In recent years, persistent disparities in compensation and representation between genders have been revealed in various sectors, most notably in computer science and engineering. This ongoing issue accentuates a critical inequality that necessitates comprehensive and systematic actions. A significant contributing factor is women's relative lack of interest in technology and computing domains. This topic has not received ad-equate investigation in broader discussions surrounding Science, Technology, Engineering, and Mathematics (STEM) fields. As an incentive for the retention and entrance of women in the field, initiatives have emerged in Brazil, such as the Meninas Digitais program, an extension project to encourage and support women in information technology and the sciences. These initiatives aim to combat stigmas and promote gender equality in STEM. This research aims to thoroughly analyze women's participation and performance within computing and technology programmes. The present investigation concerns the low enrollment rates of women in technology-related higher education programs, which contributes to a predominantly male workforce and exacer-bates existing gender disparities, potentially stifling technological advancement. Utilizing a decade's data from undergraduate affiliates at the Universidade Federal de Santa Catarina (UFSC), this study employed machine learning techniques to examine trends surrounding dropout rates, admissions, retention, and academic performance of female students in technology and engineering. The findings reveal that women often achieve strong academic results but remain significantly underrepresented in these fields. This scenario underscores an urgent need for targeted educational policies that foster gender equality and inclusivity. Additionally, the research identifies potential shortcomings within the Brazilian primary education system and highlights specific challenges female students face in transitioning to university life, particularly those linked to socioeconomic factors.

Gender-based violence affects millions of women worldwide, transcending cultural, economic, and social boundaries.According to an ONU survey, home is the most dangerous place.In a large number of cases, even with the possibility of local telephone numbers for help, at-risk women cannot make a call simply because the aggressor is close to them.Considering the accessibility of communication for the deaf, there are tools to teach American Sign Language (ALS), which is also used in countries other than the United States.The Signal for Help was created by the Canadian Women's Foundation, allowing women to silently send an SOS.The idea presented in this work combines a fake app to teach sign language with a way to use this signal without the possible note of any neighbor aggressor.This is a silent and effective request to help at-risk women.After implementing the first version, it was tested by users who suggested significant improvements, and a second version was developed.This version was also sent to users for experimentation, and an update to the current version is presented here.Thus, there are useful functionalities in the application that fulfill the needs of daily use, such as access by voice activation, login with facial recognition, interface with contrasting colors, and the possibility of setting the language of the screen texts and menu words, among others.The geographical location of the woman sending the signal helps in her location, allows quick contact with the protection service in the region, and helps in identifying areas of greater risk (geographic tracking map) for the regional public security authorities.

This paper presents the framework SIM@ ClOUD that optimizes cost-related resource allocation decisions for simulation jobs in cloud environments. SIM@ CLOUD offers comprehensive management of simulations throughout their execution life-cycle in the cloud, including the selection of Virtual Machine (VM) types across different regions and markets. By leveraging Spot VMs and application checkpointing, the framework transparently reduces the monetary costs associated with the execution without client intervention. Historical data analysis enables the prediction of simulation execution times, which is refined further by a dynamic predictor for adaptive VM selection. SIM@ CLOUD is being deployed in an industrial setting and employs a cachebased storage solution to improve access latency to in-house data by VMs located in geographically distinct regions. An evaluation carried out on AWS EC2, using real oil reservoir simulations, demonstrates the effectiveness of the framework.

In information technology (IT), there is an increasing demand for professionals. However, recruiters need help hiring qualified professionals due to intense competition and a lack of information about the ones seeking employment. This work uses the Engagement by Design to reflect on a solution for hiring IT professionals focusing on crowdsourcing using engagement concepts. This study aims to identify the opportunities and challenges of using Engagement by Design to create an innovative solution for hiring an IT professional. The study made it possible to identify some items that can improve user engagement in a program aimed at recruiting and retaining IT talent.

