Reviewerly/exHarmony huggingface dataset:
Mixed Reality Technology Influences Motivation for Learning in Medical Students

<ns4:p>This article was migrated. The article was not marked as recommended. Introduction: Motivation plays an important role in the learning process and strongly influences learning outcomes. However, it remains unclear how the use of technologies like mixed reality (MR) affect student motivation for learning. Our study aims to assess differences in student motivation for learning using MR technology in comparison to large group, didactic learning. Methods: First-year medical students (n = 18) were given a survey to measure three components of motivation: attention, relevance, and confidence. The survey was given after classroom learning on cardiovascular anatomy and physiology was completed, and again after completing a learning activity on the same topic using MR technology. Results: The analysis showed overall student motivation to learn in the classroom and from the MR activity were similar (p &gt; 0.05). However, the specific motivating factors differed between the two instructional methods. Students were motivated to learn using the MR activity because it better held their attention (p &lt; 0.05). In contrast, classroom instruction motivated students to learn primarily because students viewed it as more worthwhile and useful (p &lt; 0.05). There was no difference in student confidence as a motivating factor to learn. Conclusions: These findings suggest MR instructional activities may be designed to motivate student learning by holding their attention because of the variety of interactive learning options it is capable of presenting. However, instructional activities using MR technology may not be perceived by students as being as worthwhile and useful as formal classroom instruction.</ns4:p>
TreeXP—An Instantiation of XPattern Framework

Most of the data generated from social media, Internet of Things, etc. are semi-structured or unstructured. XML is a leading semi-structured data commonly used over cross-platforms. XML clustering is an active research area. Because of the complexity of XML clustering, it remains a challenging area in data analytics, especially when Big Data is considered. In this paper, we focus on clustering of XML based on structure. A novel method for representing XML documents, Compressed Representation of XML Tree, is proposed following the concept of frequent pattern tree structure. From the proposed structure, clustering is carried out with a new algorithm, TreeXP, which follows the XPattern framework. The performances of the proposed representation and clustering algorithm are compared with a well-established PathXP algorithm and found to give the same performance, but require very less time.
Research on Music Teaching Reform and Innovation Development in the Era of Big Data

Information technology under the background of big data era provides a diversified platform for teachers' music teaching and students' music knowledge learning. This paper points out the forms and characteristics of music teaching and its existing problems under the background of big data era through analyzing the relationship between big data and music teaching and the reform of music teaching in the era of big data. At the same time, this paper probes into the specific ways of diversified design of music teaching reform in the era of big data, providing scientific theoretical basis for the development and evaluation research of music teaching model driven by innovative concepts in the era of big data.
A Comprehensive Survey on Federated Cloud Computing and its Future Research Directions

Shishira, S. R. Kandasamy, A.The cloud computing paradigm is popular due to its pay-as-you-go model. Due to its increasing demand for service, the user has a huge advantage in paying for the service currently needed. In a federated cloud environment, there is one or more number of cloud service providers who share their servers to service the user request. It improves minimizing cost, utilization of services and improves performance. Clients will get benefited as there is a Service Level Agreement between both. In the present paper, survey is provided on the benefits of the federated environment, its architecture, provision of resources and future research directions. Paper also gives the comparative study on the above aspects.
SCRAM: A Platform for Securely Measuring Cyber Risk

We develop a new cryptographic platform called SCRAM (Secure Cyber Risk Aggregation and Measurement) that allows multiple entities to compute aggregate cyber-risk measures without requiring any entity to disclose its own sensitive data on cyberattacks, penetrations, and losses.Using the SCRAM platform, we present results from two computations in a pilot study with six large private-sector companies: (1) benchmarks of the adoption rates of 171 critical security measures and (2) links between monetary losses from 49 security incidents and the specific sub-control failures implicated in each incident.These results provide insight into problematic cyberrisk-control areas that need additional scrutiny and/or investment, but in a completely anonymized and privacypreserving way.
Folk Physics for Crows?

