Ebook: New Trends in Intelligent Software Methodologies, Tools and Techniques

Applied intelligence, integrated with software, is an essential enabler for science and the new economy. It creates new markets and new directions for a more reliable, flexible and robust society. It empowers the exploration of our world in ever more depth. However, software often falls short of our expectations. Current software methodologies, tools, and techniques remain neither robust enough nor sufficiently reliable for a constantly changing and evolving market, and many promising approaches have proved to be no more than case-by-case oriented methods that are not fully automated.

This book explores new trends and theories which illuminate the direction of developments in this field which we believe will lead to a transformation in the role of software and science integration in tomorrow’s global information society. By discussing issues ranging from research practices to techniques and methodologies and proposing and reporting on the solutions needed for global world business, it offers an opportunity for the software-science community to think about where we are today and where we are going.

The book aims to capture the essence of a new state-of-the-art in software science and its supporting technologies, and to identify the challenges that such technologies will have to master. It contains extensively reviewed papers presented at the 23rd edition of the International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, (SoMeT_24) held in Cancun, Mexico, with the collaboration of University of Instituto Politécnico Nacional, Mexico, from 24 to 25 September 2024, https://atenea.esimecu.ipn.mx/

SoMeT_24 is celebrating its 23rd edition (Previous related events are: SoMeT_02 (the Sorbonne, Paris, 2002); SoMeT_03 (Stockholm, Sweden, 2003); SoMeT_04 (Leipzig, Germany, 2004); SoMeT_05 (Tokyo, Japan, 2005); SoMeT_06 (Quebec, Canada, 2006); SoMeT_07 (Rome, Italy, 2007); SoMeT_08 (Sharjah, UAE, 2008); SoMeT_09 (Prague, Czech Republic, 2009); SoMeT_10 (Yokohama, Japan, 2010), and SoMeT_11 (Saint Petersburg, Russia), SoMeT_12 (Genoa, Italy), SoMeT_13 (Budapest, Hungary), SoMeT_14 (Langkawi, Malaysia), SoMeT_15 (Naples, Italy), SoMeT_16 (Larnakes, Cyprus), SoMeT_17 (Kitakyushu, Japan), SoMeT_18 (Granada, Spain), SoMeT_19 (Sarawak, Malaysia), SoMeT_20 (Kitakyushu, Japan), SoMeT_2021 (Cancun, Mexico), SoMeT_22 (Kitakyushu, Japan), SoMeT_2023 (Naples, Italy)), and the 2024 event was supported by the i-SOMET Incorporated Association, (www.i-somet.org) established by Prof. Hamido Fujita.

This edition of the conference brought together researchers and practitioners to share their original research results and practical development experience in software science and related new technologies. It forms part of the conference and the SoMeT series, providing an opportunity for the exchange of ideas and experiences in the field of software technology, opening up new avenues for software development, methodologies, tools, and techniques, particularly with regard to intelligent software, by applying artificial intelligence techniques in software development, and tackling human interaction in the development process for better high-level interface. The emphasis has been placed on human-centric software methodologies, end-user development techniques, and emotional reasoning, for an optimally harmonized performance between the design tool and the user. The word “intelligent” in SOMET emphasizes the need for the consideration of artificial intelligence issues in software design for systems application, for example. in disaster recovery and other systems supporting civil protection.

A major goal of this publication was to assemble the work of scholars from the international research community which discuss and share research experiences of new software methodologies and techniques. One of the important issues addressed is the handling of cognitive issues in software development so as to adapt it to the user’s mental state, and tools and techniques related to this aspect form part of the contributions to this book. Other subjects raised at the conference were intelligent software design in software ontology, and conceptual software design in the practice of human-centric information system application.

The book also investigates other theories and practices in software science, including emerging technologies from their computational foundations in terms of models, methodologies, and tools. This is essential both for a comprehensive overview of information systems and research projects, and to assess their practical impact on real-world software problems. This represents another milestone in mastering the new challenges of software and its promising technology addressed by the SoMeT conferences, and provides the reader with new insights, inspiration and concrete material to further the study of this new technology.

