Accurate and justifiable : new algorithms for explainable recommendations.

Websites and online services thrive with large amounts of online information, products, and choices, that are available but exceedingly difficult to find and discover. This has prompted two major paradigms to help sift through information: information retrieval and recommender systems. The broad family of information retrieval techniques has given rise to the modern search engines which return relevant results, following a user\u27s explicit query. The broad family of recommender systems, on the other hand, works in a more subtle manner, and do not require an explicit query to provide relevant results. Collaborative Filtering (CF) recommender systems are based on algorithms that provide suggestions to users, based on what they like and what other similar users like. Their strength lies in their ability to make serendipitous, social recommendations about what books to read, songs to listen to, movies to watch, courses to take, or generally any type of item to consume. Their strength is also that they can recommend items of any type or content because their focus is on modeling the preferences of the users rather than the content of the recommended items. Although recommender systems have made great strides over the last two decades, with significant algorithmic advances that have made them increasingly accurate in their predictions, they suffer from a few notorious weaknesses. These include the cold-start problem when new items or new users enter the system, and lack of interpretability and explainability in the case of powerful black-box predictors, such as the Singular Value Decomposition (SVD) family of recommenders, including, in particular, the popular Matrix Factorization (MF) techniques. Also, the absence of any explanations to justify their predictions can reduce the transparency of recommender systems and thus adversely impact the user\u27s trust in them. In this work, we propose machine learning approaches for multi-domain Matrix Factorization (MF) recommender systems that can overcome the new user cold-start problem. We also propose new algorithms to generate explainable recommendations, using two state of the art models: Matrix Factorization (MF) and Restricted Boltzmann Machines (RBM). Our experiments, which were based on rigorous cross-validation on the MovieLens benchmark data set and on real user tests, confirmed that our proposed methods succeed in generating explainable recommendations without a major sacrifice in accuracy

Stereoscopic vision in vehicle navigation.

Traffic sign (TS) detection and tracking is one of the main tasks of an autonomous vehicle which is addressed in the field of computer vision. An autonomous vehicle must have vision based recognition of the road to follow the rules like every other vehicle on the road. Besides, TS detection and tracking can be used to give feedbacks to the driver. This can significantly increase safety in making driving decisions. For a successful TS detection and tracking changes in weather and lighting conditions should be considered. Also, the camera is in motion, which results in image distortion and motion blur. In this work a fast and robust method is proposed for tracking the stop signs in videos taken with stereoscopic cameras that are mounted on the car. Using camera parameters and the detected sign, the distance between the stop sign and the vehicle is calculated. This calculated distance can be widely used in building visual driver-assistance systems

New Explainable Active Learning Approach for Recommender Systems

Introduction and Motivations Recommender Systems are intelligent programs that analyze patterns between items and users to predict the user’s taste. Objective Design an efficient Active Learning Strategy to increase the explainability and the accuracy of an “Explainable Matrix Factorization” model

Perceptions of Local Parents and School Staff on Childhood Obesity Prevention Interventions in Iran

Background and Objectives: Childhood obesity is an increasing public health problem in Iran, and there is no evidence for effective prevention strategies to date. The aim of this qualitative study was to identify and prioritise perceived potential interventions by parents and school staff to help inform the development of an obesity prevention intervention for Iranian school children. Materials and Methods: Focus groups were held with the parents of primary school aged children and school staff working in primary schools in Tehran city. Additionally, three interviews were held with two physical education teachers and one school nurse. The participants were asked about the causes of obesity and what activities they believed would help children to maintain healthy weight. Then they were asked to prioritise the activities that would have the greatest impact on children to maintain their healthy weight. Thematic analysis was used to analyse the data. Parents were selected from a range of socio-economic backgrounds to include two groups from each of high, medium, and low socio-economic districts of Tehran. Eleven focus groups were held with a total of 85 participants. Results: Public policy interventions included the provision of valid nutrition information, physical activity promotion, and accessibility to healthy foods. School-based interventions included improving physical education, providing organised physical activity, provision of good quality education for children, parents and school staff, improving school shops, and using rewards and competitions. The findings suggest that close liaison should be established between the school, the family, and the broader community. Conclusions: This study provided important contextual data on where the emphasis should be placed in developing the childhood obesity prevention interventions for the school children in Tehran. The findings further highlight the importance of involving a wide range of stakeholders, and including multiple components to maximise the chances of success. Keywords: Child, Obesity, Prevention, Intervention, Qualitative research, Ira

Determination of Benzoate Level in Canned Pickles and Pickled Cucumbers in Food Producing Factories in Markazi Province and those that their Products were Sold in Arak City, Iran

Background: Anecdotal information has suggested that sodium benzoate is used with more than permissible doses during production steps of food products especially pickles and pickled cucumbers in food producing factories in Markazi province and other food producing factories . The present study was done to evaluate factual concentration of sodium benzoate in these products. Methods: In this study, 8 samples from canned pickled cucumbers and 10 samples from canned pickles were randomly gathered from food production factories in Markazi province between March and September 2010. Also, 25 samples from canned pickled cucumbers and 15 samples from canned pickles and 7 samples of bulk cargo pickled cucumbers were collected from the other provinces in Arak city. Sodium benzoate level was determined in the samples using UV-VIS spectrophotometry method. The determined values were analyzed by N-par test using SPSS software version 16.0. Results: Sodium benzoate level was near zero in the samples of canned pickles and pickled cucumbers from producing factories. This was 200-400 PPM in 7 samples from bulk cargo pickled cucumbers which was higher than permissible dose. There was not a statistically significant difference between mean benzoate level of canned pickles and pickled cucumbers produced in Markazi providence factories and other food factories. Benzoate level was significantly higher than permissible dose in bulk cargo pickled cucumbers. Conclusion: Food products from production factories do not have higher than permissible level of sodium benzoate; however, this is higher in bulk cargo pickled cucumbers. Hence, stricter control on bulk cargo pickled cucumber products is recommended

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems