The ethical attitudes of information technology professionals: a comparative study between the USA and the Middle East

This paper aims at investigating comparatively the ethical orientation of information technology (IT) professionals in the Middle East and the United States. It tests for attitudes toward and awareness of ethically-related issues, namely intellectual property, privacy and other general ethical IT aspects. In addition, through a comparison between the two regions, this paper intends to examine whether differences in IT professional demographics and characteristics, including gender and academic level, have any impact on attitudes to business ethics. A t test is used to establish significant differences between the targeted samples, while an ANOVA F-test is conducted to determine significant differences among the sample countries on a group basis. The results show a general awareness of ethical issues concerning information technology, and no significant differences are found between the two samples. However, different ethical attitudes are reported among respondents in terms of their reactions to the targeted IT ethical aspects. On an individual sample basis, the results about gender support the claim that male and female respondents are different, while mixed results are revealed for the influence of academic level on attitudes towards IT ethics. For intellectual property, the results are significant regarding ethical attitude differences between Middle-Eastern professionals and their counterparts in the US, while no significance differences are reported in terms of privacy.     

Adaptive pseudospectral methods for solving constrained linear and nonlinear time-delay optimal control problems

In this paper, we first develop an adaptive shifted Legendre–Gauss (ShLG) pseudospectral method for solving constrained linear time-delay optimal control problems. The delays in the problems are on the state and/or on the control input. By dividing the domain of the problem into a uniform mesh based on the delay terms, the constrained linear time-delay optimal control problem is reduced to a quadratic programming problem. Next, we extend the application of the adaptive ShLG pseudospectral method to nonlinear problems through quasilinearization. Using this scheme, the constrained nonlinear time-delay optimal control problem is replaced with a sequence of constrained linear-quadratic sub-problems whose solutions converge to the solution of the original nonlinear problem. The method is called the iterative-adaptive ShLG pseudospectral method. One of the most important advantages of the proposed method lies in the case with which nonsmooth optimal controls can be computed when inequality constraints and terminal constraints on the state vector are imposed. Moreover, a comparison is made with optimal solutions obtained analytically and/or other numerical methods in the literature to demonstrate the applicability and accuracy of the proposed methods.     

A micropillar array for sample concentration via in-plane evaporation

We present a method to perform sample concentration within a lab-on-a-chip using a microfluidic structure which controls the liquid-gas interface through a micropillar array fabricated in polydimethylsiloxane between microfluidic channels. The microstructure confines the liquid flow and a thermal gradient is used to drive evaporation at the liquid-gas-interface. The evaporation occurs in-plane to the microfluidic device, allowing for precise control of the ambient environment. This method is demonstrated with a sample containing 1 μm, 100 nm fluorescent beads and SYTO-9 labelled Escherichia coli bacteria. Over 100 s, the fluorescent beads and bacteria are concentrated by a factor of 10.     

An empirical investigation of entrepreneurship intensity in Iranian state universities

The purpose of this study is to propose a framework to evaluate the entrepreneurship intensity (EI) of Iranian state universities. In order to determine EI, a hybrid multi-method framework consisting of Delphi, Analytic Network Process (ANP), and VIKOR is proposed. The Delphi method is used to localize and reduce the number of criteria extracted from a deep literature review, according to the social and economic conditions of Iranian state universities by using an expert panel, including sixty-eight country-wide academicians and practitioners. After that, a group approach to ANP was utilized as an evaluation method to derive the weights of each criterion. Next, the evaluation data were gathered through a questionnaire, and, finally, the compromise ranking of universities was calculated using the VIKOR method. Moreover, this study applies weight-variance analysis (WVA) to suggest improvement actions. The paper proposes an evaluation framework for determining the performance of entrepreneurship development initiatives in universities. By using this framework, twenty-two Iranian state universities are evaluated in terms of their EI, and the results are discussed.     

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.     

Students’ Alternative Conceptions about Electricity and Effect of Inquiry-Based Teaching Strategies

This study attempted to investigate the alternative conceptions that a group of 12 Lebanese students in a grade 9 class hold about electricity. It also attempted to evaluate learning outcomes of implementing in that class an inquiry-based module for the acquisition of conceptual understanding of basic concepts in electricity. Fourteen mostly subjective tests were administered throughout the implementation phase of the inquiry-based module to assess the evolution of participants’ conceptions. The instrument DIRECT (Version 1.0) focusing on conceptual understanding was used as a post-instructional test to measure acquisition of understanding. The findings revealed that most of the alternative conceptions reported in literature were found amongst the participants. Results of the post-testing showed that the implemented inquiry-based approach was successful in enhancing participants’ conceptual understanding of the targeted DC circuit concepts.     

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.