Reviewerly/exHarmony huggingface dataset:
Piecewise Iterative Extrapolation Method for Bandlimited Signal

In some high spectrally efficient communication structures, the Gerchberg-Papoulis extrapolation algorithm can be used to reconstruct nonorthogonal signals. However, the extrapolation efficiency and accuracy degrades when the energy of known signal is small or the iterative filter bandwidth is large. Focused on these drawbacks, a piecewise iterative extrapolation method is proposed to extrapolate bandlimited signals with higher extrapolation efficiency and accuracy. In this paper, the amplitude change of the unknown part over iterations is analyzed and the feasibility of the proposed extrapolation method is discussed. Numerical simulation shows the accuracy superiority of proposed method with the frequency offset compared to the GP algorithm with fixed computational complexity. Besides, the feasibility of the proposed method is verified in fractional Fourier domain with performance advantages.
Dense-to-Question and Sparse-to-Answer: Hybrid Retriever System for Industrial Frequently Asked Questions

The term “Frequently asked questions” (FAQ) refers to a query that is asked repeatedly and produces a manually constructed response. It is one of the most important factors influencing customer repurchase and brand loyalty; thus, most industry domains invest heavily in it. This has led to deep-learning-based retrieval models being studied. However, training a model and creating a database specializing in each industry domain comes at a high cost, especially when using a chatbot-based conversation system, as a large amount of resources must be continuously input for the FAQ system’s maintenance. It is also difficult for small- and medium-sized companies and national institutions to build individualized training data and databases and obtain satisfactory results. As a result, based on the deep learning information retrieval module, we propose a method of returning responses to customer inquiries using only data that can be easily obtained from companies. We hybridize dense embedding and sparse embedding in this work to make it more robust in professional terms, and we propose new functions to adjust the weight ratio and scale the results returned by the two modules.
Joint Dense Residual and Recurrent Attention Network for DCE-MRI Breast Tumor Segmentation

Breast cancer detection largely relies on imaging characteristics and the ability of clinicians to easily and quickly identify potential lesions. Magnetic resonance imaging (MRI) of breast tumors has recently shown great promise for enabling the automatic identification of breast tumors. Nevertheless, state-of-the-art MRI-based algorithms utilizing deep learning techniques are still limited in their ability to accurately separate tumor and healthy tissue. Therefore, in the current work, we propose an automatic and accurate two-stage U-Net-based segmentation framework for breast tumor detection using dynamic contrast-enhanced MRI (DCE-MRI). This framework was evaluated using T2-weighted MRI data from 160 breast tumor cases, and its performance was compared with that of the standard U-Net model. In the first stage of the proposed framework, a refined U-Net model was utilized to automatically delineate a breast region of interest (ROI) from the surrounding healthy tissue. Importantly, this automatic segmentation step reduced the impact of the background chest tissue on breast tumors' identification. For the second stage, we employed an improved U-Net model that combined a dense residual module based on dilated convolution with a recurrent attention module. This model was used to accurately and automatically segment the tumor tissue from healthy tissue in the breast ROI derived in the previous step. Overall, compared to the U-Net model, the proposed technique exhibited increases in the Dice similarity coefficient, Jaccard similarity, positive predictive value, sensitivity, and Hausdorff distance of 3%, 3%, 3%, 2%, and 16.2, respectively. The proposed model may in the future aid in the clinical diagnosis of breast cancer lesions and help guide individualized patient treatment.
Mixed Reality Support for Bridge Inspectors

Bridge inspectors work for the safety of our infrastructure and mobility. In regular intervals, they conduct structural inspections - a manual task with a long-lasting and firmly normed analogue tradition. We propose a mixed analogue and digital workflow that includes Mixed Reality views that can be ready-to-hand for bridge inspectors during their work at and in a bridge. Our presented demonstrator was iteratively designed in a collaborative research project and turned into a tablet-based application to digitally support that work. It employs BIM data that contains 3D geometry-data and additional data about the structure, such as previous damage reports.
The effect of business intelligence practices on job satisfaction in the Saudi Electricity Company in the Asir Region

