Reviewerly/exHarmony huggingface dataset:
Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection

Neural networks have proven to be a good alternative in application fields such as healthcare, time-series forecasting and artificial vision, among others, for tasks like regression or classification. Their potential has been particularly remarkable in unstructured data, but recently developed architectures or their ensemble with other classical methods have produced competitive results in structured data. Feature selection has several beneficial properties: improve efficacy, performance, problem understanding and data recollection time. However, as new data sources become available and new features are generated using feature engineering techniques, more computational resources are required for feature selection methods. Feature selection takes an exorbitant amount of time in datasets with numerous features, making it impossible to use or achieving suboptimal selections that do not reflect the underlying behavior of the problem. We propose a nonparametric neural network layer which provides all the benefits of feature selection while requiring few changes to the architecture. Our method adds a novel layer at the beginning of the neural network, which removes the influence of features during training, adding inherent interpretability to the model without extra parameterization. In contrast to other feature selection methods, we propose an efficient and model-aware method to select the features with no need to train the model several times. We compared our method with a variety of popular feature selection strategies and datasets, showing remarkable results
Application of Sandwich Teaching Method Based on Network Platform in Rehabilitation Nursing Teaching in Postepidemic Era

Rehabilitation nursing teaching is one of the key points of nursing teaching. The quality of rehabilitation nursing teaching directly affects the comprehensive ability of clinical interns. Therefore, teachers must further deepen the reform of clinical nursing teaching, improve the teaching system, and explore effective clinical nursing teaching methods. This paper introduces the application of the network teaching mode based on sandwich in rehabilitation nursing teaching, which provides guidance and help for the clinical nursing teaching mode. Through sandwich teaching activities for third year students of the nursing department, taking mastering theoretical knowledge, changing critical thinking ability, and changing learning initiative as indicators, this paper analyzes the sandwich teaching mode and discusses the cultivation of nursing students’ ability. The experimental results showed that after the intervention, there was a statistically significant difference in the learning initiative of students in the experimental group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>t</mi><mo>=</mo><mo>−</mo><mn>29.713</mn><mo>,</mo><mi>p</mi><mo>≤</mo><mn>0.001</mn></math> ). After the intervention, there was a significant difference in the learning initiative of the control group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mi>t</mi><mo>=</mo><mo>−</mo><mn>3.368</mn><mo>,</mo><mi>p</mi><mo>≤</mo><mn>0.001</mn></math> ).
Performance releaser with smart anchor learning for arbitrary‐oriented object detection

Abstract Arbitrary‐oriented object detection is widely used in aerial image applications because of its efficient object representation. However, the use of oriented bounding box aggravates the imbalance between positive and negative samples when using one‐stage object detectors, which seriously decreases the detection accuracy. We believe that it is the anchor learning strategy (ALS) used by such detectors that needs to take the responsibility. In this study, three perspectives on ALS design were summarised and ALS—Performance Releaser with Smart Anchor Learning (PRSAL) was proposed. Performance Releaser with Smart Anchor Learning is a dynamic ALS that utilises anchor classification ability as an equivalent indicator to anchor box regression ability, this allows anchors with high detection potential to be filtered out in a more reasonable way. At the same time, PRSAL focuses more on anchor potential and it is able to automatically select a number of positive samples that far exceed that of other methods by activating anchors that previously had a low spatial overlap, thereby releasing the detection performance. We validate the PRSAL using three remote sensing datasets—HRSC2016, DOTA and UCAS‐AOD as well as one scene text dataset—ICDAR 2013. The experimental results show that the proposed method gives substantially better results than existing models.
Conceptual Foundations of Emerging and Mobile Technologies, ICT-Enabled Training, and Traditional Methods for Examinations in the Indian Civil Service

