Reviewerly/exHarmony huggingface dataset:
Optimal control for Hamiltonian parameter estimation in non-commuting and bipartite quantum dynamics

The ability to characterise a Hamiltonian with high precision is crucial for the implementation of quantum technologies. In addition to the well-developed approaches utilising optimal probe states and optimal measurements, the method of optimal control can be used to identify time-dependent pulses applied to the system to achieve higher precision in the estimation of Hamiltonian parameters, especially in the presence of noise. Here, we extend optimally controlled estimation schemes for single qubits to non-commuting dynamics as well as two interacting qubits, demonstrating improvements in terms of maximal precision, time-stability, as well as robustness over uncontrolled protocols.
ATTITUDE TOWARDS YOGA EDUCATION AMONG UNDERGRADUATE STUDENTS

The present study was aimed at assessing the attitude of undergraduate students towards yoga education. The study analyzed the data from 295 undergraduate students. Out of these, 161 students were male and 134 female. A questionnaire related to attitude towards yoga education was developed and standardized by the investigators and was used to collect responses from the students. The findings of this study revealed that there is no significant difference existing between male vs. female and rural college vs. urban college undergraduate students regarding their attitude toward yoga education. Another finding of this study also showed that there is no significant difference existing between arts and science undergraduate students regarding their attitude toward yoga education.
Hierarchical Identity-Based Online/Offline Encryption Scheme with Leakage Resilience

The adversary is able to obtain some secret information from side channel attacks, which further damages the security for the system. To resolve this issue, we provide a hierarchical identity-based online/offline encryption scheme which resists side channel attacks. In our scheme, most encryption operations are preprocessed in the offline stage and only a small amount of lightweight calculation needs to be performed in the online stage for generating ciphertext. The presented scheme greatly reduces the workload of online encryption and is suitable for the resource-constrained device. The security of the proposed scheme is proved by the dual system technique. The leakage performance analysis shows that the presented scheme is resilient to leakage for almost the whole symmetric key.
Talismans of Digital Literacy: A Statistical Overview

The individuals living in the 21st century have become the consumers of digital innovations and have to adapt, adopt and adapt to the new norm of surviving and thriving in the digital society. Familiarity with the latest technologies is not the only requirement for survival. One also needs to have relevant digital competencies to complete tasks with optimized outputs and efficiently deal with the chain of digital changes. The current study introduces the South Pacific digital literacy framework (SPDLF) driven by 6 essential literacies and sixty attributes. The study intends to provide a three-stage statistical validation for the South Pacific digital literacy framework. The three stages of validation include;(1) evaluating the strongest predictors of digital literacy from the six literacies in the SPDLF, (2) evaluating the significant predictors of each of the six literacies and (3) evaluating the significant attributes from a total of sixty attributes in the SPDLF. The results show that all attributes in the SPDLF are statistically significant, therefore, all attributes are significant contributors to digital literacy in the South Pacific digital literacy framework.
TARDYS Quantifiers: Extracting Temporal and Reversible DYnamical Symmetries

One of the great challenges in complex and chaotic dynamics is to reveal the details of its underlying determinism. This can be manifest in the form of temporal correlations or structured patterns in the dynamics of a measurable variable. These temporal dynamical structures are sometimes a consequence of hidden global symmetries. Here, we identify the temporal (approximate) symmetries of a semiconductor laser with external optical feedback, based on which we define the Temporal And Reversible DYnamical Symmetry (TARDYS) quantifiers to evaluate the relevance of specific temporal correlations in a time series. We show that these symmetries are also present in other complex dynamical systems, letting us extrapolate one system’s symmetries to characterize and distinguish chaotic regimes in other dynamical systems. These symmetries, natural of the dynamics of the laser with feedback, can also be used as indicators in forecasting regular-to-chaos transitions in mathematical iterative maps. We envision that this can be a useful tool in experimental data, as it can extract key features of the deterministic laws that govern the dynamics of a system despite the lack of knowledge of those specific quantitative descriptions.
A framework for evaluating security risk in system design

Abstract Design and development of ubiquitous computer network systems has become increasingly difficult as technology continues to grow. From the introduction of new technologies to the discovery of existing threats, weaknesses, and vulnerabilities there is a constantly changing landscape of potential risks and rewards. The cyber security community, and industry at large, is learning to account for these increasing threats by including protections and mitigations from the beginning of the design V process. However, issues still come from limitations in time for thoroughly exploring a potential design space and the knowledge base required to easily account for potential vulnerabilities in each. To address this problem we propose the G-T-S framework, which is an automated tool that allows a user to provide a set of inputs relating to the desired design space and returns a monetary security risk evaluation of each. This methodology first generates a series of potential designs, then dissects their contents to associate possible vulnerabilities to device elements, and finally evaluates the security risk poised to a central asset of importance. We exemplify the tools, provide methodologies for required background research, and discuss the results in evaluating a series of IoT Home models using the GTS framework. Through implementation of our framework we simplify the information an individual will require to begin the design process, lower the bar for entry to perform evaluating security risk, and present the risk as an easily understood monetary metric.
Quantum Fisher information for estimating N partially coherent point sources

