Reviewerly/exHarmony huggingface dataset:
A new method for identifying a fault in T-connected lines based on multiscale S-transform energy entropy and an extreme learning machine

Due to the characteristics of T-connection transmission lines, a new method for T-connection transmission lines fault identification based on current reverse travelling wave multi-scale S-transformation energy entropy and limit learning machine is proposed. S-transform are implemented on the faulty reverse traveling waves measured by each traveling wave protection unit of the T-connection transmission line, the reverse travelling wave energy entropies under eight different frequencies are respectively calculated, and a T-connection transmission line fault characteristic vector sample set are thus formed. Establish an intelligent fault identification model of extreme learning machines, and use the sample set for training and testing to identify the specific faulty branch of the T-connection transmission line. The simulation results show that the proposed algorithm can accurately and quickly identify the branch where the fault is located on the T-connection transmission line under various operation conditions.
A Fast Multi-RGBD-Camera Calibration

Calibrating multiple depth cameras to a common coordinate space can be a laborious and time-consuming task, and often relies on expensive motion capture tracking systems. However, if accuracy constraints can be relaxed then a consumer-grade virtual reality tracking system is sufficient to support the calibration process. In this paper, we present a fast and convenient camera calibration method aimed at such systems. The calibration process can be carried out in about ten minutes and the resulting calibration is sufficiently accurate to achieve spatial coherence when targeting reconstructions on an 8 mm grid over a $\approx 2.2\text{m}^{3}$ capture volume.
Cybersecurity Threats and Solutions in the Current E-Healthcare Environment: A Situational Analysis

The need for cybersecurity measures in the field of healthcare is becoming more prescient given both the increased sophistication of cyber criminal activity and the increased prevalence of low-security mobile devices1. Also, hospitals have been slower to increase cybersecurity, but has become an increasing target due to the increased security of the financial industry2. The implementation of the Health Information Portability and Accountability Act (HIPAA) has made patient records more portable and accessible, and created more opportunities for breaches1. The Health Information Technology for Economic and Clinical Health Act (HITECH) promoting greater implementation of electronic systems for coordination and records. The medical field is also becoming increasing dependent on connectivity, and people are wary of the threats to automated and robotic safety procedures. The threat of high dependence on technology for medical practices presents risks in and of itself, such as cases where it disrupts communication between healthcare professionals, or fails to account for user failures that could be prevented with better feedback before implementation. Healthcare providers are in a difficult position, trying to balance the need for quick, responsive systems for users, and need for cybersecurity measures that can slow down the computer system. In the following pages, Lewin's field mapping approach will facilitate an analysis of the current largest cybersecurity threats and current research on the best methods of thwarting these vulnerabilities.
A Q-Learning based PSK Symbol Synchronizer

Timing recovery loops are the state-of-the-art systems in telecommunication receivers with the purpose to recover the correct sampling time. They retrieve the symbol phase maximizing the Modulation Error Rate (MER) performance. In this paper, we propose a PSK symbol synchronizer based on a Reinforcement Learning method called Q-Learning. We designed an intelligent system which autonomously becomes able to minimize the symbol amplitude variance by a trial-and-error iterative process. This results in a correct timing synchronization. The experimental results show that, below a E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> =22.5dB channel noise level, the MER figures are lower (2 to 2.5dB) yet comparable with a Mueller and Müller loop. Above such a noise threshold, we measured 1dB of MER improvement. Moreover, the system response to impulsive, step and ramp phase perturbation was characterized in terms of number of symbols to recover a stable locked state: 65 in the first, 420 in the second and 750 in the third case.
Business Processes and Chains of Kazakhstan How do Organisations Start Blockchain Projects and Evolve Throughout?

The blockchain is an emerging technology, which promises to revolutionise the way trust in the digital world is perceived. Nonetheless, it has been an issue that we know little of how and when to apply it in a modern business setting. In particular, how does one identify a business process under the greatest threat or the one, which would bring the most benefit being blockchained. Paper presents our first attempt to understand the reasoning behind the introduction of the blockchain in numerous organisations. We were especially interested in how decisions were made, what perception of the technology was at a time, and which changes had to be implemented to facilitate the introduction. The research method was qualitative - a set of semi-structured interviews as there is no academic source information that we could identify. Obtained results show that there is a worrying difference in technology perception between technical and managerial staff, and decision on technology adaption has clear hype-driven and top-down nature to it. Eventually, we conclude that there is a clear need for further study using quantitative tools and a wider selection of respondents to develop recommendations or come up with some systematic approach.
Performance Analysis of Mobile Cross-platform Development Approaches based on Typical UI Interactions

The market for mobile apps is projected to generate revenues of nearly $ 190 billion by 2020. Besides native development approaches, in which developers are required to maintain a unique code base for each mobile platform they want to support, mobile cross-platform development (MCPD) approaches can be used to develop mobile apps. MCPD approaches allow building and deploying mobile apps for several mobile platforms from one single code base. The goal of this paper is to analyze the performance of MCPD approaches based on UI interactions. For this, we developed three mobile apps, one native and two developed using MCPD approaches. Using an automated test, we measured CPU usage and memory consumption of these apps when executing one selected UI interaction, that is, swiping through a virtual scrollable list. The results indicate that the CPU usage of the two apps developed using MCPD approaches is about twice as high compared to the native app, the memory consumption is even substantially higher than in the native app. This papers confirms results of previous studies and extends the body of knowledge by testing UI interactions.
A robust policy bootstrapping algorithm for multi-objective reinforcement learning in non-stationary environments

Multi-objective Markov decision processes are a special kind of multi-objective optimization problem that involves sequential decision making while satisfying the Markov property of stochastic processes. Multi-objective reinforcement learning methods address this kind of problem by fusing the reinforcement learning paradigm with multi-objective optimization techniques. One major drawback of these methods is the lack of adaptability to non-stationary dynamics in the environment. This is because they adopt optimization procedures that assume stationarity in order to evolve a coverage set of policies that can solve the problem. This article introduces a developmental optimization approach that can evolve the policy coverage set while exploring the preference space over the defined objectives in an online manner. We propose a novel multi-objective reinforcement learning algorithm that can robustly evolve a convex coverage set of policies in an online manner in non-stationary environments. We compare the proposed algorithm with two state-of-the-art multi-objective reinforcement learning algorithms in stationary and non-stationary environments. Results showed that the proposed algorithm significantly outperforms the existing algorithms in non-stationary environments while achieving comparable results in stationary environments.
Collision Detection for a Mobile Robot using Logistic Regression

Collisions cannot be entirely avoided during normal operation of an autonomous mobile robot. Therefore, mobile robots need to detect collisions and react appropriately when they happen. We investigate whether logistic regression on acceleration data can be used for collision detection. We have collected training data from an acceleration sensor during normal driving behavior of a small mobile robot. Collisions were manually marked by a human operator. Accelerations occurring in a direction opposite to the current direction of motion are more likely to be actual collisions. Hence, we combine accelerometer data and motor commands in the logistic regression model. The trained model was able to detect 13 out of 14 collisions on a separate test set with no false positives.
Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing

Cloud computing consists of a cluster of computing resources that are delivered over a network, which is accomplished by utilizing virtualization technologies to consolidate and allocate resources suitable for various different software applications.Therefore, an efficient task scheduling in the cloud would be required to improve the performance of the cloud.In this paper, implementation of a model that seeks to improve load balancing algorithm for virtual machine load balancing was performed using simulations.A method by which average burst time was used as the time quantum for the round robin load balancing algorithm to achieve more effective time sharing.Results obtained from the simulations along with performance evaluation carried out shows response time and data center processing time achieved using the improved model is slightly minimal compared to the other algorithms.This shows more effective load balancing by achieving a better overall throughput.
Autonomous Robot Navigation with Signaling Based on Objects Detection Techniques and Deep Learning Networks

The autonomous navigation of robots is one of the main problems of robots due to its complexity and level of dynamics, as it depends on environmental conditions and the environment. Deep learning has become an interesting line of research in the area of robotics and computer vision. In this work, we have proposed the application of neural networks with deep learning applying the Single Shot Detector algorithm that provides robustness in speed and precision that mainly allows the detection of persons, objects through the use of the neural network trained for autonomous navigation of robots. The objective of this research is to achieve that a robot is able to make a route in an unknown environment and that by means of the parameterization of a signal at the moment of the detection of an object to have a visible mark in the map, allowing this way the acquisition of information that will help in the future explorations of the robot in the same environment.
Second-Order Response Transform Attention Network for Image Classification

Embedding second-order operations into deep convolutional neural networks (CNNs) has recently shown impressive performance for a number of vision tasks. Specifically, the two-branch second-order response transform (SoRT) network introduces the element-wise product transform into intermediate layers of CNNs, which facilitates the cross-branch response propagation and achieves promising classification accuracy. However, it fails to adaptively rescale responses of feature maps and largely changes the topology of the original backbone networks, leading to the limitation of generalizability. In order to overcome above problems, we propose a novel Second-order Response Transform Attention Network (SoRTA-Net) for classification tasks. The core of SoRTA-Net is the designed refined second-order response transform (RSoRT) module integrating reasonably the attention Squeeze-and-Excitation (SE) block and second-order response transform. Firstly, SoRTA-Net recalibrates adaptively feature responses by the SE block, and then the outputs are sequentially passed through the second-order response transform block, capturing approximately co-occurrence statistics and providing more nonlinearity. Finally, a shortcut branch is naturally combined with the output of the module to boost propagation. The proposed RSoRT module can be flexibly inserted into existing CNNs without any modification of network topology. Our SoRTA-Net extensively evaluated on three datasets (CIFAR-10, CIFAR-100, and SVHN). The experiments have shown that SoRTA-Net is superior to its baseline and achieves competitive performance.
Reinforcement Learning - A Technical Introduction

Reinforcement learning provides a cognitive science perspective to behavior and sequential decision making provided that RL-algorithms introduce a computational concept of agency to the learning problem. Hence it addresses an abstract class of problems that can be characterized as follows: An algorithm confronted with information from an unknown environment is supposed to find stepwise an optimal way to behave based only on some sparse, delayed or noisy feedback from some environment, that changes according to the algorithm's behavior. Hence reinforcement learning offers an abstraction to the problem of goal-directed learning from interaction. The paper offers an opintionated introduction in the algorithmic advantages and drawbacks of several algorithmic approaches such that one can understand recent developments and open problems in reinforcement learning.
How to Improve the Adaptation Phase of the CBR in the Medical Domain

This paper reviews the Case-based reasoning (CBR) approach and its usability in the medicine and presents a new concept on how to improve its adaptation phase. We use the CBR as a supporting method for decision support like diseases diagnostics or therapy identification. We investigated existing approaches, studies, and research works to solve one of the most critical problems in the CBR cycle - adaptation, which is often done manually by the experts in the relevant field. Based on the findings and our experiences with medical diagnostics through suitable data analytical methods, we proposed a new solution to solve this challenge. This approach is based on a comparison of the stored decision rules with the new one related to the current case. This comparison can result in three alternative states: (1) case base contains a similar case, and relevant rule can be applied. (2) The new case is very different from the stored ones, so the input from participated experts is needed, and a new rule will be stored. (3) The new case is partially similar satisfying adaptability conditions, in such a situation we adopt related decision rule to the new conditions under the supervision of the expert. We plan to experimentally test and verify this concept within available medical samples from our previous experiments.
ETNet: Error Transition Network for Arbitrary Style Transfer

Numerous valuable efforts have been devoted to achieving arbitrary style transfer since the seminal work of Gatys et al. However, existing state-of-the-art approaches often generate insufficiently stylized results under challenging cases. We believe a fundamental reason is that these approaches try to generate the stylized result in a single shot and hence fail to fully satisfy the constraints on semantic structures in the content images and style patterns in the style images. Inspired by the works on error-correction, instead, we propose a self-correcting model to predict what is wrong with the current stylization and refine it accordingly in an iterative manner. For each refinement, we transit the error features across both the spatial and scale domain and invert the processed features into a residual image, with a network we call Error Transition Network (ETNet). The proposed model improves over the state-of-the-art methods with better semantic structures and more adaptive style pattern details. Various qualitative and quantitative experiments show that the key concept of both progressive strategy and error-correction leads to better results. Code and models are available at https://github.com/zhijieW94/ETNet.
A Regularization-based Framework for Bilingual Grammar Induction

Grammar induction aims to discover syntactic structures from unannotated sentences.In this paper, we propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language.Unlike previous work on multilingual grammar induction, our approach does not rely on any external resources, such as parallel corpora, word alignments or linguistic phylogenetic trees.We propose three regularization methods that encourage similarity between model parameters, dependency edge scores, and parse trees respectively.We deploy our methods on a state-ofthe-art unsupervised discriminative parser and evaluate it on both transfer grammar induction and bilingual grammar induction.Empirical results on multiple languages show that our methods outperform strong baselines.
CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums

In contrast to many decades of research on oral code-switching, the study of written multilingual productions has only recently enjoyed a surge of interest.Many open questions remain regarding the sociolinguistic underpinnings of written code-switching, and progress has been limited by a lack of suitable resources.We introduce a novel, large, and diverse dataset of written code-switched productions, curated from topical threads of multiple bilingual communities on the Reddit discussion platform, and explore questions that were mainly addressed in the context of spoken language thus far.We investigate whether findings in oral code-switching concerning content and style, as well as speaker proficiency, are carried over into written code-switching in discussion forums.The released dataset can further facilitate a range of research and practical activities.
Interweaved Prediction for Affine Motion Compensation

With affine motion compensation (AMC) in the emerging next generation video coding standard Versatile Video Coding (VVC), a coding-block is divided into sub-blocks, and each sub-block is assigned with an individual motion vector derived by the affine model. The sub-block-based design for AMC faces a dilemma. With smaller sub-blocks, AMC can achieve a better coding performance but suffers a higher complexity burden. In this contribution, an interweaved prediction approach is proposed for AMC to address the dilemma. With the interweaved prediction, a coding block is divided into sub-blocks with two different dividing patterns. Then two auxiliary predictions are generated by AMC with the two dividing patterns respectively. The final prediction is calculated as a weighted-sum of the two auxiliary predictions. The interweaved prediction is only applied on the luma-component for affine-coded blocks with uni-prediction. Simulation results show 0.53% Bjøntegaard-Delta rate savings in average can be achieved compared to VTM-3.0 under random access configurations. The coding gain on sequences with affine motions is up to 3.3%.
THE IMPORTANCE OF EARLY READING LEARNING IN THE FACE OF 21st CENTURY EDUCATION

The increasing technological developments currently have an impact on the world of education, especially in early reading learning. Reading is one of the skills that must be possessed by students, therefore reading skills must be taught at the beginning of entering the educational level. In the 21st century reading skills are included in literacy. It shows that mastery of reading skills is important to be mastered in the face of the 21st century. This research aims to know the importance of reading the beginning to face the 21st century education. The research uses descriptive qualitative methods, with data collection techniques including interviews, observation and documentation. The data validity technique using source trials. Data analysis using data reduction procedures, data presentation, and drawing conclusions. The results of this study can be seen that the beginning of reading learning is emphasized to be mastered by students, considering that in 21st century student must have a skill. Mastered reading skills can help learners to think critically, solve a problem and master the various technologies available.
Look-up and Adapt: A One-shot Semantic Parser

Computing devices have recently become capable of interacting with their end users via natural language.However, they can only operate within a limited "supported" domain of discourse and fail drastically when faced with an out-of-domain utterance, mainly due to the limitations of their semantic parser.In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch.Our parser maintains a memory consisting of a representative subset of the seen utterances paired with their logical forms.Given an unseen utterance, our parser works by looking up a similar utterance from the memory and adapting its logical form until it fits the unseen utterance.Moreover, we present a data generation strategy for constructing utterance-logical form pairs from different domains.Our results show an improvement of up to 68.8% on one-shot parsing under two different evaluation settings compared to the baselines.
Multilingual Grammar Induction with Continuous Language Identification

The key to multilingual grammar induction is to couple grammar parameters of different languages together by exploiting the similarity between languages.Previous work relies on linguistic phylogenetic knowledge to specify similarity between languages.In this work, we propose a novel universal grammar induction approach that represents language identities with continuous vectors and employs a neural network to predict grammar parameters based on the representation.Without any prior linguistic phylogenetic knowledge, we automatically capture similarity between languages with the vector representations and softly tie the grammar parameters of different languages.In our experiments, we apply our approach to 15 languages across 8 language families and subfamilies in the Universal Dependency Treebank dataset, and we observe substantial performance gain on average over monolingual and multilingual baselines.