Technology companies increasingly need to recruit and select qualified Information Technology professionals, as the technology market is fast-paced, and there is a shortage of these professionals. We used Design Thinking supported by remote communication technologies to reflect on this problem and generate solutions. In this context, we seek to answer: What are the opportunities and challenges when using Design Thinking to support the creation of an innovative solution for recruiting and selecting Information Technology professionals suitable for the market’s demands? We found that the main opportunities are to identify and integrate different profiles of professionals in the same environment in the selective process. The main challenges were contacting and recruiting participants during the COVID19 pandemic, developing solutions quickly, and applying the ideas generated in building a prototype for recruiting Information Technology professionals to meet market demand.

Urban population growth creates problems such as congestion and resource scarcity. These problems contribute to poor quality of life and negative environmental impacts. In this context, Information and Communication Technologies appear to improve sustainability solutions. Smart Mobility emerges as a dimension of the Smart City and includes technologies and applications that assist transport services. Among these services, the applications directed to the cyclist segment stand out. In our work, we present a review of bicycle applications, and we perform a comparative function analysis and their relationship with the factors that contribute to the practice of cycling filtering the most relevant functions. We aim to find the most attractive features for urban cyclists and the limitations of what is offered in the market. In addition, we will provide guidance to improve the development of cycling apps and the implementation of new features, collaborating with the development of new technologies and future research.

The database project is the process of engineering a database model from conceptual modeling to database implementation.Existing tools allow the conceptual modeling of relational and geographical databases separately, but none integrates both in a single solution.This paper presents an Model-Driven Development framework for creating relational and geographical database models.The framework comprises an extended relational metamodel to adhere to geographical database concepts present in the OMT-G, an Entity-Relationship modeling tool, Query View Transformation rules between OMT-G and the extended metamodel, and Model-To-Text transformations to generate ANSI SQL/SFS code.

Abstract Object detection (OD) is used for visual quality control in factories. Images that compose training datasets are often collected directly from the production line and labeled with bounding boxes manually. Such data represent well the inference context but might lack diversity, implying a risk of overfitting. To address this issue, we propose a dataset construction method based on an automated pipeline, which receives a CAD model of an object and returns a set of realistic synthetic labeled images (code publicly available). Our approach can be easily used by non‐expert users and is relevant for industrial applications, where CAD models are widely available. We performed experiments to compare the use of datasets obtained by the two different ways—collecting and labeling real images or applying the proposed automated pipeline—in the classification of five different industrial parts. To ensure that both approaches can be used without deep learning expertise, all training parameters were kept fixed during these experiments. In our results, both methods were successful for some objects but failed for others. However, we have shown that the combined use of real and synthetic images led to better results. This finding has the potential to make industrial OD models more robust to poor data collection and labeling errors, without increasing the difficulty of the training process.

Nowadays, applications that use crowdsourcing are becoming more and more widespread. However, for the solution to be successful, it needs to go beyond usage, users must be engaged with them. User’s engagement (UE) is the quality of user experience in applications that highlights the good aspects of their interaction and failing at engage users can be crucial to a system. We have developed an 18-cards deck that provides domain-specific insights for designers on user engagement in crowdsourcing initiatives. We have applied the cards in three online design workshops, offer participants insights, findings that were lifted in the workshop, and discuss how they support to advance of the design research and design practice in crowdsourcing initiatives in the context of engagement.

Abstract The number of malicious software applications, or malware programs, increases every year. Their development becomes more sophisticated as new techniques are used to bypass program scanning software applications, such as antiviruses. Thereby, deep learning‐based methods emerge as a new promising way to identify these threats. Our main purpose and contribution in this work is proposing and implementing a successful approach to tackle both binary and multiclass malware classification problems. We used unsupervised word embedding algorithms for representing software applications to be analyzed and long‐short term memory for classifying the software applications. For evaluating our pipeline, we introduce a new dataset for binary and multiclass malware classification because we could not find large datasets containing sufficient samples of cleanware and the various malware types for multiclass classification that could be used to evaluate classification models. Our experimental results reached an accuracy of 88.94% for binary classification and 75.13% for multiclass classification. These results suggest that the proposed dataset is challenging, and using it can help in the training of better malware classifiers, improving security.

