To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Instance-level examinations, such as attribution techniques, are commonly employed to interpret the model decisions. However, when interpreting misclassified decisions, human intervention may be required. Analyzing the attributions across each class within one instance can be particularly laborintensive and influenced by the bias of the human interpreter. In this paper, we present a novel framework to uncover the weakness of the classifier via counterfactual examples. A prober is introduced to learn the correctness of the classifier's decision in terms of binary code - hit or miss. It enables the creation of the counterfactual example concerning the prober's decision. We test the performance of our prober's misclassification detection and verify its effectiveness on the image classification benchmark datasets. Furthermore, by generating counterfactuals that penetrate the prober, we demonstrate that our framework effectively identifies vulnerabilities in the target classifier without relying on label information on the MNIST dataset.

Most machine learning classifiers only concern classification accuracy, while certain applications (such as medical diagnosis, meteorological forecasting, and computation advertising) require the model to predict the true probability, known as a calibrated estimate. In previous work, researchers have developed several calibration methods to post-process the outputs of a predictor to obtain calibrated values, such as binning and scaling methods. Compared with scaling, binning methods are shown to have distribution-free theoretical guarantees, which motivates us to prefer binning methods for calibration. However, we notice that existing binning methods have several drawbacks: (a) the binning scheme only considers the original prediction values, thus limiting the calibration performance; and (b) the binning approach is non-individual, mapping multiple samples in a bin to the same value, and thus is not suitable for order-sensitive applications. In this paper, we propose a feature-aware binning framework, called Multiple Boosting Calibration Trees (MBCT), along with a multi-view calibration loss to tackle the above issues. Our MBCT optimizes the binning scheme by the tree structures of features, and adopts a linear function in a tree node to achieve individual calibration. Our MBCT is non-monotonic, and has the potential to improve order accuracy, due to its learnable binning scheme and the individual calibration. We conduct comprehensive experiments on three datasets in different fields. Results show that our method outperforms all competing models in terms of both calibration error and order accuracy. We also conduct simulation experiments, justifying that the proposed multi-view calibration loss is a better metric in modeling calibration error.

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called "smart pig" in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.

The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this reason, flows is a suitable approach for this situation, which in future 5G networks will have to be used, as the number of packets will increase dramatically. If this is also combined with unsupervised learning models, it can detect new threats for which it has not been trained. This paper presents a systematic review of the literature on unsupervised learning algorithms for detecting anomalies in network flows, following the PRISMA guideline. A total of 63 scientific articles have been reviewed, analyzing 13 of them in depth. The results obtained show that autoencoder is the most used option, followed by SVM, ALAD, or SOM. On the other hand, all the datasets used for anomaly detection have been collected, including some specialised in IoT or with real data collected from honeypots.

Open-Vocabulary Multi-Object Tracking (OV-MOT) aims to enable approaches to track objects without being limited to a predefined set of categories. Current OV-MOT methods typically rely primarily on instance-level detection and association, often overlooking trajectory information that is unique and essential for object tracking tasks. Utilizing trajectory information can enhance association stability and classification accuracy, especially in cases of occlusion and category ambiguity, thereby improving adaptability to novel classes. Thus motivated, in this paper we propose \textbf{TRACT}, an open-vocabulary tracker that leverages trajectory information to improve both object association and classification in OV-MOT. Specifically, we introduce a \textit{Trajectory Consistency Reinforcement} (\textbf{TCR}) strategy, that benefits tracking performance by improving target identity and category consistency. In addition, we present \textbf{TraCLIP}, a plug-and-play trajectory classification module. It integrates \textit{Trajectory Feature Aggregation} (\textbf{TFA}) and \textit{Trajectory Semantic Enrichment} (\textbf{TSE}) strategies to fully leverage trajectory information from visual and language perspectives for enhancing the classification results. Extensive experiments on OV-TAO show that our TRACT significantly improves tracking performance, highlighting trajectory information as a valuable asset for OV-MOT. Code will be released.