A partially coherent object’s localization parameters are shown to be theoretically estimable with higher precision than those of an incoherent object, and the maximum number of independent parameters that have non-vanishing precision in the sub-Rayleigh regime is 3 (compared to 2 for an incoherent object). Normalization schemes, which are crucial in the proper interpretation of quantum Fisher information results in the presence of partial coherence, are introduced and detailed.
BHC-IoT: A Survey on Healthcare IoT Security Issues and Blockchain-Based Solution

The Internet of Things (IoT) has become one of the most widely utilized technologies in the recent decade, particularly in healthcare systems. With the usage of wearable and mobile devices, IoT-based healthcare systems have significantly increased the value of the healthcare sector, resulting in the effective use of health data exchange for enhanced, accurate, and quick diagnosis. These devices communicate, process, compute and monitor real-time scenarios via wireless or wired connections. Such devices are heterogeneous, having limited memory and computational power. The adoption of the IoT system in healthcare raises security and privacy concerns. Healthcare records containing sensitive patient data complicate this system since there is a danger of a privacy breach, privacy leakage, tampering, and so on. Blockchain technology has recently emerged as a promising solution to such breaches and problems. In this study, we consider various challenges to healthcare IoT implementation for widespread practical use, considering the security concerning the IoT layered architecture. We have categorized various layered security issues faced by IoT and specific attacks and security issues faced in communication, computation, and authentication IoT-based healthcare systems. Then we demonstrate how blockchain technology can be a key enabler in resolving numerous healthcare IoT security issues. We concluded that blockchain provides a secure and efficient way to resolve healthcare issues.
Anomaly Detection and Early Warning Model for Latency in Private 5G Networks

Different from previous generations of communication technology, 5G has tailored several modes especially for industrial applications, such as Ultra-Reliable Low-Latency Communications (URLLC) and Massive Machine Type Communications (mMTC). The industrial private 5G networks require high performance of latency, bandwidth, and reliability, while the deployment environment is usually complicated, causing network problems difficult to identify. This poses a challenge to the operation and maintenance (O&amp;M) of private 5G networks. It is needed to quickly diagnose or predict faults based on high-dimensional data of networks and services to reduce the impact of network faults on services. This paper proposes a ConvAE-Latency model for anomaly detection, which enhances the correlation between target indicators and hidden features by multi-target learning. Meanwhile, transfer learning is applied for anomaly prediction in the proposed LstmAE-TL model to solve the problem of unbalanced samples. Based on the China Telecom data platform, the proposed models are deployed and tested in an Automated Guided Vehicles (AGVs) application scenario. The results have been improved compared to existing research.
Fresher Experience Plays a More Important Role in Prioritized Experience Replay

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to measure the value of experiences and adjusts the sampling probability of experiences. Although PER can sample valuable experiences according to the TD-error, freshness is also an important character of experiences. It implicitly reflects the potential value of experiences. Fresh experiences are produced by virtue of the current networks and they are more valuable for updating the current network parameters than the past. The sampling of fresh experiences to train the neural networks can increase the learning speed of the agent, but few algorithms can perform this job efficiently. To solve this issue, a novel experience replay method is proposed in this paper. We first define that the experience freshness is negatively correlated with the number of replays. A new hyper-parameter, the freshness discounted factor μ, is introduced in PER to measure the experience freshness. Further, a novel experience replacement strategy in the replay buffer is proposed to increase the experience replacement efficiency. In our method, the sampling probability of fresh experiences is increased by raising its priority properly. So the algorithm is more likely to choose fresh experiences to train the neural networks during the learning process. We evaluated this method in both discrete control tasks and continuous control tasks via OpenAI Gym. The experimental results show that our method achieves better performance in both modes of operation.
Modeling of Optical Matrix Multipliers Using Transposed Convolutional Neural Networks

We demonstrate a data-driven model for optical matrix multipliers utilizing Mach-Zehnder interferometer meshes. For a fabricated chip, a transposed convolutional neural network model learns from experimental measurements offline and predicts the weights across 100 frequency channels in the C-band with high precision (RMSE<0.8 dB).
Integrated e-learning for knowledge management and its impact on innovation performance among Jordanian manufacturing sector companies

E-learning in knowledge management was examined in this study, specifically on how it assists organizations in improving knowledge transfer and e-learning management, to increase performance and employee knowledge management. In this study, e-learning and knowledge management systems and technology were jointly implemented, and its impact on organizational performance was examined. Organizational management was also explored. The present study investigated the relationship between knowledge management (KM) and innovation performance (IP). The mediating effect of knowledge Management was deeply explored. Randomly selected managers from 57 Jordanian manufacturing companies were the study samples, and there were 470 managers involved in this study, from strategic, tactical, and operational levels. Questionnaires were used to gather data, and the questionnaire items covered the constructs of knowledge management, organizational learning (OL), knowledge-oriented leadership (KOL) and IP. A research model was proposed and was tested using structural equation modeling (SEM). The findings were as follows: KOL positively affected KM; KOL positively affected IP; OL negatively affected IP; KOL positively affected KM; OL positively affected KM; KM positively affected IP and KM mediated the relationship between KOL, OL and IP.
The effect of reliable data transfer and efficient computer network features in Jordanian banks accounting information systems performance based on hardware and software, database and number of hosts

