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Artificial intelligence systems are becoming ubiquitous in everyday life as well as in high-risk environments, such as autonomous driving, medical treatment, and medicine. The opaque nature of the deep neural network raises concerns about its adoption in high-risk environments. It is important for researchers to explain how these models reach their decisions. Most of the existing methods rely on softmax to explain model decisions. However, softmax is shown to be often misleading, particularly giving unjustified high confidence even for samples far from the training data. To overcome this shortcoming, we propose Bayesian model uncertainty for producing counterfactual explanations. In this paper, we compare the counterfactual explanation of models based on Bayesian uncertainty and softmax score. This work predictively produces minimal important features, which maximally change classifier output to explain the decision-making process of the Bayesian model. We used MNIST and Caltech Bird 2011 datasets for experiments. The results show that the Bayesian model outperforms the softmax model and produces more concise and human-understandable counterfactuals.

The purpose of this paper is to compare the strategies of companies with data science practices and methodologies and the data specificities/variables that can influence the definition of a data science strategy in pharma companies. The current paper is an empirical study, and the research approach consists of verifying against a set of statistical tests the differences between companies with a data science strategy and companies without a data science strategy. We have designed a specific questionnaire and applied it to a sample of 280 pharma companies. The main findings are based on the analysis of these variables: overwhelming volume, managing unstructured data, data quality, availability of data, access rights to data, data ownership issues, cost of data, lack of pre-processing facilities, lack of technology, shortage of talent/skills, privacy concerns and regulatory risks, security, and difficulties of data portability regarding companies with a data science strategy and companies without a data science strategy. The paper offers an in-depth comparative analysis between companies with or without a data science strategy, and the key limitation is regarding the literature review as a consequence of the novelty of the theme; there is a lack of scientific studies regarding this specific aspect of data science. In terms of the practical business implications, an organization with a data science strategy will have better direction and management practices as the decision-making process is based on accurate and valuable data, but it needs data scientists skills to fulfil those goals. info:eu-repo/semantics/acceptedVersion

This research aims to understand how Business process management (BPM) can be applied for the improvement of Information Technology service management (ITSM) processes. A case study is conducted for the improvement of the time performance of the incident management process, since it is pointed as a quick win for ITSM. The results obtained identified three best practices—activity automation, activity elimination and integral technology—as the best suited for the improvement of the time performance of the incident management process. Using a simulation tool for business processes, it was revealed that the employment of these best practices in the analysed incident management process eliminates the effort required in the 1st support level and reduces in 10.7% the average processing time in the 2nd support level.

The applications related to ultra-low power wearables like internet of things, robotics, intrusion detection, and image security have become a major computing paradigm and the latest disruptive technology after artificial intelligence. Ability of ultra-low power wearables to connect with other systems and applications from low to high level has prompted unprecedented array of applications in all fields of human activity from science, engineering, health, leisure and everyday life, etc. The technological growth in ultra-low power wearables is going hand-by-hand with enormous research efforts by an ever increasing community in a fascinating multidisciplinary field. The motivation of this special issue is to offer an essential guide to the readers on the applications: internet of things security, wireless sensor network, image processing, intrusion detection, antenna, optimization, and cryptography. The accepted manuscripts are used as a reference text for graduate and undergraduate studies. Hence, the manuscripts are written in plain and easy to follow language and explain every main concept the first time it appears, helping readers with no prior background in the field. It is a “must-read” guide to the subject matter. This special issue intends to provide a platform for researchers to share innovative work in many applications like internet of things security, wireless sensor network, image processing, intrusion detection, antenna, optimization, and cryptography. After a double-blinded peer-review procedure, 14 papers have been accepted and included in this special issue, which contains different methods to solve different kinds of complex problems.

With the rapid development of Internet information technology, the advantages of social media in terms of speed, content, form, and effect of communication are becoming increasingly significant. In recent years, more and more researchers have paid attention to the special value and role of social media tools in disaster information emergency management. Weibo is the most widely used Chinese social media tool. To effectively mine and apply the emergency function of disaster situation microblogs, a disaster situation information discovery and collection system capable of online incremental identification and collection are constructed for massive and disordered disaster microblog text streams. First, based on the deep learning- trained word vector model and a large-scale corpus, an unsupervised short-text feature representation method of disaster situation Weibo information is developed. According to the experimental results of the feature combination test and the training set scale test, the SVM algorithm was selected for disaster microblog information classification, which realized effective identification of disaster situation micro-bloggings. Then, the temporal information similarity and geographic information similarity are used to improve the single text similarity algorithm, and a Chinese disaster event online real-time detection model is constructed. Furthermore, the disaster-affected areas can be achieved in real-time based on the detection results. By crawling and classifying the micro-bloggings from the disaster-affected areas, it is possible to realize the incremental identification and collection of online disaster situation Weibo information. Finally, the empirical analysis of disaster events such as the “Leshan Earthquake” shows that the real- time intelligent identification and collection system for disaster situation Weibo micro-bloggings developed in this paper can obtain large-scale and useful data for disaster emergency management, which proving that this system is effective and efficient.

This Special Issue is dedicated to current issues in Decision-making and Negotiation in Innovation & Research in Information Science. Despite all the research around Decision-making and Negotiation the main theoretical and practical importance of this issue is the focus on studies oriented to innovation & research (I&R) in information science. The purpose is to analyse the decision-making processes for practical problem-solving and to understand the ways information systems can be a trigger in the process. To achieve this objective several articles will present models, tools and frameworks to help improving the decision-making and negotiation processes, and the research findings support the conclusion that information systems can help to achieve better decisions and facilitate the negotiation processes.

Agriculture industry is the foundation of Indian economy where quality fruit production plays an important role. Apple or pome fruits are always in demand because of rich nutrients in it. Hence, to analyze and recognize the nutrients deficiency in fruits, a deep neural-based model is being proposed. This model automatically classifies and recognizes the type of deficiency present in apple. In this paper, a database has been created for four major types of nutrients deficiency in apples and used for training and validation of the proposed deep convolutional network. The model is tuned with k-fold cross-validation. The hyper-parameters such as epoch are set at 100 and batch size kept at 5. Finally, the model is tested with the testing data and achieved an average accuracy of 98.24% with k-fold cross-validation set to 15. The model accuracy depends on the hyper-parameters. The process of features optimization reduces the risk of overfitting of the model. Hence, careful selection of hyper-parameters is important for the convergence of cost function to the global minima that results in minimum misclassification.

Skin cancer is one of the diseases which lead to death if not detected at an early stage. Computer-aided detection and diagnosis systems are designed for its early diagnosis which may prevent biopsy and use of dermoscopic tools. Numerous researches have considered this problem and achieved good results. In automatic diagnosis of skin cancer through computer-aided system, feature extraction and reduction plays an important role. The purpose of this research is to develop computer-aided detection and diagnosis systems for classifying a lesion into cancer or non-cancer owing to the usage of precise feature extraction technique. This paper proposed the fusion of bag-of-feature method with speeded up robust features for feature extraction and quadratic support vector machine for classification. The proposed method shows the accuracy of 85.7%, sensitivity of 100%, specificity of 60% and training time of 0.8507 s in classifying the lesion. The result and analysis of experiments are done on the PH2 dataset of skin cancer. Our method improves performance accuracy with an increase of 3% than other state-of-the-art methods.

The present article, placed within the epistemological framework of Political Communication, analyses citizens’ reaction to politicians’ messages on the social network Twitter during the presidential elections in Argentina, Peru, Ecuador, Honduras, and Chile, held between 2015 and 2017. Through a script developed for the present research, almost 200,000 tweets have been studied according to the following questions: What are citizens’ emotional reactions to the messages of presidential candidates? Does digital public opinion analysis have a predictive nature from the electoral point of view? As a result, we note the existence of “sympathy currents” and “antipathy currents” on social media, where positive emotions prevail, especially towards candidates on the right side of the ideological spectrum, with progressive politicians generating a higher anger and sadness index than conservative ones. Similarly, emotions on social media largely correlate to the subsequent electoral result.

Presently, the fruit industry requires a fast and efficient method for classification and recognition of the quality of fruits in bulk processing. Fruit recognition based on computer vision is quite challenging as it is based on the intensity, size, contour, and texture features extraction from fruits along with their suitable classifier selection. In this paper, the pixels containing the defected regions are segmented and their features are extracted. Further, a support vector machine (SVM) classifier is used to identify the defects and recognizes the cause with its stage. During the process of classification, fruits are categorized into two groups, defected and no-defect. The sample image observed defected further classified into three categories as the first, second and final stage of fruit defect. The sample testing at an early stage helps one to further proceed with the production or halt based on the outcome of a computer vision-based recognition system.

Higher education institutions (HEIs) are complex and dynamic organizations in terms of information management, forcing their information systems to respond to enormous challenges and threats. In order to evaluate the HEIs’ information systems, we propose the development of a maturity model capable of supporting the role of HEI’s managers, as well as accreditation agencies, in the assessment of the maturity of these systems, thus, promoting continuous improvement. In this paper, we present and discuss our proposal for an architecture of the maturity model being developed. This one is based on a two-dimensional architecture composed of vertical and horizontal dimensions. We selected a multi-case study approach, based on five Portuguese HEIs, and reviewed the literature to identify the dimensions. This case study was supported by interviews with experts from the selected HEI. The results of this research work were both encouraging and promising amongst the interviewed experts, revealing a high level of acceptance of the general model architecture, as well as positive expectations about its usefulness in the future. The development of our maturity model is carried out by following a formal methodology specially designed to support the construction of this type of model.

Maturity models have been introduced over the last five decades as guides and references for information system (IS) management in organizations from different sectors. In the education sec...

In the last five decades, maturity models have been introduced as reference frameworks for Information System (IS) management in organizations within different industries. In the healthcare domain, maturity models have also been used to address a wide variety of challenges and the high demand for hospital IS (HIS) implementations. The increasing volume of data, is exceeded the ability of health organizations to process it for improving clinical and financial efficiencies and quality of care. It is believed that careful and attentive use of Data Analytics in healthcare can transform data into knowledge that can improve patient outcomes and operational efficiency. A maturity model in this conjuncture, is a way of identifying strengths and weaknesses of the HIS maturity and thus, find a way for improvement and evolution. This paper presents a proposal to measure Hospitals Information Systems maturity with regard to Data Analytics. The outcome of this paper is a maturity model, which includes six stages of HIS growth and maturity progression.

This Special Issue is dedicated to current issues in strategic knowledge management in the Era of digital. Despite all the research around knowledge management the main theoretical and practical importance of this special issue is the focus on research oriented to the identification, achievement and management of critical issues related with strategic knowledge management implementation within a holistic approach to business strategy, where different components impact the long term competitive advantage for the organisations.

In the last five decades, maturity models have been introduced as guides and reference frameworks for information system (IS) management in organizations within different industries and sectors. In the healthcare domain, maturity models have also been used to address a wide variety of challenges, complexities and the high demand for hospital IS (HIS) implementations. The present paper describes a research project focused on the development of a comprehensive maturity model applied in the HIS context. The outcome of this research is the HIS maturity model (hereafter referred to as HISMM), which includes six stages of HIS growth and maturity progression. The HISMM has the peculiarity of congregating a set of key maturity-influencing factors and respective characteristics, enabling not only the assessment of the global maturity of a HIS, but also the individual maturity of its different dimensions.

Nowadays, we are forced to face off, more than ever before, an exponential growth of tremendous quantities of data necessary to be local or remotely transferred within cabled or wireless communication networks. We present in this work a methodology used for a short survey and the main findings following queries in important scientific databases. As a result of this research in databases, a sort presentation of achievement related to data rates and optical link, are chronological presented. The Optical Wireless Communication (OWC), complementary to the classical wireless transmission technologies into the radiofrequency (RF) spectrum, consists of Visible Light Communication (VLC), Optical Camera Communication (OCC), Light Fidelity (LiFi), Free Space Optics (FSO) and Infrared (IR). All these technologies and applications are briefly described here with their key characteristics. Various implementations of OWC in different areas so far, as well as the challenges and achievements reached during the last decade of intensive research, are also addressed in this work.

The article is devoted to the assessment of existing level of development of information and consulting system in agricultural and industrial complex. The relevance of covering methodological issues is due to the current level of development of information and consulting services in regions, the rapid and large-scale expansion of the Internet space, the established world experience in introducing innovations, and the need to implement state policy in the field of agriculture. The paper considers the works of Russian scientists and economists on improving the system of agricultural consulting. The positive world experience of spreading an effective system of innovative and consulting services of rural areas is studied. For discussion, the authors propose a methodology for choosing a model of an innovative service center in rural areas. The effectiveness of application of comprehensive information system with the involvement of educational organizations is justified. The experience of functioning of Center for Support of Rural Development on the basis of regional university is presented. The authors formulate proposals for improving the innovation and consulting infrastructure in the agricultural and industrial complex in order to increase the efficiency of providing information and consulting services, increase the level of knowledge of rural population, the personnel of agricultural producers, disseminate information about the achievements of scientific and technical community, innovative developments and methods of management, effective interaction of agricultural entities, authorities, scientific and educational organizations.