Aiming to comprehend the importance of training civil servants, a feature that has become an integral part of modern personnel management, the author attempts to make a reference to the topic of civil servant training in developing countries as an integral part of the training process. There is no doubt that all governments realize that effective and appropriate training needs to be provided to their civil servants as a matter of maintaining an efficient and contemporary administration as stated in the paper. During the past few years, government functions have grown and expanded rapidly, while the administration itself has become more complex, specialized, and technological in nature. Merit-based recruitment policies and programs in the civil service are aimed at selecting the most qualified and competent candidates for employment in the civil service. In light of the fact that most of the selected candidates possess a degree or diploma, there is no doubt that they are highly qualified and highly educated. In spite of this, an MBA or a university degree does not necessarily qualify them as the best administrators when it comes to their respective fields. The purpose of this study is to examine the relationship between the traditional lecture method and the modern technological method of training for civil service exams. It is also to determine the degree to which these two methods are interrelated. It is pertinent to note that both classes were taught by the same trainer, who ensured that the requirements for both classes were the same. Due to the use of technology in two of the classes, they both provided significant rigor to the training process. Furthermore, both provided participants with valuable information. According to the results of this study, there is a positive correlation between traditional lecture methods and modern technology-based training methods for civil service examinations. Based on the results of the study, this conclusion can be drawn.
DPED: Bio-inspired dual-pathway network for edge detection

Edge detection is significant as the basis of high-level visual tasks. Most encoder-decoder edge detection methods used convolutional neural networks, such as VGG16 or Resnet, as the encoding network. Studies on designing decoding networks have achieved good results. Swin Transformer (Swin) has recently attracted much attention in various visual tasks as a possible alternative to convolutional neural networks. Physiological studies have shown that there are two visual pathways that converge in the visual cortex in the biological vision system, and that complex information transmission and communication is widespread. Inspired by the research on Swin and the biological vision pathway, we have designed a two-pathway encoding network. The first pathway network is the fine-tuned Swin; the second pathway network mainly comprises deep separable convolution. To simulate attention transmission and feature fusion between the first and second pathway networks, we have designed a second-pathway attention module and a pathways fusion module. Our proposed method outperforms the CNN-based SOTA method BDCN on BSDS500 datasets. Moreover, our proposed method and the Transformer-based SOTA method EDTER have their own performance advantages. In terms of FLOPs and FPS, our method has more benefits than EDTER.
The effect of touch simulation in virtual reality shopping

Abstract This study aims to explore the effect of touch simulation on virtual reality (VR) store satisfaction mediated by VR shopping self-efficacy and VR shopping pleasure. The moderation effects of the autotelic and instrumental need for touch between touch simulation and VR store satisfaction are also explored. Participants wear a head-mounted display VR device (Oculus Go) in a controlled laboratory environment, and their VR store experience is recorded as data. All participants’ responses (n = 58) are analyzed using SPSS 20.0 for descriptive statistics, reliability analysis, exploratory factor analysis, and the Process macro model analysis. The results show that touch simulation positively influences VR store satisfaction, which is mediated by the self-efficacy and by the dual path of the self-efficacy and the pleasure. Furthermore, the relation between touch simulation and pleasure is moderated by need for touch. For individuals with a high level of autotelic need for touch, the effect of touch simulation on the pleasure is heightened. However, instrumental need for touch does not moderate the path of touch simulation on the self-efficacy.
A Blockchain-Based Privacy-Preserving Publish-Subscribe Model in IoT Multidomain Data Sharing

With the dramatically increasing deployment of intelligent devices, the Internet of Things (IoT) has attracted more attention and developed rapidly. It effectively collects and shares data from the surrounding environment to achieve better IoT services. For data sharing, the publish-subscribe (PS) paradigm provides a loosely coupled and scalable communication model. However, due to the loosely coupled nature, it is vulnerable to many attacks, resulting in some security threats to the IoT system, but it cannot provide the basic security mechanisms such as authentication and confidentiality to ensure the data security. Thus, in order to protect the system security and users’ privacy, this paper presents a secure blockchain-based privacy-preserving access control scheme for the PS system, which adopt the fully homomorphic encryption (FHE) to ensure the confidentiality of the publishing events and leverage the ledger to store the large volume of data events and access crossdomain information. Finally, we analyze the correctness and security of our scheme; moreover, we deploy our proposed prototype system on two computers and evaluate its performance. The experimental results show that our PS system can efficiently achieve the equilibrium between the system cost and the security requirement.
Analysis on the Regional Spatial Distribution of Employment of College Students and Evaluation of Employment Quality

