This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B (Wizardlm), and Vicuna 7B (Vicuna) to generate synthetic datasets. These synthetic data are employed in two use-case scenarios: (a) exclusively as training datasets composed of synthetic job postings (situations where no real data is available) and (b) as an augmentation method to bolster underrepresented job title categories. To evaluate our proposed method, we relied on two well-established approaches: the feedforward neural network (FFNN) and the BERT model. Both the use cases and training methods were assessed against a genuine job posting dataset to gauge classification accuracy. Our experiments substantiated the benefits of using synthetic data to enhance job posting classification. In the first scenario, the models’ performance matched, and occasionally exceeded, that of the real data. In the second scenario, the augmented classes consistently outperformed in most instances. This research confirms that AI-generated datasets can enhance the efficacy of NLP algorithms, especially in the domain of multi-class classification job postings. While data augmentation can boost model generalization, its impact varies. It is especially beneficial for simpler models like FNN. BERT, due to its context-aware architecture, also benefits from augmentation but sees limited improvement. Selecting the right type and amount of augmentation is essential.
{"title":"Deep Learning Approaches for Big Data-Driven Metadata Extraction in Online Job Postings","authors":"Panagiotis Skondras, Nikos Zotos, Dimitris Lagios, Panagiotis Zervas, Konstantinos C. Giotopoulos, Giannis Tzimas","doi":"10.3390/info14110585","DOIUrl":"https://doi.org/10.3390/info14110585","url":null,"abstract":"This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B (Wizardlm), and Vicuna 7B (Vicuna) to generate synthetic datasets. These synthetic data are employed in two use-case scenarios: (a) exclusively as training datasets composed of synthetic job postings (situations where no real data is available) and (b) as an augmentation method to bolster underrepresented job title categories. To evaluate our proposed method, we relied on two well-established approaches: the feedforward neural network (FFNN) and the BERT model. Both the use cases and training methods were assessed against a genuine job posting dataset to gauge classification accuracy. Our experiments substantiated the benefits of using synthetic data to enhance job posting classification. In the first scenario, the models’ performance matched, and occasionally exceeded, that of the real data. In the second scenario, the augmented classes consistently outperformed in most instances. This research confirms that AI-generated datasets can enhance the efficacy of NLP algorithms, especially in the domain of multi-class classification job postings. While data augmentation can boost model generalization, its impact varies. It is especially beneficial for simpler models like FNN. BERT, due to its context-aware architecture, also benefits from augmentation but sees limited improvement. Selecting the right type and amount of augmentation is essential.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"61 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113706","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}
Manuel Domínguez-Dorado, Francisco J. Rodríguez-Pérez, Javier Carmona-Murillo, David Cortés-Polo, Jesús Calle-Cancho
Public sector organizations are facing an escalating challenge with the increasing volume and complexity of cyberattacks, which disrupt essential public services and jeopardize citizen data and privacy. Effective cybersecurity management has become an urgent necessity. To combat these threats comprehensively, the active involvement of all functional areas is crucial, necessitating a heightened holistic cybersecurity awareness among tactical and operational teams responsible for implementing security measures. Public entities face various challenges in maintaining this awareness, including difficulties in building a skilled cybersecurity workforce, coordinating mixed internal and external teams, and adapting to the outsourcing trend, which includes cybersecurity operations centers (CyberSOCs). Our research began with an extensive literature analysis to expand our insights derived from previous works, followed by a Spanish case study in collaboration with a digitization-focused public organization. The study revealed common features shared by public organizations globally. Collaborating with this public entity, we developed strategies tailored to its characteristics and transferrable to other public organizations. As a result, we propose the “Wide-Scope CyberSOC” as an innovative outsourced solution to enhance holistic awareness among the cross-functional cybersecurity team and facilitate comprehensive cybersecurity adoption within public organizations. We have also documented essential requirements for public entities when contracting Wide-Scope CyberSOC services to ensure alignment with their specific needs, accompanied by a management framework for seamless operation.
{"title":"Boosting Holistic Cybersecurity Awareness with Outsourced Wide-Scope CyberSOC: A Generalization from a Spanish Public Organization Study","authors":"Manuel Domínguez-Dorado, Francisco J. Rodríguez-Pérez, Javier Carmona-Murillo, David Cortés-Polo, Jesús Calle-Cancho","doi":"10.3390/info14110586","DOIUrl":"https://doi.org/10.3390/info14110586","url":null,"abstract":"Public sector organizations are facing an escalating challenge with the increasing volume and complexity of cyberattacks, which disrupt essential public services and jeopardize citizen data and privacy. Effective cybersecurity management has become an urgent necessity. To combat these threats comprehensively, the active involvement of all functional areas is crucial, necessitating a heightened holistic cybersecurity awareness among tactical and operational teams responsible for implementing security measures. Public entities face various challenges in maintaining this awareness, including difficulties in building a skilled cybersecurity workforce, coordinating mixed internal and external teams, and adapting to the outsourcing trend, which includes cybersecurity operations centers (CyberSOCs). Our research began with an extensive literature analysis to expand our insights derived from previous works, followed by a Spanish case study in collaboration with a digitization-focused public organization. The study revealed common features shared by public organizations globally. Collaborating with this public entity, we developed strategies tailored to its characteristics and transferrable to other public organizations. As a result, we propose the “Wide-Scope CyberSOC” as an innovative outsourced solution to enhance holistic awareness among the cross-functional cybersecurity team and facilitate comprehensive cybersecurity adoption within public organizations. We have also documented essential requirements for public entities when contracting Wide-Scope CyberSOC services to ensure alignment with their specific needs, accompanied by a management framework for seamless operation.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"48 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168400","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}
Francisca Barros, Beatriz Rodrigues, José Vieira, Filipe Portela
Due to the amount of data emerging, it is necessary to use an online analytical processing (OLAP) framework capable of responding to the needs of industries. Processes such as drill-down, roll-up, three-dimensional analysis, and data filtering are fundamental for the perception of information. This article demonstrates the OLAP framework developed as a valuable and effective solution in decision making. To develop an OLAP framework, it was necessary to create the extract, transform and load the (ETL) process, build a data warehouse, and develop the OLAP via cube.js. Finally, it was essential to design a solution that adds more value to the organizations and presents several characteristics to support the entire data analysis process. A backend API (application programming interface) to route the data via MySQL was required, as well as a frontend and a data visualization layer. The OLAP framework was developed for the ioCity project. However, its great advantage is its versatility, which allows any industry to use it in its system. One ETL process, one data warehouse, one OLAP model, six indicators, and one OLAP framework were developed (with one frontend and one API backend). In conclusion, this article demonstrates the importance of a modular, adaptable, and scalable tool in the data analysis process and in supporting decision making.
{"title":"Pervasive Real-Time Analytical Framework—A Case Study on Car Parking Monitoring","authors":"Francisca Barros, Beatriz Rodrigues, José Vieira, Filipe Portela","doi":"10.3390/info14110584","DOIUrl":"https://doi.org/10.3390/info14110584","url":null,"abstract":"Due to the amount of data emerging, it is necessary to use an online analytical processing (OLAP) framework capable of responding to the needs of industries. Processes such as drill-down, roll-up, three-dimensional analysis, and data filtering are fundamental for the perception of information. This article demonstrates the OLAP framework developed as a valuable and effective solution in decision making. To develop an OLAP framework, it was necessary to create the extract, transform and load the (ETL) process, build a data warehouse, and develop the OLAP via cube.js. Finally, it was essential to design a solution that adds more value to the organizations and presents several characteristics to support the entire data analysis process. A backend API (application programming interface) to route the data via MySQL was required, as well as a frontend and a data visualization layer. The OLAP framework was developed for the ioCity project. However, its great advantage is its versatility, which allows any industry to use it in its system. One ETL process, one data warehouse, one OLAP model, six indicators, and one OLAP framework were developed (with one frontend and one API backend). In conclusion, this article demonstrates the importance of a modular, adaptable, and scalable tool in the data analysis process and in supporting decision making.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135169438","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}
Aristeidis Karras, Christos Karras, Spyros Sioutas, Christos Makris, George Katselis, Ioannis Hatzilygeroudis, John A. Theodorou, Dimitrios Tsolis
This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data pertinent to these farms. A feature of this system is its ability to calculate disease transmission intervals between individual cages and broader fish farm entities, providing crucial insights into the spread dynamics. These data then act as an entry point to our expert system. To enhance the predictive precision, we employed various machine learning strategies, ultimately focusing on a reinforcement learning (RL) environment. This RL framework, enhanced by the Multi-Armed Bandit (MAB) technique, stands out as a powerful mechanism for effectively managing the flow of virus transmissions within farms. Empirical tests highlight the efficiency of the MAB approach, which, in direct comparisons, consistently outperformed other algorithmic options, achieving an impressive accuracy rate of 96%. Looking ahead to future work, we plan to integrate buffer techniques and delve deeper into advanced RL models to enhance our current system. The results set the stage for future research in predictive modeling within aquaculture health management, and we aim to extend our research even further.
{"title":"An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture","authors":"Aristeidis Karras, Christos Karras, Spyros Sioutas, Christos Makris, George Katselis, Ioannis Hatzilygeroudis, John A. Theodorou, Dimitrios Tsolis","doi":"10.3390/info14110583","DOIUrl":"https://doi.org/10.3390/info14110583","url":null,"abstract":"This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data pertinent to these farms. A feature of this system is its ability to calculate disease transmission intervals between individual cages and broader fish farm entities, providing crucial insights into the spread dynamics. These data then act as an entry point to our expert system. To enhance the predictive precision, we employed various machine learning strategies, ultimately focusing on a reinforcement learning (RL) environment. This RL framework, enhanced by the Multi-Armed Bandit (MAB) technique, stands out as a powerful mechanism for effectively managing the flow of virus transmissions within farms. Empirical tests highlight the efficiency of the MAB approach, which, in direct comparisons, consistently outperformed other algorithmic options, achieving an impressive accuracy rate of 96%. Looking ahead to future work, we plan to integrate buffer techniques and delve deeper into advanced RL models to enhance our current system. The results set the stage for future research in predictive modeling within aquaculture health management, and we aim to extend our research even further.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315628","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}
In this paper, we present a new method for multitask learning applied to ultrasound beamforming. Beamforming is a critical component in the ultrasound image formation pipeline. Ultrasound images are constructed using sensor readings from multiple transducer elements, with each element typically capturing multiple acquisitions per frame. Hence, the beamformer is crucial for framerate performance and overall image quality. Furthermore, post-processing, such as image denoising, is usually applied to the beamformed image to achieve high clarity for diagnosis. This work shows a fully convolutional neural network that can learn different tasks by applying a new weight normalization scheme. We adapt our model to both high frame rate requirements by fitting weight normalization parameters for the sub-sampling task and image denoising by optimizing the normalization parameters for the speckle reduction task. Our model outperforms single-angle delay and sum on pixel-level measures for speckle noise reduction, subsampling, and single-angle reconstruction.
{"title":"Deep-Learning-Based Multitask Ultrasound Beamforming","authors":"Elay Dahan, Israel Cohen","doi":"10.3390/info14100582","DOIUrl":"https://doi.org/10.3390/info14100582","url":null,"abstract":"In this paper, we present a new method for multitask learning applied to ultrasound beamforming. Beamforming is a critical component in the ultrasound image formation pipeline. Ultrasound images are constructed using sensor readings from multiple transducer elements, with each element typically capturing multiple acquisitions per frame. Hence, the beamformer is crucial for framerate performance and overall image quality. Furthermore, post-processing, such as image denoising, is usually applied to the beamformed image to achieve high clarity for diagnosis. This work shows a fully convolutional neural network that can learn different tasks by applying a new weight normalization scheme. We adapt our model to both high frame rate requirements by fitting weight normalization parameters for the sub-sampling task and image denoising by optimizing the normalization parameters for the speckle reduction task. Our model outperforms single-angle delay and sum on pixel-level measures for speckle noise reduction, subsampling, and single-angle reconstruction.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412987","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}
Alexandros Z. Spyropoulos, Evangelos Ioannidis, Ioannis Antoniou
The early intervention of law enforcement authorities to prevent an impending terrorist attack is of utmost importance to ensuring economic, financial, and social stability. From our previously published research, the key individuals who play a vital role in terrorist organizations can be timely revealed. The problem now is to identify which attack strategy (node removal) is the most damaging to terrorist networks, making them fragmented and therefore, unable to operate under real-world conditions. We examine several attack strategies on 4 real terrorist networks. Each node removal strategy is based on: (i) randomness (random node removal), (ii) high strength centrality, (iii) high betweenness centrality, (iv) high clustering coefficient centrality, (v) high recalculated strength centrality, (vi) high recalculated betweenness centrality, (vii) high recalculated clustering coefficient centrality. The damage of each attack strategy is evaluated in terms of Interoperability, which is defined based on the size of the giant component. We also examine a greedy algorithm, which removes the node corresponding to the maximal decrease of Interoperability at each step. Our analysis revealed that removing nodes based on high recalculated betweenness centrality is the most harmful. In this way, the Interoperability of the communication network drops dramatically, even if only two nodes are removed. This valuable insight can help law enforcement authorities in developing more effective intervention strategies for the early prevention of impending terrorist attacks. Results were obtained based on real data on social ties between terrorists (physical face-to-face social interactions).
