Reviewerly/exHarmony huggingface dataset:
Library makerspaces in China: A comparison of public, academic, and school libraries

This exploratory study identified and compared the organization, services, challenges of and motivations for makerspaces in public, academic, and school libraries in China. Although there is a significant body of literature on makerspaces in libraries, this study is one of the first ones that provides a comparison of library makerspace organization and operation by library type. Data was collected using paper and online surveys from 158 librarians. Supporting learning was the most frequently identified motivation for establishing a makerspace by all three categories of librarians. While makerspaces in academic libraries were mostly operated by library staff, school libraries more evenly relied on teaching staff, volunteers, library staff members, and paid instructors to operate their makerspaces. Makerspaces in public and academic libraries were funded mostly from the libraries’ budget, while school libraries were funded more by other units on the campus and institutional or individual investments. The most frequently selected technologies were 3D printing and modeling technologies, and makerspaces in academic libraries were better equipped than makerspaces in the other two types of libraries. Group study rooms and learning commons centers were the most frequently occurring physical spaces in academic and public library makerspaces. School library makerspaces differentiated themselves by offering wooden crafts centers more often than other library makerspaces. While participants selecting budget limitation and inadequate equipment as barriers to implementing makerspaces was not surprising, public and academic librarians also often cited the lack of professional instructors. Based on the findings, several suggestions were offered to the practice of planning and operating a makerspace in libraries such as bringing together internal and external funding to support makerspaces, consolidating the required physical space of makerspaces and the existing space arrangement of libraries, and developing additional training programs to address the problem of a lack of professional instructors.
Artificial Cell Swarm Optimization

Traditional meta-heuristic optimization algorithms, such as the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and bat algorithm (BA) played a vital role to provide impressive near to the optimum solutions for linear/nonlinear complex problems in numerous applications. Nevertheless, in some case, such algorithms may suffer from becoming trapped in local optima with long computational time for convergence. Thus, in order to enhance a broader view over the optimization domain, still further refined studies are carried out to develop these algorithms and to explore new ones based on the inspiration from nature. Thus, a novel meta-heuristic optimization algorithm has been proposed in the present work by employing the concept of artificial cells, which are inspired by biological living cells. An efficient application of artificial cell division (ACD) algorithm has been employed to traverse the search space while decreasing the search time. The inherent properties of ACD algorithm prevent it from premature convergence to local optima. The current work designed a novel artificial cell swarm optimization (ACSO) algorithm. The results compared the proposed algorithm performance to GA, PSO, and the bat algorithm by using seven known benchmark functions. The results established that the performance of proposed ACSO algorithm in terms of the number of iterations required to reach the expected accuracy outperformed the GA, PSO, and the Bat Algorithms. The ACSO achieved the fastest convergence with the benchmark functions with accuracies range 100 or 99% compared to the other optimization algorithms in the current study.
SDRNet: An end-to-end shadow detection and removal network

Abstract Image shadow detection and removal can effectively recover image information lost in the image due to the existence of shadows, which helps improve the accuracy of object detection, segmentation and tracking. Thus, aiming at the problem of the scale of the shadow in the image, and the inconsistency of the shadowed area with the original non-shadowed area after the shadow is removed, the multi-scale and global feature (MSGF) is used in the proposed method, combined with the non-local network and dense dilated convolution pyramid pooling network. Besides, aiming at the problem of inaccurate detection of weak shadows and complicated shape shadows in existing methods, the direction feature (DF) module is adopted to enhance the features of the shadow areas, thereby improving shadow segmentation accuracy. Based on the above two methods, an end-to-end shadow detection and removal network SDRNet is proposed. SDRNet completes the task of sharing two feature heights in a unified network without adding additional calculations. Experimental results on the two public datasets ISDT and SBU demonstrate that the proposed method achieves more than 10% improvement in the BER index for shadow detection and the RMSE index for shadow removal, which proves that the proposed SDRNet based on the MSGF module and DF module can achieve the best results compared with other existing methods.
The Stemming Application on Affixed Javanese Words by using Nazief and Adriani Algorithm