The book is a collection of carefully selected papers, refereed by the reviewing committee listed, the members of which carefully selected revised articles of the highest quality for publication. Referees from the program committee first carefully reviewed all the submissions received, and 40 papers were selected on the basis of technical soundness, relevance, originality, significance, and clarity. These were then revised on the basis of the review reports before being selected by the SoMeT_24 international reviewing committee, with each paper being reviewed by three or four reviewers.

This book is the result of a collective effort from many industrial partners and colleagues throughout the world. We would like to express our gratitude for the support provided by the University of Instituto Politécnico Nacional, Mexico and to all those authors who contributed their invaluable support to this work. Most especially, we wish to thank the program committee, reviewing committee and all those who participated in the rigorous reviewing process and the lively discussion and evaluation meetings which led to the selection of the papers which appear in this book. Last, but not least, we would also like to thank the Microsoft Conference Management Tool team for their expert guidance on the use of the Microsoft CMT System as a conference-support tool during all the phases of SoMeT_24.

The Editors

Creating effective sightseeing trip plans requires consideration of both positive factors, such as travelers’ preferences and the type of experience they want, and negative factors, such as travel time and travel costs. However, it is difficult for travelers to create completely satisfactory trip plans. Additionally, although it is easy to outsource trip planning to a travel agency or use an existing travel tour, the resultant plans often cost extra or are unsatisfactory. The purpose of this research was to facilitate the creation of highly satisfying sightseeing trip plans that meet the needs of travelers. In this study, a database was created of sightseeing trip plans that included positive and negative factors and had relatively high satisfaction levels. A sightseeing trip plan search service is proposed that allows users to search for sightseeing trip plans that meet their needs. To confirm the effectiveness of the proposed service, a web-based system was built that allows users to view 72 sightseeing trip plans created by students at Yamato University in Osaka and Iwate Prefectural University in Iwate, Japan. A database system was then constructed that allows users to input and search for trip plans. This paper provides an overview of these systems, evaluates the effectiveness of the proposed service, and highlights future issues to consider in fully implementing a search service for sightseeing trip plans.

Nowadays online learning is growing strongly, with many diverse options. Learners can utilize electronic devices and internet platforms for studying, searching for materials, and looking up knowledge. In this study, a method for design a knowledge chatbot system in education is proposed. This method includes a knowledge base, which is a knowledge model integrating ontology of relations and operators and knowledge graph, and the algorithms for solving problems on intellectual querying. The proposed method is utilized to build an intelligent chatbot system in the course of Foudation of Database. This chatbot can assist the online learning by querying subject knowledge content, categorizing concepts, and providing support for various types of exercises in the subject. The experimental results show that the built system meets the requirements of an intelligent educational system and outperforms than currently GenAI systems.

The rapid advancement in deepfake technology has enabled the creation of highly realistic fake images and videos, posing significant risks, especially in the context of explicit content. Such content, which often involves the alteration of an individual’s identity in sexually explicit material, can lead to defamation, harassment, and blackmail. This paper focuses on the detection of deepfakes in explicit content using a state-of-the-art ID-unaware Binary Classification method. We evaluate its effectiveness in real-world scenarios by analyzing three versions of the model with different backbones: ResNet34, EfficientNet-B3, and EfficientNet-B4. To facilitate this evaluation, we curated a dataset of 200 videos, consisting of 100 genuine videos and their corresponding deepfake counterparts, ensuring a direct comparison between genuine and altered content. Our analysis revealed a significant decrease in detection performance when applying the state-of-the-art method to explicit content. Specifically, the AUC score dropped from 93% on standard datasets such as FaceForensics++ to 62% on our explicit content dataset. Additionally, the accuracy for detecting deepfakes plummeted to around 25%, while the accuracy for genuine videos remained high at approximately 90%. We identified specific factors contributing to this decline, including unconventional makeup, lighting issues, and facial blurring due to camera distance. These findings underscore the challenges and the necessity for robust detection methods to address the unique problems posed by explicit content deepfakes, ultimately aiming to protect individuals from the potential harms associated with this technology.

