Reviewerly/exHarmony huggingface dataset:
Effect of Systematic Nursing Intervention on Psychological Condition, Blood Pressure Change, Self-management and Quality of Life of Patients with Obstructive Sleep Apnea Hypopnea Syndrome

Objective: To explore the influence of systematic nursing intervention on patients with bstructive sleep apnea hypopnea syndrome (OSAHS). Methods: From March 2017 to July 2018, 102 patients with OSAHS in our hospital were collected. Among them, 50 cases in Group A (GA) received routine nursing intervention. Fifty-two patients in Group B (GB) received systematic nursing intervention on the basis of GA. Before and after nursing, the psychological changes, blood pressure changes and self-management were observed and compared between the two groups. After nursing, the sleep quality, quality of life and nursing satisfaction were observed in the two groups. Results: After nursing, the anxiety of patients in GB was better than that in GA (P&lt;0.05); The depression of patients in GB was better than that in GA (P&lt;0.05); The blood pressure of patients in GB was better than that in GA (P&lt;0.05); The self-management of patients in GB was better than that in GA (P&lt;0.05); The quality of life and sleep quality of patients in GB were better than those in GA (P&lt;0.05); The nursing satisfaction of patients in GB was higher than that in GA (P&lt;0.05). Conclusion: Systematic nursing intervention can improve the psychological condition, blood pressure changes and quality of life of patients with OSAHS.
Hive and MySQL - Query performance comparison for a multidimensional model

This paper presents a performance comparison of queries in multidimensional database stored in two different platforms, MySQL, which is one of the most popular database management systems for relational data, and Hive, which is a data warehouse and Big Data platform. We analyze the impact of the increase in the data volume on the query processing time on both platforms. The Brazilian Military Service data set was used with four data warehouse queries. Both Hive and MySQL environments were created in Google Cloud, using MySQL version 5.7 and two clusters for Hive, the first one with two workers, and the second one with four workers. The results showed that, as the volume of data grew, Hive's four workers cluster combined with Parquet file format had an equivalent or better performance than MySQL for the queries executed.
Probabilistic Inference and Dynamic Programming: A Unified Approach to Multi-Agent Autonomous Coordination in Complex and Uncertain Environments

We present a unified approach to multi-agent autonomous coordination in complex and uncertain environments, using path planning as a problem context. We start by posing the problem on a probabilistic factor graph, showing how various path planning algorithms can be translated into specific message composition rules. This unified approach provides a very general framework that, in addition to including standard algorithms (such as sum-product, max-product, dynamic programming and mixed Reward/Entropy criteria-based algorithms), expands the design options for smoother or sharper distributions (resulting in a generalized sum/max-product algorithm, a smooth dynamic programming algorithm and a modified versions of the reward/entropy recursions). The main purpose of this contribution is to extend this framework to a multi-agent system, which by its nature defines a totally different context. Indeed, when there are interdependencies among the key elements of a hybrid team (such as goals, changing mission environment, assets and threats/obstacles/constraints), interactive optimization algorithms should provide the tools for producing intelligent courses of action that are congruent with and overcome bounded rationality and cognitive biases inherent in human decision-making. Our work, using path planning as a domain of application, seeks to make progress towards this aim by providing a scientifically rigorous algorithmic framework for proactive agent autonomy.
C3-STISR: Scene Text Image Super-resolution with Triple Clues

Scene text image super-resolution (STISR) has been regarded as an important pre-processing task for text recognition from low-resolution scene text images. Most recent approaches use the recognizer's feedback as clues to guide super-resolution. However, directly using recognition clue has two problems: 1) Compatibility. It is in the form of probability distribution, has an obvious modal gap with STISR - a pixel-level task; 2) Inaccuracy. it usually contains wrong information, thus will mislead the main task and degrade super-resolution performance. In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution. Here, visual clue is from the images of texts predicted by the recognizer, which is informative and more compatible with the STISR task; while linguistical clue is generated by a pre-trained character-level language model, which is able to correct the predicted texts. We design effective extraction and fusion mechanisms for the triple cross-modal clues to generate a comprehensive and unified guidance for super-resolution. Extensive experiments on TextZoom show that C3-STISR outperforms the SOTA methods in fidelity and recognition performance. Code is available in https://github.com/zhaominyiz/C3-STISR.
Cognitive segmentation and fluid reasoning in childhood