Analyzing the spatial distribution of the employment of college students and developing strategies to improve the quality of their employment are the key to helping college students get employed and retained in the region. However, there are few systematic and comprehensive research results on the employment of college students, and the existing employment quality indicator systems are not objective enough. To this end, this paper analyzes the regional spatial distribution of employment of college students and carries out extended study on employment quality evaluation. Firstly, an analysis model map of the regional spatial distribution of employment of college students was given. Based on the spatial lag model, the regional spatial distribution of employment of college students was preliminarily analyzed, with the influencing factors to the regional spatial distribution of employment of college students as the independent variables, and the regional spatial agglomeration level of employment of college students as the dependent variable. A spatial econometric model for the employment quality of college students was proposed, which introduced spatial effects, and realized the quantitative analysis on the contributions of the influencing factors to the regional spatial distribution of employment of colleges students. Finally, the analysis results were given, and the corresponding employment quality evaluation results were also provided.
Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction

Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising systems. In such systems, rich user behavior (viz. long- and short-term) has been proved to be of great value in capturing user interests. Both industry and academy have paid much attention to this topic and propose different approaches to modeling with long-term and short-term user behavior data. But there are still some unresolved issues. More specially, (1) rule and truncation based methods to extract information from long-term behavior are easy to cause information loss, and (2) single feedback behavior regardless of scenario to extract information from short-term behavior lead to information confusion and noise. To fill this gap, we propose a Graph based Long-term and Short-term interest Model, termed GLSM. It consists of a multi-interest graph structure for capturing long-term user behavior, a multi-scenario heterogeneous sequence model for modeling short-term information, then an adaptive fusion mechanism to fused information from long-term and short-term behaviors. Comprehensive experiments on real-world datasets, GLSM achieved SOTA score on offline metrics. At the same time, the GLSM algorithm has been deployed in our industrial application, bringing 4.9% CTR and 4.3% GMV lift, which is significant to the business.
"Essential ‘Human’ Features of the Cyber-Physical Nurse"

Maya Dimitrova* Author Affiliations Institute of Robotics, Bulgarian Academy of Sciences, Sofia, Bulgaria Received: September 01, 2022 | Published: September 07, 2022 Corresponding author: Maya Dimitrova, Institute of Robotics, Bulgarian Academy of Sciences, Str. Bl. 2, Floor 4, 1113 Sofia, Bulgaria DOI: 10.26717/BJSTR.2022.46.007285
A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model

Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.
Transformer-Based Value Function Decomposition for Cooperative Multi-Agent Reinforcement Learning in StarCraft

The StarCraft II Multi-Agent Challenge (SMAC) was created to be a challenging benchmark problem for cooperative multi-agent reinforcement learning (MARL). SMAC focuses exclusively on the problem of StarCraft micromanagement and assumes that each unit is controlled individually by a learning agent that acts independently and only possesses local information; centralized training is assumed to occur with decentralized execution (CTDE). To perform well in SMAC, MARL algorithms must handle the dual problems of multi-agent credit assignment and joint action evaluation. This paper introduces a new architecture TransMix, a transformer-based joint action-value mixing network which we show to be efficient and scalable as compared to the other state-of-the-art cooperative MARL solutions. TransMix leverages the ability of transformers to learn a richer mixing function for combining the agents' individual value functions. It achieves comparable performance to previous work on easy SMAC scenarios and outperforms other techniques on hard scenarios, as well as scenarios that are corrupted with Gaussian noise to simulate fog of war.
Medical Image Encryption Based on Frequency Domain and Chaotic Map

Medical images typically have diagnostic and confidential data and information about the patient and are usually sent using public networks. Due to the sensitivity of medical images, their security has become a challenging requirement that must be addressed. Traditional cryptographical algorithms are inadequate to ensure appropriate and fine security while encrypting them, because of the correlation between each pixel, high redundancy of the image and its major size. Chaotic systems with their properties and partial encryption based on frequency domain are the best candidates for securing the storage and transfer of digital images. The paper shows new criteria for encryption of medical image. It is designed to improve performance and meet the increasing need for better security for medical image encryption. At first, the pixel's correlation is eliminated by scrambling the input image and diffusing them using secret sharing based on polynomials. Various frequency domains of the image are accomplished by applying the integer wavelet transform of the scrambled image, namely, the associated detail of (HL, LH and HH) and the LL (approximation coefficient) through using the AES algorithm. The (LL) part is encrypted to originate the diffusion image and by using the inverse of the Haar wavelet transform to produce a reliable, unbreakable and secure form. The designed algorithm is used to reverse and shuffle every frequency sign of the transformed image before transformation image back to the pixel domain. The original image form is restored through the reverse decryption algorithm. The suggested algorithm is measured and evaluated in a statistical way and normal standard security; The outcome of the proposed algorithm shows a strong resistant to the familiar attacks and extra secure than the other algorithms in the domain of image cryptography.
Template Attack of LWE/LWR-Based Schemes with Cyclic Message Rotation

