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Forest fires have devastating consequences for the environment, the economy and human lives. Understanding their dynamics is therefore crucial for planning the resources allocated to combat them effectively. In a world where the incidence of such phenomena is increasing every year, the demand for efficient and accurate computational models is becoming increasingly necessary. In this study, we perform a revision of an initial proposal which consists of a two-dimensional propagation model based on cellular automata (2D-CA), which aims to understand the dynamics of these phenomena. We identify the key theoretical weaknesses and propose improvements to address these limitations. We also assess the effectiveness and accuracy of the model by evaluating improvements using real forest fire data (Beneixama, Alicante 2019). Moreover, as a result of the theoretical modifications performed, we introduce a novel intelligent architecture that seeks to capture relationships between system cells from the data. This new architecture has the ability to advance our understanding of forest fire dynamics, contributing to both the evaluation of existing protocols and more efficient firefighting resource management.

Denial of Wallet (DoW) attacks refers to a type of cyberattack that aims to exploit and exhaust the financial resources of an organization by triggering excessive costs or charges within their cloud or serverless computing environment. These attacks are particularly relevant in the context of serverless architectures due to characteristics like pay-as-you-go model, auto-scaling, limited control and cost amplification. Serverless computing, often referred to as Function-as-a-Service (FaaS), is a cloud computing model that allows developers to build and run applications without the need to manage traditional server infrastructure. Serverless architectures have gained popularity in cloud computing due to their flexibility and ability to scale automatically based on demand. These architectures are based on executing functions without the need to manage the underlying infrastructure. However, the lack of realistic and representative datasets that simulate function invocations in serverless environments has been a challenge for research and development of solutions in this field. The aim is to create a dataset for simulating function invocations in serverless architectures, that is a valuable practice for ensuring the reliability, efficiency, and security of serverless applications. Furthermore, we propose a methodology for the generation of the dataset, which involves the generation of synthetic data from traffic generated on cloud platforms and the identification of the main characteristics of function invocations. These characteristics include SubmitTime, Invocation Delay, Response Delay, Function Duration, Active Functions at Request, Active Functions at Response. By generating this dataset, we expect to facilitate the detection of Denial of Wallet (DoW) attacks using machine learning techniques and neural networks. In this way, this dataset available in Mendeley data repository could provide other researchers and developers with a dataset to test and evaluate machine

Wildfires have significant impacts on both environment and economy, so understanding their behaviour is crucial for the planning and allocation of firefighting resources. Since forest fire management is of great concern, there has been an increasing demand for computationally efficient and accurate prediction models. In order to address this challenge, this work proposes applying a parameterised stochastic model to study the propagation of environmental events, focusing on the bias introduced by climatic variables such as wind. This model’s propagation occurs in a grid where cells are classified into different compartments based on their state. Furthermore, this approach generalises previous non-stochastic models, which are now considered particular cases within this broader framework. The use of the Monte Carlo method is highlighted, which allows for obtaining probabilistic estimates of the state of the cells in each time step, considering a level of confidence. In this way, the model provides a tool to obtain a quantitative estimate of the probability associated with each state in the spread of forest fires.

Migration (irregular and forced) represents one of the major challenges the international community faces today. Inasmuch as the phenomenon of irregular and forced migration is the marker of the state of socio-economic systems around the world, the response to and the ways of navigating the resulting multi-scalar challenges mirror not only the efficiency of the global regulatory frameworks, but also our civility. Recognizing the potential inherent in sophisticated information and communication technology (ICT), specifically the blockchain technology and smart contracts, this paper focuses on the special case of “welcome centers” that irregular migrants enter in the hope of acquiring international legal protection and thus refugee status. Since the process may be time-consuming and the living conditions undignified, this paper proposes a tool, named here “responsible wallet”, aka rWallet, that bears the promise of navigating some of these challenges. rWallet derives from the recognition that in modern societies ICT should serve the purpose of improving the quality of life of all people.

