Similarities of Testing Programmed and Learnt Software

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel CNN architecture is evaluated by conducted comprehensive experiments on two benchmark intrusion detection datasets. The designed CNN performance was compared with four other ML models such as logistic regression, k-nearest neighbors, decision trees, and random forest. The experimental results revealed that the proposed CNN architecture outperformed the traditional ML models with highest accuracy of 97.68% and 97.39% and highest F1-score of 97.68% and 97.40% for. UNSW-NB15 and CICIDS2017 datasets, respectively. The proposed CNN architecture serves as a promising alternative to traditional ML models, offering improved performance in identifying and mitigating network attacks.

Graph theory provides a robust framework for modelling complex relationships in medical data, enhancing classification accuracy through relational learning. Unlike traditional machine learning (ML) models that treat data points independently, graph-based approaches support structural dependencies to improve feature representation. This study explores the application of Graph Attention Networks (GAT), GraphSAGE, Graph Convolutional Network(GCN) and Graph Isomorphism Network (GIN) for breast cancer classification, employing k-nearest neighbor (KNN) graphs to construct a structured dataset where nodes represent patients and edges capture feature similarities. The effectiveness of the graph-based approaches is evaluated against traditional ML classifiers, including Decision Trees, Random Forest, LightGBM, and XGBoost. Experimental results indicate that GCN, GIN, GAT and GraphSAGE are beat conventional methods, with GCN, GAT and GraphSAGE achieved 100% test-accuracy and with GIN achieved 99.42%, to confirm the percentage of accuracy, authors conducted extensive experiments, including robustness testing by reducing KNN connections, introducing noise, and shuffling train-test splits. Results demonstrate that graph-based models are significantly best than traditional ML models, and these graph-based models maintain same classification accuracy while maintaining stability under robustness tests. The findings confirm that graph-based learning provides a scalable, interpretable, and highly accurate alternative for medical classification tasks, proving its effectiveness in distinguishing between benign and malignant tumors.

Purpose The blood–brain barrier (BBB) is a major obstacle in neurological drug development, restricting most drugs from entering the brain. To address this, computational models leveraging Artificial Intelligence (AI) and machine learning (ML) have been explored for predicting BBB permeability. This meta-review explores various computational strategies leveraging AI and ML to improve BBB permeability prediction. Methods 31 publications were included in this review following a search in PubMed Central and in the Journal of Cheminformatics. Models are categorized into three groups: (1) traditional ML models using physiochemical descriptors, (2) graph/image-based models leveraging molecular structure, and (3) encoder-based methods using SMILES representations. Results Traditional ML models achieve greater predictive accuracy due to their reliance on explicitly defined features, whereas deep learning methods, particularly graph neural networks (GNNs), show promise but require large-scale datasets and pretraining. Encoder-based methods underperform compared to traditional ML and GNNs, likely due to inadequate feature extraction. Conclusion Despite advancements, challenges such as dataset biases, model interpretability, and the need for experimental validation remain. Future research should explore multi-modal integration and generative AI to enhance BBB permeability prediction and aid drug discovery.

Accurate rainfall forecasting is crucial in activities such as agricultural planning, water resource management, and flood preparedness, so failure to accurately forecast rainfall might undermine these activities. This study aimed to improve the accuracy of daily rainfall prediction in Hai district, Kilimanjaro region, Tanzania, a region characterised by a bimodal rainfall pattern, by comparing rainfall prediction accuracies of Traditional Machine Learning (TML) and Deep Learning (DL) models. The 25-year daily rainfall dataset for Hai district was first pre-processed by cleaning, normalisation, and splitting into training (first 60%), validation (next 20%), and test (last 20%) sets. TML models (K-Nearest Neighbours (KNN) through its variant KNN Regressor and Support Vector Machine (SVM) through its variant Support Vector Regressor (SVR)) were developed, trained, and their performances compared with developed and trained DL models (Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)). Each model’s test performance was evaluated using the Root Mean Squared Error (RMSE) metric. The findings revealed that the DL models, LSTM and GRU, achieved test RMSE scores of 6.35 mm and 6.49 mm, respectively, while the TML models, KNN and SVM, achieved test RMSE scores of 6.61 mm and 10.42 mm, respectively. The study demonstrated that DL models are more effective for accurate rainfall forecasting than TML models in regions with complex rainfall patterns. The insights gained will guide the development of more reliable, location-specific weather prediction systems, ultimately supporting informed decision-making in agriculture, environmental management, and flood risk reduction in Tanzania and other regions facing similar climatic challenges

Scene graph generation takes an image and derives a graph representation of key objects in the image and their relations. This core computer vision task is often used in autonomous driving, where traditional software and machine learning (ML) components are used in tandem. However, in such a safety-critical context, valid scene graphs can be further restricted by consistency constraints captured by domain or safety experts. Existing ML approaches for scene graph generation focus exclusively on relation-level accuracy but provide little to no guarantee that consistency constraints are satisfied in the generated scene graphs. In this paper, we aim to complement existing ML-based approaches by a post-processing step using constraint optimization over probabilistic scene graphs that can (1) guarantee that no consistency constraints are violated and (2) improve the overall accuracy of scene graph generation by fixing constraint violations. We evaluate the effectiveness of our approach using well-known, and novel metrics in the context of two popular ML datasets augmented with consistency constraints and two ML-based scene graph generation approaches as baselines.

