Mining urban events from the tweet stream through a probabilistic mixture model

<div>Background:<p>Tumor mutational burden (TMB) has been investigated as a biomarker for immune checkpoint blockade (ICB) therapy. Increasingly, TMB is being estimated with gene panel–based assays (as opposed to full exome sequencing) and different gene panels cover overlapping but distinct genomic coordinates, making comparisons across panels difficult. Previous studies have suggested that standardization and calibration to exome-derived TMB be done for each panel to ensure comparability. With TMB cutoffs being developed from panel-based assays, there is a need to understand how to properly estimate exomic TMB values from different panel-based assays.</p>Design:<p>Our approach to calibration of panel-derived TMB to exomic TMB proposes the use of probabilistic mixture models that allow for nonlinear relationships along with heteroscedastic error. We examined various inputs including nonsynonymous, synonymous, and hotspot counts along with genetic ancestry. Using The Cancer Genome Atlas cohort, we generated a tumor-only version of the panel-restricted data by reintroducing private germline variants.</p>Results:<p>We were able to model more accurately the distribution of both tumor-normal and tumor-only data using the proposed probabilistic mixture models as compared with linear regression. Applying a model trained on tumor-normal data to tumor-only input results in biased TMB predictions. Including synonymous mutations resulted in better regression metrics across both data types, but ultimately a model able to dynamically weight the various input mutation types exhibited optimal performance. Including genetic ancestry improved model performance only in the context of tumor-only data, wherein private germline variants are observed.</p>Significance:<p>A probabilistic mixture model better models the nonlinearity and heteroscedasticity of the data as compared with linear regression. Tumor-only panel data are needed to properly calibrate tumor-only panels to exomic TMB. Leveraging the uncertainty of point estimates from these models better informs cohort stratification in terms of TMB.</p></div>

Thanks to online social networks services such as Facebook, Twitter, YouTube, etc.…, users have the opportunity to communicate easily without constraints by providing them with free and open platforms. Because of their crucial role in spreading information between users, these platforms have become an integral part of everyday life. Privacy and security are major concerns for many social media users. When users share information (such as data, photos etc.…) with their friends, they are not always safe; they can make their friends easy targets attacked by malicious users who try with multiple methods to access their personal data. Unfortunately, with the continued expansion of a user’s social network, privacy settings alone are often insufficient to protect the user’s profile. In this paper, we have proposed a new approach to limit the spread and dissemination of spam content and malicious profiles in social networks. In another way, prohibit the creation of malicious profiles by automated robots as well as the falsification of identity when publishing spam content in social networks. Given that when using online social networking services, users trust them to process their information; have seen that it is necessary not to neglect this part; but rather to make a set of changes that will be consistent with our proposal to strengthen the protection of personal data of users of these platforms.

Fault diagnosis of rolling bearings is of great significance to ensure the production efficiency of rotating machinery as well as personal safety. In recent years, machine learning has shown great potential in signal feature extraction and pattern recognition, and it is superior to traditional fault diagnosis methods in dealing with big data. However, most of the current intelligent diagnostic methods are based on the ideal conditions that bearing data set and label information are sufficient, which are often not always available in engineering practice. In response to this problem, this paper proposes to use probabilistic mixture model (PMM) to approximate the data distribution of the bearing signal, and then use Markov Chain Monte Carlo (MCMC) algorithm to sample the probabilistic model to expand the fault data set. In addition, Semi-supervised Ladder Network (SSLN) can achieve the effect of supervised learning classifier with only a few labeled samples. Based on Case Western Reserve University (CWRU) Bearing Database, the recognition accuracy of the proposed PMM-SSLN model can reach 99.5%, and the experimental results show that this model is applicable to the case where both bearing data set and label information are insufficient.

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with the state-of-the-art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover high-quality latent topics from noisy short texts.

The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.

TopicStriKer: A topic kernels-powered approach for text classification

The demand for wearable devices that can detect anxiety and stress when driving is increasing. Recent studies have attempted to use multiple biosignals to detect driving stress. However, collecting multiple biosignals can be complex and is associated with numerous challenges. Determining the optimal biosignal for assessing driving stress can save lives. To the best of our knowledge, no study has investigated both longitudinal and transitional stress assessment using supervised and unsupervised ML techniques. Thus, this study hypothesizes that the optimal signal for assessing driving stress will consistently detect stress using supervised and unsupervised machine learning (ML) techniques. Two different approaches were used to assess driving stress: longitudinal (a combined repeated measurement of the same biosignals over three driving states) and transitional (switching from state to state such as city to highway driving). The longitudinal analysis did not involve a feature extraction phase while the transitional analysis involved a feature extraction phase. The longitudinal analysis consists of a novel interaction ensemble (INTENSE) that aggregates three unsupervised ML approaches: interaction principal component analysis, connectivity-based clustering, and K-means clustering. INTENSE was developed to uncover new knowledge by revealing the strongest correlation between the biosignal and driving stress marker. These three MLs each have their well-known and distinctive geometrical basis. Thus, the aggregation of their result would provide a more robust examination of the simultaneous non-causal associations between six biosignals: electrocardiogram (ECG), electromyogram, hand galvanic skin resistance, foot galvanic skin resistance, heart rate, respiration, and the driving stress marker. INTENSE indicates that ECG is highly correlated with the driving stress marker. The supervised ML algorithms confirmed that ECG is the most informative biosignal for detecting driving stress, with an overall accuracy of 75.02%.