In today’s digital landscape, the prevention of cyber attacks has become exceptionally crucial. This is especially true for safety-critical systems, where safeguarding against these threats is of paramount importance. To address this concern, the MITRE Corporation has developed ATT&CK, an extensive framework comprising data matrices. This framework serves the purpose of assessing a company’s security preparedness and pinpointing vulnerabilities that may exist within its infrastructure.

By leveraging the capabilities of MITRE ATT&CK, including its tactics and techniques, in conjunction with the LDA4CPS tool, we have devised a novel approach to identify the most critical vulnerabilities in a susceptible system. Furthermore, incorporating MITRE ATT&CK mitigations tailored to the discovered vulnerability empowers the blue team (defensive side) with tangible, practical measures to fortify their security posture. This approach enhances their capability to effectively counter cyber threats, bolstering their overall defensive capabilities.

The development of efficient audio coding schemes allowing an increasing information security and reduction of the storage requirement, is problem that has attracted the researchers interest over the last several years. To this end several schemes have been proposed, that allows an efficient compression and encryption of digital information. In most cases the information is firstly compressed before the encryption process. Because in several situations, to achieve a real time communication process, it is desirable to compress and encrypt the audio signal now when it is captured, it would be desirable to implement coding schemes able to encrypt and compress sensitive information simultaneously. A suitable approach to achieve this goal, is to use a compressing sensing-based systems which allows a simultaneous compression and encryption of the signal to be transmitted. This paper presents an audio encoding scheme using compressive sensing techniques which firstly segments the signal to be encoded signal in segments of M frames, each one with N samples. These are then transformed into a set of M sparse frames using the Discrete Cosine Transform (DCT) and multiplied by a properly designed random matrix of size M × N. The resulting vectors are then concatenated to generate the compressed and encrypted matrix. It is then feed to a chaotic mixing scheme to further increase the security of proposed system. Evaluation results shows that the proposed system achieves and efficient and secure compression and encryption, while satisfying the extended Wyner secrecy criterion (EWS).

Cyberbullying, marked by its persistent and intentional aggression online, yields severe repercussions for its victims, extending beyond immediate distress to long-lasting effects such as heightened anxiety, depression, and social withdrawal. Individuals subjected to Cyberbullying often grapple with diminished self-esteem, compromised academic performance, and strained interpersonal relations. Given the escalating prevalence of this digital menace, there is a pressing need for advanced methodologies to address it effectively. This paper introduces an approach to Cyberbullying detection, integrating techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses, along with the parallel processing capabilities of Convolutional Neural Networks (CNNs) and the contextual comprehension provided by Bidirectional Long Short-Term Memory (BiLSTM) networks. Through an experimentation on the latest Ejaz-Choudhury-Razi Cyberbullying dataset, our framework exhibits satisfactory performance in identifying instances of online hostility. These results underscore the potential of our approach to significantly contribute to ongoing efforts aimed at combating Cyberbullying in digital environments.

Quick Response (QR) two-dimensional codes were designed to send information in a simple and versatile way, such as sharing a product or company website. Nowadays they can be found in documents with sensitive information, such as personal documents (passports, visas, bank transfers) and documents with restricted information of companies (financial reports, legal documents), for this reason it is important to take care of the information that is sent or shared, depending on its use, it is important to add levels of security to the information encoded in QR codes, since in some cases sensitive information is sent. In this paper we propose to add two levels of security to QR codes that will share sensitive information. A first level of security is the encryption of the original information through the Privacy Guard (GPG) method, a second level of security is the multiplexing of two pieces of information in a chromatic QR code, where each color represents an encoded information, the incorporation of the two levels of security will keep the information shared by the users secure.

In the field of medical image management, the security and privacy of patient data are of utmost importance. This research focuses on the application of the Jarvis halftone technique for image reconstruction aimed at enhancing data hiding capabilities. We employed data hiding in binary image with high payload method to embed binary characters into the modified contours of Jarvis halftone images, thereby increasing the bandwidth for data hiding. To enhance the hiding capacity, the original patient image was modified by adding an artificial contour that creates vectors in selected pixels, thus optimizing the halftone technique used. The processed images were embedded into DICOM medical images using a pseudo-random walk technique, which allowed the generation of two keys for concealment, embedded in layers 12 and 16 of the image. This resulted in a modified DICOM image with a Structural Similarity Index measure. Subsequently, data were extracted using the generated keys, thus retrieving the Jarvis halftone image with modified contours. This process was followed by a conversion of the halftone image to grayscale for patient authentication, highlighting the effectiveness of the Jarvis halftone in preserving image quality after the data hiding and extraction process. This study not only demonstrates the feasibility of integrating advanced halftone and data hiding techniques in the medical field but also opens ways to future research on security in the transmission and storage of medical images, ensuring the confidentiality and integrity of patient information.

The selection between Deep Learning (DL) approaches is not easy for URL phishing due to the variety of attacks and scammers. There are various DL techniques to detect phishing URLs, and choosing the suitable algorithm and affecting the model formed is very important. Wrong-choosing DL techniques might lead to low maturity and produce bias. The trained model’s performance and accuracy would also be unsatisfactory if the wrong algorithms and methods were used. It often happens when the attackers change their phishing strategies frequently to target the system’s weaknesses and users’ naivety. The robust characteristics of DL algorithms have led the researchers to develop several URL phishing mitigation strategies. The techniques have been used to detect phishing attacks by using various URL features like URL length, URL domain, and other known features and further incorporating the new features. From the perspective of the Natural Language Processing (NLP) technique perspective, transformers are models designed to handle sequential text, such as summarizing and translating. One of the well-known transformers, called Keras Embedding, has a good application in detecting spam emails. As the transformers proved their usage in URL phishing detection, it is further hypothesized that the URLs can directly parse out the contextual meaning of the string and identify whether the website is benign or phishing. Therefore, this paper provides a URL phishing detection model with a combination of deep learning and natural language processing methods. As shown in the experiments, the result produces and improves with high performance and accuracy for URL phishing detection. We also examined and compared the findings of the proposed solution with deep learning only and NLP-only URL phishing detection approaches.

This paper is about a special configuration of engineering model and modeling platform for model development that serves widely connected research in applied informatics considering demands by highly automated achievements. Initiative for this special application of engineering modeling platform was motivated by engineering purposed software development towards reactive, autonomous, and widely contextual solutions to accommodate tasks in extending variety of integrated issues. After characterizing the multiphysical way of multidisciplinary model powered engineering research, this paper introduces novel model mediated scenario in which research specific engineering software related activities are placed with the emphasis on flexible model and software configuration considering composition of complex multidisciplinary solutions for multiphysical engineering representations and simulations those are extended to human organ and other bioinspired issues. Additional contributions in this paper are about capabilities of reactive autonomous models, extended representation of behaviors, and novel experimental model for research in applied informatics. Finally, application of the above experimental model in research using scientifically renewable world level engineering modeling platform at the Virtual Research Laboratory, and evaluation and advantages of the proposed research are introduced.

In the software development life cycle, the implementation of stringent security requirements is essential to promote the creation of robust and secure code, thereby avoiding the need for extensive post-implementation revisions. A wide variety of methodologies are commonly employed to examine source code authorship, ranging from adherence to strict standards and guidelines to the application of best practices. However, these reviews are often very laborious and demand a broad spectrum of specialized knowledge from various DevOps task groups to effectively address underlying vulnerabilities. To streamline and enhance the efficiency of the review process, advanced Machine Learning techniques are increasingly being adopted as a critical factor in improving the precision of transitions to secure code structures. This manuscript introduces an innovative transformation system that leverages the contextual adaptability provided by the renowned advanced language model, CodeBERT, integrated with a Generative Adversarial Network (GAN). This synergistic combination allows for the precise classification of insecure code segments in different programming languages and the subsequent generation of their secure counterparts. Empirical results confirm the system’s ability to detect up to 98.3% of insecure tokens and reconstruct secure versions with an accuracy of up to 95.67%.