New Caledonian crows are a key model species for testing whether the ability to reason about unobservable causal mechanisms is unique to humans.Here, I review the growing body of evidence for the absence of this ability in this species.
Bounds in query learning

We introduce new combinatorial quantities for concept classes, and prove lower and upper bounds for learning complexity in several models of query learning in terms of various combinatorial quantities. Our approach is flexible and powerful enough to enough to give new and very short proofs of the efficient learnability of several prominent examples (e.g. regular languages and regular $\omega$-languages), in some cases also producing new bounds on the number of queries. In the setting of equivalence plus membership queries, we give an algorithm which learns a class in polynomially many queries whenever any such algorithm exists. We also study equivalence query learning in a randomized model, producing new bounds on the expected number of queries required to learn an arbitrary concept. Many of the techniques and notions of dimension draw inspiration from or are related to notions from model theory, and these connections are explained. We also use techniques from query learning to mildly improve a result of Laskowski regarding compression schemes.
Image editing-based data augmentation for illumination-insensitive background subtraction

Purpose A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model. Design/methodology/approach In our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally. Findings Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place. Originality/value Such data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation
Vehicle Counting using Deep Learning Models: A Comparative Study

Recently, there has been a shift to deep learning architectures for better application in vehicle traffic control systems.One popular deep learning library used for detecting vehicle is TensorFlow.In TensorFlow, the pre-trained model is very efficient and can be transferred easily to solve other similar problems.However, due to inconsistency between the original dataset used in the pre-trained model and the target dataset for testing, this can lead to low-accuracy detection and hinder vehicle counting performance.One major obstacle in retraining deep learning architectures is that the network requires a large corpus training dataset to secure good results.Therefore, we propose to perform data annotation and transfer learning from an existing model to construct a new model for vehicle detection and counting in the real world urban traffic scenes.Then, the new model is compared with the experimental data to verify the validity of the new model.Besides, this paper reports some experimental results, comprising a set of innovative tests to identify the best detection algorithm and system performance.Furthermore, a simple vehicle tracking method is proposed to aid the vehicle counting process in challenging illumination and traffic conditions.The results showed a significant improvement of the proposed system with the average vehicle counting of 80.90%.
Relation learning in a neurocomputational architecture supports cross-domain transfer

People readily generalise prior knowledge to novel situations and stimuli. Advances in machine learning and artificial intelligence have begun to approximate and even surpass human performance in specific domains, but machine learning systems struggle to generalise information to untrained situations. We present and model that demonstrates human-like extrapolatory generalisation by learning and explicitly representing an open-ended set of relations characterising regularities within the domains it is exposed to. First, when trained to play one video game (e.g., Breakout). the model generalises to a new game (e.g., Pong) with different rules, dimensions, and characteristics in a single shot. Second, the model can learn representations from a different domain (e.g., 3D shape images) that support learning a video game and generalising to a new game in one shot. By exploiting well-established principles from cognitive psychology and neuroscience, the model learns structured representations without feedback, and without requiring knowledge of the relevant relations to be given a priori. We present additional simulations showing that the representations that the model learns support cross-domain generalisation. The model's ability to generalise between different games demonstrates the flexible generalisation afforded by a capacity to learn not only statistical relations, but also other relations that are useful for characterising the domain to be learned. In turn, this kind of flexible, relational generalisation is only possible because the model is capable of representing relations explicitly, a capacity that is notably absent in extant statistical machine learning algorithms.
THE IMPLEMENTATION OF ENGLISH LANGUAGE TUTORING "ENGLISH IS EASY" AND CHEAP " :Activity as a Method to Improved English Language Skill of Children and Adolescents at Makassar City

