Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool, since it requires less human coding. However, scholars still need many human-labeled documents to train automated classifiers. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. Moreover, we replicate two recently published articles and reach the same substantive conclusions with only a small proportion of the original labeled data used in those studies. We provide activeText, an open-source software to implement our method.

This study develops a framework for unmanned aerial systems (UASs) to monitor fall hazard prevention systems near unprotected edges and openings in high-rise building projects. A three-step machine-learning-based framework was developed and tested to detect guardrail posts from the images captured by UAS. First, a guardrail detector was trained to localize the candidate locations of posts supporting the guardrail. Since images were used in this process collected from an actual job site, several false detections were identified. Therefore, additional constraints were introduced in the following steps to filter out false detections. Second, the research team applied a horizontal line detector to the image to properly detect floors and remove the detections that were not close to the floors. Finally, since the guardrail posts are installed with approximately normal distribution between each post, the space between them was estimated and used to find the most likely distance between the two posts. The research team used various combinations of the developed approaches to monitor guardrail systems in the captured images from a high-rise building project. Comparing the precision and recall metrics indicated that the cascade classifier achieves better performance with floor detection and guardrail spacing estimation. The research outcomes illustrate that the proposed guardrail recognition system can improve the assessment of guardrails and facilitate the safety engineer's task of identifying fall hazards in high-rise building projects.

Dozens of new tools and technologies are being incorporated to help developers, which is becoming a source of consternation as they struggle to choose one over the others. For example, there are at least ten frameworks available to developers for developing web applications, posing a conundrum in selecting the best one that meets their needs. As a result, developers are continuously searching for all of the benefits and drawbacks of each API, framework, tool, and so on. One of the typical approaches is to examine all of the features through official documentation and discussion. This approach is time-consuming, often makes it difficult to determine which aspects are the most important to a particular developer and whether a particular aspect is important to the community at large. In this paper, we have used a benchmark API aspects dataset (Opiner) collected from StackOverflow posts and observed how Transformer models (BERT, RoBERTa, DistilBERT, and XLNet) perform in detecting software aspects in textual developer discussion with respect to the baseline Support Vector Machine (SVM) model. Through extensive experimentation, we have found that transformer models improve the performance of baseline SVM for most of the aspects, i.e., `Performance', `Security', `Usability', `Documentation', `Bug', `Legal', `OnlySentiment', and `Others'. However, the models fail to apprehend some of the aspects (e.g., `Community' and `Potability') and their performance varies depending on the aspects. Also, larger architectures like XLNet are ineffective in interpreting software aspects compared to smaller architectures like DistilBERT.

Mercer kernels play an important role in machine learning and is a mathematical basis of such techniques as kernel density estimation and spline models [14], Support Vector Machines [11], kernel principal components analysis [10], regularization of neural networks [13] and many others. According to Aronszajn's theorem, any Mercer kernel induces a reproducing kernel Hilbert space (RKHS) and vice versa, any RKHS corresponds to a kernel. A relationship between the latter two notions is decribed in the classical Mercer's theorem. A goal of this note is torefine this theoremandgive some estimates onthe speedof uniformconvergencestated in it.

In classification problems, a classifier is a function that mimics the relationship between the data vectors and their class labels. Support vector machine(SVM) is a popular classifier, which was proposed by Cortes and Vapnik [1] as a maximum margin classifier. The success of the SVM has encouraged further research into extensions to the more general multiclass cases, which has been an active topic of research interest [2-4]. Shilton et al.[5] proposed the conicsegmentation support vector machine (CS-SVM) by introducing the concept of target space into the problem formulation and showed that some other multiclassfication model are special cases of this framework. The standard CS-SVM is dealing with the situation where the exact values of the data points are known.

Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for $K$-class problems (Wu, Zhang and Liu, 2010; Wang, Zhang and Wu, 2019), where $K$ is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in $K$. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in $K$. Though not being most efficient in computation, the OVA offers the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate finite sample performance.