{"title":"Interoperability and Targeted Attacks on Terrorist Organizations Using Intelligent Tools From Network Science","authors":"Alexandros Z. Spyropoulos, Evangelos Ioannidis, Ioannis Antoniou","doi":"10.3390/info14100580","DOIUrl":"https://doi.org/10.3390/info14100580","url":null,"abstract":"The early intervention of law enforcement authorities to prevent an impending terrorist attack is of utmost importance to ensuring economic, financial, and social stability. From our previously published research, the key individuals who play a vital role in terrorist organizations can be timely revealed. The problem now is to identify which attack strategy (node removal) is the most damaging to terrorist networks, making them fragmented and therefore, unable to operate under real-world conditions. We examine several attack strategies on 4 real terrorist networks. Each node removal strategy is based on: (i) randomness (random node removal), (ii) high strength centrality, (iii) high betweenness centrality, (iv) high clustering coefficient centrality, (v) high recalculated strength centrality, (vi) high recalculated betweenness centrality, (vii) high recalculated clustering coefficient centrality. The damage of each attack strategy is evaluated in terms of Interoperability, which is defined based on the size of the giant component. We also examine a greedy algorithm, which removes the node corresponding to the maximal decrease of Interoperability at each step. Our analysis revealed that removing nodes based on high recalculated betweenness centrality is the most harmful. In this way, the Interoperability of the communication network drops dramatically, even if only two nodes are removed. This valuable insight can help law enforcement authorities in developing more effective intervention strategies for the early prevention of impending terrorist attacks. Results were obtained based on real data on social ties between terrorists (physical face-to-face social interactions).","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"119 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135511101","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}
Francis Marco Maria Cirianni, Antonio Comi, Agata Quattrone
The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) and to identify network and service configurations by comparing phenomena in similar contexts, as well as support the implementation of measures for managing demand that achieve sustainable goals. In this paper, an in-depth analysis of scenarios, with an IT (Information Technology) framework based on emerging technologies and AI to support sustainable and cooperative digital mobility, is provided. Therefore, the definition of the functional architecture of an AI-based mobility control centre is defined, and the process that has been implemented in a medium-large city is presented.
{"title":"Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts","authors":"Francis Marco Maria Cirianni, Antonio Comi, Agata Quattrone","doi":"10.3390/info14100581","DOIUrl":"https://doi.org/10.3390/info14100581","url":null,"abstract":"The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) and to identify network and service configurations by comparing phenomena in similar contexts, as well as support the implementation of measures for managing demand that achieve sustainable goals. In this paper, an in-depth analysis of scenarios, with an IT (Information Technology) framework based on emerging technologies and AI to support sustainable and cooperative digital mobility, is provided. Therefore, the definition of the functional architecture of an AI-based mobility control centre is defined, and the process that has been implemented in a medium-large city is presented.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"98 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135512390","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}
Poetic devices implicitly work towards inducing the reader to associate intended and expressed meaning to the sounds of the poem. In turn, sounds may be organized a priori into categories and assigned presumed meaning as suggested by traditional literary studies. To compute the degree of harmony and disharmony, I have automatically extracted the sound grids of all the sonnets by William Shakespeare and have combined them with the themes expressed by their contents. In a first experiment, sounds have been associated with lexically and semantically based sentiment analysis, obtaining an 80% of agreement. In a second experiment, sentiment analysis has been substituted by Appraisal Theory, thus obtaining a more fine-grained interpretation that combines dis-harmony with irony. The computation for Francis Webb is based on his most popular 100 poems and combines automatic semantically and lexically based sentiment analysis with sound grids. The results produce visual maps that clearly separate poems into three clusters: negative harmony, positive harmony and disharmony, where the latter instantiates the need by the poet to encompass the opposites in a desperate attempt to reconcile them. Shakespeare and Webb have been chosen to prove the applicability of the method proposed in general contexts of poetry, exhibiting the widest possible gap at all linguistic and poetic levels.