The side-channel security of lattice-based post-quantum cryptography has gained extensive attention since the standardization of post-quantum cryptography. Based on the leakage mechanism in the decapsulation stage of LWE/LWR-based post-quantum cryptography, a message recovery method, with templates and cyclic message rotation targeting the message decoding operation, was proposed. The templates were constructed for the intermediate state based on the Hamming weight model and cyclic message rotation was used to construct special ciphertexts. Using the power leakage during operation, secret messages in the LWE/LWR-based schemes were recovered. The proposed method was verified on CRYSTAL-Kyber. The experimental results demonstrated that this method could successfully recover the secret messages used in the encapsulation stage, thereby recovering the shared key. Compared with existing methods, the power traces required for templates and attack were both reduced. The success rate was significantly increased under the low SNR, indicating a better performance with lower recovery cost. The message recovery success rate could reach 99.6% with sufficient SNR.
A Novel Adaptive Deskewing Algorithm for Document Images

Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image's layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC'2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.
An Order-Preserving Encryption Scheme Based on Weighted Random Interval Division for Ciphertext Comparison in Wearable Systems

With the rapid development of wearable devices with various sensors, massive sensing data for health management have been generated. This causes a potential revolution in medical treatments, diagnosis, and prediction. However, due to the privacy risks of health data aggregation, data comparative analysis under privacy protection faces challenges. Order-preserving encryption is an effective scheme to achieve private data retrieval and comparison, but the existing order-preserving encryption algorithms are mainly aimed at either integer data or single characters. It is urgent to build a lightweight order-preserving encryption scheme that supports multiple types of data such as integer, floating number, and string. In view of the above problems, this paper proposes an order-preserving encryption scheme (WRID-OPES) based on weighted random interval division (WRID). WRID-OPES converts all kinds of data into hexadecimal number strings and calculates the frequency and weight of each hexadecimal number. The plaintext digital string is blocked and recombined, and each block is encrypted using WRID algorithm according to the weight of each hexadecimal digit. Our schemes can realize the order-preserving encryption of multiple types of data and achieve indistinguishability under ordered selection plaintext attack (IND-OCPA) security in static data sets. Security analysis and experiments show that our scheme can resist attacks using exhaustive methods and statistical methods and has linear encryption time and small ciphertext expansion rate.
DeepFocus: A visual focus of attention detection framework using deep learning in multi-object scenarios

In recent years, recognizing the visual focus of attention (VFoA) has attracted much attention among computer vision experts due to its various Human–Computer Interaction (HCI) or Human-Robot Interaction (HRI) applications. Although eye gaze is a potential cue to determine someone’s focus of attention (FOA), it is challenging to determine FOA alone when the interacting partners are far away or the camera cannot capture high-resolution images from long-distance. Therefore, the head pose can be used as an approximation to recognize the focus of someone’s attention. This paper proposes a vision-based framework to detect the FOA of humans using nine head poses consisting of four main modules: face detection and facial key-point selection (FDKPSM), head pose classification (HPCM), object localization and classification (OLCM), and focus of attention estimation (FoAEM). The FDKPSM uses the Multi-task Cascaded Neural Network (MTCNN) framework to detect head poses, and the HPCM classifies them into nine classes using the ResNet18. To estimate the FoA, the FoAEM uses a mapping Algorithm (EFoA) which integrates head poses on the focused object. Experimental results show that the proposed model outperformed other deep learning models by achieving the highest accuracy on three datasets: BIWI-M (96.97%), Pointing’04-M (96.04%) and HPoD 9 (98.99%). The visual focus of the attention model gained an accuracy of 94.12% in the multi-object scenario.