New Information and Communications Technologies (ICT) are changing the way in which the world works. These technologies provide new tools to face the issues of contemporary society (poverty, migrations, sustainable development challenges, governance, etc.). Among them, blockchain emerge as a disruptive technology able to make things in a completely different and innovative way. They can provide solutions where before there were none. Recently, these technologies have become subject of intense research and development by industry and academia. In consequence, a large amount of interesting works is published in many areas, proposing solutions, services, frameworks and applications based on these technologies. The research focusing on the previous challenges are especially interesting in current society. However, the proposals arrive at higher speed than society can assimilate them, and sometimes they are not properly exploited. Thus, this paper provides a comprehensive overview of the blockchain technology to identify what are its key attributes that enable to successfully address the social challenges. The review has been conducted from three point of views: service-delivery, resource-management and city-administration, and has been focused on the Sustainable Development Goals of the United Nations, in order to cover the full agenda of current social issues. The contributions of this work are an exhaustive compilation of the existing research on blockchain technology to tackle the social challenges from several angles, and the identification of the key features and the conditioning factors of the practical applicability of the technology.

Nowadays, the emergence of large-scale and highly distributed cyber-physical systems (CPSs) in applications including Internet of things (IoT), cloud computing, mobility, Big Data, and sensor networks involves that architecture models have to work in an open and highly dynamic world. This fact increasingly highlights the importance of designing real-time and intelligent CPSs that are capable of decision making. In this paper, an Intelligent Architecture model for CPS management (IA-CPS) based on online processing is proposed for this purpose. Specifically, it can manage simple and complex events based on a service-oriented architecture and directed by an event-driven architecture, using event processing technology as Complex Event Processing (CEP). The novelty of our approach relies on the fact that the proposed architecture is service-oriented, which models the functionalities of the event-driven system. This gives us the possibility to offer a flexible service catalog, allowing us to connect to the system on any kind of device and interact in different scenarios. The model has been applied to two use cases: processing images from video surveillance cameras, and processing of consumption data captured by water and energy sensors installed in end-user environments.

3D engines for real time CAD geometry representation have been improved extremely in recent years, both, thanks to the evolution of the computer industry itself and the new technologies used for computer graphics, like OpenGL shaders and their implementation in the last graphic cards generation. Raytracing and radiosity rendering techniques are achieving more and more close to reality results thanks to these improvements. Many manufacturing industries are taking advantage of these technologies, like footwear industry, where 3D CAD shoe models are been rendering in real time with a huge qualitative leap in terms of efficiency, costs and close-to-reality visualization results. All the same, the performance of the computational models used for the representation of virtual materials, texture maps (like normal or roughness map), specular and diffuse reflection models, illumination or shadows among many other elements, is leading to having different visualization results depending on the 3D engine that it is been used, even though we work with the same CAD model together with its materials and representation scene. Furthermore, if we bring all these implementations to web technologies, like WebGL, we must tackle not only the visualization problem but also, data optimization and computational capacity issues due to the fact of the web browsers and Internet nature. This paper proposes a computational model for hyper-realistic representation in 3D engines that offers correct results for both, 3D engines optimized for computers and 3D engines for web environments. The proposed model adapts the different characteristics of both environments and makes possible an identical visualization of a 3D CAD shoe model whatever the environment chosen.

Cloud Computing is one of the most disruptive technologies of this century. This technology has been widely adopted in many areas of the society. In the field of manufacturing industry, it can be used to provide advantages in the execution of the complex geometric computation algorithms involved on CAD/CAM processes. The idea proposed in this research consists in outsourcing part of the load to be computed in the client machines to the cloud through the Mobile Cloud Computing paradigm. This practice gives substantial benefits to both the clients and the software-provider in terms of costs, flexibility, ubiquity and performance. In this document, an outsourcing architecture is proposed based on this paradigm. Extensive experiments have been done using highly parallelizable computational geometry operations to show the strengths and weaknesses of the proposal in combination of specialized computing platforms in the cloud. The results suggest that there are some issues that affect the overall performance and the stability of the QoS: the network communication delay, and the number of simultaneous clients and multiple requests. Some solutions have been proposed to face these challenges.

User-generated content about brands is an important source of big data that can be transformed into valuable information. A huge number of items are reviewed and rated by consumers on a daily basis, and managers have a keen interest in real-time monitoring of this information to improve decision-making. The main challenge is to mine reliable textual consumer opinions, and automatically use them to rate the best products or brands. We propose a framework to automatically analyse these reviews, transforming negative and positive user opinions in a quantitative score. Sentiment analysis was employed to analyse online reviews on Amazon. The Fake Review Detection Framework—FRDF— detects and removes fake reviews using Natural Language Processing technology. The FRDF was tested on reviews of products from high-tech industries. Brands were rated according to consumer sentiment. The findings demonstrate that brand managers and consumers would find this tool useful, in combination with the 5-Star score, for more comprehensive decision-making. For instance, the FRDF ranks the best products by price alongside their respective sentiment value and the 5-Star score.

Science and engineering education are mostly based on content assimilation and development of skills. However, to adequately prepare students for today's world, it is also necessary to stimulate critical thinking and make them reflect on how to improve current practices using new tools and technologies. In this line, the main motivation of this research consists in exploring ways supported by technology to enhance the learning process of students and to better prepare them to face the challenges of today's world. To this end, the purpose of this work is to design an innovative learning project based on collaborative work among students, and research its impact in achieving better learning outcomes, generating of collective intelligence and further motivation. The proposed collaborative working model is based on peer review assessment methodology implemented through a learning web-platform. Thus, students were encouraged to peer review their classmates' works. They had to make comments, suggest improvements, and assess final assignments. Teaching staff managed and supervised the whole process. Students were selected from computer science engineering at the University of Alicante (Spain). Results suggested greater content assimilation and enhanced learning in several scientific skills. The students' final grade exceeded what any student could produce individually, but we cannot conclude that real collective intelligence was generated. Learning methodologies based on the possibilities of Information and Communication Technologies (ICT) provide new ways to transmit and manage knowledge in higher education. Collaborating in peer assessment enhances the students' motivation and promotes the active learning. In addition, this method can be very helpful and time saving for instructors in the management of large groups.

The advent of big data analytics and cloud computing technologies has resulted in wide-spread research on the data placement problem. Since data-intensive services require access to multiple datasets within each transaction, traditional schemes of uniformly partitioning the data into distributed nodes, as employed by many popular data stores like HDFS or Cassandra, may cause network congestion thereby affecting system throughput. In this article, we propose a scalable and unified framework for data-intensive service data placement into geographically distributed clouds. The proposed framework introduces a new paradigm for partitioning a set of data-items into geo-distributed clouds using Spectral Clustering on Hypergraphs, and is therefore called SpeCH. Scaling spectral methods to large workloads is challenging, since computing the spectra of the hypergraph laplacian is a computationally intensive task. SpeCH provides two solutions to tackle this problem: (1) an algorithm, called SpectralApprox, that leverages randomized techniques for obtaining low-rank approximations of the hypergraph matrix with bounded guarantees, thereby significantly improving the efficiency of spectral clustering while also providing high quality solutions in practice; (2) an algorithm, called SpectralDist, that exploits the highly parallel nature of the spectral clustering algorithm and uses Apache Spark to speed-up the process while retaining the same quality guarantees as the exact algorithm. Additionally, being distributed in nature, SpectralDist enables SpeCH to perform data placement on workloads that require resources beyond the capacity of a single machine. Experiments on a real-world trace-based online social network dataset show that the SpeCH is effective, efficient, and scalable. Empirically, SpectralApprox is comparable in efficacy on the evaluated metrics, while being up to 10 times faster in execution time when compared to state-of-the-art techniques. On the other hand, though

The advances in Information and Communication Technologies (ICTs), together with social commerce initiatives, collaborative economy and education, are working together to move the gears of sustainable development. All of these aspects have the potential to revolutionize social business and contribute to future sustainable development. In the past few years, the economy has seen the emergence of modern business models and innovative ideas, mainly driven by ICT. Concepts such as social commerce, collaborative economy and virtual currencies establish new business models, in which participants seek equity exchanges, trust, cooperation, and a better redistribution of incomes. Education cannot be exempted from this evolution. Education should play an important role in society since it generates and transfers new knowledge and contributes to developing appropriate competencies on this matter. The objective of this paper is to analyse the role played by these components to promote sustainable development. The research method conducted in this work is twofold. First, a conceptualization of the key terms involved in this research was. Next, a constructivist approach centred on the student was conducted to enable the creation of learning proposals to prepare students to take advantage of the possibilities offered by the progress of technology to promote sustainable development and social commerce initiatives.

Nowadays malware is a major threat to the security of cyber activities. The rapid development of the Internet and the progressive implementation of the Internet of Things (IoT) increase the security needs of networks. This research presents a theoretical model of malware propagation for mobile computer devices. It is based on the susceptible-exposed-infected-recovered-susceptible (SEIRS) epidemic model. The scheme is based on a concrete connection pattern between nodes defined by both a particular neighbourhood which fixes the connection between devices, and a local rule which sets whether the link is infective or not. The results corroborate the ability of our model to perform the behaviour patterns provided by the ordinary differential equation (ODE) traditional method.

Anatomical insoles and additions have a corrective action on the footwear user. They are intended to reduce and adequately distribute plantar pressure among support points, thus minimising the stress these points can undergo. Such customised components have traditionally been manufactured either handcrafted or by subtractive techniques, i.e. by milling a sheet of material. Latest advances in additive manufacturing (AM) techniques and, in particular, the popularisation of 3D printing by fused deposition modelling (FDM), have opened new ways for the production of anatomical insoles. These technologies allow additional functionalities to be added, as for instance the use of materials with antimicrobial properties, or, at a structural level, zonal control in 3D design to increase cushioning capacity. The latter cannot be achieved by traditional manufacturing techniques, in that the inside of the element is not accessible. However, there are no CAD tools available for the design and production of insoles, which are specifically oriented to take advantage of the benefits that AM can bring about. Based on a previous study about the possibilities for functionalisation of anatomical insole materials and structures, this paper intends to review certain CAD methodologies for the design and manufacture of insoles by means of additive manufacturing techniques. These techniques will be employed to design and produce prototypes through which it is possible to assess such techniques. In order to study the feasibility of using this technology for the manufacture of customised insoles in a real production environment, this paper presents a comparative analysis of the proposed technology and the technology that is currently being used.

The evolving trends of mobility, cloud computing and collaboration have blurred the perimeter separating corporate networks from the wider world. These new tools and business models enhance productivity and present new opportunities for competitive advantage although they also introduce new risks. Currently, security is one of the most limiting issues for technological development in fields such as Internet of Things or Cyber-physical systems. This work contributes to the cyber security research field with a design that can incorporate advanced scheduling algorithms and predictive models in a parallel and distributed way, in order to improve intrusion detection in the current scenario, where increased demand for global and wireless interconnection has weakened approaches based on protection tasks running only on specific perimeter security devices. The aim of this paper is to provide a framework to properly distribute intrusion detection system (IDS) tasks, considering security requirements and variable availability of computing resources. To accomplish this, we propose a novel approach, which promotes the integration of personal and enterprise computing resources with externally supplied cloud services, in order to handle the security requirements. For example, in a business environment, there is a set information resources that need to be specially protected, including data handled and transmitted by small mobile devices. These devices can execute part of the IDS tasks necessary for self-protection, but other tasks could be derived to other more powerful systems. This integration must be achieved in a dynamic way: cloud resources are used only when necessary, minimizing utility computing costs and security problems posed by cloud, but preserving local resources when those are required for business processes or user experience. In addition to satisfying the main objective, the strengths and benefits of the proposed framework can be explored in future research. This

This paper proposes a new framework that takes advantage of the computing capabilities provided by the Internet of Thing (IoT) paradigm in order to support collaborative applications. It looks at the requirements needed to run a wide range of computing tasks on a set of devices in the user environment with limited computing resources. This approach contributes to building the social dimension of the IoT by enabling the addition of computing resources accessible to the user without harming the other activities for which the IoT devices are intended. The framework mainly includes a model of the computing load, a scheduling mechanism and a handover procedure for transferring tasks between available devices. The experiments show the feasibility of the approach and compare different implementation alternatives.

Cyber-Physical Systems typically consist of a combination of mobile devices, embedded systems and computers to monitor, sense, and actuate with the surrounding real world. These computing elements are usually wireless, interconnected to share data and interact with each other, with the server part and also with cloud computing services. In such a heterogeneous environment, new applications arise to meet ever-increasing needs and these are an important challenge to the processing capabilities of devices. For example, automatic driving systems, manufacturing environments, smart city management, etc. To meet the requirements of said application contexts, the system can create computing processes to distribute the workload over the network and/or a cloud computing server. Multiple options arise in relation to what network nodes should support the execution of the processes. This paper focuses on this problem by introducing a distributed computational model to dynamically share these tasks among the computing nodes and considering the inherent variability of the context in these environments. Our novel approach promotes the integration of the computing resources, with externally supplied cloud services, to fulfill modern application requirements. A prototype implementation for the proposed model has been built and an application example has been designed to validate the proposal in a real working environment.

