Hailing from the Caribbean coast of Colombia, Daniel is a writer and freelance translator with a background in biology. When not word-smithing, you will probably find him chasing frogs somewhere around the tropical belt.

An illegal connection might be detected from the vibrations caused by an outflow where one is not supposed to be… The idea is that [the AI tool] can be used to quickly narrow down the location of any water losses, allowing operators using geophones to pinpoint the exact site. In one case, in Votorantim, a city in São Paulo state, it would have taken the local water company two years to survey its network of pipes using two operators armed with geophones." He says it's situations like that which highlight differences between what is legal in the laws of war and what is morally right -- something that autonomous weapons might not distinguish. "That is one of the concerns that people raise about autonomous weapons is a lack of an ability to feel empathy and to engage in mercy in war. And that if we built these weapons, they would take away a powerful restraint in warfare that humans have."

OLD hands at some water companies still on occasion whip out a pair of dousing rods or find a Y-shaped twig to search for a leak in an underground pipe. Dousing, or water witching as it is known in America, has no basis in scientific fact. A somewhat more reliable method involves using acoustic equipment called geophones to listen for escaping water. The trouble is it takes an experienced ear to distinguish the sound of a leak from the normal gurgle of water passing though pipes, let alone to predict from that sound where any trouble might be found. As this is a problem of pattern recognition, which is something that artificial intelligence can be good at, a Brazilian startup has used AI to develop an acoustic device that makes tracing leaks a lot easier.

Journal of Parallel and Distributed Computing 104, 19-35.

In mulsemedia applications, traditional media content (text, image, audio, video, etc.) can be related to media objects that target other human senses (e.g., smell, haptics, taste). Such applications aim at bridging the virtual and real worlds through sensors and actuators. Actuators are responsible for the execution of sensory effects (e.g., wind, heat, light), which produce sensory stimulations on the users. In these applications sensory stimulation must happen in a timely manner regarding the other traditional media content being presented. For example, at the moment in which an explosion is presented in the audiovisual content, it may be adequate to activate actuators that produce heat and light. It is common to use some declarative multimedia authoring language to relate the timestamp in which each media object is to be presented to the execution of some sensory effect. One problem in this setting is that the synchronization of media objects and sensory effects is done manually by the author(s) of the application, a process which is time-consuming and error prone. In this paper, we present a bimodal neural network architecture to assist the synchronization task in mulsemedia applications. Our approach is based on the idea that audio and video signals can be used simultaneously to identify the timestamps in which some sensory effect should be executed. Our learning architecture combines audio and video signals for the prediction of scene components. For evaluation purposes, we construct a dataset based on Google's AudioSet. We provide experiments to validate our bimodal architecture. Our results show that the bimodal approach produces better results when compared to several variants of unimodal architectures.

A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling. An RBM lacks the capacity to retain memory, making it inappropriate for dynamic data modeling as in time-series analysis. In this paper we address this issue by proposing the p-RBM model, a generalization of the regular RBM model, capable of retaining memory of p past states. We further show how to train the p-RBM model using contrastive divergence and test our model on the problem of predicting the stock market direction considering 100 stocks of the NASDAQ-100 index. Obtained results show that the p-RBM offer promising prediction potential.

Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cut costs or both. Web Mining is the application of data mining techniques to discover patterns from the World Wide Web. It can be divided into three different types - Web usage mining, Web content mining and Web structure mining. Web usage mining itself can be classified further depending on the kind of usage data considered: Web Server Data, Application Server Data, and Application Level Data. Web log Mining includes three main stages: Data Pre-Processing, Pattern Discovery and Pattern Analysis. A) Data Pre-Processing: Web Server Data contains information such as who accessed the web site, what pages were accessed, Time of request etc. In pre-processing [3] stage, irrelevant data fields are removed and unique users are identified [4]. Transaction table is created through the user sessions.

KDnuggets poll finds that Machine Learning Engineer, Researcher, and Data Scientist have the highest job satisfaction. Job satisfaction usually starts high, but drops significantly after 4 years on the job. Data professionals in Asia and Latin America are most unsatisfied.

Last week I attended the 2018 INFORMS Conference on Business Analytics & Operations Research in Baltimore, MD. Among the activities of the conference, several teams of researchers from universities and industries were competing in challenges to show how their work is influencing the world. I had the honor to be among the finalist teams for the 2018 Syngenta Crop Challenge in Analytics. The Syngenta Crop Challenge in Analytics was established in 2015 with funding provided by prize winnings awarded to Syngenta in connection with its receipt of the 2015 Franz Edelman Award for Achievement in Operations Research and the Management Sciences. This year's Challenge asked participants to develop a quantitative framework for predicting corn hybrids performance in new, untested locations.

On a fine early afternoon in late March a young German-Iranian friend and I walked into the Garage Gallery at the Fire Station in Doha, Qatar - and we wondered. We were there to see the famous Chinese artist Ai Weiwei's "Laundromat": "A traveling installation", as the official description of the exhibition says, "that brings the current European migrant crisis into sharp focus." We had read before that "the work is centered around a vast makeshift camp near the village of Idomeni, on the border with the Republic of Macedonia. As part of his recently released documentary Human Flow, Ai Weiwei has borne witness to the brutal plight of refugees worldwide." Does the brutal plight of refugees worldwide - those from Syria in particular - need a witness?