Differential privacy enables organizations to collect accurate aggregates over sensitive data with strong, rigorous guarantees on individuals' privacy. Previous work has found that under differential privacy, computing multiple correlated aggregates as a batch, using an appropriate \emph{strategy}, may yield higher accuracy than computing each of them independently. However, finding the best strategy that maximizes result accuracy is non-trivial, as it involves solving a complex constrained optimization program that appears to be non-linear and non-convex. Hence, in the past much effort has been devoted in solving this non-convex optimization program. Existing approaches include various sophisticated heuristics and expensive numerical solutions. None of them, however, guarantees to find the optimal solution of this optimization problem. This paper points out that under ($\epsilon$, $\delta$)-differential privacy, the optimal solution of the above constrained optimization problem in search of a suitable strategy can be found, rather surprisingly, by solving a simple and elegant convex optimization program. Then, we propose an efficient algorithm based on Newton's method, which we prove to always converge to the optimal solution with linear global convergence rate and quadratic local convergence rate. Empirical evaluations demonstrate the accuracy and efficiency of the proposed solution.

Noname manuscript No. (will be inserted by the editor) Abstract Nowadays large amounts of GPS trajectory data is being continuously collected by GPSenabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the POIs in the process of SVL generation. NNQ in some scenarios is not accurate enough. To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations. In this paper we propose a novel method for estimating the POIs and their GOIs, which consists of three phases: (i) extracting the geometries of the stay regions; (ii) constructing the geometry of destination regions based on the extracted stay regions; and (iii) constructing the GOIs based on the geometries of the destination regions. Using the geometric similarity to known GOIs as the major evaluation criterion, the experiments we performed using long-term GPS trajectory data show that our method outperforms the existing approaches. Keywords Trajectory Data, Spatio-Temporal Partitioning, Geometry of Interest, Time-Value, Time-Weighted Centroid, Destination Extraction 1 Introduction In recent years, GPS trajectory data has become abundant due to the many GPS enabled devices used on a daily basis. Mining these GPS trajectories for gathering useful information for applications has received a growing amount of attention in the recent literature. In this field, researchers have tried to derive knowledge for solving practical problems (e.g. The applications dealing with data analysis on trajectory data often need to have access to information about the significant places which a mobile object frequently travels and stay. These significant places are referred to as the points of interest (POIs).

Community annotation of biological entities with concepts from multiple bio-ontologies has created large and growing repositories of ontology-based annotation data with embedded implicit relationships among orthogonal ontologies. Development of efficient data mining methods and metrics to mine and assess the quality of the mined relationships has not kept pace with the growth of annotation data. In this study, we present a data mining method that uses ontology-guided generalization to discover relationships across ontologies along with a new interestingness metric based on information theory. We apply our data mining algorithm and interestingness measures to datasets from the Gene Expression Database at the Mouse Genome Informatics as a preliminary proof of concept to mine relationships between developmental stages in the mouse anatomy ontology and Gene Ontology concepts (biological process, molecular function and cellular component). In addition, we present a comparison of our interestingness metric to four existing metrics. Ontology-based annotation datasets provide a valuable resource for discovery of relationships across ontologies. The use of efficient data mining methods and appropriate interestingness metrics enables the identification of high quality relationships.

Artificial intelligence is getting smarter by leaps and bounds -- within this century, research suggests, a computer AI could be as "smart" as a human being. And then, says Nick Bostrom, it will overtake us: "Machine intelligence is the last invention that humanity will ever need to make." A philosopher and technologist, Bostrom asks us to think hard about the world we're building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values -- or will they have values of their own?

Research indicates that IoT and Machine Learning are more valuable to utilities when used in combination but there are hurdles to overcome first. Machine learning and IoT will enable utilities to better realize the next generation of the power grid: a distributed system with power flows among millions of things like distributed energy resources (DERs), microgrids and in-home devices. All of which will help utilities deliver clean reliable energy and greater customer choice. Utility respondents to new research from SAS and Zpryme, The Autonomous Grid, indicated that IoT and machine learning were more than market hype. These technologies are already delivering actionable results, say respondents.

Creating applications with the ability to learn from available data and making decisions based on the prediction results obtained from the learned models has become a trend-setting aspect in the software industry. Therefore, almost all applications that do predictive modeling uses one or more of these already available machine learning engines. With the ability to directly interact with these engines with simple API calls, intricacy of integration has alleviated rapidly. One of the common practices is training models with machine learning engines and building predictive APIs on top of them to retrieve prediction results for a new event. This article discusses how to train a machine learning model with WSO2 Machine Learner and create a predictive API with WSO2 API Manager with the trained model.

LONDON--(BUSINESS WIRE)--According to the latest research study released by Technavio, the global industrial robotics market in the rubber and plastic industry is expected to record a CAGR of over 18% until 2020. This research report titled'Global Industrial Robotics Market in the Rubber and Plastic Industry 2016-2020', provides an in-depth analysis of market growth in terms of revenue and emerging market trends. To calculate the market size, the report considers the revenue generated primarily through the sales and services of various industrial robotics such as cartesian, articulated, and others for different applications in the rubber and plastic industry. "China accounted for about 25% of the overall production of plastics, followed by European countries that accounted for 20%. Key findings of this report show that the demand for plastics is anticipated to grow during the forecast period, and positively impact the market. Significant improvements in gripper technology, such as the development of Versaball by Empire Robotics for handling materials of any shape and size, is also slated to play a critical role in the expansion of robots in the rubber and plastic industry," said Bharath Kanniappan, one of Technavio's lead analysts for robotics research.

Let's skip to the chase: Self-driving cars are going to be The Next Big Thing (tm). Here's how you can make big money from this revolution, no matter which carmaker or technology platform comes out on top. Since Alphabet started leading this futuristic idea out into the mainstream, the car industry itself has turned in that direction. Name a carmaker, and I bet the company has developed at least the embryo of a self-driving platform. You can already find traces of this upcoming revolution inside current cars, powering automatic parallel-parking systems or highway-speed autopilots.

HP Inc. (NYSE: HPQ) has launched a new corporate venture arm targeting early stage companies with innovative technologies. HP Tech Ventures will operate across teams in Palo Alto, Calif., and Tel Aviv, Israel, to pursue strategic investments and partnerships in technology areas including: 3D transformation, immersive computing, hyper-mobility, Internet of Things, artificial intelligence, and smart machines. Led by Andrew Bolwell, HP Tech Ventures will leverage its technology network, its channel and distribution partners, as well as manufacturing and supply chain relationships to help startups scale. The HP Tech Ventures group reports into Shane Wall, HP Chief Technology Officer and head of HP Labs. Palo Alto, Calif., and Tel Aviv, Israel, May 10, 2016 --HP Inc. (NYSE: HPQ) today announced the launch of HP Tech Ventures, a new corporate venture arm, targeting early stage companies with cutting edge technologies.

Bill Gates speaks during the Forbes' 2015 Philanthropy Summit Awards Dinner on June 3, 2015 in New York City. Many are still wary about the effects of future artificial intelligence in humanity. But Microsoft cofounder and the world's richest man Bill Gates says AI won't be a threat, instead it will be "extremely helpful" in managing human lives. The actively evolving field of artificial intelligence has revolutionized the healthcare, business and education sectors around the world. AI also continues to prove its ubiquity by making great advances in technology and robotics but many warn about the existential risks of artificial intelligence.