Reviewerly/exHarmony huggingface dataset:
Improving Generalization and Robustness with Noisy Collaboration in Knowledge Distillation.

Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at different levels in a knowledge distillation framework. We introduce Fickle Teacher which provides variable supervision signals to the student for the same input. We observe that the response variability from the teacher results in a significant generalization improvement in the student. We further propose Soft-Randomization as a novel technique for improving robustness to input variability in the student. This minimizes the dissimilarity between the student's distribution on noisy data with teacher's distribution on clean data. We show that soft-randomization, even with low noise intensity, improves the robustness significantly with minimal drop in generalization. Lastly, we propose a new technique, Messy-collaboration, which introduces target variability, whereby student and/or teacher are trained with randomly corrupted labels. We find that supervision from a corrupted teacher improves the adversarial robustness of student significantly while preserving its generalization and natural robustness. Our extensive empirical results verify the effectiveness of adding constructive noise in the knowledge distillation framework for improving the generalization and robustness of the model.
Republishing OpenStreetMap’s Roads as Linked Routable Tiles

Route planning providers manually integrate different geo-spatial datasets before offering a Web service to developers, thus creating a closed world view. In contrast, combining open datasets at runtime can provide more information for user-specific route planning needs. For example, an extra dataset of bike sharing availabilities may provide more relevant information to the occasional cyclist. A strategy for automating the adoption of open geo-spatial datasets is needed to allow an ecosystem of route planners able to answer more specific and complex queries. This raises new challenges such as (i) how open geo-spatial datasets should be published on the Web to raise interoperability, and (ii) how route planners can discover and integrate relevant data for a certain query on the fly. We republished OpenStreetMap’s road network as “Routable Tiles” to facilitate its integration into open route planners. To achieve this, we use a Linked Data strategy and follow an approach similar to vector tiles. In a demo, we show how client-side code can automatically discover tiles and perform a shortest path algorithm. We provide four contributions: (i) we launched an open geo-spatial dataset that is available for everyone to reuse at no cost, (ii) we published a Linked Data version of the OpenStreetMap ontology, (iii) we introduced a hypermedia specification for vector tiles that extends the Hydra ontology, and (iv) we released the mapping scripts, demo and routing scripts as open source software.
Distance-driven User Interface for Collaborative Exhibit Viewing in Augmented Reality Museum

The rapid advancement of augmented reality technology brings museum visitors enhanced interaction and immersion experience. However, most existing augmented reality museums adopt individual-based user interfaces that hinder joint interaction across multiple users. We present the distance-driven user interface (DUI) to enable collaborative exhibit viewing in augmented reality museum. We classify the users in four groups according to the user-exhibit distance and assign each group specific interaction privileges - put simply, the DUI elicits the users standing far from an exhibit to explore more of the desired exhibit by approaching closer to the exhibit. We describe the DUI architecture and preliminarily evaluate users' acceptance and effectiveness of the DUI and find that, the DUI is interpretable, improves users' awareness of collaboration, and increases user interests to the exhibit with considerably improved willingness of approaching the exhibit.
Building Dynamic Knowledge Graphs from Text-based Games

We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate elementary KG operations. Furthermore, we introduce a new dataset for KG extraction built upon text-based game transitions (over 300k data points). We conduct experiments and discuss the results.
AI, Connectivity and Cyber-Security in Avionics

This paper introduces the three key pillars for the avionics platform of the future, and presents the associated challenges. The term “Avionics Platform” addresses here includes (1) all the embedded systems required by an aircraft to fly (whatever the aircraft is, commercial, fighter, transport, drone or flying cabs) and (2) all the embedded critical and less critical functions required for flying (flight command control, propulsion management), piloting (fuel management, anti-icing, TCAS, TAWS, etc.), utilities (doors and slides, toilets, air conditioning, etc.), maintenance, and connectivity. AI, Connectivity and Cyber-Security in Avionics are the three pillar and main challenges we face. In this paper, we present the current status of our work.
Introduction of Group-Subscriptions for RESTful OPC UA clients in IIoT environments

In the automation domain, OPC UA can be considered one of the most important communication protocols for IIoT applications. According to a study of McKinsey the potential economic impact of the IoT within factories may reach up to 3,7 Trillion Dollar by 2025. To unlock this potential it is very important to bridge the interoperability gap. Since V1.04 OPC UA also supports session-less clients and therefore allows the development of RESTful clients which can be used to address the cross-domain interoperability issue. However, currently, RESTful clients cannot use Subscriptions, because the corresponding Service Sets are not supported for session-less clients. In this paper, we will present how Subscriptions can be introduced for session-less clients. Furthermore, we outline how Group-Subscriptions can be introduced for session-less as well as session-based clients. Finally, our evaluation shows that Group-Subscriptions significantly outperform standard Subscriptions if the Subscription can be shared by several clients.
An Overview on Technical Characteristics of Blockchain Platforms