Abstract The Javanese language has various forms of affixed words. It makes people from other regions find it difficult to understand the Javanese language. One of the ways to understand the Javanese language is by studying and understanding Javanese vocabulary in the Javanese language dictionary or learning directly from ones who master the Javanese language. Both offline and online dictionaries have not been able to translate affixed words. This research used Nazief and Adriani Algorithm to find out the stems/root words in the Javanese language. The objective of this research is to apply the Nazief and Adriani Algorithm in stemming affixed words in the Javanese language. The subject discussed in this research is the application of stemming affixed Javanese words by using Nazief and Adriani algorithm. The stages started with identifying the affixes in the Javanese language. Nazief and Adriani algorithm has five steps those are detecting words that have affixes, cutting words that contain inflection suffixes, derivation suffixes and derivation prefixes, and last recording. The result of this research is a stemming application for affixed Javanese words that applies Nazief and Adriani algorithm method. Based on the accuracy test, it appears that 93.6% of words are accurate from the total of 219 words tested.
Tile-based Proactive Virtual Reality Streaming via Online Hierarchial Learning

Wireless virtual reality (VR) can provide unconstrained immersive experience, which however is resource-demanding to satisfy the unique user experience, i.e., motion-to-photon latency and degree of overlap (DoO). To improve the quality of experience with constrained resource, in this paper we propose tile-based proactive VR streaming, which selects, transmits and computes the tiles in a segment most likely requested in future before playback. To select the tiles to be delivered under limited communication and computing resources, it is necessary to learn the user behaviour in requesting tiles in an online manner. We formulate a joint communication and computing duration allocation and tile selection problem to maximize the average DoO for a VR video under the communication and computing resource constraints. To reduce computational complexity and implicitly predict the tile request information, we decouple the original problem into two subproblems, which are respectively solved via convex optimization and hierarchial online learning. Simulation results on a real dataset demonstrate evident gain of the proposed method over the first-predict-then-optimize scheme.
Unsupervised Anomaly Detection and Root Cause Analysis in Mobile Networks

Telecommunication networks are designed for high reliability; however, when they do fail, it is difficult to detect and diagnose problems in a timely manner as the networks are characteristically complex and the problems may be subtle rather than catastrophic. Within the overall objective of network anomaly detection, the focus of this paper is on three aspects: Dimensionality reduction through feature selection to determine the minimum set of features that provide the most information regarding the network state; the application of the multivariate unsupervised learning technique, Principal Component Analysis (PCA), to detect anomalies with low detection latency; and root cause analyses using finite state machines. For anomaly detection, we apply PCA on the normal data, and find the subspace where the variation of the normal data is small. By characterizing the variation of the normal data in the subspace, we derive a boundary of the normal data, and develop an anomaly detection model based on it. Once the anomalies are detected, we compare the message patterns of the anomaly data to those of the normal data to determine where the problems are occurring. Moreover, we examine the error codes in the anomaly data to better understand the underlying problems. The algorithms are developed and tested with Per Call Measurement Data (PCMD) records from a 4G-LTE network. The impact of our work is the proactive detection and root cause analysis of network anomalies in mobile networks, thereby improving network reliability and availability.
Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

Accurate medical images analysis plays a vital role for several clinical applications. Nevertheless, the immense and complex data volume to be processed make difficult the design of effective algorithms. The first aim of this paper is to examine this area of research and to provide some relevant reference sources related to the context of medical image analysis. Then, an effective hybrid solution to further improve the expected results is proposed here. It allows to consider the benefits of the cooperation of different complementary approaches such as statistical-based, variational-based and atlas-based techniques and to reduce their drawbacks. In particular, a pipeline framework that involves different steps such as a preprocessing step, a classification step and a refinement step with variational-based method is developed to identify accurately pathological regions in biomedical images. The preprocessing step has the role to remove noise and improve the quality of the images. Then the classification is based on both symmetry axis detection step and non linear learning with SVM algorithm. Finally, a level set-based model is performed to refine the boundary detection of the region of interest. In this work we will show that an accurate initialization step could enhance final performances. Some obtained results are exposed which are related to the challenging application of brain tumor segmentation.
Evaluating the Use of Policy Gradient Optimization Approach for Automatic Cloud Resource Provisioning

Reinforcement learning is a very active field of research with many practical applications. Success in many cases is driven by combining it with Deep Learning. In this paper we present results of our attempt to use modern advancements in this area for automated management of resources used to host distributed software. We describe the use of three policy training algorithms from the policy gradient optimization family, to create a policy used to control the behavior of an autonomous management agent. The agent is interacting with a simulated cloud computing environment, which is processing a stream of computing jobs. We discuss and compare the policy performance aspects and the feasibility to use them in real-world scenarios.
Improving the performance of Hadoop MapReduce Applications via Optimization of concurrent containers per Node

Apache Hadoop is a distributed platform for storing, processing and analyzing of big data on commodity machines. Hadoop has tunable parameters and they affect the performance of MapReduce applications significantly. In order to improve the performance, tuning the Hadoop configuration parameters is an effective approach. Performance optimization is usually based on memory utilization, disk I/O rate, CPU utilization and network traffic. In this paper, the effect of MapReduce performance is experimented and analyzed by varying the number of concurrent containers (cc) per machine on yarn-based pseudo-distributed mode for MapReduce applications. In this experiment, we also measure the impact of performance by using different suitable Hadoop Distributed File System (HDFS) block size. From our experiment ,we found that tuning cc per node improve performance compared to default parameter setting, we also observed the further performance improvement via optimizing cc along with different HDFS block size.
Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study

The application of machine learning to Automatic Modulation Classification (AMC) has typically used transfer learning from architectures found in the image classification domain. This work examines deviations from the image classification architectures by drawing from traditional expert feature systems within the AMC domain. Two types of `expert architectures' are contrasted against the traditional image processing architectures; the first utilizes a more traditional one-versus-all binary classification with decision fusion approach, while the second inherits a hierarchical decision tree structure that leverages expert knowledge of the classes. When compared with a typical image processing architecture there are marginal classifier performance gains associated with the structures taken from expert AMC systems; however, the expert architectures allow for greater intuition, adaptability, and future-proofing in general.
Improving Students Skills to Solve Elementary Equations in K-12 Programs Using an Intelligent Tutoring System

This full paper of research to practice category describes an experiment using an Intelligent Tutoring System to support teaching and learning that evolve solutions of first-degree equations. The difficulties encountered in solving algebra problems can make students consider math and related science a difficult task. We propose a Tutor to promote the learning of this class of problems. In this article, we describe the functioning and architecture of the tutor composed by the Interface, Student Model, Tutorial Strategies and Domain Model. Also, a Virtual Assistant was included to motivate students in the learning process. The tutoring strategies that comprise the tutor are projected from models found in the literature. To approximate the interactions of the tutor with the actions of the teachers, Lev Vygotsky's Concept of Zone Proximal Development was used. The methodology used in this project was the case study applied in a real-life context, obtaining a combination of quantitative and qualitative evidence about technical and pedagogical usability that show the degree of satisfaction of the students regarding the proposed approach.
Hyperpartisan News and Articles Detection Using BERT and ELMo

Fake news and articles are misleading the readers.This leads to the increasing studies of fake news article detection over the decades.Hyperpartisan news is news riddled with twisted and untruth and extremely one-sided.This news can spread more successfully than others.Besides that, hyperpartisan news can mimic the form of regular news articles.This study aims to identify and classify the hyperpartisan news with BERT and ELMo.Two distinct models, BERT and ELMo, were created to classify hyperpartisan news from two datasets, namely by-article and by-publisher.Few other models with different settings and training designed to test and optimise the performance of both models.The results of the optimised BERT and ELMo models can achieve 68.4% and 60.8%, respectively.
General Quantum Entanglement Purification Protocol using a Controlled‐Phase‐Flip Gate

Abstract Entanglement purification is an important method to guarantee the fidelity of long‐distance quantum communication. Here, a general entanglement purification protocol (EPP) for mixed state with bit‐flip error and phase‐flip error is proposed, resorting to unilateral operations and a controlled‐phase‐flip (CPF) gate. The CPF gate is working with a high fidelity under balance condition of quantum dot embedded in a double‐sided optical cavity. This general EPP scheme can purify the mixed state with both bit‐flip error and phase‐flip error to a high fidelity entangled state relatively fast in some regimes, owing to the unilateral operations and high‐fidelity CPF gate, which can largely decrease the resource consumption. This general EPP provides a convenient way for increasing the entanglement of different quantum systems, which has great potential for guaranteeing the fidelity of long‐distance quantum communication in the future.
A Weakly Supervised Strategy for Learning Object Detection on a Humanoid Robot

Research in Computer Vision and Deep Learning has recently proposed numerous effective techniques for detecting objects in an image. In general, these employ deep Convolutional Neural Networks trained end-to-end on large datasets annotated with object labels and 2D bounding boxes. These methods provide remarkable performance, but are particularly expensive in terms of training data and supervision. Hence, modern object detection algorithms are difficult to be deployed in robotic applications that require on-line learning. In this paper, we propose a weakly supervised strategy for training an object detector in this scenario. The main idea is to let the robot iteratively grow a training set by combining autonomously annotated examples, with others that are requested for human supervision. We evaluate our method on two experiments with data acquired from the iCub and R1 humanoid platforms, showing that it significantly reduces the number of human annotations required, without compromising performance. We also show the effectiveness of this approach when adapting the detector to a new setting.
Forced Spatial Attention for Driver Foot Activity Classification

This paper provides a simple solution for reliably solving image classification tasks tied to spatial locations of salient objects in the scene. Unlike conventional image classification approaches that are designed to be invariant to translations of objects in the scene, we focus on tasks where the output classes vary with respect to where an object of interest is situated within an image. To handle this variant of the image classification task, we propose augmenting the standard cross-entropy (classification) loss with a domain dependent Forced Spatial Attention (FSA) loss, which in essence compels the network to attend to specific regions in the image associated with the desired output class. To demonstrate the utility of this loss function, we consider the task of driver foot activity classification - where each activity is strongly correlated with where the driver's foot is in the scene. Training with our proposed loss function results in significantly improved accuracies, better generalization, and robustness against noise, while obviating the need for very large datasets.
Language-Based Mechanisms for Privacy-by-Design

The privacy by design principle has been applied in system engineering. In this paper, we follow this principle, by integrating necessary safeguards into the program system design. These safeguards are then used in the processing of personal information. In particular, we use a formal language-based approach with static analysis to enforce privacy requirements. To make a general solution, we consider a high-level modeling language for distributed service-oriented systems, building on the paradigm of active objects. The language is then extended to support specification of policies on program constructs and policy enforcement. For this we develop (i) language constructs to formally specify privacy restrictions, thereby obtaining a policy definition language, (ii) a formal notion of policy compliance, and (iii) a type and effect system for enforcing and analyzing a program's compliance with the stated polices.
A Robust End-To-End Information Extraction System for Vietnamese Identity Cards

These days, automatic information extraction from identity documents is essential for many applications which require users authentication. In previous researches, this task was carried out by using image processing. However, the document identity recognition from images captured by smartphones encounters problems such as various backgrounds, perspective, blurring. Moreover, some cards may have dated backgrounds and diverse fonts printed manually by old typewriters. To solve these problems, this paper proposes an end-to-end system for extracting information from the Vietnamese identity cards captured by camera devices. Firstly, the ID card is aligned with the standard aspect ratio and orientation based on corner detection and national emblem detection. The proposed solution can locate and crop precisely the foreground target area of the oriented ID card in various contexts. In the text localization stage, a deep convolutional neural network model is used to bounding the required texts which are then classified by their positions. Finally, the text recognition is implemented based on the Attention OCR architecture. In experiments, this proposed method reduces processing time in comparison with the state of the art and the average accuracy achieved is higher 91%.
Build systems à la carte: Theory and practice

Abstract Build systems are awesome, terrifying – and unloved. They are used by every developer around the world, but are rarely the object of study. In this paper, we offer a systematic, and executable, framework for developing and comparing build systems, viewing them as related points in a landscape rather than as isolated phenomena. By teasing apart existing build systems, we can recombine their components, allowing us to prototype new build systems with desired properties.
Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation. The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.