Purpose The purpose of this study is to determine the effect of business intelligence on job satisfaction in the Saudi Electricity Company in the Asir. It aims to evaluate employee job satisfaction after adopting a BI system for job practices. Design/methodology/approach A mixed-method including descriptive, questionnaire for data collection and analytical approach was used. The random sample population consists of 354 employees out of 3,000. Findings It is found that the implementation of a BI system and the associated practices have a statistically significant effect on employee job satisfaction. The study recommends the adoption of BI systems for organizational activities. Such organizations must consider the latest BI tools for solving business problems. Originality/value To the best of authors' knowledge, the current study is the first attempt to evaluate the performance of BI practices in the region and in context of employee job satisfaction. This will contribute to understanding the job satisfaction in similar organizational environment.
Clustering based sentiment analysis on Twitter data for COVID-19 vaccines in India

Coronavirus is a new and rapidly spreading viral disease. It is essential to have a vaccine in order to reduce the virus's impact. Vaccination-related sentiments can influence an individual's decision to accept the vaccines. Evaluating the sentiments is a time-consuming and challenging process. Sentiment analysis (SA) could have an impact on the vaccination initiatives as well as changes in people's opinions and behaviour around immunizations. Since social media is widely utilized to disseminate information, mining this data is a popular area of study these days. On Twitter, a wide range of opinions about the negative effects of licensed vaccines have been expressed over time. In this research, tweets are gathered, pre-processed to remove extraneous data, and then utilized for sentiments analysis utilizing the Lexicons-based technique and machine learning. After feature extraction, the clustering is performed using MEEM approach. This research proposed a Clustering Based Twitter sentiments analysis of COVID 19 (C-SAT COVID 19) vaccinations in India. An enhanced random forest classifier is implemented in this research to classify the sentiment scores provided by the sentiment analysis. A classification is performed based on the negative, neutral, and positive sentiment analysis to examine people's emotions towards vaccinations accessible in India.
Studying the Effect of Physical Realism on Time Perception in a HAZMAT VR Simulation

Our research focuses on how physical props in virtual reality (VR) can affect users&#x0027; time perception. We designed an experiment with the goal of comparing users&#x0027; perception of time when using physical props in VR as compared to standard controllers and only virtual elements. In order to quantify this effect, time estimates for both conditions are compared to time estimates for a matching real-world task. In this experiment, participants assume the role of a firefighter trainee, going through a HAZMAT scenario, where they touch and interact with different physical props that match the virtual elements of the scene.
Right Invariant SE<sub>2</sub> (3) - EKF for Relative Navigation in Learning-based Visual Inertial Odometry

The visual inertial odometry(VIO) has shown great potential localization ability in robots&#x0027; autonomy tasks. Although the learning-based VIO method shows promising results as it is robust to different lighting conditions without sensors calibration. It is not necessary to learn the kinematics of IMU as its model is well studied. In this work, we propose a learning based VIO framework which uses a right invariant SE<inf>2</inf> (3)-EKF to combine the measurements from IMU and relative poses from learning-based visual odometry. The novel SE<inf>2</inf> (3) - EKF defines the right invariant error of the relative navigation model on matrix Lie group and derives the associated error state differential equations. As the relative pose of the learning-based VO is compatible with the right invariant error, the consistence issue of tradition EKF is overcame. The experiments on the KITTI datasets have shown the superior performance of the proposed method compared to the other learning-based methods.
GPU‐accelerated and mixed norm regularized online extreme learning machine

Summary Extreme learning machine (ELM) is a prominent example of neural network with its fast training speed, and good prediction performance. An online version of ELM called online sequential extreme learning machine (OS‐ELM) has also been proposed for the sequential training. Combined with the need for regularization to prevent over‐fitting in addition to the large number of neurons required in the hidden layer, OS‐ELM demands huge amount of computation power for the large‐scale data. In this article, a mixed norm () regularized online machine learning algorithm (MRO‐ELM) that is based on alternating direction method of multipliers (ADMM) is proposed. A linear combination of the mixed norm and the Frobenius norm regularization is applied using the ADMM framework and update formulas are derived. Graphics processing unit (GPU) accelerated version of MRO‐ELM (GPU‐MRO‐ELM) is also proposed to reduce the training time by processing appropriate parts in parallel using the implemented custom kernels. In addition, a novel automatic hyper‐parameter tuning method is incorporated to GPU‐MRO‐ELM using progressive validation with GPU acceleration. The experimental results show that the MRO‐ELM algorithm and its GPU version outperform OS‐ELM in terms of training speed, and testing accuracy. Also, compared to the cross validation, the proposed automatic hyper‐parameter tuning demonstrates dramatical reduction in the tuning time.
Recent Applied Techniques for Open Dialog Generation Systems

