Reviewerly/exHarmony huggingface dataset:
High-efficiency and robust binary fringe optimization for superfast 3D shape measurement

By utilizing 1-bit binary fringe patterns instead of conventional 8-bit sinusoidal patterns, binary defocusing techniques have been successfully applied for high-speed 3D shape measurement. However, simultaneously achieving high accuracy and high speed remains challenging. To overcome this limitation, we propose a high-efficiency and robust binary fringe optimization method for superfast 3D shape measurement, which consists of 1D optimization and 2D modulation. Specifically, for 1D optimization, the three-level OPWM technique is introduced for high-order harmonics elimination, and an optimization framework is presented for generating the ‘best’ three-level OPWM pattern especially for large fringe periods. For 2D modulation, a single-pattern three-level OPWM strategy is proposed by utilizing all the dimensions for intensity modulation to decrease the required projection patterns. Thus, the proposed method essentially belongs to the 2D modulation technique, yet iterative optimization is carried out along one dimension, which drastically improves the computational efficiency while ensuring high accuracy. With only one set of optimized patterns, both simulations and experiments demonstrate that high-quality phase maps can be consistently generated for a wide range of fringe periods (e.g., from 18 to 1140 pixels) and different amounts of defocusing, and it can achieve superfast and high-accuracy 3D shape measurement.
IMODEII: an Improved IMODE algorithm based on the Reinforcement Learning

The success of differential evolution algorithm depends on its offspring breeding strategy and the associated control parameters. Improved Multi-Operator Differential Evolution (IMODE) proved its efficiency and ranked first in the CEC2020 competition. In this paper, an improved IMODE, called IMODEII, is introduced. In IMODEII, Reinforcement Learning (RL), a computational methodology that simulates interaction-based learning, is used as an adaptive operator selection approach. RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. Different from IMODE, only two mutation strategies have been used in IMODEII. We tested the performance of the proposed IMODEII by considering 12 benchmark functions with 10 and 20 variables taken from CEC2022 competition on single objective bound constrained numerical optimisation. A comparison between the proposed IMODEII and the state-of-the-art algorithms is conducted, with the results demonstrating the efficiency of the proposed IMODEII.
Why, How and Where of Delays in Software Security Patch Management: An Empirical Investigation in the Healthcare Sector

Numerous security attacks that resulted in devastating consequences can be traced back to a delay in applying a security patch. Despite the criticality of timely patch application, not much is known about why and how delays occur when applying security patches in practice, and how the delays can be mitigated. Based on longitudinal data collected from 132 delayed patching tasks over a period of four years and observations of patch meetings involving eight teams from two organisations in the healthcare domain, and using quantitative and qualitative data analysis approaches, we identify a set of reasons relating to technology, people and organisation as key explanations that cause delays in patching. Our findings also reveal that the most prominent cause of delays is attributable to coordination delays in the patch management process and a majority of delays occur during the patch deployment phase. Towards mitigating the delays, we describe a set of strategies employed by the studied practitioners. This research serves as the first step toward understanding the practical reasons for delays and possible mitigation strategies in vulnerability patch management. Our findings provide useful insights for practitioners to understand what and where improvement is needed in the patch management process and guide them towards taking timely actions against potential attacks. Also, our findings help researchers to invest effort into designing and developing computer-supported tools to better support a timely security patch management process.
Statistical verifications and deep-learning predictions for satellite-to-ground quantum atmospheric channels

Abstract Laser communications from small satellite platforms empowers the establishment of quantum key distribution (QKD), relying on quantum superposition states of single photons to realize unconditional security between distant parties at a global scale. Although recent breakthrough experiments have demonstrated the feasibility of satellite-to-ground QKD links, the underlying statistical characteristics of quantum atmospheric channels have not been well-understood and experimentally verified in the literature. In this paper, we highlight that classical atmospheric statistical models can be applied for describing random fluctuations of the quantum channels. To verify this fact, we report a statistical verification study of quantum atmospheric channels from the world’s first low-Earth orbit (LEO) 50-kg-class microsatellite-to-ground quantum-limited communication experiment. The verified statistical model is then applied to numerically investigate the quantum bit-error rate (QBER) and secret-key length (SKL) of a decoy-state efficient Bennett-Brassard 1984 (BB84) QKD protocol with optimized parameters considering finite-key effects, implemented over a LEO 6-unit (6U)-CubeSat-to-ground link. Important insights of the physical channel effects including pointing errors and atmospheric turbulence on the QBER and SKL are then revealed. Finally, we present a study using a deep-learning-based long short-term memory (LSTM) recurrent neural network (RNN) for predicting photon-count fluctuations over quantum atmospheric channels.
Few-Shot Object Detection in Remote Sensing Image Interpretation: Opportunities and Challenges

Recent years have witnessed rapid development and remarkable achievements on deep learning object detection in remote sensing (RS) images. The growing improvement of the accuracy is inseparable from the increasingly complex deep convolutional neural network and the huge amount of sample data. However, the under-fitting neural network will damage the detection performance facing the difficulty of sample acquisition. Thus, it evolves into few-shot object detection (FSOD). In this article, we first briefly introduce the object detection task and its algorithms, to better understand the basic detection frameworks followed by FSOD. Then, FSOD design methods in RS images for three important aspects, such as sample, model, and learning strategy, are respectively discussed. In addition, some valuable research results of FSOD in computer vision field are also included. We advocate a wide research technique route, and some advice about feature enhancement and multi-modal fusion, semantics extraction and cross-domain mapping, fine-tune and meta-learning strategies, and so on, are provided. Based on our stated research route, a novel few-shot detector that focuses on contextual information is proposed. At the end of the paper, we summarize accuracy performance on experimental datasets to illustrate the achievements and shortcomings of the stated algorithms, and highlight the future opportunities and challenges of FSOD in RS image interpretation, in the hope of providing insights into future research.
Generalizable Pancreas Segmentation Modeling in CT Imaging via Meta-Learning and Latent-Space Feature Flow Generation

Accurate pancreas segmentation is highly crucial for diagnosing and treating pancreatic diseases. Although CNN has demonstrated promising outcomes, the performance on unseen data can be significantly compromised by the wide appearance-style variations induced by different imaging factors. Thus, we propose a generalizable pancreas segmentation model based on a meta-learning strategy and latent-space feature flow generation method. Our approach enhances the generalizability by systematically reducing the interference from the cluttered background and appearance-style discrepancies through a coarse-to-fine workflow. Specifically, the integrity-preserving coarse segmentation module is designed to adaptively balance the pancreas coverage and segmentation accuracy with the meta-learning strategy for filtering out background clutter. It also enhances the generalization of the coarse model to reasonably-accurate ROIs thereby promoting the stability of fine segmentation. Subsequently, the appearance-style feature flow generation method is developed to generate a series of progressively-varying style-related intermediate representations between two latent spaces. This feature flow effectively models the distribution variations caused by appearance-style discrepancies, and thus enhances the adaptability of the fine model. Our method achieves superior performance on three pancreas datasets and outperforms state-of-the-art generalization methods. Besides, it can be easily integrated into other workflows, leading to a potential paradigm for enhancing generalization performance.
Comparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets

Deep learning algorithms for object detection used in autonomous vehicles require a huge amount of labeled data. Data collecting and labeling is time consuming and, most importantly, in most cases useful only for a single specific sensor application. Therefore, in the course of the research which is presented in this paper, the LiDAR pedestrian detection algorithm was trained on synthetically generated data and mixed (real and synthetic) datasets. The road environment was simulated with the application of the 3D rendering Carla engine, while the data for analysis were obtained from the LiDAR sensor model. In the proposed approach, the data generated by the simulator are automatically labeled, reshaped into range images and used as training data for a deep learning algorithm. Real data from Waymo open dataset are used to validate the performance of detectors trained on synthetic, real and mixed datasets. YOLOv4 neural network architecture is used for pedestrian detection from the LiDAR data. The goal of this paper is to verify if the synthetically generated data can improve the detector's performance. Presented results prove that the YOLOv4 model trained on a custom mixed dataset achieved an increase in precision and recall of a few percent, giving an F1-score of 0.84.
Boundary TextSpotter: Toward Arbitrary-Shaped Scene Text Spotting

Reading arbitrary-shaped text in an end-to-end fashion has received particularly growing interested in computer vision. In this paper, we study the problem of scene text spotting, which aims to detect and recognize text from cluttered images simultaneously and propose an end-to-end trainable neural network named Boundary TextSpotter. Different from existing methods that describe the shape of text instance with bounding box or shape mask, Boundary TextSpotter formulates it as a set of boundary points. Besides, the representation of such boundary points provides the order of reading text. Benefiting from the representation on both detection and recognition, Boundary TextSpotter can easily deal with the text of arbitrary shapes. Further, to efficiently detect the boundary points of the text, a single-stage text detector is proposed, which can almost perform at a real-time speed. Experiments on three challenging datasets, including ICDAR2015, Total-Text and CTW1500 demonstrate that the proposed method achieves state-of-the-art or competitive results, meanwhile significantly improving the inference speed.
Perceived benefits of training, individual readiness for change, and affective organizational commitment among employees of national jordanian banks

