Brazilian startup Cobli has specialized in technological solutions for vehicle fleet monitoring and management. It is currently focusing on safety and refining a tool to identify driver behavioral patterns by analyzing data collected by a solar-powered tracker. The project is based on machine learning, an application of artificial intelligence, and had the support) from the São Paulo Research Foundation - FAPESP through its Innovative Research in Small Business Program (PIPE http://www.bv.fapesp.br/en/3). "The algorithm uses the data collected to establish a driving profile with more than 90% accuracy," says engineer Rodrigo Mourad, a partner and co-founder of Cobli. According to Mourad, in one or two weeks of use, the system can glean a sufficient amount of data - on speed, acceleration, braking and curve angles - to produce a profile of the driver's vehicle handling habits.

In an increasingly complex scenario for network management, a solution that allows configuration in more autonomous way with less intervention of the network manager is expected. This paper presents an evaluation of similarity functions that are necessary in the context of using a learning strategy for finding solutions. The learning approach considered is based on Case-Based Reasoning (CBR) and is applied to a network scenario where different Bandwidth Allocation Models (BAMs) behaviors are used and must be eventually switched looking for the best possible network operation. In this context, it is required to identify and configure an adequate similarity function that will be used in the learning process to recover similar solutions previously considered. This paper introduces the similarity functions, explains the relevant aspects of the learning process in which the similarity function plays a role and, finally, presents a proof of concept for a specific similarity function adopted. Results show that the similarity function was capable to get similar results from the existing use case database. As such, the use of similarity functions with CBR technique has proved to be potentially satisfactory for supporting BAM switching decisions mostly driven by the dynamics of input traffic profile.

The variety of pedestrians detectors proposed in recent years has encouraged some works to fuse pedestrian detectors to achieve a more accurate detection. The intuition behind is to combine the detectors based on its spatial consensus. We propose a novel method called Content-Based Spatial Consensus (CSBC), which, in addition to relying on spatial consensus, considers the content of the detection windows to learn a weighted-fusion of pedestrian detectors. The result is a reduction in false alarms and an enhancement in the detection. In this work, we also demonstrate that there is small influence of the feature used to learn the contents of the windows of each detector, which enables our method to be efficient even employing simple features. The CSBC overcomes state-of-the-art fusion methods in the ETH dataset and in the Caltech dataset. Particularly, our method is more efficient since fewer detectors are necessary to achieve expressive results.

Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end. Therefore, we investigate end-to-end source separation in the time-domain, which allows modelling phase information and avoids fixed spectral transformations. Due to high sampling rates for audio, employing a long temporal input context on the sample level is difficult, but required for high quality separation results because of long-range temporal correlations. In this context, we propose the Wave-U-Net, an adaptation of the U-Net to the one-dimensional time domain, which repeatedly resamples feature maps to compute and combine features at different time scales. We introduce further architectural improvements, including an output layer that enforces source additivity, an upsampling technique and a context-aware prediction framework to reduce output artifacts. Experiments for singing voice separation indicate that our architecture yields a performance comparable to a state-of-the-art spectrogram-based U-Net architecture, given the same data. Finally, we reveal a problem with outliers in the currently used SDR evaluation metrics and suggest reporting rank-based statistics to alleviate this problem.

A Tesla's autopilot function was engaged in the minutes before a fiery crash that killed its driver in California earlier this year, according to a federal inquiry. In the roughly 20 minutes before the vehicle slammed into a barrier near Mountain View and burst into flames, the car's autopilot feature was in "continuous operation", the National Transportation Safety Board (NTSB) found in its initial investigation. During the critical 60 seconds leading up to the crash, the NTSB reported, the car's driver repeatedly placed his hands on the steering wheel. Tesla crashes into parked police car in Autopilot mode Wall Street blasts Elon Musk's'truly bizarre' Tesla earnings call Tesla faces labour investigation after allegation of injury undercount But six seconds before the accident, evidence suggests the driver had removed his hands from the steering wheel. The vehicle also accelerated in the final three seconds.

Robots appear to be in the middle of a gradual but persistent transition from automated tools that perform specific tasks to artificially intelligent entities that we interact with socially and emotionally. It's not at all clear where this is going to end up--people toss around the idea of robot companionship and even robot love with some frequency, for example. What hasn't been explored nearly as much is the idea of robots in a religious context. We've seen a few examples of robots assisting in religious tasks, but what if robots could take things a step farther, and become sacred objects, embodying divinity within a robot itself? At the ACM/IEEE International Conference on Human Robot Interaction (HRI) in March, Gabriele Trovato from Waseda University in Japan (with colleagues from Pontificia Universidad Católica del Perú) presented a paper taking a look at whether divine robots might be possible, and why it could be useful to develop such robots in the first place.

Food tech is a booming industry and one that has witnessed some seriously staggering innovations that have completely transformed the food business. From hassle-free order placement to quick deliveries to curated options, the food tech space has everything covered. We spoke with José Daniel Leal Avila, CEO, and Founder at Foodstamp.tech, a promising young food tech brand, to shed light on the trends in the food tech space and to share valuable advice for businesses looking to startup in the food tech space. We started Foodstamp while studying for a project that involved understanding the issues that new restaurants faced. At the time, coincidentally, one of our favorite burger joints went out of business and we were puzzled as to why that happened.

Fans of "Days of Our Lives" and other NBC shows will have to take a day off from their favorite programs on Thursday, June 7, as the programming schedule has been altered due to coverage of the French Open. According to NBC's programming schedule for the day, "The Today Show" will still air until 11 a.m. EDT with Megyn Kelly's block of the broadcast beginning at 9 a.m., followed by Kathie Lee Gifford and Hoda Kotb's block at 10 a.m. However, at 11 a.m. EDT, the programming will switch to the network's coverage of the French Open. This will continue until 2 p.m. EDT, preempting the 11:00 News, "New York Live," "Days of Our Lives," and "Access Hollywood Live." A normal Thursday schedule resumes at 2:00 with "Steve." These scheduling changes will also be in effect on Friday, June 8.

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.

Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixture (GM) functions as a new approach for providing an efficient combination procedure for these systems through the use of dynamic weights in the combination process. Therefore, we present three GM functions to be applied as a combination method. The main advantage of these functions is that they can define dynamic weights at the member outputs, making the combination process more efficient. In order to evaluate the feasibility of the proposed approach, an empirical analysis is conducted, applying classifier ensembles to 25 different classification data sets. In this analysis, we compare the use of the proposed approaches to ensembles using traditional combination methods as well as the state-of-the-art ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed approaches to the traditional ones as well as comparable results with the state-of-the-art methods.