Using learning automata in brain emotional learning for speech emotion recognition

We propose an improved version of brain emotional learning (BEL) model trained via learning automata (LA) for speech emotion recognition. Inspiring from the limbic system in mammalian brain, the original BEL model is composed of two neural network components, namely amygdala and orbitofrontal cortex. In this modified BEL model, named brain emotional learning based on learning automata (BELBLA), we have employed the theory of the stochastic LA in error back-propagation to train the BEL model in decreasing the high computational complexity of the traditional gradient method. Hence, the performance of the model can be enhanced. For a speech emotion recognition task, we extract the usual features, such as energy, pitch, formants, amplitude, zero crossing rate and MFCC, from average short-term signals of the emotional Berlin dataset. The experimental results show that the BELBLA outperforms some opponents, like hidden Markov model, Gaussian mixture model, k-nearest neighbor, support vector machines and artificial neural networks, for this application.     

Synthesis of polyaniline based composite material and its analytical applications for the removal of highly toxic Hg<Superscript>2+</Superscript> metal ion: Antibacterial activity against <Emphasis Type="Italic">E. coli</Emphasis>

A composite material polyaniline-Zr(IV) phosphoborate (PZPB) was synthesized via sol-gel method by the combination of Zr(IV) phosphoborate and polyaniline. The PZPB composite material was characterized by various analytical techniques. The PZPB composite material was found to be selective for Hg²⁺+ metal ion due to the high distribution coefficient values for Hg²⁺+ metal ion in all mediums. The PZPB composite material was used for Hg²⁺+ removal under different experimental conditions. The antibacterial activity of PZPB composite material was also studied against E. coli.     

Automating test oracles from restricted natural language agile requirements

Manual testing of software requirements written in natural language for agile or any other methodology requires more time and human resources. This leaves the testing process error prone and time consuming. For satisfied end users with bug‐free software delivered on time, there is a need to automate the test oracle process for natural language or informal requirements. The automation of the test oracle is relatively easier with formal requirements, but this task is difficult to achieve with natural language requirements. This study proposes an approach called Restricted Natural Language Agile Requirements Testing (ReNaLART) to automate the test oracle from restricted natural language agile requirements. For this purpose, it uses an existing user story template with some modifications for writing user stories. This helps in identifying test input and expected output for a user story. For comparison of expected and observed outputs it makes use of a regex pattern and string distance functions. It is capable of assigning different types of verdicts automatically depending upon the similarity/dissimilarity between observed and expected outputs of user stories. ReNaLART is validated using several case studies of different domains, namely, OLX Pakistan, Mental Health Tests, McDelivery Pakistan, BlueStacks, Power Searching with Google, TensorFlow Playground, w3Schools 2018 offline and Touch'D. It revealed several faults in five of the above listed eight applications. Plus, the proposed test oracle on an average took 0.02 s for test data generation, expected output generation and verdict assignment. Both these facts show the fault revealing effectiveness and efficiency of ReNaLART.     

Instagram Mobile Application Digital Forensics

In this research, we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application, Instagram. This plugin extracts personal details from Instagram users, e.g., name, user name, mobile number, ID, direct text or audio, video, and picture messages exchanged between different Instagram users. While developing the plugin, we identified resources available in both Android and IOS-based devices holding key forensics artifacts. We highlighted the poor privacy scheme employed by Instagram. This work, has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed, and how the traces, as well as the URL links of visual messages, can be used to access the privacy of any Instagram user without any critical credential verification. We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file, which any criminal mind can use to set up or trap someone else. The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.     