The term “digital twin” has gained an ever-increasing popularity since the last five years. Digital twins promote the existence of a virtual counterpart of any physical system for monitoring the system behaviour and suggesting possible improvements automatically. Given many industries’ interest on developing and using digital twin systems, it becomes highly crucial to develop such systems with least cost and high quality. This can be addressed with the architecture-centric design approach. However, the literature still lacks in any approach that can be applied for the specifications of any digital twin system architectures and any concrete modeling notation set. In this paper, we propose an architecture modeling language called DTLang for the high-level specifications of any digital twin architectures. DTLang is based on the well-regarded C4 architecture model and thus offers 4 different architecture viewpoints which are context, container, component, and code viewpoints. Each viewpoint addresses a different concern and is related to another viewpoint for the hierarchical (i.e., traceable) specifications of digital twin system architectures. With DTLang, the viewpoint models can be specified separately using distinct graphical notation sets and also can easily be traced between each other. We demonstrate the use of DTLang via the building fire protection case-study.

This paper presents a systemic model for the simulation of the design of a nozzle for R-Candy. This research is focused on a complex system using the methodology of 5 phases based on the tools of systemic modeling and general systems theory. The rocket motor was designed using Solidworks, which consists of the igniter system, the combustion chamber, and the nozzle. Usually, a Laval-type nozzle (convergent-divergent) is used for rocket motors, taking advantage of the exothermic properties of the potassium nitrate and sorbitol fuel mixture in a solid state. The simulation was carried out using the computational fluid dynamics software FEATool, evaluating temperature, velocity, and pressure. The results obtained by the R-Candy software give guidelines for a broad preliminary panorama for the manufacture of rocket motors in experimental rocketry.

Breast cancer is a dangerous disease, contributing to a high mortality rate in women. Early detection plays a pivotal role in enhancing survival rates. Breast ultrasound is considered an effective method to help diagnose breast diseases early. Breast ultrasound is inexpensive, easy to perform, non-invasive and painless, so it is often prescribed by doctors in cases where it is necessary to examine the nature of clinically palpable lesions or related symptoms in the breast. In this paper, we introduce a method based on transfer learning and deep feature fusion to classify breast cancer using ultrasound images. The results from our experiments involving 780 breast ultrasound images across three categories (benign, malignant, and normal) indicated that the model using max fusion of deep features outperformed an original CNN in terms of performance, the combination of the maximum value between deep features has a higher performance level with an accuracy of about 1% to 4% compared to the original model. The concatenation fusion of VGG19 and ViT features delivers 1% - 4% times more accuracy than the original model alone

This paper proposes an innovative deep learning algorithm, denominated as Neuroevolution of Hybrid Neural Networks in a Robotic Agent (NRNH-AR). This algorithm stands out in computational efficiency, evolutionary stability, and classification speed, surpassing other existing methods such as FAM-HGNN, I3A, DQN, among others. The triptych design of the NRNH-AR optimizes resource usage and exhibits superior adaptability in dynamic environments, which is a critical factor in robotics. The integration of artificial vision techniques enhances the capability for rapid classification of observations. Throughout 300 epochs in a dynamic environment, the importance of a minimal database for the algorithm’s effective evolution was evidenced. Moreover, it was found that the accuracy of the Convolutional Neural Network (CNN) directly impacts the performance of the NRNH-AR. An additional study of 850 epochs in a static environment provided a deeper understanding of the evolution and adaptation of learning networks. The NRNH-AR is positioned as a viable and promising solution in the field of applied deep learning in robotics.

This study introduces a Preference-Based Reinforcement Learning (PbRL) approach tailored for autonomous vehicle (AV) applications within a simulated environment. Traditional RL methods often struggle with the complexities of reward function engineering, failing to perform behaviors of human desire. The proposed framework integrates human preferences directly into the training loop, our framework offers a novel methodology for enhancing the decision-making processes of autonomous system. Our results demonstrate that PbRL can refine the strategies of AVs to align more closely with human-like decision-making, highlighting the potential for increased adaptability and safety in autonomous technologies.