{"title":"Computing the Sound–Sense Harmony: A Case Study of William Shakespeare’s Sonnets and Francis Webb’s Most Popular Poems","authors":"Rodolfo Delmonte","doi":"10.3390/info14100576","DOIUrl":"https://doi.org/10.3390/info14100576","url":null,"abstract":"Poetic devices implicitly work towards inducing the reader to associate intended and expressed meaning to the sounds of the poem. In turn, sounds may be organized a priori into categories and assigned presumed meaning as suggested by traditional literary studies. To compute the degree of harmony and disharmony, I have automatically extracted the sound grids of all the sonnets by William Shakespeare and have combined them with the themes expressed by their contents. In a first experiment, sounds have been associated with lexically and semantically based sentiment analysis, obtaining an 80% of agreement. In a second experiment, sentiment analysis has been substituted by Appraisal Theory, thus obtaining a more fine-grained interpretation that combines dis-harmony with irony. The computation for Francis Webb is based on his most popular 100 poems and combines automatic semantically and lexically based sentiment analysis with sound grids. The results produce visual maps that clearly separate poems into three clusters: negative harmony, positive harmony and disharmony, where the latter instantiates the need by the poet to encompass the opposites in a desperate attempt to reconcile them. Shakespeare and Webb have been chosen to prove the applicability of the method proposed in general contexts of poetry, exhibiting the widest possible gap at all linguistic and poetic levels.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616765","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}
Raquel Hijón-Neira, Celeste Pizarro, John French, Pedro Paredes-Barragán, Michael Duignan
Students in their first year of computer science (CS1) at universities typically struggle to grasp fundamental programming concepts. This paper discusses research carried out using a Java-based visual execution environment (VEE) to introduce fundamental programming concepts to CS1 students. The VEE guides beginner programmers through the fundamentals of programming, utilizing visual metaphors to explain and direct interactive tasks implemented in Java. The study’s goal was to determine if the use of the VEE in the instruction of a group of 63 CS1 students from four different groups enrolled in two academic institutions (based in Madrid, Spain and Galway, Ireland) results in an improvement in their grasp of fundamental programming concepts. The programming concepts covered included those typically found in an introductory programming course, e.g., input and output, conditionals, loops, functions, arrays, recursion, and files. A secondary goal of this research was to examine if the use of the VEE enhances students’ understanding of particular concepts more than others, i.e., whether there exists a topic-dependent benefit to the use of the VEE. The results of the study found that use of the VEE in the instruction of these students resulted in a significant improvement in their grasp of fundamental programming concepts compared with a control group who received instruction without the use of the VEE. The study also found a pronounced improvement in the students’ grasp of particular concepts (e.g., operators, conditionals, and loops), suggesting the presence of a topic-dependent benefit to the use of the VEE.
{"title":"Improving CS1 Programming Learning with Visual Execution Environments","authors":"Raquel Hijón-Neira, Celeste Pizarro, John French, Pedro Paredes-Barragán, Michael Duignan","doi":"10.3390/info14100579","DOIUrl":"https://doi.org/10.3390/info14100579","url":null,"abstract":"Students in their first year of computer science (CS1) at universities typically struggle to grasp fundamental programming concepts. This paper discusses research carried out using a Java-based visual execution environment (VEE) to introduce fundamental programming concepts to CS1 students. The VEE guides beginner programmers through the fundamentals of programming, utilizing visual metaphors to explain and direct interactive tasks implemented in Java. The study’s goal was to determine if the use of the VEE in the instruction of a group of 63 CS1 students from four different groups enrolled in two academic institutions (based in Madrid, Spain and Galway, Ireland) results in an improvement in their grasp of fundamental programming concepts. The programming concepts covered included those typically found in an introductory programming course, e.g., input and output, conditionals, loops, functions, arrays, recursion, and files. A secondary goal of this research was to examine if the use of the VEE enhances students’ understanding of particular concepts more than others, i.e., whether there exists a topic-dependent benefit to the use of the VEE. The results of the study found that use of the VEE in the instruction of these students resulted in a significant improvement in their grasp of fundamental programming concepts compared with a control group who received instruction without the use of the VEE. The study also found a pronounced improvement in the students’ grasp of particular concepts (e.g., operators, conditionals, and loops), suggesting the presence of a topic-dependent benefit to the use of the VEE.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616968","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}
Analysing and understanding donor behaviour in nonprofit organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an artificial-intelligence-enabled decision-support system (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a design science research approach to evaluate an AI-enabled DSS’s initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment by conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The evaluation of the conceptual design results led to the recommendation for efficiency, effectiveness, flexibility, and usability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSSs for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such an analysis of donors who donate money or volunteer time for NPOs.
{"title":"A Conceptual Design of an AI-Enabled Decision Support System for Analysing Donor Behaviour in Nonprofit Organisations","authors":"Idrees Alsolbi, Renu Agarwal, Bhuvan Unhelkar, Tareq Al-Jabri, Mahendra Samarawickrama, Siamak Tafavogh, Mukesh Prasad","doi":"10.3390/info14100578","DOIUrl":"https://doi.org/10.3390/info14100578","url":null,"abstract":"Analysing and understanding donor behaviour in nonprofit organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an artificial-intelligence-enabled decision-support system (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a design science research approach to evaluate an AI-enabled DSS’s initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment by conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The evaluation of the conceptual design results led to the recommendation for efficiency, effectiveness, flexibility, and usability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSSs for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such an analysis of donors who donate money or volunteer time for NPOs.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617896","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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