(English) Machine Learning (ML) systems allow the efficient implementation of functionalities that can be hard to program by traditional software due to the high spectrum of inputs that hinder the definition of a specific procedural rule set. This characteristic of ML systems has encouraged their adoption in applications such as object detection or image classification in several safety-related domains, which are subject to safety certification. This certification is usually achieved by adhering to traditional functional safety standards such as IEC 61508 or ISO 26262. However, these standards were not devised to accommodate technologies such as ML in safety-related systems due to their development process, which is based on probabilistic models generated from training data, as opposed to traditional software components coded from specifications. Additionally, new challenges arise due to the fact that these ML algorithms need to process large volumes of data, and this requires High-Performance Embedded Computing (HPEC) platforms with computing capabilities far superior to traditional safety systems, such as multicore devices and GPU accelerators. Current functional safety standards do not provide explicit guidance for the use of HPEC platforms in safety-relevant systems, and the inherent complexity of those highly parallel architectures challenges certifications. With this Thesis, we attempt to address these challenges and give a step forward towards the functional safety certification of safety control systems integrating ML components in HPEC platforms. (Español) Los sistemas de aprendizaje automático (ML) permiten la implementación eficiente de funcionalidades que pueden ser difíciles de programar por el software tradicional debido al elevado espectro de entradas que dificultan la definición de un conjunto de reglas procedimentales específicas. Esta característica de los sistemas ML ha fomentado su adopción en aplicaciones como la detección de objetos o la clasificación de imágenes en diversos ámbitos relacionados con la seguridad, que están sujetos a certificación de seguridad. Esta certificación suele lograrse mediante la adhesión a las normas tradicionales de seguridad funcional, como IEC 61508 o ISO 26262. Sin embargo, estas normas no se concibieron para dar cabida a tecnologías como el ML en los sistemas relacionados con la seguridad debido a su proceso de desarrollo, que se basa en modelos probabilísticos generados a partir de datos de entrenamiento, a diferencia de los componentes de software tradicionales codificados a partir de especificaciones. Además, surgen nuevos retos debido a que estos algoritmos de ML necesitan procesar grandes volúmenes de datos, lo que requiere plataformas de computación embebidas de alto rendimiento (HPEC) con capacidades de cálculo muy superiores a las de los sistemas de seguridad tradicionales, como dispositivos multinúcleo y aceleradores con GPUs. Las actuales normas de seguridad funcional no proporcionan orientaciones explícitas para el uso de plataformas HPEC en sistemas relevantes para la seguridad, y la complejidad inherente a esas arquitecturas altamente paralelas supone un reto para las certificaciones. Con esta Tesis, intentamos abordar estos retos y dar un paso adelante hacia la certificación de seguridad funcional de los sistemas de control de seguridad que integran componentes ML en plataformas HPEC.

Physics-supervised ensemble learning model for predicting failure modes of reinforced concrete columns

Since its establishment, the major objective of the Tropos methodology has been to develop an approach for the systematic engineering of agentoriented information systems. In this chapter we illustrate a number of approaches to deal with complexity, which address different activities in software development and are deemed to be used in combination. We begin with handling complexity at requirements levels. In particular we examine how modularization can be improved using some of Aspect Oriented Software Development Principles. We then examine how model-based testing applied in parallel to requirements analysis and design can support incremental validation and testing of software components, as well as help to clarify ambiguities in requirements. We also look at how Tropos can help to address complexity in social context when making design decisions. Last but not least, we show how to tackle complexity at the process modelling level. We explore iterative development extension to Tropos as well as perspectives taken from software project management. This allows us to deal with the complexity of large real world projects.

A vibration-based 1DCNN-BiLSTM model for structural state recognition of RC beams