In the realm of forensic science, precise identification of individuals holds paramount importance in both investigative procedures and legal proceedings. Hands and palms recognition has emerged as a valuable biometric modality within forensic applications, owing to the distinct and intricate features inherent to these anatomical regions. The elaborate patterns of veins, creases, and ridges present on palms and fingers serve as rich sources of biometric data, crucial for accurate identification purposes. Furthermore, given the frequent involvement of hands and palms in criminal activities such as theft and assault, their recognition becomes imperative for establishing links between suspects and crime scenes. However, developing robust recognition systems tailored for forensic applications poses notable challenges, including variations in hand poses, lighting conditions, and image quality. To address these hurdles, sophisticated deep learning techniques, notably transfer learning, have been employed. By harnessing pre-trained deep learning models namely NasNetLarge, NasNetMobile, and EfficientNet, initially trained on expansive datasets for general image recognition tasks, we can adapt these models to the specific task of hands and palms recognition in forensic contexts. Our findings reveal that all three models consistently achieved over 92% accuracy across all metrics evaluated, demonstrating their efficacy as strong contenders for the hands-and-palms recognition task. Notably, the EfficientNet model exhibited superior performance compared to its counterparts, boasting more than 95.8% accuracy, precision, F1-score and recall, along with more than 98.6% specificity and 99.4% AUC.

With the growing importance of marine resources and the increasing demand for exploration of underwater environments, underwater target detection technology has become one of the key technologies in the fields of ocean engineering, underwater archaeology, and intelligent agriculture. However, due to the complexity and uncertainty of underwater environments, such as light attenuation, water turbidity, and dynamic changes of water currents, current target detection methods often perform poorly in underwater scenes. To solve the corresponding problems, this paper proposes the YOLOv5_OD_Conv model, which aims to improve the accuracy and generalisation ability of the model by introducing the OD_Conv full-dimensional dynamic convolution in the YOLOv5 Neck part. Simulation and experimental results show that the proposed method increases the detection accuracy P by 1.05%, the precision mAP0.5 by 1.5%, and the recall R by 0.43% compared to YOLOv5s. The improvement of detection effect is obvious, which proves the effectiveness of the method.

This study investigates the application of machine learning to enhance the accuracy of saturated flow boiling heat transfer correlations. Traditional correlations often exhibit limitations, motivating the exploration of alternative approaches. A comprehensive dataset encompassing 2770 experimental observations, compiled from over 15 published research papers, serves as the foundation for this research. The data incorporates flow boiling heat transfer for pure and mixture refrigerants in smooth tubes. The study compares three machine learning models including neural network, linear regression, and SVM. The data is strategically divided: 80% for training the models and 20% for testing. The wide neural network shows that the best-performing model achieves a Root Mean Square Error (RMSE) of 0.202, demonstrating exceptional prediction accuracy, followed by the linear regression, and SVM training models. Heat Transfer Coefficient (HTC) is predicted with an error of less than 5% for all refrigerants, further confirmed by a high coefficient of determination (0.99). This signifies a significant improvement in traditional experimental-based correlations. Furthermore, the study demonstrates the efficacy of machine learning models in predicting heat transfer enhancement methods. In conclusion, this work highlights the potential of machine learning for refining saturated flow boiling heat transfer predictions.

Video Anomaly Detection (VAD) is a well-established area of research with significant potential for enhancing video surveillance in urban public transportation. However, current VAD systems often propose powerful methodologies but overlook their use in extreme environments like public transportation, necessitating a balance between performance and computational efficiency. In this paper, we evaluate a key component in many VAD frameworks: feature extractors. We investigate five extractors: Inflated 3D ConvNets (I3D), 3D Convolutional Neural Networks (C3D), Unified Transformer (UniFormer) in Small (UniFormer-S) and Base (UniFormer-B) versions, and Temporal Shift Module (TSM). These are integrated into a VAD architecture employing Bidirectional Encoder Representations from Transformers (BERT) with Multiple Instance Learning (MIL), chosen for its modularity and clear separation between the feature extractor and anomaly detector module. UniFormer-S demonstrated a processing rate of 4.64 clips per second with a computational demand of 28.717 GFLOPs on edge devices like the Jetson Orin NX (8GB RAM, 20W power). On the UCF-Crime dataset, UniFormer-S with BERT + MIL achieves an AUC of 79.74%. These findings highlight the promise of UniFormer-S and the use of edge devices like the Jetson Orin NX in public transportation due to their balance of performance and efficiency.