Optimization is an entire field that aims to improve efficiency and effectiveness across various domains. Its primary objective is to minimize costs, time, and risks while maximizing gains, quality, and efficiency. In this context, the 0/1 1-D bin packing problem is one of combinatorial optimization’s most challenging and extensively studied problems. This problem holds significant practical applications in supply chain management, packaging design, and resource optimization. This work solves the 0/1 1-D bin packing problem using a nature-inspired golden eagle optimizer. The hunting behavior of golden eagles inspires this bio-solver, and it employs swarm intelligence-based strategies to approximate solutions. We perform a comparative analysis of the bio-inspired algorithm to evidence its yield. We use twenty instances of the 1-D bin packing problem. Computational results show that the golden eagle optimizer exhibits better results in convergence time than well-known bio-inspired algorithms.

Mammography is known as one of the best forms to screen possible breast cancer in women, and recently deep learning models have been developed to assist the radiologist in the diagnosis. However, their lack of interpretability has become a significant drawback to their extended use in clinical practice. This paper introduces a novel approach for detecting and localising pathological findings in mammography exams through the use of a EfficientNet-based deep learning model. The model is trained using cropped segments of labelled pathological findings from Vindr Mammography Dataset. Achieving an average F1-score of 72.7 %, and reaching on mass and suspicious calcifications an F1-Score of 79.9 % and 84.5 % respectively. Using this classifier we propose a method to visualise from local information the regions of interest where pathological findings could be present on the complete image. Plus, we describe the limitations regarding area coverage of these patches on the model's capability of generalization and certainty on its predictions, explaining its functionality.

This paper focuses on modeling and solving the Manufacturing Cell Design Problem (MCDP) by using the Harmony Search (HS) metaheuristic. The MDCP consists on grouping machines and parts that they process, into groups called cells. So, the idea is to identify an organization of cells such that the number of times that a piece is transported between these cells is minimized. To this end, we use the HS optimization algorithm, which is based on the process of improvisation performed by musicians to find a perfect musical harmony. The experimental results demonstrate the efficiency of the proposed approach which is able to reach all global optimums for a set of 90 well-known MDCP instances.

In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.

The COVID-19 outbreak has shown that Modeling and Simulation (MS) methodologies are an important aspect to study the spread of the disease and assess the effect of different measures to diminish its negative effect. Although traditional models have been widely used, there is a need to build new, highly configurable disease models to explore multiple scenarios quickly. We present an MS framework to perform rapid prototyping of pandemic spread using the Cell-DEVS space-based discrete-event modeling approach. This method supports age segmentation of the population, hospital-capacity-dependent deaths, and enforcing mobility restriction policies. This method is useful for studying the spread of the disease, as well as combining the simulation results with different visualization tools.

The Model-Driven Development paradigm aims to represent all the information system features through models. Conceptual-Model Programming offers a similar approach, but with a focus on automatic code generation. Both approaches consider modeling and traceability of different abstraction levels, where each level can be tackled with different modeling methods. This heterogeneity introduces a challenge for the quality of the traceability and transformations among models, especially when aiming for automatic code generation. In this paper, we introduce a holistic conceptual-model programming method to generate code from different abstraction levels (from the problem space to the solution space), through three modeling languages whose consistency has been ontologically ensured by two transformation techniques. Particularly, we focus on transformations from the strategic layer using i*, to business process layer using Communication Analysis (CA), and to the system conceptual model layer with OO-Method, which can automatically generate fully functional systems. Even though there are previous works that have proposed partial transformations among these modeling methods, this paper is the first one that deals with the perspective of putting together all the models in a single development method. For each transformation, we discuss what parts can be automatically performed and what parts need human intervention.

Detecting and extracting findings in a radiological report is crucial for text mining tasks in several applications. In this case, a labeled process for the image associated with the radiological report in mammography and Spanish context for a computer vision model is required. This paper shows the methodology and process generated for this goal. This paper presents a Named Entity Recognition (NER) approach based on a transformer deep learning model, using a labeled corpus and fine-tuning process to find three concepts that compose a typical finding in a mammographic radiological report: laterality, location, and the finding. We add another concept in the labeled process, the negation, necessary to identify falses positive inside the text that writes the radiologist. Our model achieves an F1 score of 88.24% classifying the three principal concepts for a finding, product of the labeled and fine-tuning process. The results presented here will be used as input for future training work on a computer vision model.