The blockchain is an emerging technology which has a web of applications and potentials. A wide range of blockchain platforms has been developed to meet different technical and non-technical requirements and issues. Due to the diverse type of these platforms and the rapid evolution of blockchain technology, it would be necessary to have a big picture of existing blockchain platforms. Determining the characteristics of technology is an important factor to have such a total view and to standardize and expand technology. Despite the research has already been done on blockchain, due to the agile growth, rapid expansion and immaturity of this technology, determining its features is still challenging. In this paper, the results of studying and assessing the current state of blockchain technology from a technical point of view are presented. Furthermore, technical aspects of the blockchain are categorized as a two level taxonomy of characteristics and features.
Efficient Representation of Ducts and Cluttered Spaces for Realistic Motion Planning of Hyper-Redundant Robots through Confined Paths

Application of highly articulated hyper-redundant robots to manoeuvre in narrow and confined spaces is gaining popularity due to their obvious advantages. In this paper, we describe an optimization based approach for motion planning of hyper-redundant robots, which results in a natural motion of the links through ducts and confined spaces. It is shown that for a desired motion of the end-effector or the head of the hyper-redundant robot, the motion of the subsequent links attenuate and all the links avoid collision with the walls of the ducts and any other obstacles in the confined spaces. We discuss several ways to represent ducts in 2D and 3D space and also how the proposed algorithm is applied in these representations. It is shown that the complexity of the algorithm, with m constraints is at most O(m3.5) and in case where the ducts can be modeled with polyhedra, the complexity can be as low as O(m1.5). The proposed approach is also used to determine the largest link length in the hyper-redundant robot which can traverse the confined path. The concepts developed in this paper are demonstrated using simulations conducted on three practical scenarios: 1) hyper-redundant manipulators inspecting an industrial pipeline, 2) motion of an endoscopic robot through gastro-intestinal (GI) tract and 3) motion of hyper-redundant manipulators in search and rescue operations. Analysis on the computational complexity and the simulations shows that the method is feasible for practical implementation.
Phase-shifting coding method for absolute phase retrieval

Absolute phase retrieval has been widely studied, as it is not much influenced by the discontinuity and occlusion of objects in phase-measuring profilometry. In the practical measurement, however, fringe order determination is by no means easy when the grayscale of deformed patterns is degraded because of uneven reflectivity or the large curvature of measured objects. We introduce a phase-shifting coding (PSC) method to overcome that problem. In the PSC method, fringe order is encoded by the phase-shifting amounts of additional fringe patterns, and the decoding algorithm to retrieve absolute phase is designed on the basis of phase difference and neighborhood consistency. The special statistical filtering and self-correcting filtering methods are utilized to eliminate absolute phase errors. Experiment results demonstrate that the proposed PSC method is effective and has strong robustness in grayscale degradation regions.
Scalable Performance Modeling and Evaluation of MapReduce Applications

Big Data frameworks are becoming complex systems which have to cope with the increasing rate and diversity of data production in nowadays applications. This implies an increase in number of the variables and parameters to set in the framework for it to perform well. Therefor an accurate performance model is necessary to evaluate the execution time before actually executing the application. Two main and prominent Big Data frameworks are Hadoop and Spark, for which multiple performance models have been proposed in literature. Unfortunately, these models lack enough scalability to compete with the increasing size and complexity of the frameworks and of the underlying infrastructures used in production environments. In this paper we propose a scalable Lumped SRN model to predict execution time of multi-stage MapReduce and Spark applications, and validate the model against experiments on TPC-DS benchmark using the CINECA Italian super computing center. Results show that the proposed model enables analysis for multiple simultaneous jobs with multiple users and stages for each job in reasonable time and predicts execution time of an application with an average error about 14.5%.
An Improved Algorithm for Solving Scheduling Problems by Combining Generative Adversarial Network with Evolutionary Algorithms