The ability to solve novel complex problems predicts success in a wide range of areas. Recent research suggests that the ability to cognitively segment complex problems into smaller parts constrains nonverbal reasoning in adults. This study aimed to test whether cognitively segmenting problems improves nonverbal reasoning performance for children as it does for adults. A total of 115 children aged 6–10 years completed two versions of a modified traditional matrix reasoning task in which demands on working memory, integration, and processing speed were minimised, such that the only significant requirement was to break each problem into its constituent parts. In one version of the task, participants were presented with a traditional 2×2 Matrix and asked to draw the missing matrix item into a response box below. In a second version, the problem was broken down into its component features across three separate cells, reducing the need for participants to segment the problem. As with adults, performance was better in the condition in which the problems were separated into component parts. Children with lower fluid intelligence did not benefit more in the separated condition than children with higher fluid intelligence, and there was no evidence that segmenting problems was more beneficial for younger than older children. This study demonstrates that cognitive segmentation is a critical component of complex problem-solving for children, as it is for adults. By forcing children to focus their attention on separate parts of a complex visual problem, their performance can be dramatically improved.
What is New in Industry? [Industrial and Governmental Activities]

Welcome to a new column of the IEEE Computational Intelligence Magazine devoted to Industrial Activities. In a previous communication <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> , I described the vision for the newly formed IEEE CIS Industrial and Governmental Activities Committee (IGAC). With IGAC, we strive to provide services, products, and offerings of interest to Industry, ranging from webinars to tutorials, conferences, publications, standards, and other aspects of CIS activities, as illustrated in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> , adapted from reference <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref> .
Surface-Free Multi-Stroke Trajectory Reconstruction and Word Recognition Using an IMU-Enhanced Digital Pen

Efficient handwriting trajectory reconstruction (TR) requires specific writing surfaces for detecting movements of digital pens. Although several motion-based solutions have been developed to remove the necessity of writing surfaces, most of them are based on classical sensor fusion methods limited, by sensor error accumulation over time, to tracing only single strokes. In this work, we present an approach to map the movements of an IMU-enhanced digital pen to relative displacement data. Training data is collected by means of a tablet. We propose several pre-processing and data-preparation methods to synchronize data between the pen and the tablet, which are of different sampling rates, and train a convolutional neural network (CNN) to reconstruct multiple strokes without the need of writing segmentation or post-processing correction of the predicted trajectory. The proposed system learns the relative displacement of the pen tip over time from the recorded raw sensor data, achieving a normalized error rate of 0.176 relative to unit-scaled tablet ground truth (GT) trajectory. To test the effectiveness of the approach, we train a neural network for character recognition from the reconstructed trajectories, which achieved a character error rate of 19.51%. Finally, a joint model is implemented that makes use of both the IMU data and the generated trajectories, which outperforms the sensor-only-based recognition approach by 0.75%.
COMPARATIVE PERFORMANCE EVALUATION OF EFFICIENCY FOR HIGH DIMENSIONAL CLASSIFICATION METHODS

This paper aimed to determine the efficiency of classifiers for high-dimensional classification methods. It also investigated whether an extreme minimum misclassification rate translates into robust efficiency. To ensure an acceptable procedure, a benchmark evaluation threshold (BETH) was proposed as a metric to analyze the comparative performance for high-dimensional classification methods. A simplified performance metric was derived to show the efficiency of different classification methods. To achieve the objectives, the existing probability of correct classification (PCC) or classification accuracy reported in five different articles was used to generate the BETH value. Then, a comparative analysis was performed between the application of BETH value and the well-established PCC value ,derived from the confusion matrix. The analysis indicated that the BETH procedure had a minimum misclassification rate, unlike the Optimal method. The results also revealed that as the PCC inclined toward unity value, the misclassification rate between the two methods (BETH and PCC) became extremely irrelevant. The study revealed that the BETH method was invariant to the performance established by the classifiers using the PCC criterion but demonstrated more relevant aspects of robustness and minimum misclassification rate as compared to the PCC method. In addition, the comparative analysis affirmed that the BETH method exhibited more robust efficiency than the Optimal method. The study concluded that a minimum misclassification rate yields robust performance efficiency.
Empirical Analysis of Deep Learning for Big Data and Its Applications using CNN

