Reviewerly/exHarmony huggingface dataset:
Qualitative Systematic Review in Applied Linguistics: A Synthesis

Qualitative Systematic Review (QSR) is the second-generation literature review that aims to synthesize primary qualitative studies. Its use is growing rather rapidly in medicine, nursing, education and other fields, but it remains underutilized in applied linguistics. In order to invite more qualitative systematic reviews in applied linguistics, this paper first provides the definition of QSR, outlines the key steps for its conduct, elaborates on the core procedure, i.e., synthesis methods, then illustrates with published examples of qualitative systematic reviews in applied linguistics, and finally explores the future directions.
Learning Heterogeneous Spatial–Temporal Context for Skeleton-Based Action Recognition

Graph convolution networks (GCNs) have been widely used and achieved fruitful progress in the skeleton-based action recognition task. In GCNs, node interaction modeling dominates the context aggregation and, therefore, is crucial for a graph-based convolution kernel to extract representative features. In this article, we introduce a closer look at a powerful graph convolution formulation to capture rich movement patterns from these skeleton-based graphs. Specifically, we propose a novel heterogeneous graph convolution (HetGCN) that can be considered as the middle ground between the extremes of (2 + 1)-D and 3-D graph convolution. The core observation of HetGCN is that multiple information flows are jointly intertwined in a 3-D convolution kernel, including spatial, temporal, and spatial-temporal cues. Since spatial and temporal information flows characterize different cues for action recognition, HetGCN first dynamically analyzes pairwise interactions between each node and its cross-space-time neighbors and then encourages heterogeneous context aggregation among them. Considering the HetGCN as a generic convolution formulation, we further develop it into two specific instantiations (i.e., intra-scale and inter-scale HetGCN) that significantly facilitate cross-space-time and cross-scale learning on skeleton graphs. By integrating these modules, we propose a strong human action recognition system that outperforms state-of-the-art methods with the accuracy of 93.1% on NTU-60 cross-subject (X-Sub) benchmark, 88.9% on NTU-120 X-Sub benchmark, and 38.4% on kinetics skeleton.
Cross-Modal Object Detection Via UAV

UAV-based object detection aims at locating and recognizing targets in aerial images, which is widely applied to traffic surveillance, disaster rescue and anomaly monitoring. However, due to expensive sensors and complicated architectures, it is unrealistic to deploy precise but heavy multi-modal object detectors into UAV nodes. To get over the dilemma, inspired by model compression and cross-modal signal processing techniques, this paper proposes a cross-modal knowledge distillation (CKD) enabled object detection paradigm, which achieves comparable detection performance with multi-modal techniques, yet requires less computational resource. On the one hand, in order to avoid transferring redundant feature knowledge, we design a Selective Feature Imitation (SFI) to selectively shorten the distance between cross-modal features according to their types. On the other hand, in order to transfer the most valuable prediction knowledge, we design an Adaptive Prediction Imitation (API). It evaluates the quality of prediction knowledge, and then adaptively adjusts the distillation intensity for cross-modal prediction. Extensive experiments on the DroneVehicle dataset have shown the performance improvement of the proposed scheme.
Congestion Control for Datacenter Networks: A Control-Theoretic Approach

In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoCC</i> , a robust congestion control approach for datacenter networks based on RDMA. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoCC</i> leverages switch queue size as an input to a PI controller, which computes the fair data rate of flows in the queue. The PI parameters are self-tuning to guarantee stability, rapid convergence, and fair and near-optimal throughput in a wide range of congestion scenarios. Our simulation and DPDK implementation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoCC</i> can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$7\times$</tex-math></inline-formula> reduction in PFC frames generated under high load levels, compared to DCQCN. At the same time, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoCC</i> can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.7 - 4.5\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.4 - 3.9\times$</tex-math></inline-formula> lower tail latency for long flows and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2.1-7\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3.5-8.2\times$</tex-math></inline-formula> lower tail latency for short flows, compared to DCQCN and HPCC, respectively. We also find that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoCC</i> does not require PFC. The functional components of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoCC</i> can be efficiently implemented in P4 and FPGA-based switch hardware.
A helmet detection scheme based on improved YOLOv3

