A family of proportionate normalized subband adaptive filter algorithms

In this paper, the concept of proportionate adaptation is extended to the normalized subband adaptive filter (NSAF), and seven proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are proportionate normalized subband adaptive filter (PNSAF), μ‐law PNSAF (MPNSAF), improved PNSAF (IPNSAF), the improved IPNSAF (IIPNSAF), the set-membership IPNSAF (SM-IPNSAF), the selective partial update IPNSAF (SPU-IPNSAF), and SM-SPU-IPNSAF which are suitable for sparse system identification in network echo cancellation. When the impulse response of the echo path is sparse, the PNSAF has initial faster convergence than NSAF but slows down dramatically after initial convergence. The MPNSAF algorithm has fast convergence speed during the whole adaptation. The IPNSAF algorithm is suitable for both sparse and dispersive impulse responses. The SM-IPNSAF exhibits good performance with significant reduction in the overall computational complexity compared with the ordinary IPNSAF. In SPU-IPNSAF, the filter coefficients are partially updated rather than the entire filter at every adaptation. In SM-SPU-IPNSAF algorithm, the concepts of SM and SPU are combined which leads to a reduction in computational complexity. The simulation results show good performance of the proposed algorithms.     

An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities

Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning.     

Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels’ reviews using morphological,syntactic and semantic features

This research presents an enhanced approach for Aspect-Based Sentiment Analysis (ABSA) of Hotels’ Arabic reviews using supervised machine learning. The proposed approach employs a state-of-the-art research of training a set of classifiers with morphological, syntactic, and semantic features to address the research tasks namely: (a) T1:Aspect Category Identification, (b) T2:Opinion Target Expression (OTE) Extraction, and (c) T3: Sentiment Polarity Identification. Employed classifiers include Naïve Bayes, Bayes Networks, Decision Tree, K-Nearest Neighbor (K-NN), and Support-Vector Machine (SVM).The approach was evaluated using a reference dataset based on Semantic Evaluation 2016 workshop (SemEval-2016: Task-5). Results show that the supervised learning approach outperforms related work evaluated using the same dataset. More precisely, evaluation results show that all classifiers in the proposed approach outperform the baseline approach, and the overall enhancement for the best performing classifier (SVM) is around 53% for T1, around 59% for T2, and around 19% in T3.     

Modern standard Arabic speech corpus for implementing and evaluating automatic continuous speech recognition systems

This paper presents our work towards developing a new speech corpus for Modern Standard Arabic (MSA), which can be used for implementing and evaluating Arabic speaker-independent, large vocabulary, automatic, and continuous speech recognition systems. The speech corpus was recorded by 40 (20 male and 20 female) Arabic native speakers from 11 countries representing three major regions (Levant, Gulf, and Africa). Three development phases were conducted based on the size of training data, Gaussian mixture distributions, and tied states (senones). Based on our third development phase using 11 hours of training speech data, the acoustic model is composed of 16 Gaussian mixture distributions and the state distributions tied to 300 senones. Using three different data sets, the third development phase obtained 94.32% and 8.10% average word recognition correctness rate and average Word Error Rate (WER), respectively, for same speakers with different sentences (testing sentences). For different speakers with same sentences (training sentences), this work obtained 98.10% and 2.67% average word recognition correctness rate and average WER, respectively, whereas for different speakers with different sentences (testing sentences) this work obtained 93.73% and 8.75% average word recognition correctness rate and average WER, respectively.     

Kinetic study of low density lipoprotein oxidation by copper

Oxidation of Low Density Lipoprotein (LDL) is regarded as a key event in the development of atherosclerosis. The aim of this study was to investigate effect of various copper concentrations on LDL oxidation kinetic profile as a mechanism in atherosclerosis process. LDL was isolated from plasma and its oxidation with copper was investigated by monitoring the formation of conjugated dienes. Based on time course of the formation of conjugated diene was observed at concentrations of 0.5 to 10 μM copper, represented the conventional kinetics of LDL oxidation with an inhibition period followed by a propagation phase. In contrast, at concentrations of 20 to 50 μM copper, LDL oxidation proceeded after a negligibly short lag-time followed by a distinct propagation phase. At lower copper concentrations of about 0.5 μM, LDL oxidation can be combined in 4 consecutive oxidation phase. The increasing copper concentration (to 10 μM) lowered the first propagation and shortened the seconded inhibition period until they melted into one apparent kinetic phase. But in copper concentrations of about 20 to 50 μM, increasing copper concentration increased the first propagation and the second inhibition but lowered the second propagation phase. The results of this investigation on the copper dependence of the oxidation kinetics suggest that LDL contains two different copper binding sites. Copper bound to the low affinity binding sites with molar ratio of 200 to 500 of copper / LDL. These ions bound to the high affinity binding sites with molar ratio of copper / LDL of 5 to 100.     

