Reviewerly/exHarmony huggingface dataset:
A Study of Digital Camera Spectral Reconstruction Based on BP Neural Networks and Polynomial Expansions

The application of multispectral imaging technology in color science is becoming more and more extensive, and how to reconstruct high-precision spectral reflectance information is a key issue currently facing. In order to solve the problem of pixel shift between channels in the band-pass filter spectral imaging system and reduce the complexity of multispectral image acquisition, a spectral reconstruction algorithm based on camera response expansion combined with BP neural network is proposed. Taking spectral root mean square error and CIEDE2000 color difference as evaluation indicators, using SG140 color card as training sample and RC24 color card as test sample, and comparing with several current PCA and BP-PCA-filter methods with better reconstruction effect. The experimental results show that when the number of response expansion items is 22, the average spectral root mean square error and CIEDE2000 color difference are reduced to 0.029 and 1.432, respectively. The spectral reconstruction accuracy of the improved method is obviously better than that of PCA and BP-PCA-filter algorithms.
Smart Electricity and Consumer Management Using Hyperledger Fabric Technology

Blockchain technology comes up with the great potential to foster various section like Banking, medical, judicial, and education, with its distinctive combination of features, such as, decentralization, immutability, and transparency, etc. []. This paper propose an innovative, cost-effective solution, to optimize electricity connection and consumer management system, with minimum physical consumer interactions, and the least negative impact on his/her lifestyle, comfort, and convenience. Besides, it would also provide data privacy as well as transparency with high-security services and ease the workload of the employees in the electricity department by integrating their workspace with hyperledger fabric []. Here, developed a consummated end-to-end typical business blockchain application that have the client application where the outside world communicates using the chaincode which is deployed and executed inside a fabric network. This system will save much of the employee’s time, which was earlier taken by the traditional electricity management system.
A classical model for perfect transfer and fractional revival based on q-Racah polynomials

It is shown how choices based on the q-Racah polynomials for the masses and spring constants along a chain give new systems that exactly allow dispersionless end-to-end transmission of a pulse as well as periodic splitting of the initial momentum between the first and last mass. This “Newton's cradle” provides a classical analog of quantum spin devices that exhibit perfect state transfer and fractional revival.
Recent Advances on Mammogram Imaging for Breast Cancer Analysis: A Technological Review

Breast cancer is a leading cause of death after skin cancer and is a threat to women worldwide. The automated technique of breast cancer that is still being considered is screening X-ray mammography by radiologists, which sometimes led to false-positive and false-negative results, in turn increasing the mortality ratio in breast cancer patients. However, earlier study shows that AI can be used as a detecting aid for radiologists where either radiologists or AI can work hand in hand or AI can be used as the second opinion for better results. The main objective of this paper is to include a technical review, which mainly summarizes contemporary research progress on mammogram image analysis and an overview of various projected AI-based models such as machine learning (ML), deep learning (DL), transfer learning (TL) and extreme learning machine (ELM) and the way they function and their advantages and limitations. Also, we convey how the different models have taken into contemplation to detect breast cancer more accurately with lesser false-positive (sensitivity) or false-negative rates (specificity) using the 2D or 3D mammograms images. This article also discusses several hyperparameters that affect the model accuracy.
Path planning of mobile robot based on improved DDQN

Abstract Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm.
Uncertainty of Interval Type-2 Fuzzy Sets Based on Fuzzy Belief Entropy

Interval type-2 fuzzy sets (IT2 FS) play an important part in dealing with uncertain applications. However, how to measure the uncertainty of IT2 FS is still an open issue. The specific objective of this study is to present a new entropy named fuzzy belief entropy to solve the problem based on the relation among IT2 FS, belief structure, and Z-valuations. The interval of membership function can be transformed to interval BPA [Bel,Pl]. Then, Bel and Pl are put into the proposed entropy to calculate the uncertainty from the three aspects of fuzziness, discord, and nonspecificity, respectively, which makes the result more reasonable. Compared with other methods, fuzzy belief entropy is more reasonable because it can measure the uncertainty caused by multielement fuzzy subsets. Furthermore, when the membership function belongs to type-1 fuzzy sets, fuzzy belief entropy degenerates to Shannon entropy. Compared with other methods, several numerical examples are demonstrated that the proposed entropy is feasible and persuasive.
The digital generation: New educational needs