This study aimed to examine how employees’ perceived benefits of training impact their level of affective organizational commitment through investigating the mediating role of individual readiness for change in National Jordanian banks. The study sample included 451 employees from 16 banks in Jordan. Stratified random sampling was used for the selection of the study participants, and data were collected using a self-administered written questionnaire. Partial least squares structural equation modelling was conducted to analyze the collected data and test the study hypotheses, which were developed according to the social exchange theory and psychological contract theory. The analysis provided strong evidence for the contentions of the social exchange theory, whereby employees’ affective commitment to their banks was found to be positively influenced by their perceptions of the job-, career, and personal-related benefits of training. Moreover, individual readiness for change was shown to be positively influenced by employees’ perceived benefits of training, and employees’ affective organizational commitment was positively influenced by their readiness for change. Finally, individual readiness for change was found to act as a mediating variable between employees’ perceived benefits of training and their level of affective commitment to their banks. The current study provides bank management teams with a comprehensive understanding of employees’ affective organizational commitment as a potential outcome of training and provides evidence for the relationship between the two variables.
An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning

Nowadays, Virtual Reality is becoming more and more popular, and 360 video is a very important part of the system. 360 video transmission over the Internet faces many difficulties due to its large size. Therefore, to reduce the network bandwidth requirement of 360-degree video, Viewport Adaptive Streaming (VAS) was proposed. An important issue in VAS is how to estimate future user viewing direction. In this paper, we propose an algorithm called GLVP (GRU-LSTM-based-Viewport-Prediction) to estimate the typical view for the VAS system. The results show that our method can improve viewport estimation from 9.5% to near 20%compared with other methods.
Color Ultrasound Image Watermarking Scheme Using FRT and Hessenberg Decomposition for Telemedicine Applications

Watermarking is a valuable technique for verifying medical images obtained through the internet for diagnosis. There is a greater need for security in medical pictures with ever- increasing security risks. This research presented a Finite Ridgelet Transform (FRT)-Hessenberg based watermarking scheme in medical images. The suggested paradigm is divided into two stages. Before watermark insertion, FRT is applied to medical images. The coefficients are combined into blocks of 4 x 4 and each block is decomposed using Hessenberg decomposition. The second column of the Q matrix is used to insert the watermark using the additive quantization technique. The results obtained from our experiment have given good visual quality of the watermarked images. The high PSNR value 53.6121 and NC value 1.0 show that our scheme is performing better. Moreover, the performance of our scheme is robust against several attacks. The consequences of this result imply that the anticipated scheme is effective for medical image watermarking.
Authenticating IoT Devices issues based on Blockchain

The Internet of Things (IoT) is increasingly an authenticity today. Nevertheless, some key challenges still need to be given care so that IoT solutions further support the rising demand for connected devices and the facilities offered. Outstanding to the possible significance and sensitivity of facilities, IoT solutions should address the security and privacy worries nearby these devices and the data they assemble, generate, and process. Recently, Blockchain technology has increased much attention in IoT solutions. Its principal usage conditions are in the financial domain, where Blockchain creates a capable application world and can be leveraged to explain security and privacy issues. However, this developing technology has a great possibility in the most various technological areas and can significantly help achieve the Internet of Things view in different features, increasing the measurements of decentralization, facilitating communications, allowing new transaction models, and allowing independent coordination of the devices. The paper's goal is to provide the ideas about the structure and operation of Blockchain and, typically, analyze how the use of this technology can be used to deliver security and privacy in IoT.
Parallel optical coherent dot-product architecture for large-scale matrix multiplication with compatibility for diverse phase shifters

Photonics physically promises high-speed and low-consumption computing of matrix multiplication. Nevertheless, conventional approaches are challenging to achieve large throughput, high precision, low power consumption, and high density simultaneously in a single architecture, because the integration scale of conventional approaches is strongly limited by the insertion loss of cascaded optical phase shifters. Here, we present a parallel optical coherent dot-product (P-OCD) architecture, which deploys phase shifters in a fully parallel way. The insertion loss of phase shifters does not accumulate at large integration scale. The architecture decouples the integration scale and phase shifter insertion loss, making it possible to achieve superior throughput, precision, energy-efficiency, and compactness simultaneously in a single architecture. As the architecture is compatible with diverse integration technologies, high-performance computing can be realized with various off-the-shelf photonic phase shifters. Simulations show that compared with conventional architectures, the parallel architecture can achieve near 100× higher throughput and near 10× higher energy efficiency especially with lossy phase shifters. The parallel architecture is expected to perform its unique advantage in computing-intense applications including AI, communications, and autonomous driving.
Blind Robust Video Watermarking Based on Adaptive Region Selection and Channel Reference