In datasets, the preponderance of imbalanced classes impedes accurate cyberattack categorization. While high aggregate accuracy is sought, it's paramount to adeptly classify all attack types, especially the under-represented ones. Existing methodologies, such as Ensemble techniques and the Synthetic Minority Oversampling Technique (SMOTE), address these disparities, yet the dynamic nature of underrepresented cyberattacks in cybersecurity remains a concern. To address this, we introduce a nested cascade model tailored for diverse cyberattacks within imbalanced datasets. This model leverages binary classifiers across tiers, each targeting a specific attack type. Before initializing the cascade, SMOTE is applied to counterbalance class disparities. The cascade's classification sequence employs a dual strategy: an initial one-vs-all binary classifier approach for pending classes, followed by prioritization based on model performance. We assessed our approach using the UNSW-NB15 dataset. Preliminary results indicate approximately 80% efficiency across metrics like accuracy, recall, and Fl-score. Notably, SMOTE's in- tegration yielded significant improvements for underrepresented classes.

In this work, a new optimization method inspired on the Thermodynamic Equilibrium is described to address nonlinear problems in continuous domains. In our proposal, each decision variable is treated as the most volatile chemical component of a saturated binary liquid mixture at a determined pressure and temperature. The optimization procedure is started with an initial solution randomly generated. The search is done by changing the equilibrium state of each mixture. The search is carried out by accepting worse solutions to avoid being left trapped in local optimums. The search includes the random change of the mixtures. The algorithm was tested by using known mathematical functions as benchmark functions showing competitive results in comparison with other metaheuristics.

The binarization of continuous swarm-intelligence metaheuristics is an area of great interest in operational research. This interest is mainly due to the application of binarized metaheuristics to combinatorial problems. In this article we propose a general binarization algorithm called Percentil Transition Ranking Algorithm (PTRA). PTRA uses the percentile concept as a binarization mechanism. In particular we apply this mechanism to the Cuckoo Search metaheuristic to solve the Set Covering Problem (SCP). We provide necessary experiments to investigate the role of key ingredients of the algorithm. Finally to demonstrate the efficiency of our proposal, Set Covering benchmark instances of the literature show that PTRA competes with the state-of-the-art algorithms.

In recent years, information has become one of the most important goods for organizations as it brings insights about customer preferences and internal processes that could help to improve organizational performance, decrease costs, improve customer engagement, among other benefits. The consumption of information within an organization has been studied in the literature under several approaches associated to information system success nor information management, but the Information Consumer eXperience (ICX) has not been evaluated following a formally defined methodology. In this work, a methodology to formalize the ICX evaluation process within the organization is proposed. The main goal of this methodology is to improve ICX into the organization by generating recommendations based on information consumers perceptions under a customer experience CX approach. The proposed methodology consists of 3 sequential stages: Characterization, Experimentation and Analysis. In The Characterization Stage an exploratory diagnosis is performed, including the experimental setup planification, consumers behavior exploration, and a preliminary version of the customer Journey Map. The Experimentation Stage is focused on data collection using different instruments such as surveys, interviews, questionnaire, and a mixed qualitative and quantitative instrument to generate data about consumers expectations and perceptions. In the third stage of Analysis, the collected data is analyzed to generate a definitive Customer Journey Map, through quantitative and qualitative data analysis. Our proposed ICX evaluation methodology is the first formally described methodology for information consumer perceptions analysis and experience evaluation. Which could be used to face ICX analysis into any kind of organization that works with information.

Osteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessing fracture risk is the measurement of the areal bone mineral density with Dual-Energy X-ray Absorptiometry. Several ultrasound techniques have been presented as alternatives. It has been shown that the estimation of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission, are associated with non-traumatic fractures in postmenopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. However, this model-based inverse approach tends to fail for the patients associated with poor guided mode information. A recent study has shown the potential of an automatic classification tool, Support Vector Machine, to analyze guided wave spectrum images independently of any waveguide model. The aim of this study is to explore how the classification accuracy varies with the number of features. Optimization was done using the Particle Swarm Optimization algorithm, while adjustment was made considering age, body mass index, and cortisone intake. The results show that adjusting the data and optimizing the parameters improved classification. Moreover, the number of features was reduced from 32 to 15, with 73.5% accuracy comparable to the gold standard.