Deep Learning has taken an utmost interest in the field of big data analytics due to its feature extraction and classification properties. Traditionally, researchers used Machine Learning algorithms to classify big data; however, the feature extraction was carried out by a human-driven process. Thus, researchers discovered the deep learning approaches to carry out the feature extraction by using algorithms. In this research, a “Convolutional Neural Network” or CNN algorithm has been selected to understand the accuracy of big data analytics. Primary research has been carried out to understand how hidden layers and nodes impact the accuracy of this neural network. Moreover, CNN has been compared with other neural networks to understand if CNN is out-competing the rest algorithms or not (other two algorithms selected are “Recurrent Neural Network” or RNN and “Artificial Neural Network” or ANN). In the primary research, CNN and other algorithms were tasted for accuracy against hidden layers, nodes, training and validation time. Regression and Correlation analyses have been carried out where independent variables were: Training time, Validation time, Hidden layers and Hidden nodes. Dependent variables were CNN, ANN and RNN. Findings showed that CNN is 92% accurate whereas other neural networks possess less than 90% of accuracy in big data analytics. The hidden nodes have significant positive impact on the accuracy of CNN.
Image Quality Evaluation in Professional HDR/WCG Production Questions the Need for HDR Metrics

In the quality evaluation of high dynamic range and wide color gamut (HDR/WCG) images, a number of works have concluded that native HDR metrics, such as HDR visual difference predictor (HDR-VDP), HDR video quality metric (HDR-VQM), or convolutional neural network (CNN)-based visibility metrics for HDR content, provide the best results. These metrics consider only the luminance component, but several color difference metrics have been specifically developed for, and validated with, HDR/WCG images. In this paper, we perform subjective evaluation experiments in a professional HDR/WCG production setting, under a real use case scenario. The results are quite relevant in that they show, firstly, that the performance of HDR metrics is worse than that of a classic, simple standard dynamic range (SDR) metric applied directly to the HDR content; and secondly, that the chrominance metrics specifically developed for HDR/WCG imaging have poor correlation with observer scores and are also outperformed by an SDR metric. Based on these findings, we show how a very simple framework for creating color HDR metrics, that uses only luminance SDR metrics, transfer functions, and classic color spaces, is able to consistently outperform, by a considerable margin, state-of-the-art HDR metrics on a varied set of HDR content, for both perceptual quantization (PQ) and Hybrid Log-Gamma (HLG) encoding, luminance and chroma distortions, and on different color spaces of common use.
Simultaneous trishearing digital shearography for nondestructive testing

Digital shearography (DS) is a noncontact, whole-field, and laser-based optical interferometric method. It is a valuable tool in the field of nondestructive testing (NDT) and received considerable acceptance in the automotive and aerospace industries. Conventional DS only detects the flaws in one or two shearing/sensitive directions, which leads to the missed inspection and leaves quite a lot of suspected defects underdefined. A trishearing DS for NDT is proposed to solve the mentioned issue. The presented shearography is designed based on a multiplexed Mach–Zehnder interferometer embedded with the spatial phase shift technique. It measures the defects in three sensitive directions simultaneously and has balanced optical paths with high light efficiency. Moreover, the measurement sensitivity of each direction can be adjusted during the test, without affecting the carrier frequencies, so it meets various needs for NDT applications. The principle and the experimental validation of the proposed system are introduced in detail.
Practical multipartite entanglement distribution in noisy channels

Entanglement among multiple parties is vital for practical quantum information applications. As two kinds of typical multipartite entangled state, the W and Greenberger-Horne-Zeilinger (GHZ) states have attracted a lot of attention. In this paper, we propose two practical linear optical schemes for multipartite entanglement distribution in noisy channels. Using the time-bin or spatial-mode degree of freedom (DoF) encoding, the influence of the noise will be eliminated and perfect W or GHZ state can be achieved among different nodes in a deterministic way. Moreover, we construct quantum circuits, which can be implemented with current technology. These results contribute to long-distance quantum communication both in theoretical and experimental points of view.
Design of Blockchain-based Secure Electric Vehicle Charging System Using ECC

Greener and more energy-efficient transportation options have pushed electric vehicles to the forefront of the global transportation market. Since then, academia and the transportation industry have tried to develop sustainable protocols that ensure dependable, scalable, and secure communication. With this vision, various security researchers have suggested authentication techniques to protect the messages exchanged between entities of the charging system. In its continuation, Kim et al. have proposed a charging system for electric vehicles based on the blockchain. With a close analysis of this protocol, our team has found that their scheme suffers from message substitution attacks, session key disclosure attacks, man-in-the-middle attacks, etc. This article proposes a secure and robust blockchain-based electric vehicle charging system using the ECC cryptosystem and lightweight one-way hash functions. Furthermore, the security of the proposed protocol is being analyzed, and the results confirm that the suggested protocol can withstand significant attacks.
A 7Gbps SCA-Resistant Multiplicative-Masked AES Engine in Intel 4 CMOS

