Factors influencing cloud computing adoption in small medium enterprises

Cloud computing offers information technology (IT) infrastructure, platform, and various applications via the Internet with minimum start-up cost, network access to a shared pool of configurable computing resources, and pay-per-use services. Although the potential for cloud computing is evident and much of the extant research has been carried out on cloud computing adoption, empirical studies on the factors that influence cloud computing adoption in the Malaysian Small and Medium Enterprises (SMEs) are, however, lacking. The objective of this study was to examine the factors that influence cloud computing adoption by the SMEs. We conducted a quantitative survey-based study to examine the relationship between perceived benefits, top management support, IT resources, external pressure, and cloud computing adoption. A free-form comment provided at the end of each section of the survey questionnaire was treated as qualitative data. We find that IT resources and external pressure significantly influence cloud computing adoption. Nonetheless, there is not enough evidence to support perceived benefits and top management support as significant factors of cloud computing adoption

A systematic reading in statistical translation: From the statistical machine translation to the neural translation models

Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades.Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automatic translation as a key application in the natural language processing domain has developed many approaches, namely statistical machine translation and recently neural machine translation that improved largely the translation quality especially for Latin languages.They have even made it possible for the translation of some language pairs to approach human translation quality.In this paper, we present a survey of the state of the art of statistical translation, where we describe the different existing methodologies, and we overview the recent research studies while pointing out the main strengths and limitations of the different approaches

Identifying learning style through eye tracking technology in adaptive learning systems

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning

Arabic Text Summarization Challenges using Deep Learning Techniques: A Review

Text summarization is a challenging field in Natural Language Processing due to language modelisation and used techniques to give concise summaries.  Dealing with Arabic language does increase the challenge while taking into consideration the many features of the Arabic language, the lack of tools and resources for Arabic, and the Algorithms adaptation and modelisation. In this paper, we present several researches dealing with Arabic Text summarization applying different Algorithms on several Datasets. We then compare all these researches and we give a conclusion to guide researchers on their further work

Effets de l'histoire thermomécanique sur le comportement structural et mécanique du polyfluorure de vinylidene

Doctorat en sciences appliquéesinfo:eu-repo/semantics/nonPublishe

Changes in structural and mechanical behaviour of PVDF with processing or thermal treatment. 2. Evolution of mechanical behaviour

The following study is the continuation of our previous work, which consisted in analysing the structural evolution of PVDF after different thermomechanical treatments. In this second part, we will discuss the influence of these evolutions on the mechanical behaviour. This has been determined by tensile drawing, creep or dynamic measurements. The observed changes in behaviour are strongly influenced by the different processing routes. Therefore, the beta conformation appears only when stretching injection-moulded samples (IM) and is accompanied by an increase of rupture stress compared to yield stress, which is not the case for compressed samples (CM). The influence of annealing on mechanical behaviour varies with annealing and measurement temperature. Thus, annealing at 80°C after standardisation increases the Young's modulus at 120°C and decreases it during drawing at 23°C compared to other annealing treatments. © 2001 Elsevier Science Ltd.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

Abstract Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in this area to predict the stock market price, including regression algorithms which can be useful tools to provide good performance of financial time series prediction. Support Vector Regression is one of the most powerful algorithms in machine learning. There have been countless successes in utilizing SVR algorithm for stock market prediction. In this paper, we propose a novel hybrid approach based on machine learning and filtering techniques. Our proposed approach combines Support Vector Regression and Hodrick–Prescott filter in order to optimize the prediction of stock price. To assess the performance of this proposed approach, we have conducted several experiments using real world datasets. The principle objective of this paper is to demonstrate the improvement in predictive performance of stock market and verify the works of our proposed model in comparison with other optimized models. The experimental results confirm that the proposed algorithm constitutes a powerful model for predicting stock market prices

Changes in structural and mechanical behaviour of PVDF with processing and thermomechanical treatments. 1. Change in structure

The present work focuses on the effects of thermomechanical history on the structure and mechanical behaviour of PVDF. In the first part of our investigation differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) are used to follow the evolution of the structure of PVDF after different annealing treatments or deformation. Processing has a significant impact on the quantities of the amorphous and crystalline phases and their interphase. Subsequent annealing affects these phases in a different proportion. This influence depends on the position of the annealing temperature in comparison with the upper glass transition temperature of PVDF. Deformation induces conformational change in the injection moulded samples. Thus, the α conformation is transformed to the β conformation. The β conformation also has a noticeable influence on the mechanical behaviour of the material, which will be discussed in the second part of our study. © 2001 Elsevier Science Ltd.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A SYSTEMATIC READING IN STATISTICAL TRANSLATION: FROM THE STATISTICAL MACHINE TRANSLATION TO THE NEURAL TRANSLATION MODELS.

Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades. Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automatic translation as a key application in the natural language processing domain has developed many approaches, namely statistical machine translation and recently neural machine translation that improved largely the translation quality especially for Latin languages. They have even made it possible for the translation of some language pairs to approach human translation quality. In this paper, we present a survey of the state of the art of statistical translation, where we describe the different existing methodologies, and we overview the recent research studies while pointing out the main strengths and limitations of the different approaches.

Revisiting the Didactic Triangle in the Case of an Adaptive Learning System

In this paper we revisit the classical approach of the didactic triangle designed for the classical learning situation (face to face) and adapt it to the situation of an adaptive learning system, we discuss also the different components involved in this didactic triangle and how they interact and influence the learning process in an adaptive learning system