With1 the continuous application of evolutionary algorithms in various combinatorial optimization problems, the traditional evolutionary algorithms are prone to premature convergence and fall into local optimization solutions as the complexity of the problems increases. To solve this problem, this paper proposes a hybrid algorithm combining the Generative adversarial nets (GAN) and Genetic Algorithm (GA). The algorithm is based on Genetic Algorithm and introducted the GAN sample as another sample to the generated model. The algorithm expected more abundant sample information through GAN mining, got the advantage of sample training GAN through the GA. It makes GAN learn from the edge of sample information, which can generate more advantages of samples. The generated sample is injected into the evolution of the next generation, increasing the diversity of samples and increasing the opportunity to find the optimal solution. In this paper, the hybrid algorithm is used to solve the Permutation Flow Shop Problem to verify the algorithm's solution ability. Experimental results show that the hybrid algorithm can avoid premature local optimal solution compared with the traditional evolutionary algorithm.
Real-Time Object Detection and Tracking Using Velocity Control

Real-time object detection and tracking is a vast, vibrant yet inconclusive area of computer vision. Automatic object detection and tracking are useful in surveillance, tracking systems used in security, mobile robots, medical therapy, driver assistance systems, and analysis of sports. Algorithms proposed in existing literature use color segmentation, edge tracking, shape detection for detection, and tracking of an object. The challenges such as tracking in dynamic environment and difficult tracking of multiple objects in multiple-camera environment and expensiveRani, Geeta computation restrict the implementation of these systems for solving real-world problems. This motivates us to develop a system that is efficient in real-time object detection and tracking. In this paper, authorsJindal, Anita develop the real-time object detection and tracking system using velocity control. Experimental results prove its efficacy in detection and tracking of simple as well as complex objects in both simple and complex backgrounds. The system is effective in detecting and tracking the co-occurrence of two objects. It clearly shows the impact of color dominance or shape dominance, self-shadow, and image of an object in a mirror.
Changes in Life Skills and Learning Attitudes of Student Athletes by Applying PEAK Program

Purpose: This study aimed to identify the changes in the life skill and learning attitudes of the student-athletes according to the application of the PEAK program developed for the purpose of raising their life skills. Methods: The participants were student-athletes(n=47) at Y university in Gyeonggi province. Data were collected using life skill and learning attitude measurement. The collected data were analyzed by repeated-measure variable analysis. Partial Eta Squared was calculated to explain the effect size. Results: The two groups showed interaction effects in goal setting, time management, interpersonal skills in the life skill, and satisfaction, achievement, confidence, and interest in learning attitude according to pre & post measurement. Specifically, there was no difference in the mean score between the two groups in the pre-test, but there was a difference in the post-test. Conclusion: The student-athletes who participated PEAK program have changed positively on life skills and learning attitude.
Representing the Filter Bubble: Towards a Model to Diversification in News

Filtering techniques like recommender systems are commonly employed to help people selecting items that best fit their conceptual needs. Although many benefits, recommender systems can put the user inside a filter-bubble given their high focus on similarity measures. This effect tends to limit user experiences, discovering new things, and so on. In the news domain, filter-bubbles are quite critical once they are means of changing people opinions. Therefore we propose a diversification approach to pop the bubble through a representation model based on points of view.
Distance Learning in Higher Education Institutions: Results of an Experiment

One of the most promising forms of implementing active teaching methods is distance learning, or e-learning. During the last decade e-learning was most often associated with the so-called mass open online courses, the MOOCs. In Russian and Kazakhstan universities, e-learning is often with the traditional type of in-class education and is known as blended learning. A certain lack of research on the use of distance learning in Russian universities determined the topic of this paper. The initial assignment was to launch learning in 6 groups of students of general engineering and at the Engineering and Technology Faculty of  Elabuga Institute of the Kazan Federal University, and  Philology disciplines at the Kazakh Philology Faculty of the State university named after Shakarim in Semey city and to study the attitude of students to distance learning during 2017-2018 academic year. Methods of research involve literature review, pedagogical observation, experiment, questioning and theoretical analysis. Results of the research show some interesting observations on the attitude of students and institute professors to the use of e-learning. Also, the research shows positive results of the use of learning methodology in studying some general technical disciplines during baccalaureate program
The effects of Gamification-based Artificial Intelligence Chatbot activities on elementary English learners’ speaking performance and affective domains.

