Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9639929
P. D. Bianco, I. Mindlin, L. Lanzarini, Franco Ronchetti, W. Hasperué, F. Quiroga
As the presence of artificial intelligence has increased in a variety of different areas, the use of machine learning and deep learning techniques for creative purposes has also risen significantly in recent years. Works of this kind within the area of natural language processing (NLP) are typically neural models used for fiction or lyrics generation. Those works are in most cases in English and adapting them to other languages is not feasible. In this work, we develop a Spanish text generator system for the rap sub-genre known as freestyle. Freestyle songs present unique challenges for text generation given that performers compete with one another in a lyric improvisation contest. Given the low availability of freestyle text, especially in Spanish, we collected two separate datasets, one with freestyle lyrics and the other, larger, with rap lyrics, which are more readily available. The rap dataset can be used for pretraining, and the freestyle dataset for finetuning on the generation task. Furthermore, we design a neural network-based generation model that takes into account both the structure of freestyle and the low data availability. The model was able to generate realistic freestyle verses in Spanish.
{"title":"Structured Text Generation for Spanish Freestyle Battles using Neural Networks","authors":"P. D. Bianco, I. Mindlin, L. Lanzarini, Franco Ronchetti, W. Hasperué, F. Quiroga","doi":"10.1109/CLEI53233.2021.9639929","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9639929","url":null,"abstract":"As the presence of artificial intelligence has increased in a variety of different areas, the use of machine learning and deep learning techniques for creative purposes has also risen significantly in recent years. Works of this kind within the area of natural language processing (NLP) are typically neural models used for fiction or lyrics generation. Those works are in most cases in English and adapting them to other languages is not feasible. In this work, we develop a Spanish text generator system for the rap sub-genre known as freestyle. Freestyle songs present unique challenges for text generation given that performers compete with one another in a lyric improvisation contest. Given the low availability of freestyle text, especially in Spanish, we collected two separate datasets, one with freestyle lyrics and the other, larger, with rap lyrics, which are more readily available. The rap dataset can be used for pretraining, and the freestyle dataset for finetuning on the generation task. Furthermore, we design a neural network-based generation model that takes into account both the structure of freestyle and the low data availability. The model was able to generate realistic freestyle verses in Spanish.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"71 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72743489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9639944
Alan R. Santos, K. Aires, Francisco das Chagas Imperes Filho, L. P. Sousa, R. Veras, L. D. S. B. Neto, Antônio L. de M. Neto
Melanoma is considered the worst type of skin cancer. The early diagnosis of this disease is still a complex task due to many variables that must be analyzed. Because of this, new methodologies are becoming common in the literature due to the good results obtained. Convolutional Neural Networks are Deep Learning techniques capable of providing effective solutions in the classification of medical images. In this sense, this work developed a disease detection system using AlexNet and VGG-F convolutional architectures, trained with images of skin lesions to create feature descriptors, not classifiers. Other conventional descriptors of skin lesions were used to assess the quality of data obtained from the last layers of convolutional architectures. Data from all feature extraction processes were submitted to the conventional classifiers Support Vector Machine, Multilayer Perceptron, and K-Nearest Neighbor. The results obtained in the approach show that the feature extracting models are viable and can offer a more accurate melanoma diagnosis possibility. The VGG-F architecture obtained the best result, with an accuracy of 91.54% and a precision of 91.64% given by the K-Nearest Neighbor. It is possible to see that this result highlights the quality of data in convolutional architectures and can provide a sense of further research.