The community service activity (PKM) about language tutoring with principle English is Easy and Cheap which was done by the writer given a brief description of the process of language tutoring with used principle English is Easy and Cheap. In this community service activity (PKM), language became as a field of science that would be devoted or studied. Children and adolescents who came from poor parents' economy were the second material to be devoted or studied in this community service activity (PKM) about language tutoring with used principle English is Easy and Cheap. Some purposes or functions of this community service activity (PKM) that was in accordance with used the principles English is easy and cheap namely: the first purposed or function, to show the world, especially to wider community, especially children and adolescents that was not difficult, and not scary like a monster. The second purposed or function that was to help children and adolescents who come from poor parents' economy to learn language without collecting fees and help prepare themselves to carry out programs AFTA was a program that provided great opportunities for all Indonesian citizens to work and studied with mature readiness.Keywords: Language Tutoring, Children, Adolescents.
Local distinguishability of generalized Bell states with one ebit of entanglement

It is an interesting topic how much entanglement resources are necessary to distinguish locally indistinguishable orthogonal quantum states by local operations and classical communication (LOCC). Using only one ebit of entanglement some locally indistinguishable orthogonal product bases can be distinguished by LOCC [2019 Phys. Rev. A 99 012343]. However, it is still an open question whether one ebit of entanglement is sufficient for distinguishing a locally indistinguishable set of orthogonal maximally entangled states. We focus on the generalized Bell states (GBSs), and firstly find that any four GBSs in a 4 ⊗ 4 quantum system are always distinguished by one-way LOCC using one ebit of entanglement. Then we consider a general case, i.e., d ⊗ d quantum system (d is even), and show that the set of any GBSs can be distinguished by one-way LOCC using one ebit of entanglement if is nonempty, where is the admissible solutions set of and is the genuinely nonadmissible solutions set of . This criterion is very simple and convenient since only some congruence equations are calculated. Finally, we find a curious and counterintuitive case, i.e., in a 3 ⊗ 3 quantum system there exist four GBSs which cannot be distinguished by one-way LOCC using one ebit of entanglement, although the dimension of the subsystem is less than four. These results can help us better understand the role of entanglement as a resource in local state discrimination problems.
An Information Theoretic Image Steganalysis for LSB Steganography

Steganography hides the data within a media file in an imperceptible way. Steganalysis exposes steganography by using detection measures. Traditionally, Steganalysis revealed steganography by targeting perceptible and statistical properties which results in developing secure steganography schemes. In this work, we target LSB image steganography by using entropy and joint entropy metrics for steganalysis. First, the Embedded image is processed for feature extraction then analyzed by entropy and joint entropy with their corresponding original image. Second, SVM and Ensemble classifiers are trained according to the analysis results. The decision of classifiers discriminates cover image from stego image. This scheme is further applied on attacked stego image for checking detection reliability. Performance evaluation of proposed scheme is conducted over grayscale image datasets. We analyzed LSB embedded images by Comparing information gain from entropy and joint entropy metrics. Results conclude that entropy of the suspected image is more preserving than joint entropy. As before histogram attack, detection rate with entropy metric is 70% and 98% with joint entropy metric. However after an attack, entropy metric ends with 30% detection rate while joint entropy metric gives 93% detection rate. Therefore, joint entropy proves to be better steganalysis measure with 93% detection accuracy and less false alarms with varying hiding ratio.
Multi-robot control for a static polygon formation using Neighbor-Leader algorithm

One of the biggest open challenges to the robotic system is controlling robots formation. Its importance has been noted in a wide variety of applications, from truck platoons to robot-based standalone parcel delivery. In this paper, a new central approach to controlling a polygon-shaped formation called the Neighbor-Leader Algorithm is proposed for a group of neighbor robots randomly distributed in an unknown environment. Firstly, a localization procedure for multi-robots is used to estimate the real-time positions and orientations of a leader robot and its neighbor robots. This information is collected using an equipped RP LIDAR sensor on each robot then the collected information is sent to the leader one. Secondly, neighbor robots are rearranged into a regular distribution by moving them around the leader one in circular paths to produce a new distribution with equal angles between each two neighbor robots. In the last step, the direction of each neighbor robot is changed toward the leader robot. After that, all neighbor robots move towards the leader until they reach a virtual circle that passes through the required polygon vertices. The simulation results illustrate the integrity of this algorithm.
Genetic Algorithm Based Formula Generation for Curve Fitting in Time Series Forecasting Implemented as Mobile Distributed Computing

