Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study, for the first time, develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment. The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.

Machine Learning (ML) is widely used for predictive tasks in a number of critical applications. Recently, collaborative or federated learning is a new paradigm that enables multiple parties to jointly learn ML models on their combined datasets. Yet, in most application domains, such as healthcare and security analytics, privacy risks limit entities to individually learning local models over the sensitive datasets they own. In this work, we present the first formal study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to privately compute joint clusters on their combined sensitive datasets. First, we provide a formal definition that balances accuracy and privacy, and we present a provably secure protocol along with an optimized version for single linkage clustering. Second, we explore the integration of our protocol with existing approximation algorithms for hierarchical clustering, resulting in a protocol that can efficiently scale to very large datasets. Finally, we provide a prototype implementation and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results. For example, for a dataset of one million records and 10 dimensions, our optimized privacy-preserving approximation protocol requires 35 seconds for end-to-end execution, just 896KB of communication, and achieves 97.09% accuracy.

Many practical applications of machine learning require data-efficient black-box function optimization, e.g., to identify hyperparameters or process settings. However, readily available algorithms are typically designed to be universal optimizers and are, thus, often suboptimal for specific tasks. We therefore propose a method to learn optimizers which are automatically adapted to a given class of objective functions, e.g., in the context of sim-to-real applications. Instead of learning optimization from scratch, the proposed approach is firmly based within the famous Bayesian optimization framework. Only the acquisition function (AF) is replaced by a learned neural network and therefore the resulting algorithm is still able to exploit the proven generalization capabilities of Gaussian processes. We present experiments on several simulated as well as on a sim-to-real transfer task. The results show that the learned optimizers (1) consistently perform better than or on-par with known AFs on general function classes and (2) can automatically identify structural properties of a function class using cheap simulations and transfer this knowledge to adapt rapidly to real hardware tasks, thereby significantly outperforming existing problem-agnostic AFs.

Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain. We proposed to learn a Domain Representations over existing knowledge graph embedding models, such that entities that have similar attributes are organized into the same domain. Such hierarchical knowledge of domains can give further evidence in link prediction. Experimental results show that domain embeddings give a significant improvement over the most recent state-of-art baseline knowledge graph embedding models.

As a one-armed orphan – a disability that you might think would disqualify him from the opportunity to work as a lone assassin in 16th-century Japan – Sekiro is well acquainted with disadvantage. Still, a smooth sea never made a skilful mariner, as they used to say, and these physical and psychological handicaps have only served to strengthen this shinobi, who, with a variety of terrifying prosthetics, must now avenge his fallen master by taking down the Ashina clan. Up close, this is grindcore game-making, in which you are forced to watch the lolling of your victims' astonished mouths as you trace a katana across their necks. This world of blood, fire and pitter-patter footsteps across bamboo rooftops calls to mind Toshiya Fujita's Lady Snowblood or Akira Kurosawa's Sanjuro in both theme and body count. But in its moments of exquisite pause, it's also a game of refined cinematic style, the traumatised ninja silhouetted against a flaring sunset, while the reeds rustle and soothe.

There's a reason Gmail is far and away the world's most popular e-mail program with 1.5 billion users. It has way more features than rivals. Wouldn't you like to make free phone calls from your e-mail program or translate a French e-mail into English, right from within Gmail? Google's free e-mail program, which turned 15 this week, continues to innovate. Here are seven things you can do with Gmail that you can't do with AOL, Yahoo or Microsoft Outlook.com or Hotmail.

MOSCOW - The Russian Defense Ministry has deployed a surveillance drone with an artillery division to a group of islands controlled by Russia but claimed by Japan, a Russian newspaper reported on Monday. The drone will be used for patrolling coastal areas and surrounding waters, as well as for rescue operations, according to the newspaper, Izvestia. The artillery unit is stationed on two of the four Russian-controlled islands off the coast of Hokkaido, known in Japan as Etorofu and Kunashiri. The Orlan-10 drone, the same type as those sent by Russia to Syria, is able to operate within a 120-kilometer radius for up to 14 hours while transmitting images from a mounted camera, the Russian paper said.

Children with rare conditions could be diagnosed quicker thanks to 3D facial analysis software. Richard Palmer at Curtin University in Western Australia and his colleagues have developed a tool that can spot subtle, but important, differences in facial geometry. Around one in three rare and genetic diseases show up in facial features.

There has, for years, been one thing that just about all of the tech industry agrees on: regulation is coming. Recently, they have even realised that it's necessary. But if there is one thing that has split tech behemoths, politicians and the public apart more than perhaps any other issue, it's what that regulation should look like. Now the UK government thinks it has alighted on an answer, offering perhaps the first comprehensive attempt to limit the harm that technology companies are doing to the people – in particular the children – who use them. For the most part, the solution they have chosen focuses on shifting the responsibility for content that appears on the site onto the people who run them.

Netflix has stopped its users from sending videos to their Apple TVs from their phones. The shock decision removes one of the key features both from the Netflix apps and iPhones they are used on. Until now, it has been possible to send a Netflix video onto a TV using Apple's AirPlay, which allows people to cast the video from their phone to the TV. AirPlay is used mostly on Apple TVs at the moment, though Apple is in the process of rolling out the technology to other smart televisions. We'll tell you what's true.