According to surveys, 75% of teachers in USA believe printed books will entirely be replaced by digital learning tools. Is the Internet and technology really a game changer within the education sector? Over the past few decades, new technologies have truly transformed every aspect of our world, from scientific and industrial development to day-to-day activities in our personal space. And, whenever a new technology is introduced to the masses, the way people interact with each other and envision their lives has shifted drastically. The truth is, we only realize the redundancies of our current practices after we are introduced to a new technology that makes our daily activities efficient.

China's freeze on game approvals is winding down, although gamers and developers might not like what the thaw entails. The country has revealed the existence of an Online Game Ethics Committee that will screen games to ensure they're "healthy and beneficial" and address "social concerns," among other issues. To put it another way, the panel will clamp down on game addiction, sex, violence and short-sightedness. The committee has already reviewed 20 games, rejecting nine of them outright and demanding changes to the other 11. It didn't name the titles or say what it found objectionable. China might not lift the pause on game approvals until February.

Curriculum Learning - the idea of teaching by gradually exposing the learner to examples in a meaningful order, from easy to hard, has been investigated in the context of machine learning long ago. Although methods based on this concept have been empirically shown to improve performance of several learning algorithms, no theoretical analysis has been provided even for simple cases. To address this shortfall, we start by formulating an ideal definition of difficulty score - the loss of the optimal hypothesis at a given datapoint. We analyze the possible contribution of curriculum learning based on this score in two convex problems - linear regression, and binary classification by hinge loss minimization. We show that in both cases, the expected convergence rate decreases monotonically with the ideal difficulty score, in accordance with earlier empirical results. We also prove that when the ideal difficulty score is fixed, the convergence rate is monotonically increasing with respect to the loss of the current hypothesis at each point. We discuss how these results bring to term two apparently contradicting heuristics: curriculum learning on the one hand, and hard data mining on the other.

A robot needs contextual awareness, effective speech production and complementing non-verbal gestures for successful communication in society. In this paper, we present our end-to-end system that tries to enhance the effectiveness of non-verbal gestures. For achieving this, we identified prominently used gestures in performances by TED speakers and mapped them to their corresponding speech context and modulated speech based upon the attention of the listener. The proposed method utilized Convolutional Pose Machine [4] to detect the human gesture. Dominant gestures of TED speakers were used for learning the gesture-to-speech mapping. The speeches by them were used for training the model. We also evaluated the engagement of the robot with people by conducting a social survey. The effectiveness of the performance was monitored by the robot and it self-improvised its speech pattern on the basis of the attention level of the audience, which was calculated using visual feedback from the camera. The effectiveness of interaction as well as the decisions made during improvisation was further evaluated based on the head-pose detection and interaction survey.

Ruben A. Gevorgyan · Y enok B. Hakobyan Abstract Cluster analysis is one of the essential tasks in data mining and knowledge discovery. Each type of data poses unique challenges in achieving relatively efficient partitioning of the data into homogeneous groups. While the algorithms for numeric data are relatively well studied in the literature, there are still challenges to address in case of categorical data. The main issue is the unordered structure of categorical data, which makes the implementation of the standard concepts of clustering algorithms difficult. For instance, the assessment of distance between objects, the selection of representatives for categorical data is not as straightforward as for numeric data. Therefore, this paper presents a new framework for partitioning categorical data, which does not use the distance measure as a key concept. The Matching based clustering algorithm is designed based on the similarity matrix and a framework for updating the latter using the feature importance criteria. The experimental results show this algorithm can serve as an alternative to existing ones and can be an efficient knowledge discovery tool. Keywords categorical data · clustering algorithm · similarity matrix · feature importance Mathematics Subject Classification (2010) 62H30 · 62H17 · 62H20 Ruben A Gevorgyan Faculty of Economics and Management, Y erevan State University, Alex Manukyan 1, 0025 Y erevan, Republic of Armenia Email: rubengevorgyan@ysu.am Y enok B. Hakobyan Faculty of Economics and Management, Y erevan State University, Alex Manukyan 1, 0025 Y erevan, Republic of Armenia Email: e.hakobyan@ysu.am 1 Introduction Cluster analysis is one of the "super problems"s in data mining. Generally speaking, clustering is partitioning data points into intuitively similar groups (Saxena et al. 2017).

This survey paper presents a detailed overview of the applications for deep learning in ophthalmic diagnosis using retinal imaging techniques. The need of automated computer-aided deep learning models for medical diagnosis is discussed. Then a detailed review of the available retinal image datasets is provided. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of red lesions are reviewed.Recent deep learning models for classification of retinal disease including age-related macular degeneration, glaucoma, diabetic macular edema and diabetic retinopathy are also reported.

We propose a new formulation of Multiple-Instance Learning (MIL). In typical MIL settings, a unit of data is given as a set of instances called a bag and the goal is to find a good classifier of bags based on similarity from a single or finitely many "shapelets" (or patterns), where the similarity of the bag from a shapelet is the maximum similarity of instances in the bag. Classifiers based on a single shapelet are not sufficiently strong for certain applications. Additionally, previous work with multiple shapelets has heuristically chosen some of the instances as shapelets with no theoretical guarantee of its generalization ability. Our formulation provides a richer class of the final classifiers based on infinitely many shapelets. We provide an efficient algorithm for the new formulation, in addition to generalization bound. Our empirical study demonstrates that our approach is effective not only for MIL tasks but also for Shapelet Learning for time-series classification.

MIYAZAKI – A giant rock resembling Pac-Man is drawing climbers to a mountain in Miyazaki Prefecture, with visitors eager to snap photos to share on the internet. A 90-minute hike from the Shishigawa campground in Nobeoka, takes mountain climbers and tourists to "Pakkun Iwa" ("Pac-Man Rock"). The boulder, around 7 meters in diameter, sits on a trail leading to the top of 1,277-meter Mount Hoko. The large opening in the lower part of the rock prompts visitors to strike funny poses, as if they are being devoured like the dots and fruit the round yellow character eats in its namesake video game. The rock is in the Sobo-Katamuki-Okue mountain range, which was designated as a biosphere reserve by UNESCO last year.

Phan Hoang Tu Linh feels she has gotten the hang of working in a Japanese convenience store now, but she admits she found it tough at first. "We have three cash registers in our store but only two lines to wait in," says the 23-year-old Vietnamese national, who came to Japan to study at a Japanese-language school in Tokyo in July 2017 and started working part-time at a convenience store two months later. "One of the customers went before another customer who was supposed to be first, but I didn't see it because I was too busy," Phan says. "The customer got really angry and started shouting at me that they were supposed to be first. I felt really bad after that. My co-workers all told me that there was no need for the customer to get so angry and that it wasn't my fault. Sometimes people bring their stress from the workplace and take it out on us."

Luckily I've had the opportunity before for you and I to have a couple of conversations about DataSeers, and I was wondering if you could give our audience a brief overview of your organization and its role within the payments industry? As the name suggests, we are data seers, which means we see through data. If you look at the payments industry today, it is generating large volumes of data. It's creating a large variety of data because payments are very different when they come from different providers, different processors, and so on and so forth. And it's also coming very fast, so that volume, velocity, and variety creates a toxic mix for banks and other companies in the payments ecosystem to handle.