{"title":"Melanoma Classification Approach with Deep Learning-Based Feature Extraction Models","authors":"Alan R. Santos, K. Aires, Francisco das Chagas Imperes Filho, L. P. Sousa, R. Veras, L. D. S. B. Neto, Antônio L. de M. Neto","doi":"10.1109/CLEI53233.2021.9639944","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9639944","url":null,"abstract":"Melanoma is considered the worst type of skin cancer. The early diagnosis of this disease is still a complex task due to many variables that must be analyzed. Because of this, new methodologies are becoming common in the literature due to the good results obtained. Convolutional Neural Networks are Deep Learning techniques capable of providing effective solutions in the classification of medical images. In this sense, this work developed a disease detection system using AlexNet and VGG-F convolutional architectures, trained with images of skin lesions to create feature descriptors, not classifiers. Other conventional descriptors of skin lesions were used to assess the quality of data obtained from the last layers of convolutional architectures. Data from all feature extraction processes were submitted to the conventional classifiers Support Vector Machine, Multilayer Perceptron, and K-Nearest Neighbor. The results obtained in the approach show that the feature extracting models are viable and can offer a more accurate melanoma diagnosis possibility. The VGG-F architecture obtained the best result, with an accuracy of 91.54% and a precision of 91.64% given by the K-Nearest Neighbor. It is possible to see that this result highlights the quality of data in convolutional architectures and can provide a sense of further research.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"329 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76583108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640087
Francisco Carlos Silva, Ivan Saraiva Silva
In this work, we propose architectural solutions to cope with permanent faults in cache memories. The approach uses a FIFO and a redundant cache to detect and tolerate permanent faults in caches. During a write operation, the word is written at the same time in cache and in FIFO. A comparison is performed to evaluate if the duplicated word has the same value in both memories. In case there is a divergence between compared values, the cache line is set as faulty and it will not be used for reading or writing operations. Additionally, the word written in FIFO is copied to the redundant cache and all accesses related to the faulty address in main cache are forwarded to the redundant cache. The proposed solution was implemented using two different mapping techniques. In the first case, the main cache uses set-associative mapping with LRU replacement policy. In the second case, the main cache combines set-associative mapping, LRU and a round robin policy to reduce the number of write-back operations. In both cases, the redundant cache uses direct mapping. The proposed solution was validated using a VHDL implementation and FPGA prototyping. Simulation results show that with the proposed models is possible to obtain hit rates between 95% and 99%, even when the cache memory presents faults in up to 80% of their lines.
{"title":"A Redundant Approach to Increase Reliability of Data Cache Memories","authors":"Francisco Carlos Silva, Ivan Saraiva Silva","doi":"10.1109/CLEI53233.2021.9640087","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640087","url":null,"abstract":"In this work, we propose architectural solutions to cope with permanent faults in cache memories. The approach uses a FIFO and a redundant cache to detect and tolerate permanent faults in caches. During a write operation, the word is written at the same time in cache and in FIFO. A comparison is performed to evaluate if the duplicated word has the same value in both memories. In case there is a divergence between compared values, the cache line is set as faulty and it will not be used for reading or writing operations. Additionally, the word written in FIFO is copied to the redundant cache and all accesses related to the faulty address in main cache are forwarded to the redundant cache. The proposed solution was implemented using two different mapping techniques. In the first case, the main cache uses set-associative mapping with LRU replacement policy. In the second case, the main cache combines set-associative mapping, LRU and a round robin policy to reduce the number of write-back operations. In both cases, the redundant cache uses direct mapping. The proposed solution was validated using a VHDL implementation and FPGA prototyping. Simulation results show that with the proposed models is possible to obtain hit rates between 95% and 99%, even when the cache memory presents faults in up to 80% of their lines.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"1 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90169445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9639910
Giovanni Hernández, Álvaro Martínez, Franklin Jiménez, R. Jiménez, Alexander Baron
Actually the Software Industry in Colombia has been accumulating experience and growing. However, there has been a shortage of competent professionals for this sector. In this sense, Universities need to train this type of professionals who can quickly be linked to work. Starting from this challenge and understanding the new training demands in the world, this article presents how the first didactic transformation of a learning factory for the Software Engineering area is developed. Methodologically, a model is proposed for the representation of well-formed competencies and learning outcomes. In addition, a protocol for the development of the first didactic transformation is presented. The results show the status of the main companies in the Software Industry in Colombia. Furthermore, the competencies and learning outcomes for the services associated with the category are presented: software construction, support, and maintenance. The main contribution of this work was to propose a model to represent well-formed competencies and learning outcomes. Moreover, the proposal is used in the development of the first didactic transformation of a learning factory for the Software Engineering area.