In order to solve the problem that the target detection model is difficult to deploy in edge devices in smart grid, an improved YOLOv3 algorithm SG-YOLOv3 based on depthwise separable convolution is proposed. Use depthwise separable convolution to replace up-sampling in darknet-53 and standard convolution in residual units, respectively. The experimental results show that compared with YOLOv3, although SG-YOLOv3 has a 6.52% decrease in mAP. In addition, SG-YOLOv3 reduces the model size by 74%, while only 1/5 the amount of model parameters.
Application of Complex Network in Intra-pulse Feature Extraction of Radar Emitter Signals

Radar emitter signal identification using intra-pulse parameters plays an essential role in radar countermeasure systems. Current research has been conducted on mathematical transformation domains such as the time domain, the frequency domain, or the time-frequency domain. However, no matter how to improve the classifier model, there still exists a lower accuracy rate of deinterleaving or recognition under a complex modern radar environment. Thus, this paper proposes a feature extraction method using complex networks theory for recognition of radar emitter signals. Firstly, a dynamic segmentation model based on the BIC information criterion is introduced to construct the complex network from the signal frequency series. Secondly, the statistical dynamic characteristics which reflect topological indices of different complex networks are extracted separately. Finally, these features after principal component analysis are taken as input to the KNN model for clustering. The experimental results show that the proposed method of features extraction achieves a recognition rate of over 92% in eight typical radar emitter signals at Signal-Noise Ratios of 0dB, 10dB, 20dB.
Newspaper coverage on solidarity and personal responsibility in the COVID-19 pandemic: A content analysis from Germany and German-speaking Switzerland

Solidarity and personal responsibility have been repeatedly called upon during the COVID-19 pandemic. This study quantifies and contextualizes the use of these terms in newspaper coverage in Germany and German-speaking Switzerland based on n = 640 articles from six functionally equivalent newspapers. The term solidarity in the context of the COVID-19 pandemic was mentioned in 541/640 articles (84.5%) and was primarily used during phases with high death rates and comparatively stringent policies in place, supporting the idea that solidarity was used to explain restrictive measures to the population and motivate people to comply with these measures. German newspapers published more articles on solidarity than Swiss-German newspapers, consistent with more stringent COVID-19 policies in Germany. Personal responsibility was mentioned in 133/640 articles (20.8%), meaning that the term was less frequently discussed than solidarity. Articles covering personal responsibility included more negative evaluations during phases of high infection rates as compared to phases of low infection rates. Findings indicate that the two terms were, at least to some extent, used in newspaper reporting to contextualize and justify COVID-19 policy during phases of high infection rates. Moreover, the term solidarity was used in a high variety of different contexts and the inherent limits of solidarity were rarely mentioned. Policymakers and journalists need to take this into account for future crises to not jeopardize the positive effects of solidarity.
Voice banking to support individuals who use speech-generating devices: development and evaluation of Singaporean-accented English synthetic voices and a Singapore Colloquial English recording inventory

Voice banking involves recording an inventory of sentences produced via natural speech. The recordings are used to create a synthetic text-to-speech voice that can be installed on speech-generating devices. This study highlights a minimally researched, clinically relevant issue surrounding the development and evaluation of Singaporean-accented English synthetic voices that were created using readily available voice banking software and hardware. Processes used to create seven unique synthetic voices that produce Singaporean-accented English, and the development of a custom Singaporean Colloquial English (SCE) recording inventory, are reviewed. The perspectives of adults who spoke SCE and banked their voices for this project are summarized and were generally positive. Finally, 100 adults familiar with SCE participated in an experiment that evaluated the intelligibility and naturalness of the Singaporean-accented synthetic voices, as well as the effect of the SCE custom inventory on listener preferences. The addition of the custom SCE inventory did not affect intelligibility or naturalness of the synthetic speech, and listeners tended to prefer the voice created with the SCE inventory when the stimulus was an SCE passage. The procedures used in this project may be helpful for interventionists who wish to create synthetic voices with accents that are not commercially available.
PERSONAL FACTORS AS PREDICTORS OF ACADEMIC ADJUSTMENT IN FIRST-YEAR UNIVERSITY STUDENTS

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 707-714ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0235Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Understanding the opportunity and application of synthetic data in healthcare

The author have no conflicts of interest to disclose. No data are used in this commentary.
SPECIFICS OF DIGITAL LITERACY DEVELOPMENT DURING THE READING PROCESS IN DIGITAL LEARNING ENVIRONMENTS IN PRIMARY SCHOOL

