In software development, code comments play a crucial role in enhancing code comprehension and collaboration. This research paper addresses the challenge of objectively classifying code comments as "Useful" or "Not Useful." We propose a novel solution that harnesses contextualized embeddings, particularly BERT, to automate this classification process. We address this task by incorporating generated code and comment pairs. The initial dataset comprised 9048 pairs of code and comments written in C, labeled as either Useful or Not Useful. To augment this dataset, we sourced an additional 739 lines of code-comment pairs and generated labels using a Large Language Model Architecture, specifically BERT. The primary objective was to build classification models that can effectively differentiate between useful and not useful code comments. Various machine learning algorithms were employed, including Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting, Random Forest, and a Neural Network. Each algorithm was evaluated using precision, recall, and F1-score metrics, both with the original seed dataset and the augmented dataset. This study showcases the potential of generative AI for enhancing binary code comment quality classification models, providing valuable insights for software developers and researchers in the field of natural language processing and software engineering.

This paper presents a novel approach to enhance the performance of binary code comment quality classification models through the application of Generative Artificial Intelligence (AI). By leveraging the OpenAI API, a dataset comprising 1239 newly generated code-comment pairs, extracted from various GitHub repositories and open-source projects, has been labelled as "Useful" or "Not Useful", and integrated into the existing corpus of 9048 pairs in the C programming language. Employing a cutting-edge Large Language Model Architecture, the generated dataset demonstrates notable improvements in model accuracy. Specifically, when incorporated into the Support Vector Machine (SVM) model, a 6% increase in precision is observed, rising from 0.79 to 0.85. Additionally, the Artificial Neural Network (ANN) model exhibits a 1.5% increase in recall, climbing from 0.731 to 0.746. This paper sheds light on the potential of Generative AI in augmenting code comment quality classification models. The results affirm the effectiveness of this methodology, indicating its applicability in broader contexts within software development and quality assurance domains. The findings underscore the significance of integrating generative techniques to advance the accuracy and efficacy of machine learning models in practical software engineering scenarios.

Divorce is one of the most common social issues in developed countries like in the United States. Almost 50% of the recent marriages turn into an involuntary divorce or separation. While it is evident that people vary to a different extent, and even over time, an incident like Divorce does not interrupt the individual's daily activities; still, Divorce has a severe effect on the individual's mental health, and personal life. Within the scope of this research, the divorce prediction was carried out by evaluating a dataset named by the 'divorce predictor dataset' to correctly classify between married and Divorce people using six different machine learning algorithms- Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Na\"ive Bayes (NB), and, Support Vector Machines (SVM). Preliminary computational results show that algorithms such as SVM, KNN, and LDA, can perform that task with an accuracy of 98.57%. This work's additional novel contribution is the detailed and comprehensive explanation of prediction probabilities using Local Interpretable Model-Agnostic Explanations (LIME). Utilizing LIME to analyze test results illustrates the possibility of differentiating between divorced and married couples. Finally, we have developed a divorce predictor app considering ten most important features that potentially affect couples in making decisions in their divorce, such tools can be used by any one in order to identify their relationship condition.

Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models for two-class classification. Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but is not probabilistic in nature. On the other hand, the tuning of the regularization parameters in SVM is known to imply a high computational effort and generates pieces of information that are not fully exploited, not being used to build a probabilistic classification rule. In this paper we propose a novel approach to generate probabilistic outputs for the SVM. The new method has the following three properties. First, it is designed to be cost-sensitive, and thus the different importance of sensitivity (or true positive rate, TPR) and specificity (true negative rate, TNR) is readily accommodated in the model. As a result, the model can deal with imbalanced datasets which are common in operational business problems as churn prediction or credit scoring. Second, the SVM is embedded in an ensemble method to improve its performance, making use of the valuable information generated in the parameters tuning process. Finally, the probabilities estimation is done via bootstrap estimates, avoiding the use of parametric models as competing approaches. Numerical tests on a wide range of datasets show the advantages of our approach over benchmark procedures.

In recent years, there has been growing attention to interpretable machine learning models which can give explanatory insights on their behaviour. Thanks to their interpretability, decision trees have been intensively studied for classification tasks and, due to the remarkable advances in mixed integer programming (MIP), various approaches have been proposed to formulate the problem of training an Optimal Classification Tree (OCT) as a MIP model. We present a novel mixed integer quadratic formulation for the OCT problem, which exploits the generalization capabilities of Support Vector Machines for binary classification. Our model, denoted as Margin Optimal Classification Tree (MARGOT), encompasses maximum margin multivariate hyperplanes nested in a binary tree structure. To enhance the interpretability of our approach, we analyse two alternative versions of MARGOT, which include feature selection constraints inducing sparsity of the hyperplanes' coefficients. First, MARGOT has been tested on non-linearly separable synthetic datasets in a 2-dimensional feature space to provide a graphical representation of the maximum margin approach. Finally, the proposed models have been tested on benchmark datasets from the UCI repository. The MARGOT formulation turns out to be easier to solve than other OCT approaches, and the generated tree better generalizes on new observations. The two interpretable versions effectively select the most relevant features, maintaining good prediction quality.