Dialog Generation Systems (DGS) have emerged as a critical aspect of Natural Language Processing in recent years (NLP). It enables a diverse set of relevant applications to interact with humans in a natural and intelligent way. This study provides a systematic review of open DGS techniques that have recently been used. The major goal of this study is to discuss and analyze the most widely used approaches for implementing DGS&#x0027;s that have been published in recent years. Also, the most popular datasets for open DGS are enumerated, and some commonly used automatic evaluating metrics are presented. As a result, the explored methods are categorized into six main categories, Reinforcement Learning (RL), Hierarchical Recurrent Encoder-Decoder (HRED), Generative Adversarial Networks (GAN), Variational Auto-Encoder (VAE), Sequence to Sequence (Seq2Seq), and Pretraining Model.
Versatile, full‐spectrum, and swift network sampling for model generation

Given one task, it is difficult to generate CNN models for many different hardware platforms with extremely diverse computing power for this task. Repeating network pruning or architecture search for each platform is very time-consuming. In this paper, we propose properties that are required for this model generation problem: versatile (fits diverse applications and network structures), full-spectrum (generates models for devices with tiny to gigantic computing power), and swift (total training time for all platforms is short, and generated models have low latency). We show that existing methods do not satisfy these requirements and propose a VFS method (the V/F/S represents Versatile/Full-spectrum/Swift, respectively). VFS uses importance sampling to sample many submodels with versatile structures and with different input image resolutions. We propose new fine-tuning strategies that only need to fine-tune a best candidate submodel for few epochs for each platform. VFS satisfies all three requirements. It generates versatile models with low latency for diverse applications, is suitable for devices with a wide range of computing power differences, and the models which are generated by VFS achieve state-of-the-art accuracy.
DyAdvDefender: An instance-based online machine learning model for perturbation-trial-based black-box adversarial defense

Recent research indicates that machine learning models are vulnerable to adversarial samples that are slightly perturbed versions of natural samples. Adversarial samples can be crafted in white-box or black-box scenario. In the black-box scenario adversaries possess no knowledge of the detailed architecture and parameters of the model they attack, and they seek information by querying the model with multiple, slightly perturbed samples to achieve their attack purpose. The difficulty in recognizing adversarial samples arises from the fact that the perturbations are often imperceptible, yet effective in misleading machine learning models. Existing defense methods are static, and they cannot dynamically evolve to adapt to adversarial attacks, which unnecessarily disadvantages them. In this paper we propose a novel dynamic defense method called DyAdvDefender, and we show that a dynamic defense method can effectively utilize previous experience to defend against black-box attacks. Extensive experimental results suggest that DyAdvDefender outperforms existing static methods in terms of defense effectiveness while keeping the original classification accuracy with only limited extra time consumption.
An Improved Self-adapting Corner Detection Algorithm for Low-contrast Images

In order to suppress the impact of image dithering caused by wind-induced vibration, the features of sequential frame should be extracted. The Harris corner detection algorithm has been widely used for feature extraction. In the field of security monitoring and control, the image and video usually have the characteristics of jumbo size, high pixel and low contrast, which are difficult to obtain the corners. For the problems above, an improved self-adapting conner detection algorithm is proposed in this paper. Firstly, some of the corners are selected according to the comparison results between target pixel and the pixels around. Secondly, the selected corners are classified by reference to adaptive threshold values. Finally, false and marginal corners can be reduced or eliminated so as to select the best-matching corners. The above improved algorithm is validated in the field of sea area security monitoring and control. Simulation results show the effectiveness and feasibility of the algorithm above.
Temperature estimation of a pair of trapped ions

We apply estimation theory to a system formed by two interacting trapped ions. By using the Fisher matrix formalism, we introduce a simple scheme for estimation of the temperature of the longitudinal vibrational modes of the ions. We use the ions interaction to effectively infer the temperature of the individual ions, by optimising the interaction time evolution and by measuring only over one of the ions. We also investigate the effect of a non-thermal reservoir over the inference approach. The non-classicality of one of the ions vibrational modes, introduced due to a squeezed thermal reservoir, improves the indirect inference of the individual temperatures.
A sensor-based calibration system for three-dimensional digital image correlation