SPECIFICS OF DIGITAL LITERACY DEVELOPMENT DURING THE READING PROCESS IN DIGITAL LEARNING ENVIRONMENTS IN PRIMARY SCHOOL
INTERACTIVITY AND ANIMATION-SIMULATION TOOLS IN THE DIGITAL PRESENTATION OF EDUCATIONAL MATERIAL

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 6975-6980ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1895Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
A FRAMEWORK TO ENHANCING PROBLEM-SOLVING SKILLS OF ENTREPRENEURSHIP EDUCATION OF TECHNICAL VOCATIONAL EDUCATION AND TRAINING COLLEGE LECTURERS

A FRAMEWORK TO ENHANCING PROBLEM-SOLVING SKILLS OF ENTREPRENEURSHIP EDUCATION OF TECHNICAL VOCATIONAL EDUCATION AND TRAINING COLLEGE LECTURERS
DEEP LEARNING FOR THE TEACHING OF L2 FRENCH CLITIC OBJECT PRONOUNS

Appears in: INTED2023 Proceedings Publication year: 2023Page: 6715 (abstract only)ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1806Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Evolution of Configuration Data in CGP Format Using Parallel GA on Embryonic Fabric

Digital circuit configuration data can be optimized in the design space using genetic algorithms (GA). Further development of Cartesian Genetic Programming (CGP) has been made to better express data on circuit configuration. In order to realize digital circuits with the potential for self-repair with the least amount of resources, the embryonic fabric architecture has recently arisen. In this study, PHsClone (Parallel Half-Sibling and Clone) GA is designed and implemented for evolving configuration data in CGP format for digital circuits built on embryonic fabric architecture. Configuration data or prospective circuit solutions can be generated more quickly using parallel processing on an FPGA. The Xilinx Virtex-7 is used to implement the planned PHsClone GA architecture. The PHsClone technique was validated using well-known benchmark circuits, including a safe mode detection circuit used in flight avionics, a 1-bit adder, a 2-bit comparator for creating 4-bit adder and 8-bit comparator circuits. According to the simulation results, four concurrent PHsClone GA executions (four parallel threads) achieve convergence for the safe mode detection circuit 37 times quicker than a single HsClone GA, 10 times faster for a 1-bit adder, and 3 times faster for a 2-bit comparator.
Visual-infrared image monitoring system for moving object detection, classification and tracking using deep learning technique

In this work, the issue of moving object detection, classification, and tracking in both visual-infrared images and video streams is considered. The proposed system utilizing deep learning-based You Look Only Once -version3 (YOLOv3) algorithm as multilayers of CNN model, OpenCV, and CAMEL dataset and collected dataset as video sequence of a visual-infrared dataset. The proposed object detector system pre-trained on the dataset for streaming videos and different sizes and types of images as visual_ thermal as RGB images of different object classes and carried out by Python codes. The first part of the proposed system is to make the object or person detection and surround it by rectangle box, the center of the detected person is then specified in the second part, finally, calculate the relative accuracy of detected person and added it to the rectangle box. The obtained results proved the success of the proposed system in achieving objects detection, classification, and tracking within visual-infrared surveillance scenarios and gave satisfactory results with high accuracy.
Facial recognition using multi edge detection and distance measure

Face recognition provides broad access to several public devices, so it is essential in the midst of today's technology boom. Human face recognizing has challenge in using uncomplicated and straightforward algorithms quickly, using memory specifications are not too high, otherwise the results are quality and accurate. Face recognition using combination edge detection and Canberra distance can be recommended for applications that require fast and precise access. The application of several edge detections singly has low performance, so it requires a combination technique to obtain better results. The proposed method combined several edge detections such are Robert, Prewitt, Sobel, and Canny to recognize a face image by identification and verification. As a feature extractor, the combination edge detection forms a more robust and more specific facial pattern on the contour lines. The results show that the combination accuracy outperforms other extractor features significantly. Canberra distance produces the best performance compared to Euclidean distance and Mahalanobis distance.
B-Detection: Runtime Reliability Anomaly Detection for MEC Services With Boosting LSTM Autoencoder

By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services' large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services' current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (LSTM) Autoencoder for detecting MEC services' runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named LSTM Autoencoder to characterize the MEC services' historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services' historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance LSTM Autoencoder with a weight-based reservoir sampling technique and an LSTM boosting algorithm. The reconstruction loss of the trained LSTM Autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services' runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.
Prediction of Bitcoin prices' trends with ensemble learning models