Design of digital controllers for uncertain chaotic systems using fuzzy logic

A new and systematic method to design digital controllers for uncertain chaotic systems with structured uncertainties is presented in this paper. Takagi-Sugeno (TS) fuzzy model is used to model the chaotic dynamic system, while the uncertainties are decomposed such that the uncertain chaotic system can be rewritten as a set of local linear models with an additional disturbed input. Conventional control techniques are utilized to develop the continuous-time controllers first. Then, the digital controllers are obtained as the digital redesign of the continuous-time controllers using the state-matching approach. The performance of the proposed controller design is illustrated through numerical examples.     

Chip morphology predictions while machining hardened tool steel using finite element and smoothed particles hydrodynamics methods

Chip morphology predictions in metal cutting have always been challenging because of the complexity of the various multiphysical phenomena that occur across the tool-chip interface. An accurate prediction of chip morphology is a key factor in the assessment of a particular machining operation with regard to both tool performance and workpiece quality. Although finite element (FE) models are being developed over the last two decades, their capabilities in modeling correct material flow around the tool tip with shear localization are very limited. FE models with an arbitrary Lagrangian Eulerian (ALE) approach are able to simulate correct material flow around the tool tip. However, these models are unable to predict any shear localization based on material flow criteria. On the other hand, FE models with a Lagrangian formulation can simulate shear localization in the chip segments; they need to make use of a mesh-based chip separation criterion that significantly affects material flow around the tool tip. In this study a mesh-free method viz. smoothed particles hydrodynamics (SPH) is implemented to simulate shear localization in the chip while machining hardened steel. Unlike other SPH models developed by some researchers, this model is based on a renormalized formulation that can consider frictional stresses along the tool-chip interface giving a realistic chip shape and material flow. SPH models with different cutting parameters are compared with the traditional FE models and it has been found that the SPH models are good for predicting shear localized chips and do not need any geometric or mesh-based chip separation criteria.     

Relationship between social capital and lifestyle among older adults

ABSTRACT

Objective: This study was designed with the purpose of determining the relationship between social capital and lifestyle and their sub-variables among older adults.

Methods: This is a cross-sectional study using interviews that was conducted in 2017. This study was performed with randomly selected older people over 60 years of age in an urban area of Iran. Data were collected using a questionnaire in 3-month duration. The relationship between the study variables (lifestyle and social capital) was assessed through correlation analysis (in the case of non control of the underlying variables) and general linear modeling (in the case of control of the underlying variables). Data were analyzed using SPSS v.24 software at a 0.05 significant level.

Results: A total of 512 questionnaires were filed out. According to the Pearson correlation test, all social capital variables were associated with lifestyle components. However, in the general linear model test, feeling of trust and safety (p < 0.001), value of life (p = 0.030), and social participation (p = 0.004) had a significant relationship with the lifestyle.

Conclusions: The results indicated that social capital and lifestyle in the older adults were interconnected. Therefore, there is a need to improve social capital factors, especially social participation, feeling of trust and safety, and value of life, which is in-line with the strategies of achieving healthy older population. The results can be used to learn how to increase lifestyle in older adults.     

The role of teacher’s corrective feedback in improving Iranian EFL learners’ writing accuracy over time: is learner’s mother tongue relevant?

The purpose of the present study was to investigate the impact of feedback on writing accuracy over time and examine the relevance of the students’ mother tongue to the feedback effect. To this end, the study compared two groups of Iranian English majors (N = 56) over a period of four months: one with indirect grammar feedback and the other with no grammar feedback whatsoever. The results of the study did not show a significant effect for the teachers’ feedback. However, the results showed a main effect for practice and the interaction of practice and feedback as a result of which both groups improved their writing accuracy over the course of one semester, with the feedback group making more improvement. The results also showed significant differences between the two groups in reducing their errors in various grammatical categories. The study further revealed that the success ratio of error reduction in each category was highly sensitive to cross-linguistic differences. The students’ views, elicited after the study, support the findings obtained from the analysis of the quantitative data.