The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. This paper provides a pragmatic solution, showing how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to warm start their solver and speed up their computation significantly. For the prediction, we train linear and kernelized support vector machine classifiers, providing an out-of-sample performance guarantee if properly regularized, converting to distributionally robust classifiers. For the unit commitment problem, we solve a mixed-integer second-order cone problem. Our results based on the IEEE 6-bus and 118-bus test systems show that the kernelized SVM with proper regularization outperforms other classifiers, reducing the computational time by a factor of 1.7. In addition, if there is a tight computational limit, while the unit commitment problem without warm start is far away from the optimal solution, its warmly started version can be solved to optimality within the time limit.

Automated co-located human-human interaction analysis has been addressed by the use of nonverbal communication as measurable evidence of social and psychological phenomena. We survey the computing studies (since 2010) detecting phenomena related to social traits (e.g., leadership, dominance, personality traits), social roles/relations, and interaction dynamics (e.g., group cohesion, engagement, rapport). Our target is to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings (free-standing conversations, meetings, dyads, and crowds). We also present a comprehensive summary of the related datasets and outline future research directions which are regarding the implementation of artificial intelligence, dataset curation, and privacy-preserving interaction analysis. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively; multimodal features are prominently performing better; deep learning architectures showed improved performance in overall, but there exist many phenomena whose detection has never been implemented through deep models. We also identified several limitations such as the lack of scalable benchmarks, annotation reliability tests, cross-dataset experiments, and explainability analysis.

Authorship identification has proven unsettlingly effective in inferring the identity of the author of an unsigned document, even when sensitive personal information has been carefully omitted. In the digital era, individuals leave a lasting digital footprint through their written content, whether it is posted on social media, stored on their employer's computers, or located elsewhere. When individuals need to communicate publicly yet wish to remain anonymous, there is little available to protect them from unwanted authorship identification. This unprecedented threat to privacy is evident in scenarios such as whistle-blowing. Proposed defenses against authorship identification attacks primarily aim to obfuscate one's writing style, thereby making it unlinkable to their pre-existing writing, while concurrently preserving the original meaning and grammatical integrity. The presented work offers a comprehensive review of the advancements in this research area spanning over the past two decades and beyond. It emphasizes the methodological frameworks of modification and generation-based strategies devised to evade authorship identification attacks, highlighting joint efforts from the differential privacy community. Limitations of current research are discussed, with a spotlight on open challenges and potential research avenues.

The design and deployment of fifth-generation (5G) wireless networks pose significant challenges due to the increasing number of wireless devices. Path loss has a landmark importance in network performance optimization, and accurate prediction of the path loss, which characterizes the attenuation of signal power during transmission, is critical for effective network planning, coverage estimation, and optimization. In this sense, we utilize machine learning (ML) methods, which overcome conventional path loss prediction models drawbacks, for path loss prediction in a 5G network system to facilitate more accurate network planning, resource optimization, and performance improvement in wireless communication systems. To this end, we utilize a novel approach, nested cross validation scheme, with ML to prevent overfitting, thereby getting better generalization error and stable results for ML deployment. First, we acquire a publicly available dataset obtained through a comprehensive measurement campaign conducted in an urban macro-cell scenario located in Beijing, China. The dataset includes crucial information such as longitude, latitude, elevation, altitude, clutter height, and distance, which are utilized as essential features to predict the path loss in the 5G network system. We deploy Support Vector Regression (SVR), CatBoost Regression (CBR), eXtreme Gradient Boosting Regression (XGBR), Artificial Neural Network (ANN), and Random Forest (RF) methods to predict the path loss, and compare the prediction results in terms of Mean Absolute Error (MAE) and Mean Square Error (MSE). As per obtained results, XGBR outperforms the rest of the methods. It outperforms CBR with a slight performance differences by 0.4 % and 1 % in terms of MAE and MSE metrics, respectively. On the other hand, it outperforms the rest of the methods with clear performance differences.

Recent advances in artificial intelligence have created a paradigm shift in which models are trained on large amounts of data and can be adapted to different tasks, dubbed foundation models (Bommasani et al., 2021). These models, which often employ self-supervision, can extract valuable knowledge from various types of data, including natural language (Devlin et al., 2018; Brown et al., 2020), images (Radford et al., 2021), and biological sequencing counts (Theodoris et al., 2023). This acquired knowledge allows the model to generalize when asked to perform tasks in novel scenarios. With vast amounts of data becoming increasingly available from diverse sources, such models are of interest to leverage information that can be learned in order to build more intelligent systems (Bubeck et al., 2023). A critical aspect of intelligent systems is the ability to reason about cause-and-effect relationships (Zhang et al., 2023), which is vital to making informed decisions across various domains, including healthcare, economics, and statistics (Kube et al., 2019; Geffner et al., 2022; Zhang et al., 2022). Relying solely on correlation-based models (Harrison and March, 1984) can lead to misleading conclusions, as they do not account for the underlying causal mechanisms. This limitation is also observed in the realm of foundation models (Bubeck et al., 2023; Mahowald et al., 2023; Wolfram, 2023).