A side-channel-attack (SCA) resistant AES engine with multiplicative-masked Sboxes is fabricated in Intel 4 CMOS, achieving 1.8× lower area overhead compared to conventional additive-masked implementations. Balanced dual-rail detector circuits pre-empt zero-value attacks while providing a 34,000× increase in side-channel-attack resistance, with a measured minimum-traces-to-disclose (MTD) of 850M encryption traces.
3D Human Pose Estimation with Spatial and Temporal Transformers

Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation. However, in the field of human pose estimation, convolutional architectures still remain dominant. In this work, we present PoseFormer, a purely transformer-based approach for 3D human pose estimation in videos without convolutional architectures involved. Inspired by recent developments in vision transformers, we design a spatial-temporal transformer structure to comprehensively model the human joint relations within each frame as well as the temporal correlations across frames, then output an accurate 3D human pose of the center frame. We quantitatively and qualitatively evaluate our method on two popular and standard benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments show that PoseFormer achieves state-of-the-art performance on both datasets. Code is available at \url{https://github.com/zczcwh/PoseFormer}
The effect of the use of electronic learning aid on student satisfaction with online learning

UNNES (Universitas Negeri Semarang) has developed Electronic Learning Aid (ELENA) as an e-learning tool during the COVID-19 pandemic. The lecturers have tried to innovate in the use of ELENA by conducting many kinds of lecturing activities. This paper aims to explain the effect of the use of ELENA on the student’s satisfaction with online learning. The research is conducted at the Economics Education study program, Faculty of Economics, Semarang State University in March-June 2021. We use a questionnaire to obtain the required research data. We also use descriptive statistics to describe the students’ satisfaction with online learning. Simple regression analysis is also used to determine the influence of ELENA usage on the students’ satisfaction with online learning. The results indicate that some lecturers have been familiar with utilizing some features available in ELENA. The main lecturing activities held in ELENA are sharing files and giving assignments. The research findings also provide concrete evidence that the use of ELENA can significantly affect the students’ satisfaction with online learning process.
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation

Diverse NMT aims at generating multiple diverse yet faithful translations given a source sentence.In this paper, we investigate a common shortcoming in existing diverse NMT studies: the model is usually trained with single reference, while expected to generate multiple candidate translations in inference.The discrepancy between training and inference enlarges the confidence variance and quality gap among candidate translations and thus hinders model performance.To deal with this defect, we propose a multi-candidate optimization framework for diverse NMT.Specifically, we define assessments to score the diversity and the quality of candidate translations during training, and optimize the diverse NMT model with two strategies based on reinforcement learning, namely hard constrained training and soft constrained training.We conduct experiments on NIST Chinese-English and WMT14 English-German translation tasks.The results illustrate that our framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality.Our source code is available at https://github.com/DeepLearnXMU/MultiCanOptim.
Dependency Position Encoding for Relation Extraction

Leveraging the dependency tree of the input sentence is able to improve the model performance for relation extraction.A challenging issue is how to remove confusions from the tree.Efforts have been made to utilize the dependency connections between words to selectively emphasize target-relevant information.However, these approaches are limited in focusing on exploiting dependency types.In this paper, we propose dependency position encoding (DPE), an efficient way of incorporating both dependency connections and dependency types into the self-attention mechanism to distinguish the importance of different word dependencies for the task.In contrast to previous studies that process input sentence and dependency information in separate streams, DPE can be seamlessly incorporated into the Transformer and makes it possible to use an onestream scheme to extract relations between entity pairs.Extensive experiments show that models with our DPE significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED.
Self-supervised Representation Learning for Speech Processing

Hung-yi Lee, Abdelrahman Mohamed, Shinji Watanabe, Tara Sainath, Karen Livescu, Shang-Wen Li, Shu-wen Yang, Katrin Kirchhoff. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts. 2022.
ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language

This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection.Our system consists of finetuning a pretrained Text-to-Text-Transfer Transformer (T5) and innovatively reducing its out-of-class predictions.The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes & struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split.Our model achieves an F1 score of 0.5452 on the official test set.