Precision in computations is a considerable challenge to adequately addressing many current scientific or engineering problems. The way in which the numbers are represented constitutes the first step to compute them and determines the validity of the results. The aim of this research is to provide a formal framework and a set of computational primitives to address high precision problems of mathematical calculation in engineering and numerical simulation. The main contribution of this research is a mathematical model to build an exact arithmetical unit able to represent without error rational numbers in positional notation system. The functions under consideration are addition and multiplication because they form an algebraic commutative ring which contains a multiplicative inverse for every non-zero element. This paper reviews other specialized arithmetic units based on existing formats to show ways to make high precision computing. It is proposed a formal framework of the whole arithmetic architecture in which the operators are based. Then, the design of the addition operation is detailed and its hardware implementation is described. Finally, extensive evaluation of this operator is performed to prove its ability for exact processing.

The research developed in this work consists in proposing a set of techniques for management of social networks and their integration into the educational process. The proposals made are based on assumptions that have been proven with simple examples in a real scenario of university teaching. The results show that social networks have more capacity to spread information than educational web platforms. Moreover, educational social networks are developed in a context of freedom of expression intrinsically linked to Internet freedom. In that context, users can write opinions or comments which are not liked by the staff of schools. However, this feature can be exploited to enrich the educational process and improve the quality of their achievement. The network has covered needs and created new ones. So, the figure of the Community Manager is proposed as agent in educational context for monitoring network and aims to channel the opinions and to provide a rapid response to an academic problem.

There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method.

Emerging VLSI and ULSI integration technologies provide new possibilities for developing computational paradigms based on memories with pre-calculated data. A memory can behave like a processor with complete functionality by simulating a classic Turing machine. By means of this stored logic based architecture, the processor adopts a simple and regular internal organization. In addition, it makes the most of the inherent features in the memories and offers the possibility of reconfiguration by writing new results inside of it. This paper reviews different possibilities to use memory elements to perform computations that would replace combinational logic processing of a given function. In this research, a computational model based on stored logic is described and interesting issues concerned with memory utilization in processing are analyzed. A small functional unit based on these notions is presented.

Since radix-10 arithmetic has been gaining renewed importance over the last few years, high performance decimal systems and techniques are highly demanded. In this paper, a modification of the CORDIC method for decimal arithmetic is proposed so as to improve calculations. The algorithm works with BCD operands and no conversion to binary is needed. A significant reduction in the number of iterations in comparison to the original decimal CORDIC method is achieved. The experiments showing the advantages of the new method are described. Also, the results with regard to delay obtained by means of an FPGA implementation of the method are shown.

Although radix 10 based arithmetic has been gaining renewed importance over the last few years, decimal systems are not efficient enough and techniques are still under development. In this paper, an improvement of the CORDIC (coordinate rotation digital computer) method for decimal representation is proposed and applied to produce fast rotations. The algorithm uses BCD operands as inputs, combining the advantages of both decimal and binary systems. The result is a reduction of 50% in the number of iterations if compared with the original Decimal CORDIC method. Finally, we present a hardware architecture useful to produce BCD coordinates rotations accurately and fast, and different experiments demonstrating the advantages of the new method are shown. A reduction of 75% in a single stage delay is obtained, whereas the circuit area just increases in about 5%.

Tool path generation is one of the most complex problems in Computer Aided Manufacturing. Although some efficient strategies have been developed, most of them are only useful for standard machining. The algorithm called Virtual Digitizing computes the tool path by means of a virtually digitized model of the surface and a geometry specification of the tool and its motion, so it can even be used in non-standard machining. This algorithm is simple and robust and avoids the problem of tool-surface collision by its own definition. A Virtual Digitizing optimisation that makes the most of specific hardware in order to improve the algorithm efficiency is presented in this paper. A comparative study will show the gain achieved in terms of total computing time. We also present a FPGA-based architecture that can be used to produce rotations with more precision and speed than other well-known classic implementations.