Clustering Persian viseme using phoneme subspace for developing visual speech application

There are numerous multimedia applications such as talking head, lip reading, lip synchronization, and computer assisted pronunciation training, which entices researchers to bring clustering and analyzing viseme into focus. With respect to the fact that clustering and analyzing visemes are language dependent process, we concentrated our research on Persian language, which indeed has suffered from the lack of such study. To this end, we proposed a novel adopting image-based approach which consists of four main steps including (a) extracting the lip region, (b) obtaining Eigenviseme of each phoneme considering coarticulation effect, (c) mapping each viseme into its subspace and other phonemes’ subspaces in order to create the distance matrix so as to calculate the distance between viseme’s cluster, and finally (d) comparing similarity of each viseme based on the weight value of reconstructed one. In order to indicate the robustness of the proposed algorithm, three sets of experiments were conducted on Persian and English databases in which Consonant/Vowel and Consonant/Vowel/Consonant syllables were examined. The results indicated that the proposed method outperformed the observed state-of-the-art algorithms in feature extraction, and it had a comparable efficiency in generating adequate clusters. Moreover, obtained results reached a milestone in grouping Persian visemes with respect to the perceptual test given by volunteers.     

Cold crystallization behavior of poly(lactic acid) in its blend with acrylic rubber; the effect of acrylic rubber content

Understanding the crystallization process of polymer blends is of great importance for designing their process conditions, especially when the crystallization occurs during heating, so‐called cold crystallization. In this paper, the cold crystallization behavior of poly(lactic acid) (PLA) in its blends with acrylic rubber (ACM) was studied as a function of ACM content, using various techniques including differential scanning calorimetry, polarized optical microscopy and rheological methods. It was found that the addition of 10 wt% ? 20 wt% ACM to the PLA accelerated its cold crystallization. However, on using a greater amount of ACM up to 30%, the rate of crystallization was not further increased. In the ACM‐rich blends, the crystallizable PLA domains were distributed inside the amorphous ACM matrix and consequently confined crystallization occurred. The observed effects are discussed in terms of the interplay between chain mobility enhancement and the influence of phase boundaries. © 2017 Society of Chemical Industry     

Effects of l‐carnitine supplement on serum inflammatory cytokines,C‐reactive protein,lipoprotein (a), and oxidative stress in hemodialysis patients with Lp (a) hyperlipoproteinemia

Inflammation, oxidative stress, and high concentration of serum lipoprotein (a) [Lp (a)] are common complications in hemodialysis patients. The present study was designed to investigate the effects of l ‐carnitine supplement on serum inflammatory cytokines, C‐reactive protein (CRP), Lp (a), and oxidative stress in hemodialysis patients with Lp (a) hyperlipoproteinemia [hyper Lp (a)]. This was an unblinded, randomized clinical trial. Thirty‐six hyper Lp (a) hemodialysis patients (23 men and 13 women) were randomly assigned to either a carnitine or control group. Patients in the carnitine group received 1000 mg/d oral l ‐carnitine for 12 weeks, whereas patients in the control group did not receive any l ‐carnitine supplement. At baseline and the end of week 12, 5 mL of blood were collected after a 12‐ to 14‐hours fast and serum free carnitine, CRP, interleukin‐1β, interleukin‐6 (IL‐6), tumor necrosis factor‐α, Lp (a), and oxidized low‐density lipoprotein were measured. Serum free carnitine concentration increased significantly by 86% in the carnitine group at the end of week 12 compared with baseline (P<0.001), while serum CRP and IL‐6 showed a significant decrease of 29% (P<0.05) and 61% (P<0.001), respectively. No significant changes were observed in serum free carnitine, CRP, and IL‐6 in the control group. There were no significant differences between the two groups in mean changes of serum interleukin‐1β, tumor necrosis factor‐α, Lp (a), and oxidized low‐density lipoprotein concentrations. l ‐carnitine supplement reduces inflammation in hemodialysis patients, but has no effect on hyper Lp (a) and oxidative stress.     

On pedagogy of nanometric circuit reliability

Fast-shrinking dimensions of semiconductor devices are expected to reach sub-10 nm scale in a few years. Although smaller in size and lower in power consumption than today’s CMOS devices, the nanoscaled devices are much less reliable due to manufacturing imperfections (hard errors), and noise and radiation-induced faults (soft errors). Consequently, in addition to timing, area, and power, the reliability has to become a new design criterion. This also means that the topic of reliability has to be incorporated into the circuit design curriculum. In this paper, we propose a course on circuit reliability. We also present in detail, an automated tool for calculation of reliability which could be incorporated into the course as a means for active learning.