The article discusses the pedagogical feasibility of using innovative intellectual immersive technologies in the educational process, corresponding to the pace and needs of digital society development. The features of teaching the digital generation of students are highlighted. The specificity of educational and cognitive activity and the peculiarities of the development of cognitive interest in future IT-specialists in the process of studying specialized disciplines are determined. It is proposed to consider a constructivist approach to learning, which implies the creation of a modal, functional, communicative learning environment, which, in turn, contributes to the self-construction and development of student’ own cognitive trajectory. Arguments are given in favor of the use of Augmented Reality (AR) technology in teaching “digital natives” studying in the IT-field (direction of training 09.03.01 “Informatics and Computer Engineering”). Empirical data are presented that prove the positive influence of educational AR-content on the level of student engagement. The coefficient of student engagement is a quantitative characteristic to assess the state of interest in the educational material in the discipline “Computer Networks and Telecommunications”. The student engagement rate was established by collecting subjective data, which are not directly observable, and are revealed in the course of students’ self-assessment of their learning activities.
A proposal of adaptive parameter tuning for robust stabilizing control of N–level quantum angular momentum systems

Stabilizing control synthesis is one of the central subjects in control theory and engineering, and it always has to deal with unavoidable uncertainties in practice. In this study, we propose an adaptive parameter tuning algorithm for robust stabilizing quantum feedback control of N-level quantum angular momentum systems with a robust stabilizing controller proposed by [Liang, Amini, and Mason, SIAM J. Control Optim., 59 (2021), pp. 669-692]. The proposed method ensures local convergence to the target state. Besides, numerical experiments indicate its global convergence if the learning parameters are adequately determined.
Polytope structures for Greenberger–Horne–Zeilinger diagonal states <sup>*</sup>

We explore the polytope structures for genuine entanglement, biseparability, full biseparability and Bell inequality of multi-qubit GHZ diagonal states. We first show that biseparable GHZ diagonal states make hypersimplices inside the simplices consisting of all GHZ diagonal states. Next, we consider full biseparability which is equivalent to positive partial transpose for GHZ diagonal states, and show that they make the convex hulls of simplices and cubes. We also visualize which part of the simplex violates multipartite Bell inequality. Finally, we compute precise volumes for genuine entanglement, biseparability, full biseparability and states violating Bell inequality among all GHZ diagonal states.
Earthquake observation data grading and storage research

Abstract With the constant development of contemporary science and technology and increasing popularization of the big data technology, the concept of “database” has been integrated into people’s daily work and life. The database system also plays an indispensable role in earthquake early reports. This paper briefly analyzes introducing the mature big data technology to earthquake observation and seeks graded storage of databases to ensure a faster and more accurate retrieval of data as well as timely reports of earthquakes.
Establishment of Agricultural Economic Management Data Model Based on Block Chain Technology

With the investment of large-scale and high-speed informatization under the traditional institutional framework, serious information islands have been caused. Therefore, the establishment of data models for the agricultural economy has become an urgent requirement for the construction of agricultural economic informatization. With the rise of Bitcoin in China, the region Block chain (BC) technology has entered people’s sight, providing new possibilities for the establishment of agricultural economic management data models. Based on this, this article carried out a research on the establishment of a data model for agricultural economic management based on BC technology. This article elaborates on the establishment of a data model based on BC technology. Through an overview of BC in technology, it introduces the technical characteristics of the BC itself, and discusses several possible application scenarios of BC technology, and explains the working principle of BC technology used in agricultural economic management shows the characteristics and advantages of decentralized management. This article is aimed at the problems of data sharing in the economic network, combined with the scalability of the information technology on the BC and the privacy protection solution we proposed to use the BC to classify the nodes of each agricultural unit and department connected, data exchange is carried out through the platform, and the authenticity and reliability of the data exchange is ensured and it is not maliciously tampered with. The investigation and research results show that in the experiments of the paillier cryptosystem system in this paper, as the total length of the key generated by it gradually increases, the number of calculations that must be required in the process of generating the key gradually increases, and its increasing time and rate gradually increase by 67%; as the total length of the key decreases, the time spent in the encryption and decryption process will gradually decrease, the amount of calculation in the encryption and decryption process gradually increases by 127%, and the time required for decryption and decryption as the key length changes exponentially.
Dynamic sampling from a discrete probability distribution with a known distribution of rates

