The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website ¹ .

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website ( http://votchallenge.net ).

Stock market forecasting is one of the biggest challenges in the financial market since its time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. However, due to computing development, an intelligent model can help investors and professional analysts reduce the risk of their investments. As Deep Learning models have been extensively studied in recent years, several studies have explored these techniques to predict stock prices using historical data and technical indicators. However, as the objective is to generate forecasts for the financial market, it is essential to validate the model through profitability metrics and model performance. Therefore, this systematic review focuses on Deep Learning models implemented for stock market forecasting using technical analysis. Discussions were made based on four main points of view: predictor techniques, trading strategies, profitability metrics, and risk management. This study showed that the LSTM technique is widely applied in this scenario (73.5%). This work significant contribution is to highlight some limitations found in the literature, such as only 35.3% of the studies analysed profitability, and only two articles implemented risk management. Therefore, despite the widely explored theme, there are still interesting open areas for research and development.

The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that may lead to significant impairment in social communication, repetitive patterns of behavior, and possible fixed and restricted interests. Applied Behavior Analysis (ABA) is a well-supported and evidence-based treatment for the delays attributed to ASD. Assistive technologies, such as gamification, software apps, computer-based training (Web), and robotics; provide a standardized method of implementing ABA techniques. This review provides a synthesis of the main characteristics of these technologies. The assessed proposals focused on technologies such as Distributed Systems, Image Processing, Gamification, and Robotics. The primary objectives of these tools sought to improve social behavior, attention, communication, and reading skills. Some common limitations found in the literature was a failure to accurately define their target audience, and a failure to comply with the dimensions of ABA as defined by Baer, Wolf, and Risley in 1968.