This research examines knowledge transition in an industrial company. This study presents findings about methods and forms of interaction and knowledge transition between organizational actors in innovation processes. Methodology is qualitative and quantitative, as the data was collected through interviews and questionnaires techniques. This study contributes to the body of knowledge about knowledge transition in innovation processes, and empirically presents the impacts of knowledge transition in several dimensions of the organization activity. Finally, the study provides directions for avenues of future research, and suggests some research questions arising out of these findings that might be explored.

Big Data technology is one of the most promising organizational processes within the Healthcare and Pharmaceutical industry and crucial for any company that wants to preserve the competitive advantage in the market, where most of the organizational structures are already struggling with the right skills and knowledge to fully support existing business needs for storing and processing and even analyzing information. This paper aims to examine the extent to which new Big Data technology and data-related processes are developing different professionals skills and competencies within the Healthcare and Pharmaceutical industries, and creating sustainable development in addressing critical organizational challenges in recruiting, retaining, and discover professional skills that can fully support the advances and exponential growth of Big Data technology benefits. This research paper also highlights the significant aspects of Big Data in professional technical and process oriented skills development, and the influence it has on organizational business processes including how various internal functions will need to adapt to new circumstances with renewed competency and skills development programs for departments that are strongly connected to the business and analytical needs. We conducted a focus group with twenty-five industry based professionals’ ranges from analysts to executive directors to better assess the necessary knowledge to answer the proposed research questions: (1) which professional skills can big data influence in employee development and (2) how can organizations adapt their employee skills to big data. Regarding the key research limitations/implications most of the article and research was built on the foundation of the literature review and the performed focus group. The conceptual recommendations and observations presented provide solid empirical evidence but should be subjected to more comprehensive, large-scale empirical testing and validation. It’s recommended for future research a more extensive sample of companies, organizations, and interviewees. Studying a broader set of similar research questions in more homogeneous organizations could provide deeper insights into the process, governance, and stakeholder dimensions of Big Data within specific contexts. Therefore this study contributes to explore in-depth and systematically to what extent Big Data technology and processes are currently influencing the healthcare and pharmaceuticals industries where to the best of the authors’ knowledge, it is the first focus group dealing with the presented research questions.

“Cyber-attacks can be more dangerous than guns and tanks”. This statement was made by Junker in his speech in the EU “State of the Union” in 2017 and reflects the extreme importance of this phenomenon. The increasing variety of devices that can access to Internet has contributed to the overall growth of internet users, in all kinds of places and circumstances. These devices are now part of our lives and we use it for everything. As the number of devices and tasks that we do with it are growing, more prone we are to cyber-attacks. As well, as the number of user’s victim of these attacks are increasing, the general concerns about cybercrime are growing too. The growing list of cybercrimes includes crimes that have been made possible by computers, such as network intrusions and the dissemination of computer viruses, as well as computer-based variations of existing crimes, such as identity theft and terrorism which have become as major problem to people and nations. In this context, the European Union is leading the effort to regulate the defence against cyber-threats in both normative/legal and strategic levels. This reality is also very present in Portugal, where there is a sense of lack of control and capacity to combat this kind of threats. In this article, we present the European Strategy and Legislation for Cybersecurity and how this strategy applies and involves Portugal.

Data Science demand from Medical Affairs (MA) functions in the pharmaceutical industry are exponentially increasing, where business cases around more modern execution of activities and strategic planning are becoming a reality. MA is still lagging in terms of implementing data science and big data technology in the current times, which means a reflecting immaturity of capabilities and processes to implement these technologies better. This paper aims to identify possible gaps in the literature and define a starting point to better understand the application of Data Science for pharmaceutical MA departments through the identification and synthesis of data science criteria used in MA case studies as presented in the scientific literature. We applied a Systematic Literature Review of studies published up to (and including) 2017 through a database search and backward and forward snowballing. In total, we evaluated 2247 papers, of which 11 included specific data science methodologies criteria used in medical affairs departments. It was also made a quantitative analysis based on data from a questionnaire applied to Takeda, a Pharma organization. The findings indicate that there is good evidence in the empirical relation between Data Technostructure and Data Management dimensions of the Data Science strategy of the organization.