In this paper, we consider several efficient data structures for the problem of sampling from a dynamically changing discrete probability distribution, where some prior information is known on the distribution of the rates, in particular the maximum and minimum rate, and where the number of possible outcomes N is large. We consider three basic data structures, the Acceptance-Rejection method, the Complete Binary Tree and the Alias method. These can be used as building blocks in a multi-level data structure, where at each of the levels, one of the basic data structures can be used, with the top level selecting a group of events, and the bottom level selecting an element from a group. Depending on assumptions on the distribution of the rates of outcomes, different combinations of the basic structures can be used. We prove that for particular data structures the expected time of sampling and update is constant when the rate distribution follows certain conditions. We show that for any distribution, combining a tree structure with the Acceptance-Rejection method, we have an expected time of sampling and update of $O\left(\log\log{r_{max}}/{r_{min}}\right)$ is possible, where $r_{max}$ is the maximum rate and $r_{min}$ the minimum rate. We also discuss an implementation of a Two Levels Acceptance-Rejection data structure, that allows expected constant time for sampling, and amortized constant time for updates, assuming that $r_{max}$ and $r_{min}$ are known and the number of events is sufficiently large. We also present an experimental verification, highlighting the limits given by the constraints of a real-life setting.
Unsupervised anomaly detection in MR images using multicontrast information

Anomaly detection in magnetic resonance imaging (MRI) is to distinguish the relevant biomarkers of diseases from those of normal tissues. In this paper, an unsupervised algorithm is proposed for pixel-level anomaly detection in multicontrast MRI.A deep neural network is developed, which uses only normal MR images as training data. The network has the two stages of feature generation and density estimation. For feature generation, relevant features are extracted from multicontrast MR images by performing contrast translation and dimension reduction. For density estimation, the distributions of the extracted features are estimated by using Gaussian mixture model (GMM). The two processes are trained to estimate normative distributions well presenting large normal datasets. In test phases, the proposed method can detect anomalies by measuring log-likelihood that a test sample belongs to the estimated normative distributions.The proposed method and its variants were applied to detect glioblastoma and ischemic stroke lesion. Comparison studies with six previous anomaly detection algorithms demonstrated that the proposed method achieved relevant improvements in quantitative and qualitative evaluations. Ablation studies by removing each module from the proposed framework validated the effectiveness of each proposed module.The proposed deep learning framework is an effective tool to detect anomalies in multicontrast MRI. The unsupervised approaches would have great potentials in detecting various lesions where annotated lesion data collection is limited.
Open Science and Educational Technology Research

Over the last decade a spate of issues has been emerging in empirical research spanning diverse fields such as biology, medicine, economics, and psychological science. The crisis has already led to fundamental shifts in how research is being conducted in several fields, particularly psychological science. Broadly labelled the ‘replication crisis’, these issues place substantial doubt on the robustness of peer-reviewed quantitative research across many disciplines. In this editorial, we will delve into the replication crisis and what it means for educational technology research. We will address two key areas, describing the extent to which the replication crisis applies to educational technology research and suggestions for responses by our community.
Application of AES and DES Algorithms in File Management