Reliable data transfer protocols are algorithmic techniques that guarantee the safe and secure transport of data through networks that could experience data loss or corruption. The performance of the systems and some accounting information systems (AIS) will be negatively impacted if the real-time data is not sent. The main factors that affect the performance of computer networks are the number of users, the hardware and software and the bandwidth. The computer network performance will play a role in the performance of the banks as it is an important component of the bank infrastructure. With the advancement of information technology, network technology, and computer technology, computers have been utilized to aid AIS operations, and AIS has become an unavoidable trend of development. Therefore, the purpose of this study was to investigate Data analysis in computer networks to improve AIS performance in Jordanian banks. A questionnaire was used to obtain the information. Jordanian banks account for the bulk of the participants in the survey. A total of 115 people took part in the study. According to the conclusions of this study, communication technology networks have a statistically significant impact on the growth of Jordanian banks' improved AIS performance.
The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.
Improved Ship Object Detection in Low-Illumination Environments Using RetinaMFANet

Video-based ship object detection has long been a popular research issue that has received attention in the water transportation industry. However, in low-illumination environments, such as at night or in fog, the water environment has a complex variety of light sources, video surveillance images are often accompanied by noise, and information on the details of objects in images is worsened. These problems cause high rates of false detection and missed detection when performing object detection for ships in low-illumination environments. Thus, this paper takes the detection of ship objects in low-illumination environments at night as the research object. The technical difficulties faced by object detection algorithms in low-illumination environments are analyzed, and a dataset of ship images is constructed by collecting images of ships (in the Nanjing section of Yangtze River in China) in low-illumination environments. In view of the outstanding performance of the RetinaNet model in general object detection, a new multiscale feature fusion network structure for a feature extraction module is proposed based on the same network architecture, in such a way that the extraction of more potential feature information from low-illumination images can be realized. In line with the feature detection network, the regression and classification detection network for anchor boxes is improved by means of the attention mechanism, guiding the network structure in the detection of object features. Moreover, the design and optimization of the augmentation of multiple random images and prior bounding boxes in the training process are also carried out. Finally, on the basis of experimental validation analysis, the optimized detection model was able to improve ship detection accuracy by 3.7% with a limited decrease in FPS (frames per second), and has better results in application.
Design and Implementation of Multi-Cyber Range for Cyber Training and Testing

It is essential to build a practical environment of the training/test site for cyber training and weapon system test evaluation. In a military environment, cyber training sites should be continuously developed according to the characteristics of the military. Weapons with cyber security capabilities should be deployed through cyber security certification. Recently, each military has been building its own cyber range that simulates its battlefield environment. However, since the actual battlefield is an integrated operation environment, the cyber range built does not reflect the integrated battlefield environment that is interconnected. This paper proposes a configuration plan and operation function to construct a multi-cyber range reflecting the characteristics of each military to overcome this situation. In order to test the multi-cyber range, which has scenario authoring and operation functions, and can faithfully reflect reality, the impact of DDoS attacks is tested. It is a key to real-world mission-based test evaluation to ensure interoperability between military systems. As a result of the experiment, it was concluded that if a DDoS attack occurs due to the infiltration of malicious code into the military network, it may have a serious impact on securing message interoperability between systems in the military network. Cyber range construction technology is being developed not only in the military, but also in school education and businesses. The proposed technology can also be applied to the construction of cyber ranges in industries where cyber-physical systems are emphasized. In addition, it is a field that is continuously developing with the development of technology, such as being applied as an experimental site for learning machine learning systems.
A Local and Non-Local Features Based Feedback Network on Super-Resolution

Recent advances in Single Image Super-Resolution (SISR) achieved a powerful reconstruction performance. The CNN-based network (both sequential-based and feedback-based) performs well in local features, while the self-attention-based network performs well in non-local features. However, single block cannot always perform well due to the realistic images always with multiple kinds of features. In order to take full advantage of different blocks on different features. We have chosen three different blocks cooperating to extract different kinds of features. Addressing this problem, in this paper, we propose a new Local and non-local features-based feedback network for SR (LNFSR): (1) The traditional deep convolutional network block is used to extract the local non-feedbackable information directly and non-local non-feedbackable information (needs to cooperate with other blocks). (2) The dense skip-based feedback block is use to extract local feedbackable information. (3) The non-local self-attention block is used to extract non-local feedbackable information and the based LR feature information. We also introduced the feature up-fusion-delivery blocks to help the features be delivered to the right block at the end of each iteration. Experiments show our proposed LNFSR can extract different kinds of feature maps by different blocks and outperform other state-of-the-art algorithms.
Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging

Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.