The paper analyzes the ways in which knowledge management contributes to the success of today’s companies and leads to an increase in their effectiveness and ability to remain competitive, and furthermore, the tools and practices that they use for their knowledge management process with the role of providing a framework for their knowledge management, which is based on a proposed knowledge management life cycle, inspired by the Agile System Development Life cycle. For this, in the paper, the current life cycle models used in knowledge management have been analyzed, along with the particularities and necessities of agile companies, and based on this, a life cycle model for the knowledge management of agile companies has been proposed, and furthermore, the possibility of integrating quality management tools within each stage of the life cycle has been analyzed, and a set of quality tools has been proposed in the context of the life cycle model.

Business process management (BPM) is an approach focused on the continuous improvement of business processes, providing for this a collection of best practices. These best practices enable the redesign of business processes to meet the desired performance. IT service management (ITSM) defines the management of IT operations as a service. There are several ITSM frameworks available, consisting in best practices that propose standardizing these processes for the respective operations. By adopting these frameworks, organisations can align IT with their business objectives. The constant need for change, competitiveness and innovation in organisations compels managers to analyse its business processes and find improvement opportunities. Therefore, the objective of this research is to understand how BPM can be used to improve of ITSM processes. An exploratory case study in a multinational company based in Lisbon, Portugal, is conducted for the improvement of the time performance of an incident management process. Data were gained through documentation, archival records, interviews and focus groups with a team involved in IT support service. The results of this study demonstrate how BPM highlighted the current incident management process incongruences. The preliminary evidences of this research indicate that with the application of the remain BPM lifecycle phases may be possible to improve incident management process. During the next months the authors intend to identify the main problems and simulate the appropriate BPM heuristics to understand the impact in the business organisation.

An accurate channel model is both essential and challenging when designing a reliable wireless Visible Light Communication (VLC) system. Modeling the channel in a restricted and harsh environment such as an underground mine, is even more difficult. The objective in our work is to investigate a suitable design of a VLC link for an underground environment, while providing a reliable connectivity between the optical transmitter and optical receiver. In this paper, we present an experimental study that aims to determine a general model of an underground mine optical channel. Considering that the underground optical channel has a nonlinear dynamic behavior described by a nonlinear autoregressive exogenous (NARX) mathematical model, the system’s order is established with a feedforward neural network (NN), with a parallel architecture. In this work, in order to define a proper Underground Mine Optical Channel Model (UMOCM) for reliable VLC, we present an innovative approach based on a suitable mathematical model and neural networks (NNs).

It is a reality that wearable devices are part of our lives. We find them in different forms and answering different utilities and needs. The present research responds to an expressed necessity, namely the awareness on the psychological stress work-related syndrome of burnout, increasingly present in different fields of activity, education being among them. The research is carried out on two directions: (1) The analysis of the relationship between the organizational climate and the burnout state using a quantitative research. The results are concerning the identification of the organizational climate dimensions with the highest influence on the state of burnout and also, the fact that the studied demographic variables have no influence on the state of burnout. (2) The development, prototyping and testing of a burnout state evaluation and prediction device based on a burnout detection algorithm that allows the creation of a user profile. The findings of the research will further allow the testing of the prototype for evaluation and prediction of the burnout state in response to the stressors related to the identified high influence dimensions of the organizational climate. The research is characterized by a high degree of specificity due to the identified premises and the research context focused on the principals from Arab schools from South Israel.

This paper presents a research on the effectiveness of digital learning in organisations and how it can be measured. This research aims to bridge the gap between scientific and practice-oriented research about digital learning. The focus will be on learning analytics and measuring its impacts on organisations with the goal to identify the potential of digital learning for organisations. This research is based on a systematic literature review and a survey analysis to answer the following research questions: What are the contexts in which digital learning can take place? What are the main metrics which allows measuring the efficiency of digital learning in organisational contexts? Is there a positive influence of the digital learning contexts supported by organisations, on skills development and impacts on performance analytics that measure the impacts in organisations? And what is the digital learning analytics that measures the impacts on organisations? What is the digital learning analytics that measures the impacts on organisations? The results showed that digital learning analytics can enhance the efficacy of the learning process impact in organisations and that contexts for learning supported on mobile technologies, tablet and smartphone applications become more and more popular among the employees. Additionally, the results helped to create a model of learning analytics and identify impacts of the learning process.

