Topic-Level Bayesian Surprise and Serendipity for Recommender Systems

A recommender system that optimizes its recommendations solely to fit a user's history of ratings for consumed items can create a filter bubble, wherein the user does not get to experience items from novel, unseen categories. One approach to mitigate this undesired behavior is to recommend items with high potential for serendipity, namely surprising items that are likely to be highly rated. In this paper, we propose a content-based formulation of serendipity that is rooted in Bayesian surprise and use it to measure the serendipity of items after they are consumed and rated by the user. When coupled with a collaborative-filtering component that identifies similar users, this enables recommending items with high potential for serendipity. To facilitate the evaluation of topic-level models for surprise and serendipity, we introduce a dataset of book reading histories extracted from Goodreads, containing over 26 thousand users and close to 1.3 million books, where we manually annotate 449 books read by 4 users in terms of their time-dependent, topic-level surprise. Experimental evaluations show that models that use Bayesian surprise correlate much better with the manual annotations of topic-level surprise than distance-based heuristics, and also obtain better serendipitous item recommendation performance

Effect of Interest Rate Changes and Dividend Announcements on Stock Returns: Evidence from a Frontier Economy

This paper is motivated from previous work in the area of bank interest rate and dividend policy, and we went further to figure out whether there is any association between interest rate changes and the stock market's reaction to dividend announcements. To conduct this research paper, we used 61 Bangladeshi banks out of 66 banks (excluding central bank) from the period from 2010-2021. After using three different types of estimations (OLS, two stage least squared and GMM) we found that when savings interest rate and dividend increase stock market react positively and our result show that stock market react negatively when savings interest rate and dividend decrease. On the other hand, our results show that when loan interest rate and dividend increase stock market react more negatively and if loan interest rate and dividend decrease stock market react more positively

The Austrian and Keynesian business cycle theory and its effectiveness to combat recession-A case study in construction industry in United Kingdom

Business cycles are the “ups and downs” in economic activity, defined in terms of periods of expansion or recession. This paper attempted to find out empirical evidence of effectiveness of Austrian and Keynesian theory of business cycle when a country is in recession and how to combat the recession. The study investigates UK economic data from 2003-2013 derived from Trading Economics Website and office of the National Statics UK. This study concludes that in the boom period Keynesian theory is effective as interest rate was low and government spending was high to stimulate demand. In recessionary period it is found that government money supply was very high but production of capital goods was very poor which means Keynesian theory has not been applied. But the recent booming period evidenced that interest rate is low and govt spending high

Vitamin D status in Pulmonary Tuberculosis : a case control study

Pulmonary tuberculosis is the major cause of morbidity and mortality in the developing world. In South Asia, 80% of the apparently healthy population have vitamin D deficiency (<20 ng/mL). An association between vitamin D levels with tuberculosis has been described in several studies. But there are scarcities of studies carried out in Bangladesh to determine the association. This study, a case-control study with 2 years duration, determined and compared vitamin D concentration between pulmonary tuberculosis patients and healthy controls. Patients were recruited from the department of internal medicine, Bangabandhu Sheikh Mujib Medical University. All newly diagnosed patients of pulmonary tuberculosis who fulfiled the inclusion and exclusion criteria were selected as case; equal number of healthy subjects without pulmonary TB as control. Vitamin D level less than 20 ng/ml was considered deficiency; 21 to 29ng/ml as relative insufficiency and >30ng/ml as normal. Thirty partici- pants in each group were enrolled, from whom serum vitamin D concentration was measured, analyzed and interpreted. Mean serum vitamin D level was significantly low in case group than control group (p <0.05). The odds ratio corresponding to vitamin D deficiency in case group compared to control was 5.21 [95% CI (1.12 –27.53), (p= 0.015)]. Result indicates patients having vitamin D level <20 ng/ml has 5.21 times more risk to develop pulmonary tuberculo- sis. In conclusion, Vitamin D deficiency was significantly low in pulmonary tuberculo- sis patients and supplementation is required for them. BSMMU J 2021; 14(3): 85-9

Prediction of fetal brain gestational age using multihead attention with Xception

Accurate gestational age (GA) prediction is crucial for monitoring fetal development and ensuring optimal prenatal care. Traditional methods often face challenges in terms of precision and prediction efficiency. In this context, leveraging modern deep learning (DL) techniques is a promising solution. This paper introduces a novel DL approach for GA prediction using fetal brain images obtained via magnetic resonance imaging (MRI), which combines the strength of the Xception pretrained model with a multihead attention (MHA) mechanism. The proposed model was trained on a diverse dataset comprising 52,900 fetal brain images from 741 patients. The images encompass a GA ranging from 19 to 39 weeks. These pretrained models served as feature extraction components during the training process. The extracted features were subsequently used as the inputs of different configurable MHAs, which produced GA predictions in days. The proposed model achieved promising results with 8 attention heads, 32 dimensionality of the key space and 32 dimensionality of the value space, with an R-squared (R2) value of 96.5 %, a mean absolute error (MAE) of 3.80 days, and a Pearson correlation coefficient (PCC) of 98.50 % for the test set. Additionally, the 5-fold cross-validation results reinforce the model's reliability, with an average R2 of 95.94 %, an MAE of 3.61 days, and a PCC of 98.02 %. The proposed model excels in different anatomical views, notably the axial and sagittal views. A comparative analysis of multiple planes and a single plane highlights the effectiveness of the proposed model against other state-of-the-art (SOTA) models reported in the literature. The proposed model could help clinicians accurately predict GA

Syndromic Surveillance of Population-Level COVID-19 Burden With Cough Monitoring in a Hospital Emergency Waiting Room

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital\u27s electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value \u3c 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics