Nowadays, one of the most relevant challenges of a data center is to keep its information secure. To avoid data leaks and other security problems, data centers have to manage vulnerabilities, including determining the higher-risk vulnerabilities to prioritize. However, the current literature is scarce in the proposal of intelligent methods for the complex problem of vulnerabilities prioritization. Depending on the adopted metrics, the priority could shift, compromising simple sorting-based approaches and impairing the utilization of conflicting risk assessment metrics. Unlike the related work, this study proposes a multi-objective method that uses user-chosen vulnerabilities assessment metrics to output a complete list of these vulnerabilities ranked by their risk and overall impact in the context of an organization. The method includes a multi-objective large-scale optimization problem representation, a novel population initialization scheme, an expressive fitness function, a post-optimization process, and a custom way to select the best solution among the non-dominated ones. The dataset used in the experiments contains anonymized real-world information about database vulnerabilities obtained from a private organization. The experiments' results indicated that the proposed method can reduce the number of vulnerabilities needed to reach an organization's predefined security targets compared to the baselines simulating a security team's analysis. Multi-objective optimization achieved on average a 48,17% reduction in the vulnerabilities needed to reach the organization's target values compared to the baselines.

Cardiovascular disease (CVD) is one of the main causes of death in the world. Coronary artery disease (CAD) is one of the CVD most common disorders. CAD is mainly caused by restrictions in the heart muscle blood flow supply, called atherosclerosis. The assessment of atherosclerosis is challenging and is currently primarily achieved with angiography, which is the gold standard for geometrical assessment. The angiography is completely dependent on the physician's lesion identification and visual assessment. In this paper, we tackle this problem by proposing an architecture for automatic lesion detection, called DeepCADD. DeepCADD proposes the use of instance segmentation, replaces an original Mask R-CNN's backbone, and trains the classification layer with an angiography dataset. The backbone was replaced by a ResNet-50 pre-trained with a dataset with 2,000 images of coronary artery segments. Powered by the skip connections from ResNet-50 network which propagates small image features to deeper layers. DeepCADD is comparable with the gold standard and we discussed the performance with similar studies. Moreover, we performed a validation with specialists to understand the architecture's real performance. Our results show that DeepCADD presents a good performance in the lesion identification, reducing the false negatives and reaching a sensitivity of approximately 0.89. These pieces of evidence suggest that DeepCADD can be used as a screening tool, contributing to the narrowed lesions identification, playing a crucial role in the angiography protocol automation.

Healthcare 4.0 is a new concept that originates from the evolution of hospitals due to technological advances in medical activities. Nowadays, more and more doctors and healthcare administrators require real-time data analysis obtained from sensors and surgery monitoring. Using real-time information could make the difference between death and life in such settings. Therefore, Quality of Service (QoS) is essential in this context because, without it, the results of the applications become unreliable. Given the background, this article proposes HealthStack, a sensor middleware model for operating room facilities. HealthStack aims at improving delay and jitter from applications and, at the same time, reducing resource consumption. Our scientific contributions are twofold: 1) a middleware for operating rooms with automatic QoS support for real-time data transmission and 2) a QoS strategy based on artificial neurons to select middleware components with critical performance. We developed a prototype that uses depth cameras and ultrawideband (UWB) real-time location systems (RTLSs) to monitor workflow during surgery. The evaluation demonstrates that the strategy improves the average jitter experienced by application by 92.3%. The results also reveal a reduction of network and CPU consumption by up to 61.8% and 8.3%.

The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.

Blockchain is an emerging technology that has gained a lot of visibility in recent years due to its characteristics of transparency and immutability. Many studies have been carried out in order to use this technology in the financial industry, but there are still issues regarding scalability that need to be resolved before the technology can be used. In the literature there are works that perform Blockchain performance analysis of the private model, making comparisons between different platforms. However, no work was found regarding the analysis of the impact that changing parameters has on the performance of private model Blockchain applications. In this context, this article introduces PriBB that contributes to the literature regarding the proposal of a benchmark to evaluate the performance of private model Blockchain applications. In particular, it focuses on the impact that the parameters of block size and interval between blocks have on the performance of applications, since no equivalent work has been found in the literature. The PriBB was evaluated in a real data center environment and aims to assist in the optimization of parameterization of Blockchain applications of the private model. The results are promising and demonstrate that a given parameter can impact more than 100 times the performance in running Blockchain applications.

In the field of assisted cancer diagnosis, it is expected that the involvement of machine learning in diseases will give doctors a second opinion and help them to make a faster / better determination. There are a huge number of studies in this area using traditional machine learning methods and in other cases, using deep learning for this purpose. This article aims to evaluate the predictive models of machine learning classification regarding the accuracy, objectivity, and reproducible of the diagnosis of malignant neoplasm with fine needle aspiration. Also, we seek to add one more class for testing in this database as recommended in previous studies. We present six different classification methods: Multilayer Perceptron, Decision Tree, Random Forest, Support Vector Machine and Deep Neural Network for evaluation. For this work, we used at University of Wisconsin Hospital database which is composed of thirty values which characterize the properties of the nucleus of the breast mass. As we showed in result sections, DNN classifier has a great performance in accuracy level (92%), indicating better results in relation to traditional models. Random forest 50 and 100 presented the best results for the ROC curve metric, considered an excellent prediction when compared to other previous studies published.

Health information technology, applied to electronic health record (EHR), has evolved with the adoption of standards for defining patient health records. However, there are many standards for defining such data, hindering communication between different healthcare providers. Even with adopted standards, patients often need to repeatedly provide their health information when they are taken care of at different locations. This problem hinders the adoption of personal health record (PHR), with the patients' health records under their own control. Therefore, the purpose of this paper is to propose an interoperability model for PHR use. The methodology consisted prototyping an application model named OmniPHR, to evaluate the structuring of semantic interoperability and integration of different health standards, using a real database from anonymized patients. We evaluated health data from a hospital database with 38 645 adult patients' medical records processed using different standards, represented by openEHR, HL7 FHIR, and MIMIC-III reference models. OmniPHR demonstrated the feasibility to provide interoperability through a standard ontology and artificial intelligence with natural language processing (NLP). Although the first executions reached a 76.39% F1-score and required retraining of the machine-learning process, the final score was 87.9%, presenting a way to obtain the original data from different standards on a single format. Unlike other models, OmniPHR presents a unified, structural semantic and up-to-date vision of PHR for patients and healthcare providers. The results were promising and demonstrated the possibility of subsidizing the creation of inferences rules about possible patient health problems or preventing future problems.

The elasticity feature of cloud computing has been proved as pertinent for parallel applications, since users do not need to take care about the best choice for the number of processes/resources beforehand. To accomplish this, the most common approaches use threshold-based reactive elasticity or time-consuming proactive elasticity. However, both present at least one problem related to: the need of a previous user experience, lack on handling load peaks, completion of parameters or design for a specific infrastructure and workload setting. In this regard, we developed a hybrid elasticity service for parallel applications named SelfElastic. As parameterless model, SelfElastic presents a closed control loop elasticity architecture that adapts at runtime the values of lower and upper thresholds. Besides presenting SelfElastic, our purpose is to provide a comparison with our previous work on reactive elasticity called AutoElastic. The results present the SelfElastic’s lightweight feature, besides highlighting its performance competitiveness in terms of application time and cost metrics.

As a result of rural and suburban migration to the cities, urban life has become a significant challenge for citizens and, particularly, for city administrators who must manage the sustainable use of resources such as energy, water, and transportation. Smart cities are the biggest vision to efficiently address these challenges through a real-time monitoring, providing an intelligent planning and a sustainable urban development. However, to accomplish them we need a tightly integration among citizens, city devices, city administrators, and the data center platform where all data is stored, combined, and processed. In this context, we propose ElCity, a model that combines citizens and city devices data to enable an elastic multilevel management of energy consumption for a particular city. As design decision, this management must occur automatically without affecting the quality of already offered services. The main contribution of ElCity model concerns the exploration of the cloud elasticity concept in multiple target levels (smartphones from citizens, city devices involved in the public lightning, and data center nodes), turning on or off the resources on each level in accordance with their demands. In this way, this article presents the ElCity architecture, detailing its modules distributed along the three data sources, in addition to an experiment that uses city devices and citizens data from Rome to explore energy saving. The results are promising, with an Energy Monitor module that allows the estimation of the energy consumption of elastic applications based on CPU and memory traces with an average and median precision of 97.15 and 97.72 percent. Moreover, we proposed a reduction of more than 90 percent in the energy spent in public lightning in the city of Rome which was obtained thanks to an analysis of geolocation data from their citizens.

Today, Internet of Things (IoT) is an emergent concept in which billions of devices are connected to Internet capable of producing and exchanging data. One of the most used technologies in this area regards to the Radio Frequency Identification (RFID). It can produce large amount of data from many things like objects, persons and assets. Thus, it is needed middlewares which must support processing in large scales. However, the state-of-the-art does not present satisfactory solutions in which this kind of middlewares are capable of adapt themselves according to processing demands. In this context, this article presents a proactive cloud elasticity model called Proliot aiming at providing scalability to IoT middlewares. Proliot is capable of predicting load behavior combining time series techniques. In addition, it adapts cloud resources beforehand an overload or underload situation occurs. We evaluated our model comparing results with a reactive elasticity model. In our experiments, Proliot achieved best performance up to 76% when compared to Eliot.

Clocking represents the manner in which companies register employee data; particularly, data related to entrances to and departures from the company. Being normally implemented with wireless cards or by hand, this procedure can implies on large queues, reducing the emloyee productivity, besides being sensitive to longer complications (when the employee forget to register the clocking, for example). In this context, we developed a model named ACID (Automatic Clocking and Idleness Detection) to bring benefits for both administrators and employees through the use of wireless sensors. Besides eliminating queues, it is possible to analyze those employees that are away from their own places for more than a particular threshold. In addition, the model is also pertinent to enhance decision support when promotions take place. We developed a prototype that was evaluated through simulation, taking into account the architectural plant and the employees behavior of five real Brazilian companies. The results reveals the benefits of using IDAC both at the owner (control and productivity) and employees (the clocking actions occurs automatically) levels.

Ontologies are being increasingly used in a varied number of fields such as knowledge management, information extraction, and the semantic web. In this context, it's possible to verify the feasibility of using ontologies in healthcare. The number of seropositive people in relation to HIV, and its early detection is still a problem in today's world, especially in Brazil. Therefore, this study reports the creation and evaluation of an ontology meant to be used in healthcare applications, focused on telehealth. This ontology's main objective is to define the profiles of patients who attend the health centers of a city. The ontology proposed here can be used in a range of applications related to healthcare, not only to HIV, but also other diseases categorized as chronic. The article details the creation of ontology and its grounding. The evaluation that was made is described and its results, which are considered promising, are commented.

Summary A key characteristic of cloud computing is elasticity, automatically adjusting system resources to an application's workload. Both reactive and horizontal approaches represent traditional means to offer this capability, in which rule‐condition‐action statements and upper and lower thresholds occur to instantiate or consolidate compute nodes and virtual machines. Although elasticity can be beneficial for many HPC (high‐performance computing) scenarios, it also imposes significant challenges in the development of applications. In addition to issues related to how we can incorporate this new feature in such applications, there is a problem associated with the performance and resource pair and, consequently, with energy consumption. Further exploring this last difficulty, we must be capable of analyzing elasticity effectiveness as a function of employed thresholds with clear metrics to compare elastic and non‐elastic executions properly. In this context, this article explores elasticity metrics in two ways: (i) the use of a cost function that combines application time with different energy models; (ii) the extension of speedup and efficiency metrics, commonly used to evaluate parallel systems, to cover cloud elasticity. To accomplish (i) and (ii), we developed an elasticity model known as AutoElastic, which reorganizes resources automatically across synchronous parallel applications. The results, obtained with the AutoElastic prototype using the OpenNebula middleware, are encouraging. Considering a CPU‐bound application, an upper threshold close to 70% was the best option for obtaining good performance with a non‐prohibitive elasticity cost. In addition, the value of 90% for this threshold was the best option when we plan an efficiency‐driven execution. Copyright © 2015 John Wiley & Sons, Ltd.

Elasticity is undoubtedly one of the most striking characteristics of cloud computing. Especially in the area of high performance computing (HPC), elasticity can be used to execute irregular and CPU-intensive applications. However, the on- the-fly increase/decrease in resources is more widespread in Web systems, which have their own IaaS-level load balancer. Considering the HPC area, current approaches usually focus on batch jobs or assumptions such as previous knowledge of application phases, source code rewriting or the stop-reconfigure-and-go approach for elasticity. In this context, this article presents AutoElastic, a PaaS-level elasticity model for HPC in the cloud. Its differential approach consists of providing elasticity for high performance applications without user intervention or source code modification. The scientific contributions of AutoElastic are twofold: (i) an Aging-based approach to resource allocation and deallocation actions to avoid unnecessary virtual machine (VM) reconfigurations (thrashing) and (ii) asynchronism in creating and terminating VMs in such a way that the application does not need to wait for completing these procedures. The prototype evaluation using OpenNebula middleware showed performance gains of up to 26 percent in the execution time of an application with the AutoElastic manager. Moreover, we obtained low intrusiveness for AutoElastic when reconfigurations do not occur.

Process migration is a known technique to offer process rescheduling, being especially pertinent for Bulk Synchronous Parallel (BSP) programs. Such programs are organized in a set of supersteps, in which the slowest process always determines the synchronization time. This approach motivated us to develop a first model called MigBSP, which combines computation, communication, and migration costs metrics for process rescheduling decisions. In this paper, a new model named MigBSP++ enhances our previous work in three aspects: (i) a different algorithm for detecting imbalance situations, which considers the performance of all processes over each processor instead of their individual times; (ii) an improvement on the rescheduling reactivity through shortening the interval for the next migration call when imbalance situations arise; (iii) a new algorithm for self-organizing the migratable processes and their destinations. Particularly, this third item represents our main scientific contribution, not only in terms of the MigBSP context, but also in a broader one that covers the entire BSP landscape. We developed a MigBSP++ prototype with the Adaptive MPI (AMPI) library, which offers a standard framework for implementing migration-based load balancing policies. We tested this prototype against other built-in AMPI rescheduling policies with a fractal image compression application. The results revealed performance gains up to 41% and an overhead limited to 5% when migrations do not take place.

The use of characteristics of smart objects that have interactions features with humans, gave rise to the Internet of Things (IoT). Numerous derivations from this concept have been proposed. In this article, we focus on one of those called Social Internet of Things (SIoT). SIoT prioritizes the relationship between smart objects, where the objects can establish a connection among themselves without the interference of their owners. The purpose of this article is to explore the concept of SIoT in a University Campus, offering direct communication between intelligent devices. These devices share information based on an academic criteria and preferences informed by their owners. To evaluate the proposal, we developed a case study. The preliminary results show the viability of the proposal.

Elasticity is one of the most known capabilities related to cloud computing, being largely deployed using thresholds. In this way, limits are used to drive resource mangement actions, leading to the following problem statements: How can cloud users set the threshold values to enable elasticity in their cloud applications? And what is the impact of the application's load pattern in the elasticity? This article answers these questions for iterative high performance computing applications, showing the impact of both thresholds and load patterns on application performance and resource consumption. To accomplish this, we developed a reactive and PaaS-based elasticity model called AutoElastic and employed it over a private cloud to execute a numerical integration application. Here, we are presenting an analysis of best practices and possible optimizations regarding the elasticity and HPC pair. Considering the results, we observed that the upper threshold influences the application time more than the lower one.

In recent years, there has been growing support for more tightly coupled applications regarding heterogeneous resources. A specific way of obtaining better performance in such applications is to consider the replacement of execution entities by newer resources during the application's lifetime. Therefore, this article describes the rationale for developing jMigBSP, which is a Java programming library that offers object rescheduling for round-based applications. In this context, the proposal addresses Bulk Synchronous Parallel (BSP) applications because BSP represents one of the most often used models for writing tightly coupled parallel programs. jMigBSP's main contribution examines the rescheduling facility in two different ways: (i) using migration directives in the application code directly; and (ii) through automatic load balancing at the middleware level. Specifically, this second idea is feasible because of Java's inheritance feature, which transforms a simple jMigBSP application into a migratable one by changing only a single line of code. In addition to the description of jMigBSP, this article emphasizes the benefits of using migration over heterogeneous environments by executing scientific applications. The results indicate gains of up to 56% with object rescheduling and support the feasibility of using migration as a load balancing technique. Copyright © 2014 John Wiley & Sons, Ltd.

Electronic transactions have become the mainstream mechanism for performing commerce activities in our daily lives. Aiming at processing them, the most common approach addresses the use of a switch that dispatches transactions to processing machines using the so-called Round-Robin scheduler. Considering this electronic funds transfer (EFT) scenario, we developed a framework model denoted GetLB which comprises not only a new and efficient scheduler, but also a cooperative communication infrastructure for handling heterogeneous and dynamic environments. The GetLB scheduler uses a scheduling heuristic that combines static data from transactions and dynamic information from the processing nodes to overcome the limitations of the Round-Robin based schedulinperiodic interactiong approaches. Scheduling efficiency takes place thanks to the periodic interaction between the switching node and processing machines, enabling local decision making with up-to-date information about the environment. Besides the description of the aforementioned model in detail, this article also presents a prototype evaluation by using both traces and configurations obtained with a real EFT company. The results show improvements in transaction makespan when comparing our approach with the traditional one over homogeneous and heterogeneous clusters.

Nowadays, we can observe that electronic operations generated by debit and credit cards are increasingly common in our daily lives. Each one results in a transaction that must be processed by a service provider company. In this context, this article presents LoadEFT — a new load balancing model for EFT (electronic funds transfer) transactions. It operates at layer 7 of the OSI reference model in order to eliminate the limitations of the traditional techniques for this purpose, known as packet and connection-based load balancing. In addition, LoadEFT was developed to bypass the drawbacks of the Round-Robin scheduling which is efficient only on homogeneous systems. At model viewpoint, LoadEFT presents a scalable framework of components to handle dynamic and heterogeneous processing machines. Its scientific contribution includes a novel scheduling heuristic, which considers both characteristics of the transaction to be processed and data regarding the possible target machines. The tests were performed with real input data from a Brazilian service provider company and showed that LoadEFT overcame aforementioned technical limitations of load balancing of electronic transactions. Moreover, LoadEFT handled transactions without ony loss on both homogeneous and heterogeneous clusters. The same does not occur when employing Round-Robin.

Currently, we can observe the growing of the use of mobile devices as a media for both Internet access and carrying out purchase and sale of products and services. Commonly, E- commerce platforms are enabled by either proprietary systems or by buying ads on specialized websites. This scenario involves the adoption of strategies based on e-commerce servers, which represent a non-scalable strategy. The situation is even more critical considering the current scenario, where mobile devices has been involved in trading, the so-called m-commerce. In this context, this paper proposes the use of the P2P technology as the network substrate for supporting mobile commerce. For that, we are using the notion of ultrapeers that act as managers that receive, process and pass on requests for trade. Suppliers and consumers act as end points and access ultrapeers of their geographic region through a standardized interface. To implement the distributed scenario, changes are made over the DNS servers so that mobile devices can locate ultrapeers based on their geolocation data. Thus, suppliers who need to travel can advertise their products in the regions which they pass, boosting their business through this opportunistic approach. Finally, experimental evaluation showed that the architecture is feasible for mobile commerce environments composed by both stationary and mobile devices.

In this paper we present a view of EXEHDA middleware and a novel service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide functionality adapted to the constraints and unpredictability of the large-scale environment. EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the proposed service we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital.

Ubiquitous computing (Ubicomp) environments are characterized by high distribution, heterogeneity and dynamism. In these environments, applications must be aware of their contexts and adapt to changes in them. A major research challenge in the area of Ubicomp is related to context awareness. Considering the characteristics of ubiquitous environments, this paper presents an architecture for context awareness, called DynamiCC (Dynamic Context Composition), that includes elements to support: context modeling, contextual data collection, actuation on the environment, and composition and interpretation of contextual information. We consider that the main contributions of this work are the employ of a hybrid approach for context modeling, and the proposal of an architecture that supports the interpretation and the composition of dynamic contexts, which enables the construction of complex contexts, in runtime of applications. To assess the functionality of the DynamiCC, we did a discussion of usage scenarios, highlighting the prototypes and tests performed.

The latest technological advances, which introduced innovative and more affordable devices, have contributed to boost the practical application of research in the field of ubiquitous computing (ubicomp). For the development of applications in this area, we need an adequate software infrastructure. In order to do so, we have proposed Continuum, an infrastructure based on service-oriented architecture (SOA), making use of framework and middleware, and employing a redefinition of follow-me semantics. In this redefined vision, users can go anywhere carrying the data and application they want, which they can use in a seamlessly integrated fashion with the real world. In this article, we focus on the description of the service-based architecture proposed for Continuum. The proposal widens the Web services standards to support the mobility of services, allowing them to be deployed, copied, or moved. Besides, the abstraction provided enables the adaptation of legacy applications as Continuum pluggable services.