With the advent of the era of big data, massive data make the informationization work of academic libraries severely challenging in all aspects. The application of data mining technology to the various services of academic libraries to cope with this challenge has gradually become the mainstream trend. In order to better detect the overall trends and research hot spots of data mining in Chinese academic libraries, 329 related studies were extracted from CNKI (China National Knowledge Infrastructure) and analyzed by using bibliometrics and keyword network analyses. Bibliometrics was used to analyze the distribution of the overall trends, authors, institutions, research levels, and keywords in the related field. Cluster analysis was applied to the keyword co-occurrence network to show hot issues in the application research of data mining in Chinese academic libraries. Results indicate that findings in this area generally show an upward trend, but few high-yielding authors are found, the volume of journals is low, research institutions are scattered, and the rate of funding support is not high. At the method level, association rules and cluster analysis are hot data mining technologies in Chinese academic library research. At the application level, the fields related to personalized services and big data continue to be hot research areas.

Analizamos la literatura académica internacional centrada en los teléfonos móviles y las terapias psicosociales. Identificamos los artículos publicados entre 2000 y 2015 en las bases de datos disponibles en Internet; y ponemos en evidencia las escasas investigaciones con personas vulnerables. La literatura especializada en terapias psicosociales muestra interés en las utilidades de los teléfonos móviles en pacientes en general, con mayor producción desde la psicología y en menor medida desde el trabajo social. Destacan las investigaciones desarrolladas en los Estados Unidos y su escasa existencia en España, donde se discuten las competencias del trabajador social en este campo. Identificamos oportunidades y limitaciones en cuanto a la privacidad de los datos, al acceso a los teléfonos móviles y a su uso; para llegar a poblaciones aisladas o de escasos recursos o para facilitar el acceso a los servicios sociales y de salud. Concluimos con posibles orientaciones, hipótesis y objetivos en el diseño de nuevas investigaciones. We analyse international academic literature focused on mobile phones and psychosocial therapies. We identify articles published between 2000 and 2015 and available on Internet databases; and we highlight the scarce number of investigations focusing on vulnerable people. Literature specializing in psychosocial therapies address the usefulness of mobile phones in patients in general, with greater production from psychology and to a lesser extent from social work. We highlight the research developed in the USA and its residualness in Spain, where debates focus on the competences of the social worker in this field. We identify opportunities and limitations in terms of data privacy, access to and use of mobile phones; to reach isolated or poor populations or to facilitate access to social and health services. We conclude with possible orientations, hypotheses and goals for prospective investigation works SI

Effective knowledge management practices in organizations are focused on knowledge creation and knowledge transfer activities. Thus, intelligence and competencies matters at the organizational workplace. For most knowledge intensive organizations is fundamental the continuous availability and development of domain expertise. This paper describes an ongoing research project to develop an organizational knowledge architecture that is being specified and developed to support collaboration tasks as well as design and model predictive data analysis and insights for organizational development. The primary goal of this research is to create a suitable architecture for use, initially, in intranet (corporate portal) collaborative procedures, but also scalable for later use in more generic forms of ontology-driven knowledge management systems. The designed architecture and functionalities aim to create coherent web data layers for intranet learning and predictive analysis, defining the vocabulary and semantics for knowledge sharing and reuse projects. Regarding intellectual capital definition, this research argues that effective knowledge management are based on the dynamic nature of the organizational knowledge, and predictive data analysis and insights identification can transform and add value to an organization. This paper presents a knowledge management and engineering perspective (ontology based) for the application of predictive analysis and insights at the organizational (corporate) workplace towards the development of the organizational learning network.

Forecasting techniques have been widely used in automatic assessment in virtual teaching and learning environments, allowing educators to make decisions in both present and future planning. However, due to the element of uncertainty intrinsic to the forecasting methods, a number of studies have been carried out in order to find a more efficient model that allows for exploring and inferring the relation of a dependent variable with independent variables. In this context, we propose an alternative to solve the problem of automatic evaluation with the use of artificial neural networks that are adjusted, or trained, so that a certain input leads to a specific target output. Therefore, the research seeks to achieve the following: (1) review the state-of-the-art work published in this area, (2) propose a better forecasting model as compared to the existing model, (3) perform a comparative analysis with the previous experiments of multiple linear regression (MLR) and symbolic regression, (4) propose future research guidelines. To this end, a case study was applied to clarify the benefits of the artificial neural networks, emphasizing its efficiency and simplicity of implementation. With a margin of error of less than 2%, this proposal simulates a specialist, and automatically evaluates a student’s answer. As a result, the proposed model performance overcomes the methods of multiple linear regression and symbolic regression efficiently, eliminating the problem of randomness, reducing processing time and at the same time providing a model with higher accuracy and lower error rates.

This article presents the first phase of a research project whose purpose is to introduce a new form of collaboration and communication between the school and the family through an Electronic Booklet (EB). The EB was developed due to the increasing necessity of collaboration between the school and the family, promoting the teaching-learning process and educational success, supported by information and communication technologies. These conclusions and justifications were derived from a survey based in a questionnaire applied to guardians and teachers of several private and public Portuguese schools of the primary and secondary education level. Both groups – guardians and teachers stressed the importance of communication between school and family and expressed interest in the EB as a communication tool. The analysis of the survey results made possible the construction of an Electronics Booklet prototype.

This article provides an overview of the different nodes that accommodate the architecture of the OpenStack project. Throughout this article, we use a statistical approach to show how OpenStack is currently being developed under the control of the OpenStack Foundation and how this new platform is influencing the global market for cloud computing. We also discuss community participation in this project and present an overview of how these different components achieve independence from each other, while using asynchronous queues to send messages among themselves in order to reliably maintain communication. As a result, we have achieved a systematic review covering the period from the start of the OpenStack project until the present. This longitudinal study can be used to examine variations and changes in characteristics and different historical versions of the OpenStack project (Kilo, Juno, Icehouse etc.) throughout the years.

Advances in computing techniques, data acquisition technology, hardware, and networks have mutually promoted the development of multimedia analysis approaches. Many machine learning, signal/image p...

The problem discussed in this paper is the paradox associated with hundreds of contributions of the scientific community over three decades, mainly leveraged by the Strategic Alignment Model (SAM) and its perspectives, but whose benefits arising from its applicability to the real world are scarce. The context of public sector organizations, which does not fit into any of the four perspectives presented by Henderson and Venkatraman (1993), led to the intertwining of the theme with Data Science. The hypothesis formulation involves interconnecting convergent concepts, namely through the adoption of a replicable framework in any world-wide public services, to potentiate the change of perspective, allowing to achieve the desired strategic alignment (IT-Business). It is hoped that the suggestions and recommendations can contribute to leverage significant changes in the Public Sector context in order to optimize the services provided, anticipating the citizen’s needs.

New, emerging technologies, transform every day our life and have direct consequence on our health and well-being. More and more wearable medical devices (MD) with wireless communication technologies embedded are being developed by innovative academic community and companies. Optical wireless communication (OWC) consisting of Visible Light Communication (VLC), infrared (IR), Optical Camera Communication (OCC) and Light Fidelity (LiFi) along with the conventional Radio Frequency (RF) wireless communication are suitable technologies to be used for hybrid Wireless Integrated Medical Assistance Systems (WIMAS). The WIMAS addressed in this paper consists of two Wireless Medical Body Area Networks (WMBAN) (an insulin wearable kit and an ECG test device with VLC/OCC are considered) and an Emergency Remote Medical Assistance (ERMA) with LiFi technology embedded. Using RF in medical facilities is subject of strict regulations due to interferences with other RF medical devices, negative effects on human health and lack of security. VLC and OCC are suitable to be embedded in MDs in order to be used by the patients with wearable WMBAN. Research on IR transdermal communication for implantable MDs has also been demonstrated as feasible and both VLC and OCC have promising future, as well. On the other hand, LiFi technology, recently deployed on the market, is mature enough to be integrated in the ERMA system addressed here.

With the evolution and popularization of mobile communication sys-tems, especially for access to data services, it becomes increasingly important to have a closer look at security systems. From analog systems to the fourth gener-ation, many changes and evolutions occurred. This makes possible to question whether mobile networks have sufficiently secure systems, and what vulnerabil-ities we should be aware of. Due to the widespread risk of attacks on mobile communications, telecommunication companies are increasingly emphasizing telecommunication security technologies specifically in fourth generation (4G) technology, in order to ensure the main security functions for to provide an effi-cient service to its customers. info:eu-repo/semantics/publishedVersion

The increasing use of a variety of devices that can access the Internet has contributed to the overall growth of Internet use. The growth of Internet use also leads to a general increase in cybercrime concerns as well as the number of users’ victims of this permanent threat. This reality is also very present in Portugal where there is a sense of lack of control and combat of this kind of cyber-threats. In this context, it is important to know some indicators associated with cybercrime, in order to take the best measures/actions in a reasoned way, to combat this phenomenon. This article presents the results of a study carried out in 2017 by the European Commission, whose analysis helps to understand the Portuguese positioning in the concerns and experiences about cybercrime, in relation to the average of the European Community citizens. Analysing this study, it was possible to perceive that the Portuguese’s concerns about cybercrime are in line with the average of the European citizens, although the percentage of Portuguese experienced these attacks is, for now, below average. Based on these results, it is possible to identify some public policies that aim to mitigate this phenomenon.

Big data analytics enables large-scale data sets integration, supporting people management decisions, and cost-effectiveness evaluation of healthcare organizations. The purpose of this article is to address the decision-making process based on big data analytics in Healthcare organizations, to identify main big data analytics able to support healthcare leaders’ decisions and to present some strategies to enhance efficiency along the healthcare value chain. Our research was based on a systematic review. During the literature review, we will be presenting as well the different applications of big data in the healthcare context and a proposal for a predictive model for people management processes. Our research underlines the importance big data analytics can add to the efficiency of the decision-making process, through a predictive model and real-time analytics, assisting in the collection, management, and integration of data in healthcare organizations.

The problem discussed in this paper is the paradox associated with the hundreds of contributions of the scientific community over three decades, mainly leveraged by the Strategic Alignment Model (SAM) and its perspectives, but whose benefits arising from its applicability to the real world are scarce. The context of public sector organizations, which does not fit into any of the four perspectives presented by Henderson and Venkatraman (1993), led to the intertwining of the theme with the Social Era, predominantly people-driven or relationship-driven. The hypothesis formulation involves interconnecting convergent concepts, such as Data Science, Artificial Intelligence, Machine Learning and e-participation, to potentiate the change of perspective, allowing to achieve the desired strategic alignment (IT-Business).

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean). This research was supported by the Cluster Research Projects Activity code # R16086 and R18027, Zayed University, Abu Dhabi, United Arab Emirates.

The primary goal of this paper is to contribute to the discussion of an effective digital transformation of organizations based on the latest trends of skills. It also intends to analyze the perceptions of individuals regarding the challenges faced by organizations and opportunities for new disruptive business. A literature review about the digital learning contexts and the identification of skills needed for organizations digital transformation was made. Moreover, it was also launched an online survey to identify the importance of skills for an effective digital transformation. As primary results, the main skills identified were artificial intelligence, nanotechnology, robotization, internet of things, augmented reality, digitalization; and the main digital learning contexts were mobile technologies, tablets, and smartphone applications — which are becoming more and more popular among the employees. This study will help organizations to rethink their strategies according to skills development to respond to the challenges of digital transformation. AISTI (Associacao Iberica de Sistemas e Tecnologias de Informacao/lberian Association for Information Systems and Technologies, Portugal)

Maturity models have been adopted in organizations from different sectors of activity, as guides and references for information system (IS) management. In the healthcare field, maturity models have also been used to deal with the enormous complexity and demands of hospital information systems (HIS). This article presents a research project that aimed to develop a new comprehensive model of maturity for a health area. HISMM (hospital information system maturity model) was developed to address the complexity of HIS and intends to offer a useful tool to meet the demands of its management. The HISMM has the peculiarity of combining a set of key maturity influence factors and their respective characteristics, enabling not only the assessment of the global maturity of an HIS but also of the individual maturities of its various dimensions. In this article, we present a methodology for the application and implementation of this model in HIS, thus contributing to its widespread practical application and acceptance.

A scientific conference is an event with a great importance within scientific community, fundamentally due the results obtained from it. Researchers of various research centers, laboratories, and universities have been meeting periodically according to their specific areas, in order to present their studies and thus contribute for the development of science. The preparation of a conference is not a simple activity; there are several processes taking into account to promote the success of a conference. Thus it is very important develop and implement software applications capable of doing the management of scientific conferences from planning to the end of the conference. Although there are several conference management systems, it is always necessary to have applications capable of increasingly facilitating and improving the activities carried out by all those involved in a scientific conference. In this paper we propose a web application to support the management of scientific conferences. Innovative features that are not evident in existing systems are implemented in this web application.