Leak detection in gas pipelines is an important and persistent problem in the Oil and Gas industry. This is particularly important as pipelines are the most common way of transporting natural gas. This research aims to study the ability of data-driven intelligent models to detect small leaks for a natural gas pipeline using basic operational parameters and then compare the intelligent models among themselves using existing performance metrics. This project applies the observer design technique to detect leaks in natural gas pipelines using a regressoclassification hierarchical model where an intelligent model acts as a regressor and a modified logistic regression model acts as a classifier. Five intelligent models (gradient boosting, decision trees, random forest, support vector machine and artificial neural network) are studied in this project using a pipeline data stream of four weeks. The results shows that while support vector machine and artificial neural networks are better regressors than the others, they do not provide the best results in leak detection due to their internal complexities and the volume of data used. The random forest and decision tree models are the most sensitive as they can detect a leak of 0.1% of nominal flow in about 2 hours. All the intelligent models had high reliability with zero false alarm rate in testing phase. The average time to leak detection for all the intelligent models was compared to a real time transient model in literature. The results show that intelligent models perform relatively well in the problem of leak detection. This result suggests that intelligent models could be used alongside a real time transient model to significantly improve leak detection results.

Frequent occurrences of extreme weather events substantially impact the lives of the less privileged in our societies, particularly in agriculture-inclined economies. The unpredictability of extreme fires, floods, drought, cyclones, and others endangers sustainable production and life on land (SDG goal 15), which translates into food insecurity and poorer populations. Fortunately, modern technologies such as Artificial Intelligent (AI), the Internet of Things (IoT), blockchain, 3D printing, and virtual and augmented reality (VR and AR) are promising to reduce the risk and impact of extreme weather in our societies. However, research directions on how these technologies could help reduce the impact of extreme weather are unclear. This makes it challenging to emploring digital technologies within the spheres of extreme weather. In this paper, we employed the Delphi Best Worst method and Machine learning approaches to identify and assess the push factors of technology. The BWM evaluation revealed that predictive nature was AI's most important criterion and role, while the mass-market potential was the less important criterion. Based on this outcome, we tested the predictive ability of machine elarning on a publilcly available dataset to affrm the predictive rols of AI. We presented the managerial and methodological implications of the study, which are crucial for research and practice. The methodology utilized in this study could aid decision-makers in devising strategies and interventions to safeguard sustainable production. This will also facilitate allocating scarce resources and investment in improving AI techniques to reduce the adverse impacts of extreme events. Correspondingly, we put forward the limitations of this, which necessitate future research.

Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone. Because of the obscure nature of sarcasm in textual data, detecting it is difficult and of great interest to the sentiment analysis research community. Though the research in sarcasm detection spans more than a decade, some significant advancements have been made recently, including employing unsupervised pre-trained transformers in multimodal environments and integrating context to identify sarcasm. In this study, we aim to provide a brief overview of recent advancements and trends in computational sarcasm research for the English language. We describe relevant datasets, methodologies, trends, issues, challenges, and tasks relating to sarcasm that are beyond detection. Our study provides well-summarized tables of sarcasm datasets, sarcastic features and their extraction methods, and performance analysis of various approaches which can help researchers in related domains understand current state-of-the-art practices in sarcasm detection.

The Resistance: Avalon is a partially observable social deduction game. This area of AI game playing is fairly undeveloped. Implementing an AI for this game involves multiple components specific to each phase as well as role in the game. In this paper, we plan to iteratively develop the required components for each role/phase by first addressing the Assassination phase which can be modeled as a machine learning problem. Using a publicly available dataset from an online version of the game, we train classifiers that emulate an Assassin. After trying various classification techniques, we are able to achieve above average human performance using a simple linear support vector classifier. The eventual goal of this project is to pursue developing an intelligent and complete Avalon player that can play through each phase of the game as any role.