In recent years, with the rapid development of computer and network technology, major enterprises and institutions have successively popularized the use of computer for office, and electronic documents have gradually replaced paper documents. Important enterprise data, such as customer information, are stored in the computer in the form of electronic files. The use of computers in office has brought us many benefits such as high efficiency and resource saving, but also correspondingly caused the safety management of electronic documents. For example, the risk of information leakage is greatly increased, and office personnel may cause irreparable damage to electronic documents due to misoperation. This article takes the current situation of electronic document security management as the research background, and proposes a solution to securely manage electronic documents. Combine the technical theory of file management with the AES encryption algorithm, combine the theoretical principle of file management with the DES encryption algorithm, and use the characteristics of file management to make up for the defects of AES and DES algorithms to achieve better encryption effects and improve Difficulty of breaking secrets. At the same time, it takes into account file query and junk file disposal; through file remote transmission, secure communication between Internet users is realized.
Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.
M2M: Learning to Enhance Low-Light Image from Model to Mobile FPGA

With the development of convolutional neural networks, the effectiveness of low-light image enhancement techniques have been greatly advanced. However, the calculate-intensive and memory-intensive characteristics of convolutional neural networks make them difficult to be implemented in mobile platform with low power and limited bandwidth. This paper proposes a complete solution for low-light image enhancement from CNN model to mobile (M2M) FPGA. The proposed solution utilizes a pseudo-symmetry quantization method to compress the low-light image enhancement model, and an accelerator to permit the processing ability of the system significantly. We implemented the whole system on a customized FPGA SOC platform (a low-cost chip, Xilinx Inc. ZYNQ\(^{TM}\)XC7Z035). Extensive experiments show that our method achieved competitive results with the other three platforms, i.e. achieved better speed compare to ARM and CPU; and achieved better power efficiency compared to ARM, CPU, and GPU.
There’s no free lunch: a study of Genetic Algorithm use in Maritime Applications

This paper surveys the applicability and performance of a variety of algorithms belonging to the Genetic Algorithm (GA) family on maritime engineering problems. Nine different GAs from the state-of-the-art are compared with the original GA. The applications are a yacht layout and a grillage structure. The aim is to understand the GA mechanisms by visualising the behaviour, find appropriate algorithms for common problems in maritime engineering and understand the common characteristics of these applications. Guidance is given on the correct algorithm selection, with cMLSGA outperforming other state-of-the-art algorithms on all the test problems.
A Study on Data Compression Algorithms for Its Efficiency Analysis

For many computerized applications, data compression is a standard requirement. In order to minimize the capacity needed for that data, it decreases the redundancy in data representation and thus therefore reduces the connectivity cost by efficiently utilizing the available bandwidth. There are a range of algorithms for data compression that are used to compress various formats of data. There are also sets of different compression algorithms for a single data form, which use different approaches. This paper assesses lossless algorithms for compression of data and compares their performance. To assess the performance of compressing text data, images, audio, a set of selected algorithms are used. Experimental findings and comparisons of algorithms for lossless compression using methods for compression. It gives contrasting conclusions on the size and time ratios toward existing research. Different information that can be compressed not only text but it can be Audio, pictures and video information. In this article, there are variety of various forms of lossless compression algorithms discussed.
SocialGuard: An adversarial example based privacy-preserving technique for social images

The popularity of various social platforms has prompted more people to share their routine photos online.However, undesirable privacy leakages occur due to such online photo sharing behaviors.Advanced deep neural network (DNN) based object detectors can easily steal users' personal information exposed in shared photos.In this paper, we propose a novel adversarial example based privacy-preserving technique for social images against object detectors based privacy stealing.Specifically, we develop an Object Disappearance Algorithm to craft two kinds of adversarial social images.One can hide all objects in the social images from being detected by an object detector, and the other can make the customized sensitive objects be incorrectly classified by the object detector.The Object Disappearance Algorithm constructs perturbation on a clean social image.After being injected with the perturbation, the social image can easily fool the object detector, while its visual quality will not be degraded.We use two metrics, privacy-preserving success rate and privacy leakage rate, to evaluate the effectiveness of the proposed method.Experimental results show that, the proposed method can effectively protect the privacy of social images.The privacy-preserving success rates of the proposed method on MS-COCO and PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively, and the privacy leakage rates on these two datasets are as low as 0.57% and 0.07%, respectively.In addition, compared with existing image processing methods (low brightness, noise, blur, mosaic and JPEG compression), the proposed method can achieve much better performance in privacy protection and image visual