{"title":"Learning factory for the Software Engineering area: First didactic transformation","authors":"Giovanni Hernández, Álvaro Martínez, Franklin Jiménez, R. Jiménez, Alexander Baron","doi":"10.1109/CLEI53233.2021.9639910","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9639910","url":null,"abstract":"Actually the Software Industry in Colombia has been accumulating experience and growing. However, there has been a shortage of competent professionals for this sector. In this sense, Universities need to train this type of professionals who can quickly be linked to work. Starting from this challenge and understanding the new training demands in the world, this article presents how the first didactic transformation of a learning factory for the Software Engineering area is developed. Methodologically, a model is proposed for the representation of well-formed competencies and learning outcomes. In addition, a protocol for the development of the first didactic transformation is presented. The results show the status of the main companies in the Software Industry in Colombia. Furthermore, the competencies and learning outcomes for the services associated with the category are presented: software construction, support, and maintenance. The main contribution of this work was to propose a model to represent well-formed competencies and learning outcomes. Moreover, the proposal is used in the development of the first didactic transformation of a learning factory for the Software Engineering area.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"63 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72822070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640222
Bryan Alexander Ulate-Caballero, Allan Berrocal Rojas, Jeisson Hidalgo-Céspedes
Pseudocode is a valuable resource used in programming education, software development, and scientific reports for designing algorithmic solutions as it is easy to write, understand, and modify. Since pseudocode is lacking in its ability to be tested, it is difficult to determine whether a pseudocode solution is correct or not. Software tools are specially required to reach this goal, e.g., helping professors find race conditions, deadlocks, or starvation issues while grading students’ concurrent pseudocode. Although there are various tools to work with sequential pseudocode, there is a lack of tools to work with concurrent pseudocode. This shortage motivated us to determine the state-of-the-art in notations and tools for testing concurrent and distributed pseudocode. We conducted a systematic literature review and found only a few related publications, confirming that this topic is understudied. We found and report about five software tools capable of interpreting concurrent or distributed pseudocode, and two software tools capable of verifying its correctness. As another result, no other literature review was found about this topic, conferring novelty to the contributions of this work.
{"title":"Concurrent and Distributed Pseudocode: A Systematic Literature Review","authors":"Bryan Alexander Ulate-Caballero, Allan Berrocal Rojas, Jeisson Hidalgo-Céspedes","doi":"10.1109/CLEI53233.2021.9640222","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640222","url":null,"abstract":"Pseudocode is a valuable resource used in programming education, software development, and scientific reports for designing algorithmic solutions as it is easy to write, understand, and modify. Since pseudocode is lacking in its ability to be tested, it is difficult to determine whether a pseudocode solution is correct or not. Software tools are specially required to reach this goal, e.g., helping professors find race conditions, deadlocks, or starvation issues while grading students’ concurrent pseudocode. Although there are various tools to work with sequential pseudocode, there is a lack of tools to work with concurrent pseudocode. This shortage motivated us to determine the state-of-the-art in notations and tools for testing concurrent and distributed pseudocode. We conducted a systematic literature review and found only a few related publications, confirming that this topic is understudied. We found and report about five software tools capable of interpreting concurrent or distributed pseudocode, and two software tools capable of verifying its correctness. As another result, no other literature review was found about this topic, conferring novelty to the contributions of this work.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"17 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91179685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9639998
Júlio César da Silva Soares, K. Aires, Alan R. Santos, R. Veras, O. P. S. Neto, G. N. Neto, Flávio H. D. Araújo
The analysis of pollen grains is a prominent task in areas such as ecology, food engineering, and others that have different purposes, such as identifying the origin of honey, as well as helping in the development of new products or evaluating the quality of the products. This research presents a CNN architecture to classify pollen grains that can have performance equal to or superior to those found in the literature. Using POLEN23E database. Two experiments were performed with this database, one of which used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case. Two experiments were performed where one of them used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case.
{"title":"Classification of pollen grain images with MobileNet","authors":"Júlio César da Silva Soares, K. Aires, Alan R. Santos, R. Veras, O. P. S. Neto, G. N. Neto, Flávio H. D. Araújo","doi":"10.1109/CLEI53233.2021.9639998","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9639998","url":null,"abstract":"The analysis of pollen grains is a prominent task in areas such as ecology, food engineering, and others that have different purposes, such as identifying the origin of honey, as well as helping in the development of new products or evaluating the quality of the products. This research presents a CNN architecture to classify pollen grains that can have performance equal to or superior to those found in the literature. Using POLEN23E database. Two experiments were performed with this database, one of which used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case. Two experiments were performed where one of them used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"29 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83080787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640112
Eduardo T. Bogue, Marcos V. A. Guimarães, T. Noronha, A. H. Pereira, I. Carvalho, S. Urrutia
This paper tackles the 2-Dimensional Guillotine Cutting Stock Problem with Stack Constraints. The problem asks for the cutting of a set of items with the minimum amount of raw material. The cutting patterns are subject to a number of constraints, including a new realistic constraint, regarding item precedence, which has just been introduced in the literature. In this case, the items are organized in stacks, where each stack represents a customer request and defines the order in which the items must be cut. That is, if item i precedes item j within a stack, then i must be cut before j. However, there is no precedence constraint between items in different stacks. This constraint comes from applications where items must be stacked and shipped in the exact order that they will be used by the customer, thus avoiding the risk of damaging fragile items (as is the case in the glass industry) or the cost of moving heavy items (as is the case in the steel industry). We propose two constructive heuristics extended from the literature for the problem, in addition to a dynamic programming based heuristic that uses as a subroutine an exact pseudo-polynomial time algorithm developed for the Rectangular Knapsack Problem with Batch Constraints. Computational experiments, performed on three sets of realistic instances, showed that the dynamic programming based heuristic found solutions with smaller optimally gaps in all instances evaluated.
{"title":"The Two-Dimensional Guillotine Cutting Stock Problem with Stack Constraints","authors":"Eduardo T. Bogue, Marcos V. A. Guimarães, T. Noronha, A. H. Pereira, I. Carvalho, S. Urrutia","doi":"10.1109/CLEI53233.2021.9640112","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640112","url":null,"abstract":"This paper tackles the 2-Dimensional Guillotine Cutting Stock Problem with Stack Constraints. The problem asks for the cutting of a set of items with the minimum amount of raw material. The cutting patterns are subject to a number of constraints, including a new realistic constraint, regarding item precedence, which has just been introduced in the literature. In this case, the items are organized in stacks, where each stack represents a customer request and defines the order in which the items must be cut. That is, if item i precedes item j within a stack, then i must be cut before j. However, there is no precedence constraint between items in different stacks. This constraint comes from applications where items must be stacked and shipped in the exact order that they will be used by the customer, thus avoiding the risk of damaging fragile items (as is the case in the glass industry) or the cost of moving heavy items (as is the case in the steel industry). We propose two constructive heuristics extended from the literature for the problem, in addition to a dynamic programming based heuristic that uses as a subroutine an exact pseudo-polynomial time algorithm developed for the Rectangular Knapsack Problem with Batch Constraints. Computational experiments, performed on three sets of realistic instances, showed that the dynamic programming based heuristic found solutions with smaller optimally gaps in all instances evaluated.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"28 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87149299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640130
J. V. Bogado, D. Stalder, C. Schaerer, Santiago Gómez-Guerrero
Dengue fever represents a public health problem and accurate forecasts can help governments take the best preventive actions. As the volume of data provided continuously increases, machine learning and deep learning (DL) models have become an attractive approach. However, it is difficult to perform accurate predictions in areas with fewer cases. In this work, we compare traditional approaches such as LASSO Regression (LR), Random Forest (RF), Support Vector Regression (SVR) vs DL models based on long short-term memory (LSTM), considering weekly dengue incidence and climate, in 217 cities in Paraguay. Several city models may present heterogeneous behaviors and poor accuracy. To mitigate this problem, a clustering analysis between time series is performed based on silhouette scores and measuring how well an observation is clustered. Our results indicate the hierarchical clustering combined with Spearman correlation is the most appropriate approach. Then several LSTM models are compared on subgroups of similar time series. The root mean squared error (RMSE) confirms that the LSTM clustered models improve the accuracy by 31.6% approximately. The main contribution of this work is that LSTM clustered models can perform predictions in cities with low incidence by combining information from similar time-series and weather data.
{"title":"Time Series Clustering to Improve Dengue Cases Forecasting with Deep Learning","authors":"J. V. Bogado, D. Stalder, C. Schaerer, Santiago Gómez-Guerrero","doi":"10.1109/CLEI53233.2021.9640130","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640130","url":null,"abstract":"Dengue fever represents a public health problem and accurate forecasts can help governments take the best preventive actions. As the volume of data provided continuously increases, machine learning and deep learning (DL) models have become an attractive approach. However, it is difficult to perform accurate predictions in areas with fewer cases. In this work, we compare traditional approaches such as LASSO Regression (LR), Random Forest (RF), Support Vector Regression (SVR) vs DL models based on long short-term memory (LSTM), considering weekly dengue incidence and climate, in 217 cities in Paraguay. Several city models may present heterogeneous behaviors and poor accuracy. To mitigate this problem, a clustering analysis between time series is performed based on silhouette scores and measuring how well an observation is clustered. Our results indicate the hierarchical clustering combined with Spearman correlation is the most appropriate approach. Then several LSTM models are compared on subgroups of similar time series. The root mean squared error (RMSE) confirms that the LSTM clustered models improve the accuracy by 31.6% approximately. The main contribution of this work is that LSTM clustered models can perform predictions in cities with low incidence by combining information from similar time-series and weather data.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"101 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85706294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9639925
Alexandre Pires Barbosa, P. Malcher, V. Farias, R. Santos
The development of smart cities requires a broad digital network, technology infrastructure, services, creativity and innovation. These are all important components of an ecosystem that offers a better quality of life to society. However, to develop systems to be part of the smart city, it is necessary to understand the challenges and opportunities involved on it, especially the informal systems that are used by citizens and do not belong to the formal structure of public administration. Informal systems are created to meet the needs of citizens. As such, this work aims to present challenges and opportunities identified in this scenario. The method adopted to increase information on this topic was an online survey research. As a result, “transport” was highlighted as the most cited opportunity and “lack of investment” as the most cited challenge to be overcome according to the participants. As a contribution, a discussion about the results is carried out and allows the analysis of the identified opportunities and challenges so that professionals can take advantage and develop systems that citizens recognize as necessary to make a smart city.
{"title":"Exploring Business Opportunities in the Domain of Smart Cities from Informal Systems","authors":"Alexandre Pires Barbosa, P. Malcher, V. Farias, R. Santos","doi":"10.1109/CLEI53233.2021.9639925","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9639925","url":null,"abstract":"The development of smart cities requires a broad digital network, technology infrastructure, services, creativity and innovation. These are all important components of an ecosystem that offers a better quality of life to society. However, to develop systems to be part of the smart city, it is necessary to understand the challenges and opportunities involved on it, especially the informal systems that are used by citizens and do not belong to the formal structure of public administration. Informal systems are created to meet the needs of citizens. As such, this work aims to present challenges and opportunities identified in this scenario. The method adopted to increase information on this topic was an online survey research. As a result, “transport” was highlighted as the most cited opportunity and “lack of investment” as the most cited challenge to be overcome according to the participants. As a contribution, a discussion about the results is carried out and allows the analysis of the identified opportunities and challenges so that professionals can take advantage and develop systems that citizens recognize as necessary to make a smart city.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"81 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86628339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640164
María Isabel Murillo, A. Pacheco, G. López, Gabriela Marín, Julio C. Guzmán
Technical debt is a concept used to describe technical decisions that can benefit companies in the short term but can produce costs and software quality issues in the long term. Technical debt management can help enterprise profitability, sustainability, and the software industry's credibility. This paper presents a replication of the InsighTD survey (a globally distributed family of industrial surveys on causes and effects of TD), focusing on Costa Rica and comparing other regional countries. In total, 145 practitioners from the Costa Rican IT industry participated. Results show that the leading cause of technical debt is not technical (not only in Costa Rica but also in the region). On the other hand, the main effects reported are delivery delay and general dissatisfaction of the parties involved. A comparative study of InsighTD survey results in various countries is also included.
{"title":"Common Causes and Effects of Technical Debt in Costa Rica: InsighTD Survey Replication","authors":"María Isabel Murillo, A. Pacheco, G. López, Gabriela Marín, Julio C. Guzmán","doi":"10.1109/CLEI53233.2021.9640164","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640164","url":null,"abstract":"Technical debt is a concept used to describe technical decisions that can benefit companies in the short term but can produce costs and software quality issues in the long term. Technical debt management can help enterprise profitability, sustainability, and the software industry's credibility. This paper presents a replication of the InsighTD survey (a globally distributed family of industrial surveys on causes and effects of TD), focusing on Costa Rica and comparing other regional countries. In total, 145 practitioners from the Costa Rican IT industry participated. Results show that the leading cause of technical debt is not technical (not only in Costa Rica but also in the region). On the other hand, the main effects reported are delivery delay and general dissatisfaction of the parties involved. A comparative study of InsighTD survey results in various countries is also included.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"1 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77941941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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