People used to invest in the stock and fund markets in the past and now have paid more attention to cryptocurrencies’ market, in which Bitcoin is the most famous and classic underlying asset. In this research, in order to improve the effects of stock prediction, difference of close price and moving average lines will be used as the labels of ensemble learning models. With the predicting results of each label, formulas are used to derive the close price in the next day. The last results show that models have less errors as the moving average prices are used as labels. Based on the models built in this research, people manage to predict the prices’ trends of Bitcoin more accurately and make investment decisions that will yield additional returns.
Space in Clouds and Clouds in Space – Dealing with Massive Amounts of EO Data

The paper deals with legal issues that arise in relation to storage, access and processing of EO data through cloud infrastructures. As for cloud infrastructures located on Earth, it addresses data security, privacy of data, as well as ownership rights. As for storage and compute capabilities in space, it explores whether and to what extent the location of the cloud in outer space has an impact on ownership rights in the cloud infrastructure and the data stored or generated therein, and whether and to what extent the location of the cloud in outer space has an impact on the legal implications under privacy law.
Online hate crimes

Hate groups and individuals espousing biased ideologies have proliferated online in the 21st century. Racist, misogynist, anti-LGBTQI+, antisemitic, anti-Muslim, anti-government, and other extremist agendas have found active supporters in various corners of the cyber-space, including websites and social media platforms. Hate activities online are manifested both as hate speech, which enables and encourages offline violent hate crimes, as well as disruptive operations against various communities and political organizations. The policy response to the growing threat of online hate crimes is fundamentally different in Europe and the U.K. in comparison to the U.S. While the E.U. and other European countries established various legal mechanisms which make online hate speech a crime - and numerous individuals have been convicted of hate crimes based on their online activities - in the U.S. the focus has been primarily on curbing disruptive attacks and hate speech has been addressed via non-legal measures. This chapter analyzes the ecosystem of online hate activities by utilizing multiple typologies of the operational and ideological characteristics of such activities. Additionally, it provides an overview of the literature and offers a summary of trends and countermeasures. We conclude by stressing the interrelated nature of online and offline hate, potential future trends in online hate crimes, and the challenges in curbing this growing threat.
Storytelling and Narrative Persuasion

Narrative transportation is the experience of becoming immersed in a story. Since Green and Brock (2000) introduced the idea that becoming transported into a story could lead to persuasion toward the story’s contents, a process known as narrative persuasion, numerous studies have explored the powerful effects stories can have on consumers. This chapter provides an overview of recent research related to stories, transportation, and persuasion, as well as a discussion of the pressing questions that remain.
Micrometer-Wide <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mrow><mml:mi>Nb</mml:mi><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math> Strips for Photon-Number-Resolving Detection

We report the development of photon-number-resolving superconducting single-photon detector with micron-scale $\mathrm{Nb}\mathrm{N}$ strips. The detector is designed as serial meander-shaped strips with resistors connected in parallel to each strip. We investigate the performance of the detectors comprising of 5--14 strips with the widths in the range $0.3$--$1\phantom{\rule{0.2em}{0ex}}\text{\ensuremath{\mu}}\mathrm{m}$ and covering an area as large as $70\ifmmode\times\else\texttimes\fi{}70\phantom{\rule{0.2em}{0ex}}\text{\ensuremath{\mu}}{\mathrm{m}}^{2}$ making it feasible for coupling to multimode fibers. We demonstrate resolution of up to six photons and analyze the sources of errors in photon-number resolution.
Single-Camera Three-Dimensional Digital Image Correlation with Enhanced Accuracy Based on Four-View Imaging

Owing to the advantages of cost-effectiveness, compactness, and the avoidance of complicated camera synchronization, single-camera three-dimensional (3D) digital image correlation (DIC) techniques have gained increasing attention for deformation measurement of materials and structures. In the traditional single-camera 3D-DIC system, the left and right view images can be recorded by a single camera using diffraction grating, a bi-prism, or a set of planar mirrors. To further improve the measurement accuracy of single-camera 3D-DIC, this paper introduces a single-camera four-view imaging technique by installing a pyramidal prism in front of the camera. The 3D reconstruction of the measured points before and after deformation is realized with eight governing equations induced by four views, and the strong geometric constraints of four views can help to improve the measurement accuracy. A static experiment, a rigid body translation experiment, and a four-point bending experiment show that the proposed single-camera 3D-DIC method can achieve higher measurement accuracy than the dual-view single-camera 3D-DIC techniques and that the single-camera 3D-DIC method has advantages in reducing both random error and systematic error.