A Unified Perspective for Disinformation Detection and Truth Discovery in Social Sensing: A Survey

With the proliferation of social sensing, large amounts of observation are contributed by people or devices. However, these observations contain disinformation. Disinformation can propagate across online social networks at a relatively low cost, but result in a series of major problems in our society. In this survey, we provide a comprehensive overview of disinformation and truth discovery in social sensing under a unified perspective, including basic concepts and the taxonomy of existing methodologies. Furthermore, we summarize the mechanism of disinformation from four different perspectives (i.e., text only, text with image/multi-modal, text with propagation, and fusion models). In addition, we review existing solutions based on these requirements and compare their pros and cons and give a sort of guide to usage based on a detailed lesson learned. To facilitate future studies in this field, we summarize related publicly accessible real-world data sets and open source codes. Last but the most important, we emphasize potential future research topics and challenges in this domain through a deep analysis of most recent methods.

Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach

As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this article, we present a focused study on the explainable personalized online course recommender system for enhancing employee training and development. Specifically, we first propose a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN), to jointly model both the employees’ current competencies and their career development preferences in an explainable way. In DCBVN, we first extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Furthermore, for handling the employees with sparse or missing skill profiles, we develop an improved version of DCBVN, called the Demand-aware Collaborative Competency Attentive Network (DCCAN) framework , by considering the connectivity among employees. In DCCAN, we first build two employee competency graphs from learning and working aspects. Then, we design a graph-attentive network and a multi-head integration mechanism to infer one’s competency information from her neighborhood employees. Finally, we can generate explainable recommendation results based on the competency representations. Extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of both of our frameworks, as well as their robustness on sparse and cold-start scenarios.

Joint Representation Learning with Relation-Enhanced Topic Models for Intelligent Job Interview Assessment

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this article, we propose three novel approaches to intelligent job interview by learning the large-scale real-world interview data. Specifically, we first develop a preliminary model, named Joint Learning Model on Interview Assessment (JLMIA), to mine the relationship among job description, candidate resume, and interview assessment. Then, we further design an enhanced model, named Neural-JLMIA, to improve the representative capability by applying neural variance inference. Last, we propose to refine JLMIA with Refined-JLMIA (R-JLMIA) by modeling individual characteristics for each collection, i.e., disentangling the core competences from resume and capturing the evolution of the semantic topics over different interview rounds. As a result, our approaches can effectively learn the representative perspectives of different job interview processes from the successful job interview records in history. In addition, we exploit our approaches for two real-world applications, i.e., person-job fit and skill recommendation for interview assessment. Extensive experiments conducted on real-world data clearly validate the effectiveness of our models, which can lead to substantially less bias in job interviews and provide an interpretable understanding of job interview assessment.

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

Single-Chamber Leadless Cardiac Pacemaker in Patients Without Atrial Fibrillation: Findings From Campania Leadless Registry

Introduction:Little is known about the clinical performance of single-chamber leadless pacemaker (LLPM) in patients without atrial fibrillation (AF) as pacing indication. The aim of this study was to describe the clinical characteristics of patients who underwent single chamber LLPM implantation at three tertiary referral centers and to compare the safety and effectiveness of the single-chamber LLPM among patients with or without AF.Materials and Methods:All the consecutive patients who underwent LLPM implantation at three referral centers were analyzed. The indications to LLPM in a real-world setting were described. The study population was divided into two groups according to AF as pacing indication. We assessed the procedure-related complications; moreover, we compared syncope, cardiac hospitalization, pacemaker syndrome, and all-cause death recurrence during the follow-up between patients with and without AF as pacing indication.Results:A total of 140 consecutive patients (mean age, 76.7 ± 11.24 years, men 64.3%) were included in the study. The indication to implantation of LLPM was permanent AF with slow ventricular response (n: 67; 47.8%), sinus node dysfunction (n: 25; 17.8%), third atrioventricular block (AVB) (n: 20; 14.2%), second-degree AVB (n: 18; 12.8%), and first degree AVB (n: 10; 7.1%). A total of 7 patients (5%) experienced perioperative complications with no differences between the AF vs. non-AF groups. During a mean follow-up of 606.5 ± 265.9 days, 10 patients (7.7%) died and 7 patients (5.4%) were reported for cardiac hospitalization; 5 patients (3.8%) experienced syncope; no patients showed pacemaker syndrome. No significant differences in the clinical events between the groups were shown. The Kaplan–Meier analysis for the combined endpoints did not show significant differences between the AF and non-AF groups [hazard ratio (HR): 0.94, 95% CI: 0.41–2.16; p = 0.88].Conclusion:Our real-world data suggest that LLPM may be considered a safe and reasonable treatment in patients without AF in need of pacing. Further studies are needed to confirm these preliminary results.

Quantum Fourier transform to estimate drive cycles

Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform.