Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific "dual--party" secure learning scenario based on two parties -- both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others. To achieve the above goal, we firstly extent -- LightGBM -- a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets.

Automated reasoning and theorem proving have recently become major challenges for machine learning. In other domains, representations that are able to abstract over unimportant transformations, such as abstraction over translations and rotations in vision, are becoming more common. Standard methods of embedding mathematical formulas for learning theorem proving are however yet unable to handle many important transformations. In particular, embedding previously unseen labels, that often arise in definitional encodings and in Skolemization, has been very weak so far. Similar problems appear when transferring knowledge between known symbols. We propose a novel encoding of formulas that extends existing graph neural network models. This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels. We provide additional links between such nodes that allow the network to recover the meaning and therefore correctly embed such nodes irrespective of the given labels. We test the proposed encoding in an automated theorem prover based on the tableaux connection calculus, and show that it improves on the best characterizations used so far. The encoding is further evaluated on the premise selection task and a newly introduced symbol guessing task, and shown to correctly predict 65% of the symbol names.

Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. "We call algorithms created with our new framework'Seldonian' after Asimov's character Hari Seldon," Thomas explains.

Placentas can provide critical information about the health of the mother and baby, but only 20 percent of placentas are assessed by pathology exams after delivery in the U.S. The cost, time and expertise required to analyze them are prohibitive. Now, a team of researchers has developed a novel solution that could produce accurate, automated and near-immediate placental diagnostic reports through computerized photographic image analysis. Their research could allow all placentas to be examined, reduce the number of normal placentas sent for full pathological examination and create a less resource-intensive path to analysis for research--all of which may positively benefit health outcomes for mothers and babies. "The placenta drives everything to do with the pregnancy for the mom and baby, but we're missing placental data on 95 percent of births globally," said Alison Gernand, assistant professor of nutritional sciences in Penn State's College of Health and Human Development. "Creating a more efficient process that requires fewer resources will allow us to gather more comprehensive data to examine how placentas are linked to maternal and fetal health outcomes, and it will help us to examine placentas without special equipment and in minutes rather than days."

Provides tools to create and manipulate probability distributions using S3. Generics random(), pdf(), cdf(), and quantile() provide replacements for base R's r/d/p/q style functions. The documentation for each distribution contains detailed mathematical notes. There are several vignettes: Intro to hypothesis testing, One-sample sign tests, One-sample T confidence interval, One-sample T-tests, Z confidence interval for a mean, One-sample Z-tests for a proportion, One-sample Z-tests, Paired tests, and Two-sample Z-tests.

GLóRIA DE DOURADOS, MATO GROSSO DO SUL, BRAZIL - 2019/08/19: In this photo illustration the Siri ... [ ] logo is displayed on a smartphone. In the early morning hours of October 2nd 2019, Duane Raible, a 52–year-old male traveling from Pennsylvania and staying at the Thompson Chicago Hotel, knew something wasn't right. He felt dizzy, his face was numb, and he recognized that he had difficulty speaking. He proceeded to call 9-1-1 on his smartphone for help. But the help he needed wasn't provided by the dispatcher.

The Artificial Intelligence in Digital Marketing report additional predicts the dimensions and valuation of the global industry throughout the forecast amount. The Artificial Intelligence in Digital Marketing Market report examines the economic status and prognosis of worldwide and major regions, in the prospect of all players, types and end-user application/industries; this report examines the most notable players in major and global regions, also divides this market by segments and applications/end businesses.

The global internet of things (IoT) in manufacturing market size was USD 27.76 Billion in 2018 and is projected to reach USD 136.83 billion by 2026, exhibiting a CAGR of 22.1% during the forecast period. The internet of things (IoT) in manufacturing comprises mechanical and electrical parts, advanced sensors, network connectivity architecture, controls, software applications, and smart devices that work together to collect and share real-time information between machines and humans. The internet of things (IoT) in manufacturing industry is gaining robust growth due to the rising adoption of AI (Artificial Intelligence) and other connected devices based on machine learning (M2M, M2P). Implementation of IoT technology in manufacturing industry is providing several organizations with new opportunities including digital transformations techniques and is enabling them to upgrade the current running operations by creating and tracking new business models. Furthermore, IoT solutions help in providing manufacturers a comprehensive vision to monitor complexities keep on arising at every intermediate point in the manufacturing process and assist in developing real-time adjustments.

This week's episode focuses on the interface between physics and computing, with deep dives into how artificial intelligence (AI) is contributing to medical physics and how silicon could form the basis of a future quantum computer. First, we hear from Tami Freeman, Physics World's resident expert on medical physics, about a new positron emission tomography (PET) scanner that can image a patient's whole body much more quickly (or at higher resolutions) than is possible with current commercial scanners. We then stick with the medical theme to discuss three recent examples of how AI is being used in medicine: firstly to diagnose skin conditions (but, disturbingly, only if the patient's skin is white); secondly to help radiologists detect lung tumours in X-rays; and thirdly to develop better radiotherapy treatment plans. There are several ways of constructing the qubits, or quantum bits, that make up a quantum computer, and this week we hear from a trio of researchers – Fernando Gonzalez-Zalba, Alessandro Rossi and Tsung-Yeh Yang – who have been developing silicon-based qubits. Their work is part of a Europe-wide collaboration between universities, government laboratories and companies called MOS-Quito, and you can read more about it in their article for the Physics World Focus on Computing.

Rest of Europe 8.4 Asia Pacific 8.4.1