The use of Crowdsourcing to solve public problems is called Citizen-Sourcing and shows the potential to increase citizen participation in the context of e-government. Successful implementations of Citizen-Sourcing applications require the citizen to continuously engage with each other and with the government through these applications. In general, citizens expect, among other things, that the government responds to their comments in an application by immediately solving the problems pointed out or indicating when and how they would be solved. In a literature review, we could not find any model for adequately assessing the quality of the service provided by local e-governments for citizens in Citizen-Sourcing scenarios. Hence, we propose an approach to analyze the quality of the government's response to the citizen through Citizen-Sourcing applications. To validate the proposed approach, we have conducted a case study using real data collected from Colab.re, a Citizen-Sourcing application very popular in Brazil. Our main contributions are a novel approach to assess the quality of interaction between local e-government and citizen and a detailed discussion regarding a local e-government's action in the platform.

In higher education, many students exceed the expected time to complete their undergraduate programs. This delay is called retention, which can lead to program abandonment. STEM undergraduate programs, in particular, have higher retention and dropout rates when compared to other non-STEM programs. The students in such programs end up exchanging or dropping out the programs before graduating, causing waste in economic, social and academic terms. In this context, Recommendation Systems can be used to support students and managers in choosing disciplines, contributing for them achieving better academic performance and thus aiming to improve student learning and engagement and mitigate retention. For constructing these Recommendation Systems, Educational Data Mining techniques, including machine learning algorithms, can be used to identify and predict retention situations and contribute to reducing their occurrence. The aim of this paper is to present a Systematic Literature Review (SLR) for identifying the use of Educational Data Mining methodologies, techniques and tools to implement Recommendation Systems with a focus on preventing student retention in higher education programs. We selected studies available in digital libraries that are international references in publications of scientific articles, in order to answer the following research question: What machine learning methods were used in Recommendation Systems in the context of Educational Data Mining? Among the various studies found to reduce student retention rates, most used methods to predict student grades. We observed that there are many papers proposing the use of machine learning methods for predicting failure in disciplines, either through regressors or classifiers. However, just a few studies have proposed Recommendation Systems to assist students in choosing subjects at the time of enrollment for the next term, which indicates a large area for the development of further future work in this field.

A radiological study is comprised by a set of images together with a medical report, which is generated by radiologists to describe the main characteristics of such images and the associated clinical findings. The radiological study provides relevant information about the patient's health condition which is necessary for physicians to accomplish diagnosis. However, some works demonstrated that the reports can be vague, incomplete, ambiguous or having inaccuracies, inconsistencies or errors. In this paper, we propose to use ontologies to support the elaboration of radiological reports. We propose an ontology to represent the medical knowledge about cranial computed tomography (CCT), which is among the most required radiological studies in emergency or regular treatments. Finally, we evaluate the quality of the reports generated based on this ontology.

Most studies analyzing medical images at some stage require the demarcation of boundaries of biological structures. This process is called segmentation. In some contexts, current techniques present satisfactory results, but in others, like breast segmentation in thermographies, it remains an open problem. Several studies have investigated the use of automated solutions for this problem. However, the automatic process does not always present a satisfactory result, requiring the active involvement of a specialist for validating it and re-segmenting images when necessary. As such task can be expensive and take too long to be completed, this scenario drives the exploration of alternative approaches for the segmentation process. Hence, in this work we propose an alternative that combines traditional techniques of image processing with techniques of collective intelligence, which is based on the wisdom of crowds to solve problems in a faster and less expensive way. We present SegMedBC, a prototype in which the methods previously mentioned are applied to improve the segmentation process. Furthermore, an experimental study is carried out to validate the involvement of lay users in this activity.

Identifying malware has always been a great challenge. Much money and time has been invested by companies and governments to mitigate the impact of these threats. Nowadays, with the increasing amount of data available, it is possible to use more precise classification techniques. However, most large datasets that include malicious and non-malicious softwares are not public, which hinders the quest for solutions based in technologies that rely on the availability of large amounts of data, such as deep learning. To overcome this limitation, this article introduces a new large dataset for malware classification, which was made publicly available. We then propose a model to train a multiclass classification recurrent neural network (RNN), more specifically a long short-term memory neural network (LSTM) on our dataset. This model for analyzing unstructured malware data is then tested on unseen programs and the accuracy obtained reaches 67.60%, including six classes with five different types of malware.