Social network contains large amounts of user interests and preferences which may therefore help improve techniques for information retrieval, such as user profile construction and query expansion. By the manual annotation, we find that query intention normally consists of several retrieval behaviors and a certain number of social network behaviors. This paper argues that a user's query intention has a strong correlation with the corresponding social network contents, which focuses on two aspects. First, we analyze the proportion of social network websites in users' retrieval records, and evaluate the degree of relationship between information retrieval behavior and social network behavior from a statistic perspective. Second, a similarity between the two components of query intention, retrieval content and social network content, is calculated employing a topic model to measure the correlation of them from a semantic perspective. The evaluation on a ground-truth data of retrieval records indicates that a long-term retrieval procedure is usually accompanied with accessing social network websites. More importantly, the experimental results show that more than 54% of the content from social network has a strong correlation with users' query intention.

This chapter introduces several fundamental models and algorithms for probabilistic clustering, including mixture models, Expectation-Maximization (EM) algorithm, and probabilistic topic models. It focuses on general framework of modeling, the probabilistic explanation, the standard algorithms to learn the model, and its applications. Mixture models are probabilistic models which are increasingly used to find the clusters for univariate and multivariate data. The chapter begins the discussion of mixture models, in which the values of the discrete latent variables can be interpreted as the assignments of data points to specific components of the mixture. It uses the Gaussian mixture model to motivate the EM algorithm in an informal way, and then gives a more general view of the EM algorithm, which is a standard learning algorithm for many probabilistic models. The chapter presents two popular probabilistic topic models, i.e., probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), for document clustering and analysis.

Discovery of clinical pathway patterns from event logs using probabilistic topic models

Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

The rapid development of large language models (LLMs) has opened up new opportunities for automated questionnaire design in academic research. However, questions remain about the validity and quality of AI-generated tools, especially when applied to theory-driven frameworks. The aim of this study is to assess the performance of AI-generated questionnaires and human-designed questionnaires in single and integrated theoretical models. To this end, we employ a hybrid approach that combines expert evaluation and unsupervised machine learning techniques. Specifically, we use latent dirichlet allocation (LDA) for topic modeling to assess semantic coverage. And, KMeans clustering was used to detect redundancy and assess semantic consistency. We created four questionnaires: two based on validated manual writing tools and two generated by the GPT-4. This covered the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extended models. 310 The questionnaires assessed program quality on multiple dimensions. And, these judgments were objectively validated using machine learning outputs. Results show that AI-generated questionnaires are fluent and objective, but perform poorly in terms of accuracy, clarity, and comprehensiveness, especially under complex modeling conditions. Redundancy and semantic drift increased with theory integration. However, the AI performed well in areas requiring standardization and neutrality.

In rarefied gas flows, discontinuity phenomena such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium state, boundary models with limited number of parameters cannot completely describe the reflection of gas molecules at the boundary. In this work, the Gaussian mixture (GM) model, which is an unsupervised machine learning technique, is employed to construct a statistical gas-solid surface scattering model based on the collisional data obtained from molecular dynamics (MD) simulations. The GM model is applied to study Couette flow for different inert gases (Ar and He) confined between two parallel infinite gold walls at different temperatures. A direct comparison between the results obtained from the GM model and the Cercignani-Lampis-Lord (CLL) scattering kernel against the MD collisional data in terms of the distribution of the predicted postcollisional velocities, and accommodation coefficients has shown that the results from the GM model are an excellent match with the MD results outperforming the CLL scattering kernel. As an example, for He gas, while the predicted energy accommodation coefficient by the CLL model is more than two times higher than the MD predictions, the value computed by the GM model is in excellent agreement with the MD results. This superior performance of the GM model confirms its high potential to derive a generalized boundary condition in systems encountered with highly nonequilibrium and complex gas flow conditions.

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios.

Technological developments have affected the decision-making phase in football matches. The viewing pleasure of fans and the result of the match is a matter of debate determined by the referees. Throughout time, the objectiveness of referees was questioned by the technological tools. Video Assistant Referee (VAR) is an example approach to ensure whether the referees react to similar positions of different matches in the same manner or not. One of the problems of this topic is the controversial decisions of some referees leading to unexpected results. In this research, as a different approach to the referees’ objectivity problem, referees are tried to be classified based on the statistical outcome of the matches using unsupervised machine learning techniques. Meaningful clusters should not be found to be able to state the referees are objective. This study is conducted on 10-years English Premier League between 2009–2018 data. Principal Component Analysis is going to be used for grouping the variables to perform exploratory data analysis. K-Means, hierarchical clustering, and Fuzzy C-Means are going to be used for dividing the referees into various subgroups. R programming language is used for examining data. In conclusion of the analysis, four different referee groups are defined.KeywordsFootballEnglish Premier LeagueRefereeClusteringMachine learningUnsupervised learning