Digital watermarking technology has a wide range of applications in video distribution and copyright protection due to its excellent invisibility and convenient traceability. This paper proposes a robust blind watermarking algorithm using adaptive region selection and channel reference. By designing a combinatorial selection algorithm using texture information and feature points, the method realizes automatically selecting stable blocks which can avoid being destroyed during video encoding and complex attacks. In addition, considering human's insensitivity to some specific color components, a channel-referenced watermark embedding method is designed for less impact on video quality. Moreover, compared with other methods' embedding watermark only at low frequencies, our method tends to modify low-frequency coefficients close to mid frequencies, further ensuring stable retention of the watermark information in the video encoding process. Experimental results show that the proposed method achieves excellent video quality and high robustness against geometric attacks, compression, transcoding and camcorder recordings attacks.
Survey of Text Document Summarization Based on Ensemble Topic Vector Clustering Model

Text document analysis has recently emerged as a promising strategy in content summarization domain. The analysis can be carried out in two stages: Text abstraction, the process of summarizing a document by including the most critical information from the original document; Text summarization, the process of generalizing redundant information in order to determine the significance of the issue. Automated text summarization is a technique used for extracting the most meaningful information from a document or group of related papers and assembling it into a concise version by retaining the overall meaning of the text document. The text abstraction model is mostly associated with the content extraction process, whereas to perform text summarization, the Natural Language Processing (NLP) technique is used to extract the necessary information from a lengthy text document. To perform efficient and automated text processing and summarization, this research study suggests a novel ensemble topic vector clustering technique, which utilizes Semantic Analysis (SA) to analyze the content. Further, the proposed study concentrates on the process of topic summarizaton to investigate various strategies and perform problem identification. Finally, the proposed study examines the significance of summarization implementation by comparing it with similar existing approaches.
Along the Time

Recent years have witnessed remarkable progress on knowledge graph embedding (KGE) methods to learn the representations of entities and relations in static knowledge graphs (SKGs). However, knowledge changes over time. In order to represent the facts happening in a specific time, temporal knowledge graph (TKG) embedding approaches are put forward. While most existing models ignore the independence of semantic and temporal information. We empirically find that current models have difficulty distinguishing representations of the same entity or relation at different timestamps. In this regard, we propose a TimeLine-Traced Knowledge Graph Embedding method (TLT-KGE) for temporal knowledge graph completion. TLT-KGE aims to embed the entities and relations with timestamps as a complex vector or a quaternion vector. Specifically, TLT-KGE models semantic information and temporal information as different axes of complex number space or quaternion space. Meanwhile, two specific components carving the relationship between semantic and temporal information are devised to buoy the modeling. In this way, the proposed method can not only distinguish the independence of the semantic and temporal information, but also establish a connection between them. Experimental results on the link prediction task demonstrate that TLT-KGE achieves substantial improvements over state-of-the-art competitors. The source code will be available on https://github.com/zhangfw123/TLT-KGE.
An AI-Based Curling Game System for Winter Olympics

The real-time application of artificial intelligence (AI) technologies in sports is a long-standing challenge owing to large spatial sports field, complexity, and uncertainty of real-world environment, etc. Although some AI-based systems have been applied to sporting events such as tennis, basketball, and football, they are replayed after the game rather than applied in real time. Here, we present an AI-based curling game system, termed CurlingHunter, which can display actual trajectories, predicted trajectories, and house regions of curling during the games via a giant screen in curling stadiums and a live streaming media platform on the internet in real time, so as to assist the game, improve the interest of watching game, help athletes train, etc. We provide a complete description of CurlingHunter' architecture and a thorough evaluation of its performances and demonstrate that CurlingHunter possesses remarkable real-time performance (~9.005 ms), high accuracy (30 ± 3 cm under measurement distance > 20 m), and good stability. CurlingHunter is the first, to the best of our knowledge, real-time system that can assist athletes to compete during the games in the history of sports and has been successfully applied in Winter Olympics and Winter Paralympics. Our work highlights the potential of AI-based systems for real-time applications in sports.