Three-dimensional digital image correlation (3D-DIC) has become a strong alternative to traditional contact-based techniques for structural health monitoring. 3D-DIC can extract the full-field displacement of a structure from a set of synchronized stereo images. Before performing 3D-DIC, a complex calibration process must be completed to obtain the stereovision system’s extrinsic parameters (i.e., cameras’ distance and orientation). The time required for the calibration depends on the dimensions of the targeted structure. For example, for large-scale structures, the calibration may take several hours. Furthermore, every time the cameras’ position changes, a new calibration is required to recalculate the extrinsic parameters. The approach proposed in this research allows determining the 3D-DIC extrinsic parameters using the data measured with commercially available sensors. The system utilizes three Inertial Measurement Units with a laser distance meter to compute the relative orientation and distance between the cameras. In this paper, an evaluation of the sensitivity of the newly developed sensor suite is provided by assessing the errors in the measurement of the extrinsic parameters. Analytical simulations performed on a 7.5 x 5.7 m field of view using the data retrieved from the sensors show that the proposed approach provides an accuracy of ~10-6 m and a promising way to reduce the complexity of 3D-DIC calibration.
Formation of Future Foreign Language Teacher’s Digital Competencies in the Pedagogical Management

The article deals with the problem of mastering a set of digital competencies that contribute to the implementation of pedagogical management in a foreign language classroom. It is noted that the necessity of the digital competencies formation is treated today as one of the most important conditions to realize teacher’s professional pedagogical activity in the digital economy. The factors of digital competencies formation in the field of pedagogical management are described. The forms of computer support of pedagogical management and the stages of implementation of digital educational products are considered. It is proposed a structural model of the main mechanisms of conceptualization future foreign language teacher’s professional pedagogical activity as a teacher-manager.
3DColAR: Exploring 3D Color Selection and Surface Painting for Head Worn Augmented Reality using Hand Gestures

Color selection and surface painting has been largely unexplored in head-worn Augmented Reality (AR) using hand gestures. In this demonstration we present 3DColAR: A system that implements several 2D and 3D techniques for color selection. We also implement two key approaches for painting a virtual 3D model using mid-air hand gestures. This includes a virtual pen which the user can grasp using their hand, akin to a real pen and the use of the user&#x0027;s fingertip directly on the virtual 3D model. We hope to explore how these various techniques effect user&#x0027;s efficiency and accuracy when performing surface painting of virtual objects using mid-air hand gestures via. several user studies.
Supervised Machine Learning Hand Gesture Classification in VR for Immersive Training

The fast adoption of immersive wearables evidences the need for more intuitive interaction methods between virtual environments and their users. Advances in wearables are making in-built real-time hand tracking mechanisms more common. However, wearable providers only include limited gestures available for developer use. This limits their use for VR-based training solutions, where users need to complete tasks that mimic real-world activities as much as possible to gain valuable insights on those tasks. We present a virtual reality application that collects data on hand characteristics and analyses the collected data to identify hand gestures towards achieving a more natural interaction.
The Security of Student Information Management System Based upon Blockchain

In recent years, with the development of science and technology, as an emerging data security technology, blockchain has been widely used in all walks of life by virtue of its immutability, uniqueness, smart contract, and decentralized organization. Examples include industry, services, and education. With the progress of China's education, the application of blockchain technology in the field of education has also been further expanded. At present, the application of blockchain in China's education field is mainly concentrated in three areas: education resource management, teaching information management, and education basic platform construction. This article aims to study the security of the student information management system based on the blockchain. The security test experiment of the student information management system is carried out based on the encryption algorithm of the blockchain, and conclusions are drawn. The security of the student information management system based on the encryption algorithm of blockchain technology has increased by 87%, which also shows that the blockchain technology has a better effect on improving the security of the student information management system.
A Saliency Prediction Model Based on Re-Parameterization and Channel Attention Mechanism

Deep saliency models can effectively imitate the attention mechanism of human vision, and they perform considerably better than classical models that rely on handcrafted features. However, deep models also require higher-level information, such as context or emotional content, to further approach human performance. Therefore, this study proposes a multilevel saliency prediction network that aims to use a combination of spatial and channel information to find possible high-level features, further improving the performance of a saliency model. Firstly, we use a VGG style network with an identity block as the primary network architecture. With the help of re-parameterization, we can obtain rich features similar to multiscale networks and effectively reduce computational cost. Secondly, a subnetwork with a channel attention mechanism is designed to find potential saliency regions and possible high-level semantic information in an image. Finally, image spatial features and a channel enhancement vector are combined after quantization to improve the overall performance of the model. Compared with classical models and other deep models, our model exhibits superior overall performance.