With the progressive abandon of traditional medical equipment containing mercury, automatic electronic sphygmomanometers (NIBP measurers) are being purchased instead of traditional sphygmomanometers, however the difference in the quality of performance (accuracy and precision) comes in question. In this paper we compare the performance of an electronic automatic NIBP measurer sold commonly in the market in contrast with a NIBP measurer module integrated in a multi-parameter monitor. Our study showed that, as expected, there was a difference between the performance of these two types of devices, but also allowed to characterize these differences showing interesting and not so obvious behaviour in the estimation of Systolic and Diastolic pressures of these devices.

Advances in health technologies have been proposed in studies aimed at improving the quality of life of older people. Ambient assisted living supports elderly adults and people with special needs during their daily activities. This concept aims to maintain the independence of users, giving them autonomy in daily life activities in addition to increasing their quality of life, well-being, and safety. In this work, two tools that are part of a ADL SoS-based Platform are presented: One low-cost sensor to detect nocturia and a complete module for identifying fall potential. Experimental results show that our work can be implemented in ambient assisted living as smart-home devices, in laboratories, or any other place, and that it is competitive compared to other developed tools.

Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

Today, natural resources are more scarce than ever, so we must make good use of them. To achieve this goal, we can use metaheuristic optimization tools as an alternative to achieve good results in a reasonable amount of time. The present work focuses on the use of adaptive techniques to facilitate the use of this type of tool to obtain good functional parameters. We use a constructive metaheuristic algorithm called Intelligent Water Drops to solve the set covering problem. To demonstrate the efficiency of the proposed method, the obtained results were compared with the standard version using the same initial configuration for both algorithms. Additionally, the Kolmogorov-Smirnov-Lilliefors, Wilcoxon signed-rank and Violin chart tests were applied to statistically validate the results, which showed that metaheuristics with autonomous search have a better behavior than do standard algorithms.

Data protection regulations impose requirements on organizations that require interdisciplinary. Conceptual modeling of information systems, particularly goal modeling, has served to communicate with stakeholders of different backgrounds for software requirements analysis. An extension for a Socio-Technical Security (STS) modeling language was proposed to include data protection modeling concepts to help represent relevant issues of the European Union’s General Data Protection Regulation. This article examines whether models designed with this extension serve as communication facilitators for privacy compliance and common ground across stakeholders. Through a series of 8 focus groups, with 21 subjects, we observed if professionals with different backgrounds (software developers, business analysts, and privacy experts) could detect discuss about the GDPR principles and identify privacy compliance “red flags” that we seeded in a use case. Using a qualitative approach to analyze the data, all the groups discussed the majority of the GDPR principles and identified more than 80% of the seeded red flags, with privacy experts identifying the most. This research provides preliminary results on using conceptual modeling as a communicator facilitator between stakeholders to contribute to a common ground between them.

International audience

Nowadays, the vast majority of university careers declare a competency-based curriculum. However, having concrete evidence to measure learning outcomes can be a complex problem in engineering. Academics make great efforts to design active learning experiences to stimulate skills development. However, more evidence is required to describe methodologies integrated with technological tools to evaluate learning outcomes. In this paper, we propose using the Lego Serious Play (LSP) methodology and Multimodal Learning Analytics (MMLA) techniques to assess learning outcomes in the Software Engineering I course. The results obtained are quite promising since it was possible to identify difficulties in acquiring and applying knowledge.

Craft fairs are spaces where artisans can exhibit their artistic pieces and interact with the public, however this activity is always affected by external factors, such as weather, stoppages, health situation (due to covid-19), among others. This proposal is an innovative contribution to this problem that makes technology available to support this sector. Through a Web platform that incorporates a 3D interface, the craftsman will reach his audience without having to leave his home, since it simulates the environment of a craft fair where you can browse and see the crafts in its 360°, admiring each detail as if the user were in person. The technological infrastructure includes a catalog of all the pieces offered by the artisans and contact details. A filter facilitates the search for parts according to established criteria. The proposal has validations and various functional tests that show the technical feasibility and usability tests that demonstrate its usefulness and ease of use.