Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation

Computer vision models learn to perform a task by capturing relevant statistics from training data. It has been shown that models learn spurious age, gender, and race correlations when trained for seemingly unrelated tasks like activity recognition or image captioning. Various mitigation techniques have been presented to prevent models from utilizing or learning such biases. However, there has been little systematic comparison between these techniques. We design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation. Using this benchmark, we provide a thorough analysis of a wide range of techniques. We highlight the shortcomings of popular adversarial training approaches for bias mitigation, propose a simple but similarly effective alternative to the inference-time Reducing Bias Amplification method of Zhao et al., and design a domain-independent training technique that outperforms all other methods. Finally, we validate our findings on the attribute classification task in the CelebA dataset, where attribute presence is known to be correlated with the gender of people in the image, and demonstrate that the proposed technique is effective at mitigating real-world gender bias.Comment: To appear in CVPR 202

Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set

Context Determination for Adaptive Navigation using Multiple Sensors on a Smartphone

Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. This paper aims to determine the behavioural and environmental contexts together, building the foundation of a context-adaptive navigation system. Both behavioural and environmental context detection results are presented. A hierarchical behavioural recognition scheme is proposed, within which the broad classes of human activities and vehicle motions are detected using measurements from accelerometers, gyroscopes, magnetometers and the barometer on a smartphone by decision trees (DT) and Relevance Vector Machines (RVM). The detection results are further improved by behavioural connectivity. Environmental contexts (e.g., indoor and outdoor) are detected from GNSS measurements using a hidden Markov model. The paper also investigates context association in order to further improve the reliability of context determination. Practical test results demonstrate improvements of environment detection in context determination

Change detection of land use and land cover in an urban region with SPOT-5 images and partial Lanczos extreme learning machine

Satellite remote sensing technology and the science associated with evaluation of land use and land cover (LULC) in an urban region makes use of the wide range images and algorithms. Improved land management capacity is critically dependent on real-time or near real-time monitoring of land-use/land cover change (LUCC) to the extent to which solutions to a whole host of urban/rural interface development issues may be well managed promptly. Yet previous processing with LULC methods is often time-consuming, laborious, and tedious making the outputs unavailable within the required time window. This paper presents a new image classification approach based on a novel neural computing technique that is applied to identify the LULC patterns in a fast growing urban region with the aid of 2.5-meter resolution SPOT-5 image products. The classifier was constructed based on the partial Lanczos extreme learning machine (PL-ELM), which is a novel machine learning algorithm with fast learning speed and outstanding generalization performance. Since some different classes of LULC may be linked with similar spectral characteristics, texture features and vegetation indexes were extracted and included during the classification process to enhance the discernability. A validation procedure based on ground truth data and comparisons with some classic classifiers prove the credibility of the proposed PL-ELM classification approach in terms of the classification accuracy as well as the processing speed. A case study in Dalian Development Area (DDA) with the aid of the SPOT-5 satellite images collected in the year of 2003 and 2007 and PL-ELM fully supports the monitoring needs and aids in the rapid change detection with respect to both urban expansion and coastal land reclamations

Predictive analytics in agribusiness industries

Agriculturally related industries are routinely among the most hazardous work environments. Workplace injuries directly impact labor-market outcomes including income reduction, job loss, and health of the injured workers. In addition to medical and indemnity costs, workplace incidents include indirect costs such as equipment damage and repair, incident investigation time, training new personnel for replacement of the injured ones, an increase in insurance premiums for the year following the incidents, a slowdown of production schedules, damage to companies’ reputation, and lowering the workers’ motivation to return to work. The main purpose of incident analysis is the derivation and development of preventative measures from injury data. Applying proper analytical tools aimed at discovering the causes of occupational incidents is essential to gain useful information that contributes in preventing those incidents in future. Insight gained from the analyses of workers’ compensation data can efficiently direct preventative activities at high-risk industries. Since incidents arise from a combination of factors rather than a single cause, research on occupational incidents must go deeper into identifying the underlying causes and their relationship through applying more comprehensive analyses. Therefore, this study aimed at identifying underlying patterns in occupational injury occurrence and costs using data mining and predictive modeling techniques instead of traditional statistical methods. Utilizing a workers’ compensation claims dataset, the objectives of this study were to: investigate the use of predictive modeling techniques in forecasting future claims costs based on historical data; identify distinctive patterns of high-cost occupational injuries; and examine how well machine learning methods work in finding the predictive relationship between factors influencing occupational injuries and workers’ compensation claims occurrence and severity. The results lead to a better understanding of injury patterns, identification of prevalent causes of occupational injuries, and identification of high-risk industries and occupations. Therefore, various stakeholders such as policymakers, insurance companies, safety standard writers, and manufacturers of safety equipment can use the findings of the study to plan for remedial actions and revise safety standards. The implementation of safety measures by agribusiness organizations can prevent occupational injuries, save lives, and reduce the occurrence and cost of such incidents in agricultural work environments