This study aims to investigate the effects of Gamification-based artificial intelligence chatbot activities on elementary English learners’ speaking performance and affective domain. To accomplish the purpose, Gamification-based AI chatbot activities were designed and implemented in experimental lessons with the experimental group. The participants were 15 students of grade 6 who performed 3 speaking practice activities and 3 speaking usage activities during experimental lessons. Their performance was, then compared to that of the control group learners. The learners’ speaking performances were recorded and analyzed quantitatively and qualitatively. Their speaking pre/post-test score, and each score of speaking performance during the experimental class were collected and analyzed. Affective aspects were analyzed through survey to assess any differences between the groups or if any changes within the group occurred during the lesson. Semi-structured interview data was also presented to reveal the effects of Gamification-based AI chatbot activity in terms of English speaking, interest, attitude, and confidence. It was found that the Gamification-based AI chatbot activity is effective as English speaking practice and usage to all level learners. Specifically, AI technology seemed to help learners have linguistic awareness and Gamification strategy made learners engaged during tasks. The findings of this study suggest designing classroom-based AI assisted language learning model. Also, more empirical evidence is required through classroom-based action research.
Reg R-CNN: Lesion Detection and Grading Under Noisy Labels

For the task of concurrently detecting and categorizing objects, the medical imaging community commonly adopts methods developed on natural images. Current state-of-the-art object detectors are comprised of two stages: the first stage generates region proposals, the second stage subsequently categorizes them. Unlike in natural images, however, for anatomical structures of interest such as tumors, the appearance in the image (e.g., scale or intensity) links to a malignancy grade that lies on a continuous ordinal scale. While classification models discard this ordinal relation between grades by discretizing the continuous scale to an unordered bag of categories, regression models are trained with distance metrics, which preserve the relation. This advantage becomes all the more important in the setting of label confusions on ambiguous data sets, which is the usual case with medical images. To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model. We show the superiority of our approach on a public data set with 1026 patients and a series of toy experiments. Code will be available at github.com/MIC-DKFZ/RegRCNN .
Wind farm water area path planning algorithm based on A* and reinforcement learning

In recent years, the scale of offshore wind farms is increasing because of the high efficiency and pollution-free wind power resources. However, the introduction of many facilities in the corresponding wind farm sea area has led to the increasing difficulty of ship navigation. Therefore, it is very important to plan safe and efficient driving path according to the corresponding starting and ending points for the navigation of ships in the increasing wind farm area. In this paper, a path planning algorithm based on the hybrid method of A* algorithm and reinforcement learning is proposed, which can plan an effective collision avoidance path for the sea area of wind farm. Then the method is used to simulate the ship's path planning in a wind farm, which proves the feasibility of the method. Finally, it shows that the method has universal reference significance for ship navigation in the wind farm waters.
Variable Selection to Determine Majors of Student using K-Nearest Neighbor and Naïve Bayes Classifier Algorithm

Abstract Appropriate student’s major placement in high school can help students to better improve their academic achievement. There are many variables which must be considered to determine the student’s majors, such as: Gender, Interests, Intelligence Quotient (IQ); Four subjects in Junior High School (JHS), average junior high school grades, matriculation score of four subjects, and average rate of matriculation. The number of variables used in the selection, causes some weaknesses among them, such as the complexity of variable, the inefficiency of variable and the existence of some variables which is only as an addition without having a significant contribution. This study aims to reduce the number of these variables so it will become easier to analyze and to be applied. The Process of reduction was done by combining experiments with Predefined attributes. A total of ten combinations were attempted using K-Nearest Neighbor (K-NN) and Naïve Bayes Classifier (NBC) which then was measured by Confusion Matrix accuracy. The experimental result showed that the combination of variables which produce the best accuracy were the 9th and 10th experiment with variable matriculation, interest, and IQ, and an accuracy of 96.77% from K-NN also 98.38% from NBC. By combining both algorithms, 99.87% of maximum accuracy was obtained from those three variables. New information which can be extracted from this research is that there are only three important variables to determine major placement in Senior High School, Average Scores of Matriculation, Interests and IQ followed by four supporting variables such as the scores of Mathematic, Physics, English and Economics in Matriculation.
Securing the Data Flow for Blockchain Technology in a Production Environment

For several years, blockchain technology have been used and tested in various prototypes for production environments. The main focus of these approaches is the feasibility of different applications. It is usually simply assumed that data in a blockchain is stored immutably. However, the path from data generation to entry into a blockchain is usually neglected. Exactly this topic is discussed in this paper and a solution proposal for a secure data flow from the machine to the blockchain is presented. For this, the hardware and software architecture of the solution is shown, followed by measurements of the operational capability of the solution.
Some codes in symmetric and linear groups

For a finite group G, a positive integer λ, and subsets X,Y of G, write λG=XY if the products xy (x∈X,y∈Y), cover G precisely λ times. Such a subset Y is called a code with respect to X, and when λ=1 it is a perfect code in the Cayley graph Cay (G, X). In this paper we present various families of examples of such codes, with X closed under conjugation and Y a subgroup, in symmetric groups, and also in special linear groups SL2(q). We also propose conjectures about the existence of some much wider families.