Through a fair looking-glass: mitigating bias in image datasets

With the recent growth in computer vision applications, the question of how fair and unbiased they are has yet to be explored. There is abundant evidence that the bias present in training data is reflected in the models, or even amplified. Many previous methods for image dataset de-biasing, including models based on augmenting datasets, are computationally expensive to implement. In this study, we present a fast and effective model to de-bias an image dataset through reconstruction and minimizing the statistical dependence between intended variables. Our architecture includes a U-net to reconstruct images, combined with a pre-trained classifier which penalizes the statistical dependence between target attribute and the protected attribute. We evaluate our proposed model on CelebA dataset, compare the results with a state-of-the-art de-biasing method, and show that the model achieves a promising fairness-accuracy combination

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Personalized driver workload inference by learning from vehicle related measurements

Adapting in-vehicle systems (e.g. Advanced Driver Assistance Systems, In-Vehicle Information Systems) to individual drivers’ workload can enhance safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in a proper manner. Rather than developing an average model for all drivers, a Personalized Driver Workload Inference (PDWI) system considering individual drivers’ driving characteristics is developed using machine learning techniques via easily accessed Vehicle Related Measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers’ workload is first automatically splitted into different categories according to its inherent data characteristics using Fuzzy C means clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating Fuzzy C-means clustering and Support Vector Machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score and prediction time. The inter-individual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness

Sustainable digital marketing under big data: an AI random forest model approach

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies

Multi-Network Feature Fusion Facial Emotion Recognition using Nonparametric Method with Augmentation

Facial expression emotion identification and prediction is one of the most difficult problems in computer science. Pre-processing and feature extraction are crucial components of the more conventional methods. For the purpose of emotion identification and prediction using 2D facial expressions, this study targets the Face Expression Recognition dataset and shows the real implementation or assessment of learning algorithms such as various CNNs. Due to its vast potential in areas like artificial intelligence, emotion detection from facial expressions has become an essential requirement. Many efforts have been done on the subject since it is both a challenging and fascinating challenge in computer vision. The focus of this study is on using a convolutional neural network supplemented with data to build a facial emotion recognition system. This method may use face images to identify seven fundamental emotions, including anger, contempt, fear, happiness, neutrality, sadness, and surprise. As well as improving upon the validation accuracy of current models, a convolutional neural network that takes use of data augmentation, feature fusion, and the NCA feature selection approach may assist solve some of their drawbacks. Researchers in this area are focused on improving computer predictions by creating methods to read and codify facial expressions. With deep learning's striking success, many architectures within the framework are being used to further the method's efficacy. We highlight the contributions dealt with, the architecture and databases used, and demonstrate the development by contrasting the offered approaches and the outcomes produced. The purpose of this study is to aid and direct future researchers in the subject by reviewing relevant recent studies and offering suggestions on how to further the field. An innovative feature-based transfer learning technique is created using the pre-trained networks MobileNetV2 and DenseNet-201. The suggested system's recognition rate is 75.31%, which is a significant improvement over the results of the prior feature fusion study

The Role of Labor Unions in Immigrant Integration

We examine if unions narrow or widen labor market gaps between natives and immigrants. We do so by combining rich Norwegian employer-employee matched register data with exogenous variation in union membership obtained through national government policies that differentially shifted the cost to workers to join a union. While union membership significantly improves the wages of natives, its positive effects diminish substantially for Western immigrants and disappear almost entirely for non-Western immigrants. The effect of unions on native wages, and the role of unions in augmenting the native-immigrant wage gap, is nonexistent in competitive labor markets while it is substantial in markets characterized by a high degree of labor concentration. This implies that unions act as a countervailing force to employer power in imperfect markets and can ameliorate the negative labor market effects of labor market concentration, but only for natives. Using unions as a means to empower workers and solve market failures caused by imperfect competition in the labor market, therefore, is likely to lead to a significant increase in societal inequality

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Doctor of Philosophy

dissertationPeristomal skin lesions are common following stoma surgery. However, there is wide variability in how those lesions are documented. The purpose of this study was to evaluate psychometric properties and feasibility of use for the Studio Alterazioni Cutanee Stomali (SACS™) instrument. Content validity was previously evaluated. This study extends that work by examining use in pediatrics, use by parents and bedside nurses, and by evaluating reliability and validity. The study was guided by the Donabedian Structure-Process-Outcome framework and psychometric theory. Data collection included questionnaire, direct observation, and rating of lesion photographs. Participants were 64 parents of children who had undergone stoma surgery, 64 bedside nurses, and 10 wound nurses, who simultaneously assessed the child's skin lesion. There were 73 lesions in 65 children, with 292 direct observations and 40 photographs. Findings supported use of the SACS™ instrument in pediatrics. The instrument was feasible for parent and nurse use. Most parents (98%) were willing to use the instrument at home. Intrarater reliability was acceptable when ratings were grouped into clinically relevant categories (78-85% agreement for lesion severity). There was strong evidence of interrater reliability, with intraclass correlation > 0.91. The contrasted groups approach supported construct validity, demonstrating that the instrument could distinguish between lesions of known severity, and that parents and bedside nurses, who have less stoma experience, rate lesions in a similar manner to each other, and differently than wound experts. Most important clinically, there was strong evidence of decision validity; the instrument was able to discriminate between lesions that needed to be seen in clinic and those that could be safely treated at home. When there was disagreement, raters consistently erred on the side of safety, rating lesions as more severe than the expert, which would have resulted in the child being assessed by a clinician. Limitations included a single setting with limited number of wound nurses, convenience sampling, and predominantly Caucasian population. Strengths included standardized methodology and strong basis in the theoretical framework. The study demonstrated that the instrument can be used in the pediatric population to document peristomal skin lesions, which should facilitate clinical decisions and communication