Indoor location-based services have become very popular, principally, because of its wide and valuable applications. On that context, Wi-fi fingerprinting based on the received signal strength indicator (RSSI) has become very popular, due the fact that RSSI values are easily acquired. On the Wi-fi fingerprint method, machine learning algorithms are trained on the constructed fingerprint database and then used on a new entry to give the indoor location based on its estimations. Choosing the correct machine learning algorithm is one of the main problems in the literature. However the database sizes used during the training phase is also one of the main concerns. In this paper, a proposed feature selection method used on the original UJIIndoorLoc database created a smaller version of it, with the 30 highest RSSIs after the APIDs responsible for then in descending order, and created even smaller database subsets. Both databases, the original UJIIndoor Loc database and ours, were split into smaller subsets that were used on the classification problem according the DESIP method proposed in [1]. Six machine learning algorithms were deployed for training and testing the two database subsets with the classification attributes modified for symbolic localization. The J48 with the AdaBoost iterative algorithm gave the best results on both database subsets. The minimized database subsets showed smaller elapsed time results for all the classifications that were done. The accuracy results show similar results for both database subsets, on building and floor classification. Although, on the region attribute, the database subset with 520 attributes got better accuracy results than the reduced one.

This work proposes an architecture for sharing devices' resources in the Internet of Things providing real sensor data for its users.The main idea is based on the fact that users such as developers and researchers do not always have access to the necessary hardware and resource sharing should impact these persons activities.Taking advantage of the Sensors as a Service model, we propose an architecture where several sensors and actuators can be coupled to environments and they also are represented virtually in a web system becoming available to be consumed by users and platforms.The architecture is composed of three layers and a model representing devices, the cloud, and clients, and how they interact with each other.A study case for testing the whole approach is also presented.

This paper proposes an architecture for sharing IoT Objects’ resources in the Internet of Things providing a model for its owners to expose devices, which can be consumed by clients inspired by the Sensor-as-a-Service model. The main idea relies on the fact that users, such as developers and researchers, do not always have access to the necessary hardware and resources. Exposing devices in IoT should impact these persons activities. Then, we present the Resource Management Architecture, where several IoT Objects endowed with sensors and actuators can be added to environments that are represented virtually in the architecture. The IoT Objects become available to be consumed by users through the use of applications. The architecture is composed of three layers: one representing devices, the cloud solution, and applications, and how they interact with each other. We also present a study case for testing the whole approach in a smart city scenario.

The use of the latent factor models technique overcomes two major problems of most collaborative filtering approaches: scalability and sparseness of the user's profile matrix.The most successful realizations of latent factor models are based on matrix factorization.Among the algorithms for matrix factorization, alternating least squares (ALS) stands out due to its easily parallelizable computations.In this work we propose a methodology for comparing the performance of two parallel implementations of the ALS algorithm, one executed with MapReduce in Apache Hadoop framework and another executed in Apache Spark framework.We performed experiments to evaluate the accuracy of generated recommendations and the execution time of both algorithms, using publicly available datasets with different sizes and from different recommendation domains.Experimental results show that running the recommendation algorithm on Spark framework is in fact more efficient, once it provides in-memory processing, in contrast to Hadoop's twostage disk-based MapReduce paradigm.

The growing practice of accumulating personal data to generate predictions about users, leverages the need for mechanisms that allow people a more effective control of their data.An emerging field of studies called Human-Data Interaction (HDI), proposes the inclusion of human at the center of the data flow, providing mechanisms for citizens to interact explicitly with the collected data.Researches in HDI have discussed ways to offer Transparency Enhancing Tools (TETs), i.e., tools that support people on HDI issues related to privacy and personal data protection.Many works conducted about TETs focuses on usability issues, exploring aspects such as efficiency, user satisfaction and ease of learning.In this work, on the other hand, we aim to assess the communicability of HDI mechanisms in TETs.Hence, we applied the Semiotic Inspection Method (SIM) to investigate if and how HDI concepts are applied in two different TETs used for personal data management.We triangulated results from the study with findings from another investigation about communicability issues carried out in the same domain, but by observing and interviewing users.

Information and Communication Technologies are indispensable components of smart cities. Its applications are present in several areas, such as urban mobility, environmental issues and medical systems. In this scenario, the use of crowdsourcing technologies comes to help people to contribute to the development and improvement of the urban digital services. However, using crowdsourced data in smart cities solutions can lead to problems with the security and the privacy of user's data. The setting of comprehensive Functional Requirements (FR) to ensure data privacy is an approach for preventing the occurrence of such issues. In this work, we intend to identify, from a literature review the main privacy requirements that have been observed in the development of applications that make use of crowdsourced data in Smart Cities scenarios.

Visualizing data in tables usually is not the best way to help users understanding large amounts of data. Instead, data visualization in graphical format, such as charts, has been used to explore available data. A good data visualization is defined as a well-designed presentation of interesting data, aiming to communicate ideas with clarity, precision and efficiency. Nowadays, considering the open data movement, many open data portals offer different types of data visualization. However, when browsing some of these portals, we can find many bad data visualizations, frequently ambiguous, confusing and unusable. Hence, it is essential to have an instrument capable of analyzing the quality of data visualizations, helping the designer to use the full capacity of a data visualization to provide a more efficient resource information to the users. This work aims to present an instrument that integrate a set of heuristics to assess the quality of data visualizations. Such heuristics were chosen due to have been previously proposed in several works in literature, and proved successful. To evaluate the proposed instrument, we conducted an experiment with a group composed by computer science graduate students. We analyzed the results using Cohen's Kappa and Any-two agreement statistical tests, which indicated that the instrument is adequate.

Several challenges arise when applying Multi-Agent System (MAS) in Ambient Intelligence scenarios such as the heterogeneity of the hardware and the domain where it is applied.There are several applications that use Agent-Oriented approaches but they provide solutions that tie the hardware to the software, and they do not provide generic architectures.So, in this paper, we propose a heterogeneous architecture for applying different microcontrollers in the design of embedded MAS for such kind of systems.An architecture and a small-scale prototype of a smart home assembled with several hardware devices connected to different ATMEGA and PIC microcontrollers are presented as proof-of-concept.Our architecture shows to be effective in several tests performed using different implementation strategies.

Ubiquitous systems consider the use of electronic components for enhancing daily objects with some kind of computational intelligence for aiding users in their tasks pervasively.Ambient Intelligence (AmI) is a branch of ubiquitous computing that provides an environment full of interconnected devices and it can provide data communication, inference mechanism based on context information and collaboration among system's devices.Similarly, the Internet of Things (IoT) provides uniquely identified devices or things in a network for helping users in their activities.Multi-Agent Systems (MAS) are intelligent systems where agents are responsible for reasoning, competing and using resources to achieve desirable goals pro-actively and autonomously.Agents have been employed in some approaches and works during the last years, but none of them considered embedded MAS responsible for smart devices in an AmI system running over an IoT network.Besides, it is also interesting that agents of the embedded MAS can interact, sharing information with agents situated in another embedded MAS using the IoT network to learn from user's experiences.This paper proposes an architecture for the development of AmI systems using embedded MAS for interfacing with sensors and actuators in a heterogenous network using an IoT middleware.

We are on the verge of a major shift in the way we perceive digital life, what may cause a significant impact to the real world. Gradually, through increasing knowledge in the areas of artificial intelligence, big data and machine learning, computers have been emulating deceased human beings and, symbolically, with the aid of technology, have been managing to conquer death. This article seeks to understand and problematize the ways in which digital immortality has manifested itself, particularly through digital memorials, chatbots and avatars. Companies like Facebook, Eter9 and Initiative 2045 allow a continuity of life after the death of its users. We analyze, through technical and philosophical questions, imbricated in this discussion, the implications of this digital immortality and how these issues are seen in a differentiated way with the presence of technology.

This research intends to analyze how users that are also HCI designers relate to the interaction with digital memorials linked to graves through QRcodes.To do so, we have carried out an immersive practice in the Consolação Cemetery (São Paulo, Brazil), where that technology is used to tag the graves of famous deceased people and to guide the visitors in the site.Those QR code tags link the graves to an online application for digital memorials called MemoriAll.To address the problem, this paper analyzes the data collected from the surveys answered by the research subjects before and after the immersive practice, along with data from a semiotic inspection of MemoriAll.

