem with rank minimization algorithms is the loss of information in the recovered image at high corruption ratios. To overcome this problem Lifting wavelet transform based Rank Minimization (LwRM), and Discrete wavelet transform based Rank Minimization (DwRM) methods are proposed, which can recover the image, if the corrupted observations are more than 80%. The evaluation of the proposed methods are accomplished by Full Reference Image Quality Assessment (FRIQA) and No Reference Image Quality Assessment (NR-IQA) metrics. The simulation results of proposed methods are superior to state-of-the-art methods.

     

Generalizing Multi-agent Graph Exploration Techniques

International Journal of Control, Automation and Systems - The system of multiple agents working in coordination for a given task has several advantages on faster completion, fault-tolerance, etc....     

Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

EPR study of Cu2+ in Na2O-NaF-B2O3-Bi2O3 glasses

Electron paramagnetic resonance (EPR) studies have been performed on the glass system (30–x) NaF-xNa₂O-50B₂O₃-20Bi₂O₃ doped with CuO as a paramagnetic probe. The calculated g values indicate that Cu²⁺ is in a tetragonally elongated octahedral co-ordination, and the Jahn-Teller character gives rise to anisotropic hyperfine structure. The spin-Hamiltonian parameters and -bonding parameter, ², are calculated. Varying the fluoride ion concentration and its effect on these parameters is also discussed.