Cochlear implants substantially improve spoken language in children with severe to profound sensorineural hearing loss, yet outcomes remain more variable than in children with healthy hearing. This variability cannot be reliably predicted for individual children using age at implant or residual hearing. Development of an artificial intelligence clinical tool to predict which patients will exhibit poorer improvements in language skills may enable an individualized approach to improve language outcomes. To compare the accuracy of traditional machine learning (ML) with deep transfer learning (DTL) algorithms to predict post-cochlear implant spoken language development in children with bilateral sensorineural hearing loss using a binary classification model of high vs low language improvers. This multicenter diagnostic study enrolled children from English-, Spanish-, and Cantonese-speaking families across 3 independent clinical centers in the US, Australia, and Hong Kong. A total of 278 children with cochlear implants were enrolled from July 2009 to March 2022 with 1 to 3 years of post-cochlear implant outcomes data. All children underwent pre-cochlear implant 3-dimensional volumetric brain magnetic resonance imaging (MRI). ML and DTL algorithms were trained to predict high vs low language improvers in children with cochlear implants using neuroanatomical features from presurgical brain MRI. Data were analyzed from August 2023 to April 2025. Cochlear implants. The accuracy, sensitivity, and specificity of prediction models based on brain neuroanatomic features using traditional ML and DTL learning. Of 278 children, 137 (49.3%) were female, and the mean (SD) age at cochlear implant was 25.7 (18.8) months. DTL prediction models using bilinear attention-based fusion strategy achieved an accuracy of 92.39% (95% CI, 90.70%-94.07%), sensitivity of 91.22% (95% CI, 89.98%-92.47%), specificity of 93.56% (95% CI, 90.91%-96.21%), and area under the curve of 0.98 (95% CI, 0.97-0.99). DTL outperformed traditional ML models in all outcome measures. The results of this diagnostic study suggest that DTL prediction of language improvement on the individual child level using neuroanatomic features demonstrates greater accuracy, sensitivity, and specificity than traditional ML prediction. DTL was substantially improved by direct capture of discriminative and task-specific information that are advantages of representation learning enabled by this approach vs ML. The results support the feasibility of a single DTL prediction model for language prediction for children served by cochlear implant programs worldwide. Prediction of low improvement may enable targeted early and customized intervention to improve language.

Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.

As Machine Learning (ML) becomes ever more ubiquitous, it is critical to increase the rigor of design and testing. In traditional software, this is done using the Requirements Engineering (RE) process, but RE looks different for ML because it is data-centric. There are different Non-Functional Requirements (NFRs) and standard testing techniques do not apply. While there are standards for verifying NFRs in traditional software, there is no standard measurement for ML NFRs, e.g. how is a model verified to meet an explainability NFR? In traditional software, NFRs are decomposed into Functional Requirements (FRs), but without clear measurements for ML NFRs, their decomposition into FRs is nearly impossible. However, recently, research has shown that provenance can help improve model transparency and reproducibility. This work builds on such literature and suggests provenance as a lower-level NFR to connect high-level NFRs, e.g. explainability and transparency, and FRs, thereby enabling concrete model verification based on requirement specifications. This work examines types of ML provenance and their use in decomposing model NFRs into verifiable FRs, thereby better aligning ML development with RE and increasing the rigor of ML testing. This paper aggregates current literature on provenance for ML and provides a method of measurement for otherwise unquantifiable NFRs. This work also analyzes trends in how provenance can decompose various ML NFRs and future directions for the field.

Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network

Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR to prevent vision loss. This paper compares and analyzes the various Machine Learning (ML) techniques, from traditional ML to advanced Deep Learning models. We compared and analyzed the efficacy of Convolutional Neural Networks (CNNs), Capsule Networks (CapsNet), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), decision trees, and Random Forests. This paper also considers determining factors in the evaluation, including contrast enhancements, noise reduction, grayscaling, etc We analyze recent research studies and compare methodologies and metrics, including accuracy, precision, sensitivity, and specificity. The findings highlight the advanced performance of Deep Learning (DL) models, with CapsNet achieving a remarkable accuracy of up to 97.98% and a high precision rate, outperforming other traditional ML methods. The Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique substantially enhanced the model’s efficiency. Each ML method’s computational requirements are also considered. While most advanced deep learning methods performed better according to the metrics, they are more computationally complex, requiring more resources and data input. We also discussed how datasets like MESSIDOR could be more straightforward and contribute to highly evaluated performance and that there is a lack of consistency regarding benchmark datasets across papers in the field. Using the DL models facilitates accurate early detection for DR screening, can potentially reduce vision loss risks, and improves accessibility and cost-efficiency of eye screening. Further research is recommended to extend our findings by building models with public datasets, experimenting with ensembles of DL and traditional ML models, and considering testing high-performing models like CapsNet.

The Turkish Music Emotion Dataset in the CL database is a dataset for music emotion classification, which contains many music samples and their corresponding emotion labels. This paper presents a comparative analysis of the performance of the traditional random forest machine learning model and the XGBoost model on the given dataset. The findings indicate that the traditional random forest machine learning model outperforms the XGBoost model in terms of accuracy, accuracy, and recall rate. The traditional random forest machine learning model achieves an accuracy of 80.8%, whereas the XGBoost model achieves an accuracy of 75%. The recall rate of the traditional random forest machine learning model is 80.8%, whereas the recall rate of the XGBoost model is 77.2%. The traditional random forest machine learning model achieved an F1 score of 80.5%, whereas the XGBoost model attained an F1 score of 75.3%. The comprehensive evaluation of the XGBoost model outperforms the traditional random forest machine learning model. Generally, the traditional random forest machine learning model demonstrates strong performance in the domain of music emotion classification. It exhibits favorable qualities such as robustness and interpretability, and possesses the ability to effectively handle noise and missing data. The XGBoost model, on the other hand, can be trained and predicted quickly, with high accuracy and generalization ability. Therefore, in practical application, it is necessary to select the appropriate model according to the specific situation, and optimize and adjust it to obtain the best classification effect.