This work proposes a context-aware middleware for medical workflow organization and efficiency improvement. In hospitals, laboratories and teleradiology companies, each physician or technician is specialized in a specific kind of diagnosis or analysis. Therefore, certain types of medical images are often forwarded to a certain physician or a certain group. This forwarding is time consuming. That is, repeatedly deciding who would be the best physician, whether he is available at a certain moment given a certain context is exhaustive and may be very inefficient. Thus, the proposed middleware has the ability to process and collect data from images analyzed by each medical staff. Based on the collected data and current clinical context, the middleware is able to infer who would be the best fit staff to receive a certain incoming medical image.

SUMMARY The process of transforming data stored in relational databases (RDBs) into sets of Resource Description Framework (RDF) triples is known as triplification or RDB2RDF. It consists of two consecutive operations, schema extraction and data conversion. Schema extraction is a process similar to creating an external schema, and contains a collection of database views. The data conversion is divided into two steps. The first step consists of deciding how to represent database schema concepts in terms of RDF classes and properties, defining an RDB2RDF mapping. The second step consists of the actual conversion of relational data to RDF data instances, based on the mapping previously defined. Although the schema extraction is very well understood, the data conversion operation is still murky. Indeed, the World Wide Web Consortium RDB2RDF Working Group has been working to define a standard language, called R2RML, to describe RDB2RDF mapping files. The definition of the R2RML, however, is still undergoing changes. In this paper, we introduce an Eclipse plug‐in that supports the entire conversion process. Its architecture takes into consideration the specificities of the triplification process by providing a modular structure that encapsulates the stable and well‐understood components separately from the volatile, change‐prone mapping strategies. The latter are accessible via a well‐defined interface to promote information hiding and separation of concerns and to facilitate evolution. Copyright © 2012 John Wiley & Sons, Ltd.

In Ambient Intelligence (AmI), reasoning is fundamental for identifying specific situations that may be meaningful and relevant to some applications. As in such systems usually not all context data is readily available to all reasoners within a system, these reasoning operations may need to evaluate context data collected from distributed sources and stored on different devices. This work proposes a middleware service for performing decentralized rule-based reasoning about context data targeting AmI systems in which we assume that there are two main interacting parties in the reasoning process, each having access to different context information.

Ubiquitous computing systems can be regarded as open systems where heterogeneous and previously unknown entities may spontaneously interact, due to intrinsic mobility of users and their devices. In this highly dynamic and heterogeneous scenario, applications must be capable of accessing the appropriate instances of the required services in each visited network or region. Some services, however, must be made available only to users and applications that fulfill some conditions. In ubiquitous systems the interaction between client and server applications happens in a physical space, involving human and artificial agents that act under social and administrative rules. We propose the integration of context-awareness with a social regulation approach to control the interaction of users and their applications in such environments. This has the advantage that access policies and rights can be defined and monitored independently of the applications for the ubiquitous system.

The development of location and context-aware applications is greatly facilitated by the use of context-provisioning middleware. However, development of such applications still remains a challenge from the point of view of software engineering. In this paper we present MoCA, a service-oriented middleware architecture that supports the development and deployment of distributed context-aware applications for mobile users. Besides explaining its main services and APIs, we discuss in which ways the MoCA architecture supports some well-known software engineering principles that apply to the design and implementation of context-aware applications. Furthermore, we give an overview of its usage and present the most notable prototype applications that have been developed on the top of MoCA.

Efficient task scheduling is fundamental for parallel applications to achieve good performance on distributed systems. While extensive work exists for scheduling tasks on homogeneous processors, fewer algorithms exist for the more common problem of scheduling in heterogeneous processor environments. In this paper, we propose coupling a replication-based clustering heuristic for homogeneous processors, with a mechanism to map the generated clusters to the heterogeneous environment. Experimental results show that this strategy compares favourably in terms of the makespan with traditional list scheduling approaches to this problem, particularly when communication costs are high.