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Nowadays, cloud computing environments have become a natural choice to host and process a huge volume of data. The combination of cloud computing and big data frameworks is an effective way to run data-intensive applications and tasks. Also, an optimal arrangement of data partitions can improve the tasks executions, which is not the case in most big data frameworks. For example, the default distribution of data partitions in Hadoop-based clouds causes several problems, which are mainly related to the load balancing and the resource usage. In addition, most existing data placement solutions are static and lack precision in the placement of data partitions. To overcome these issues, we propose a data placement approach based on the prediction of the future resources usage. We exploit Kernel Density Estimation (KDE) and Fuzzy FCA techniques to, first, forecast the workers' and tasks' future resource consumption and, second, cluster data partitions and intensive jobs according to the estimated resource usage. Fuzzy FCA is also used to exclude partitions and jobs that require less resources, which will reduce the needless migrations. To allow monitoring and predicting the workers' states and the data partitions' consumption, we modeled the big data cluster as an autonomic service-based system. The obtained results have shown that our solution outperformed existing approaches in terms of migrations rate and resource consumption. [ABSTRACT FROM AUTHOR]

Over the last years, cloud computing has emerged as a natural choice to host, manage, and provide various kinds of virtualized resources (e.g., software, business processes, databases, platforms, mobile and social applications, etc.) as on-demand services. This "servicelization" across various domains has produced a huge volume of data, leading to the emergence of a new service model, called big service. This latter consists of the encapsulation, abstraction and the processing of big data, allowing then to hide their complexity. However, this promising approach still lacks management facilities and tools. Indeed, due to the highly dynamic and uncertain nature of their hosting cloud environments, big services together with their accessed data need continuous management operations, so that to maintain a moderate state and high quality of their execution. In this context, frameworks for designing, composing, executing and managing big services become a major need. The purpose of this paper is to provide an understanding of the new emerging big service model from the lifecycle management phases' point of view. We also study the role of big data frameworks and multi-cloud strategies in the provisioning of big services. A research road map on this topic will be summarized at the end of this paper. [ABSTRACT FROM AUTHOR]

In this study, a collaborative annotation approach based on Linked Data technology (CAALDT) is proposed for improving students’ learning. An experiment was conducted to evaluate the effectiveness of the proposed approach by comparing the learning performance of the students who learned with CAALDT and those who learned with the conventional collaborative annotation approach. From the experimental results, it was found that the proposed approach improved not only the students’ learning achievements, but also their learning motivation and attitudes. Moreover, it was found that the CAALDT approach provided more challenging learning opportunities that enabled students to review the annotations of their peers or teacher as well as to expose their annotations as Linked Data, therefore contributing to the expansion of the learning contents. [ABSTRACT FROM AUTHOR]

In this study, a linked data-based annotation approach is proposed. A learning system has been developed based on the approach by providing an annotating function, a linked data enrichment function, a sharing function and faceted search function. To evaluate the effectiveness of this innovative approach, an experiment was carried out in which two classes of students participated. The first class served as the experimental group, in which the students learned with the proposed approach, and the second class served as the control group, in which the students learned with the conventional annotation approach. The experiment results show that the experimental group significantly outperformed the control group. Moreover, the cognitive load of the students in the experimental group was significantly lower than the ones in the control group. This implies that the linked data-based annotation approach not only reduced the students’ cognitive load, but also improved their learning achievement. [ABSTRACT FROM AUTHOR]

Abstract: Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer various on‐demand services (eg, software, storage, network, etc.). Software as a Service (SaaS) is one of the most popular service models. To meet the increasing demands of users, SaaS can be offered in a composite form. Although this approach offers some advantages like flexibility and reusability, it raises a question about how to manage composite SaaS in the distributed and the highly dynamic cloud environment. In this paper, we address one of the major SaaS resource management issues referred to as SaaS placement problem. As existing efforts only focus on SaaS placement problem from the perspective of resources utilization to optimize SaaS performance and minimize resource usage, in this paper, we also incorporate security concerns in SaaS placement strategy. In fact, security risk is one of the major factors influencing the efficiency of the composite SaaS. We adopt a multi‐swarm variant of particle swarm optimization to propose a security‐aware SaaS placement method. Also, a cooperative learning strategy is hybridized to the placement algorithm, which makes information of best candidate servers be used more effectively to generate better placement plan. Experiments show that our solution outperforms existing SaaS placement approaches. [ABSTRACT FROM AUTHOR]

With the emergence of the Web 2.0, collaborative annotation practices have become more mature in the field of learning. In this context, several recent studies have shown the powerful effects of the integration of annotation mechanism in learning process. However, most of these studies provide poor support for semantically structured resources, more precisely for sharing and linking educational contents using linked data approach. Adopting Semantic Web technologies, this paper proposed a new linked data-based collaborative annotation system called L2OD, which allows students to enrich their annotations with relevant resources retrieved from Linked Open Data clouds. L2OD supports two modes of annotation, a private annotation mode for the user's individual annotations, and a shared mode for all users' annotations. The experimentation shows that the learners who have used L2OD have significantly increased their learning achievements referring to the difference between L2OD's post-test and the control group's post-test. It also shows significant positive correlations between learning achievements and quantity of private and shared annotations in the experimental group, respectively. [ABSTRACT FROM AUTHOR]