In the domain of emotion recognition using body motion, the primary challenge lies in the scarcity of diverse and generalizable datasets. Automatic emotion recognition uses machine learning and artificial intelligence techniques to recognize a person's emotional state from various data types, such as text, images, sound, and body motion. Body motion poses unique challenges as many factors, such as age, gender, ethnicity, personality, and illness, affect its appearance, leading to a lack of diverse and robust datasets specifically for emotion recognition. To address this, employing Synthetic Data Generation (SDG) methods, such as Generative Adversarial Networks (GANs) and Variational Auto Encoders (VAEs), offers potential solutions, though these methods are often complex. This research introduces a novel application of the Neural Gas Network (NGN) algorithm for synthesizing body motion data and optimizing diversity and generation speed. By learning skeletal structure topology, the NGN fits the neurons or gas particles on body joints. Generated gas particles, which form the skeletal structure later on, will be used to synthesize the new body posture. By attaching body postures over frames, the final synthetic body motion appears. We compared our generated dataset against others generated by GANs, VAEs, and another benchmark algorithm, using benchmark metrics such as Fr\'echet Inception Distance (FID), Diversity, and a few more. Furthermore, we continued evaluation using classification metrics such as accuracy, precision, recall, and a few others. Joint-related features or kinematic parameters were extracted, and the system assessed model performance against unseen data. Our findings demonstrate that the NGN algorithm produces more realistic and emotionally distinct body motion data and does so with more synthesizing speed than existing methods.

Static analysis is a powerful technique for bug detection in critical systems like operating system kernels. However, designing and implementing static analyzers is challenging, time-consuming, and typically limited to predefined bug patterns. While large language models (LLMs) have shown promise for static analysis, directly applying them to scan large codebases remains impractical due to computational constraints and contextual limitations. We present KNighter, the first approach that unlocks practical LLM-based static analysis by automatically synthesizing static analyzers from historical bug patterns. Rather than using LLMs to directly analyze massive codebases, our key insight is leveraging LLMs to generate specialized static analyzers guided by historical patch knowledge. KNighter implements this vision through a multi-stage synthesis pipeline that validates checker correctness against original patches and employs an automated refinement process to iteratively reduce false positives. Our evaluation on the Linux kernel demonstrates that KNighter generates high-precision checkers capable of detecting diverse bug patterns overlooked by existing human-written analyzers. To date, KNighter-synthesized checkers have discovered 70 new bugs/vulnerabilities in the Linux kernel, with 56 confirmed and 41 already fixed. 11 of these findings have been assigned CVE numbers. This work establishes an entirely new paradigm for scalable, reliable, and traceable LLM-based static analysis for real-world systems via checker synthesis.

Predictive modelling is vital to guide preventive efforts. Whilst large-scale prospective cohort studies and a diverse toolkit of available machine learning (ML) algorithms have facilitated such survival task efforts, choosing the best-performing algorithm remains challenging. Benchmarking studies to date focus on relatively small-scale datasets and it is unclear how well such findings translate to large datasets that combine omics and clinical features. We sought to benchmark eight distinct survival task implementations, ranging from linear to deep learning (DL) models, within the large-scale prospective cohort study UK Biobank (UKB). We compared discrimination and computational requirements across heterogenous predictor matrices and endpoints. Finally, we assessed how well different architectures scale with sample sizes ranging from n = 5,000 to n = 250,000 individuals. Our results show that discriminative performance across a multitude of metrices is dependent on endpoint frequency and predictor matrix properties, with very robust performance of (penalised) COX Proportional Hazards (COX-PH) models. Of note, there are certain scenarios which favour more complex frameworks, specifically if working with larger numbers of observations and relatively simple predictor matrices. The observed computational requirements were vastly different, and we provide solutions in cases where current implementations were impracticable. In conclusion, this work delineates how optimal model choice is dependent on a variety of factors, including sample size, endpoint frequency and predictor matrix properties, thus constituting an informative resource for researchers working on similar datasets. Furthermore, we showcase how linear models still display a highly effective and scalable platform to perform risk modelling at scale and suggest that those are reported alongside non-linear ML models.

Due to climate change, the extreme wildfire has become one of the most dangerous natural hazards to human civilization. Even though, some wildfires may be initially caused by human activity, but the spread of wildfires is mainly determined by environmental factors, for examples, (1) weather conditions such as temperature, wind direction and intensity, and moisture levels; (2) the amount and types of dry vegetation in a local area, and (3) topographic or local terrian conditions, which affects how much rain an area gets and how fire dynamics will be constrained or faciliated. Thus, to accurately forecast wildfire occurrence has become one of most urgent and taunting environmental challenges in global scale. In this work, we developed a real-time Multimodal Transformer Neural Network Machine Learning model that combines several advanced artificial intelligence techniques and statistical methods to practically forecast the occurrence of wildfire at the precise location in real time, which not only utilizes large scale data information such as hourly weather forecasting data, but also takes into account small scale topographical data such as local terrain condition and local vegetation conditions collecting from Google Earth images to determine the probabilities of wildfire occurrence location at small scale as well as their timing synchronized with weather forecast information. By using the wildfire data in the United States from 1992 to 2015 to train the multimodal transformer neural network, it can predict the probabilities of wildfire occurrence according to the real-time weather forecast and the synchronized Google Earth image data to provide the wildfire occurrence probability in any small location ($100m^2$) within 24 hours ahead.