Times series forecasting has many important real life applications. Such forecasting is widely used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering and in any applied mathematics field where temporal measurements are done. In last few decades time series forecasting receives a lot of attention from the researchers in the machine learning domain. Many different forecasting models are developed with the usage of different prediction approaches. Artificial neural networks are a bright example of such forecasting technique. The main goal is learning of data dependency between past and future values of the time series when artificial neural network is used. If the weights of the artificial neural networks are taken as coefficients of a complex polynomial the forecasting can be presented as curve fitting problem. This research proposes forecasting approach a little bit different than the approach used in the artificial neural networks. Set of mathematical formulas are presented as expression trees in a genetic algorithm population. The goal in this genetic algorithm based optimization is searching of a mathematical expression which can provide the best curve fitting formula according time series values. Because of the genetic algorithms’ extremely high degree of parallelism possibilities calculations in this research are organized as distributed computing solutions on a mobile devices with Android operating system.
Risk identification method of power grid division based on power flow entropy

Abstract This research introduced a new way to identify the weak area of power grid by the maximum entropy increase. The stable quantification of grid is judged by weighted power flow entropy index. The index of weighted power flow entropy can quantitatively evaluate the ordered stability degree of system key facts. A large number of simulation calculations of IEEE39 node system show that when the entropy of weighted power flow increases to 40-50% or more, the power grid is easy to enter the self-organized critical state, that is, the risk of blackout increases significantly. This paper studies the influence of the load growth in different regions on the entropy index of the whole system, and selects the region with the largest entropy increase as the weak region. The feasibility and effectiveness of the proposed method are verified by taking an actual power grid as an example.
COVID-19 contact tracing: The Welsh experience

Contact tracing is one of the key public health response actions to control the outbreak of a novel virus. This paper describes the preparation process, activation and operational experience for contact tracing of individuals in response to confirmed COVID-19 cases in Wales.A descriptive approach has been adopted and lessons learned from our initial public health response to COVID-19 will be used to develop a new operational model for contact tracing in Wales.As part of preparations for the response in Wales, Public Health Wales formed a Contact Tracing Cell (CTC) ready to be mobilised in the event of a confirmed case.Trial activation of the CTC during the preparation period helped to resolve some issues before 'real' activation. A highly flexible approach was needed due to the constant changes to the guidance that required rapid understanding, updates to pathways and clear communication to contact tracers.Our experience and recommendations may benefit future efforts to control the spread of the virus in Wales and elsewhere, particularly in supporting COVID-19 outbreaks in enclosed settings such as care homes or in geographically localised areas. Learning from the initial public health response to COVID-19 will guide the delivery and implementation of a new contact tracing model as we move to a later stage of the pandemic when containment measures become feasible in localised outbreaks. This may include scaling-up the CTC to mobilise contact tracers to local teams and the potential use of digital technologies to support the next operational model of the CTC in Wales.
Model-Based Deep Reinforcement Learning for High-Dimensional Problems, a Survey.

Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics. Unfortunately, the sample complexity of most deep reinforcement learning methods is high, precluding their use in some important applications. Model-based reinforcement learning creates an explicit model of the environment dynamics to reduce the need for environment samples. Current deep learning methods use high-capacity networks to solve high-dimensional problems. Unfortunately, high-capacity models typically require many samples, negating the potential benefit of lower sample complexity in model-based methods. A challenge for deep model-based methods is therefore to achieve high predictive power while maintaining low sample complexity. In recent years, many model-based methods have been introduced to address this challenge. In this paper, we survey the contemporary model-based landscape. First we discuss definitions and relations to other fields. We propose a taxonomy based on three approaches: using explicit planning on given transitions, using explicit planning on learned transitions, and end-to-end learning of both planning and transitions. We use these approaches to organize a comprehensive overview of important recent developments such as latent models. We describe methods and benchmarks, and we suggest directions for future work for each of the approaches. Among promising research directions are curriculum learning, uncertainty modeling, and use of latent models for transfer learning.