Computer processing of off-line Arabic handwriting is very difficult. The cursiveness of the script and the almost variability of the handwritten symbols make the automatic recognition of Arabic a very challenging task. Character segmentation is an important pre-processing stage in any intelligent character recognition system. This paper surveys the field of off-line Arabic character segmentation. Four classes of segmentation approaches are identified based on the used features and on where segmentation points are located. Text curves analysis, outer contour detection and following, stroke scrutiny and singularities vs regularities are the most used techniques. Instructive examples of each category are described and some comments are given. [ABSTRACT FROM AUTHOR]

In this article, we suggest an automated construction of knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. First, ideal samples of the considered lexicon words are submitted to a feature extraction module which describes them using structural primitives. The analysis of these descriptions generates a symbolic knowledge base reflecting a hierarchical classification of the words. The rules are then translated into a multilayer neural network by determining precisely its architecture and initializing its connections with specific values. This construction approach provides the network with theoretical knowledge and reduces the training stage, which remains necessary because of variability in styles and writing conditions. After this empirical training stage using real examples, the network reaches its final topology, which enables it to generalize. The proposed method has been tested on the automated construction of neuro-symbolic classifiers for two Arabic lexicons: literal amounts and city names. We suggest the generalization of this approach to the recognition of handwritten words or characters in different scripts and languages. [ABSTRACT FROM AUTHOR]

The challenge of hybrid leaning systems is to use the information provided by one source of information to compensate information missing from the other source. The neuro-symbolic combination represents a promising research way. The synergy between the symbolic (theoretical) and neural (empirical) approaches makes their combination more effective than each of them used alone. In this article, we describe an Arabic literal amount recognition system that uses a neuro-symbolic classifier. For this purpose, we first extract structural features from the words contained in the amounts vocabulary. Then, we build a symbolic knowledge base that reflects a classification of words according to their features. In a third step, we use a translation algorithm (from rules to neural network) to determine the neural network architecture and to initialize its connections with specific values rather than random values, as is the case in classical neural networks. This construction approach provides the network with theoretical knowledge and reduces the training stage, which remains necessary because of styles and writing conditions variability. After this empirical training stage using real examples, the network acquires a final topology, which allows it to recognize new handwritten amounts. [ABSTRACT FROM AUTHOR]

• A distributed deep learning-based approach to classify big remote sensing images. • Satellite image classification based on distributed Convolutional-Neural-Networks. • Two steps: (1) preparing the training dataset and (2) a parallel-data training. • Experiments are conducted on a real dataset and show highly classification accuracy. • Experiments show high classification accuracy and faster speed compared with current state-of-the-art methods. Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods. [ABSTRACT FROM AUTHOR]

Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas. [ABSTRACT FROM AUTHOR]

Abstract: Automatic handwriting recognition has a variety of applications in real world problems, such as mail sorting and check processing. Recently, it has been demonstrated that combining the decisions of several classifiers and integrating multiple information sources can lead to better recognition results. This article presents an approach for recognizing handwritten Arabic literal (legal) amounts. The proposed system uses a set of holistic structural features to describe the words. These features are presented to three classifiers: multilayer neural network, nearest neighbor, and fuzzy nearest neighbor. The classification results are then combined using several schemes; we retained the score summation one for this work. A syntactic post-classification process is then carried out to find the best match among the candidate words. The performance of this approach is superior to the system which ignores all contextual information and simply relies on the recognition scores of the recognizers. [Copyright &y& Elsevier]

Due to the sharp increase in global industrial production, as well as the over-exploitation of land and sea resources, the quality of drinking water has deteriorated considerably. Furthermore, nowadays, many water supply systems serving growing human populations suffer from shortages since many rivers, lakes, and aquifers are drying up because of global climate change. To cope with these serious threats, smart water management systems are in great demand to ensure vigorous control of the quality and quantity of drinking water. Indeed, water monitoring is essential today since it allows to ensure the real-time control of water quality indicators and the appropriate management of resources in cities to provide an adequate water supply to citizens. In this context, a novel IoT-based framework is proposed to support smart water monitoring and management. The proposed framework, named SmartWater, combines cutting-edge technologies in the field of sensor clouds, deep learning, knowledge reasoning, and data processing and analytics. First, knowledge graphs are exploited to model the water network in a semantic and multi-relational manner. Then, incremental network embedding is performed to learn rich representations of water entities, in particular the affected water zones. Finally, a decision mechanism is defined to generate a water management plan depending on the water zones' current states. A real-world dataset has been used in this study to experimentally validate the major features of the proposed smart water monitoring framework. [ABSTRACT FROM AUTHOR]