Reviewerly/exHarmony huggingface dataset:
Two-photon process of a single two-level atom simultaneously absorbing or emitting two photons distributed in different cavities and its applications

We propose an approach for achieving a two-photon process in which a single two-level atom can simultaneously absorb or emit two photons separately distributed in two cavities. This two-photon process is realized by a two-level atom dispersively coupled to a classical pulse and two cavities. The main advantage of this proposal lies in the fact that because only two levels of the atom are used, i.e., no auxiliary atomic levels are utilized, decoherence from the higher energy levels of the atom is avoided. In addition, we find that the two-photon process can enable the synthesis of a hybrid atom-cavity-cavity Greenberger-Horne-Zeilinger (GHZ) entangled state. As an example, we further discuss the experimental feasibility of creating the proposed hybrid GHZ state in a circuit QED system. This proposal is quite general and can be implemented in a wide range of physical systems, such as a natural or artificial atom coupled to two optical or microwave cavities.
Slender Swarm Flamingo optimization-based residual low-light image enhancement network

Image visibility issues from a low-light background affects the interpretability or perception of the images due to the lack of resolution is the major challenge. Accordingly, in this research, Slender Swarm Flamingo (SSF) optimization-based residual Low light image enhancement network (SSF optimization-based residual Lienet) is proposed for enhancing the Image resolution. Multiple solutions are generated using the meta-heuristic algorithm named SSF optimization, which is devised by the standard hybridization of the flamingo search and the particle swarm algorithm. The final desired solution is obtained by tuning the parameters in the classifier, which provides the desired output. The utility of very deep residual networks initiates residual images with high resolution that provides enhanced image output. The proficiency of the research is proved through the analysis based on the metrics with the PSNR OF 39.65 dB.
Double Broad Reinforcement Learning Based on Hindsight Experience Replay for Collision Avoidance of Unmanned Surface Vehicles

Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-making method for the collision avoidance problem of unmanned surface vehicles (USVs), the algorithm still has the problem of over-estimation and has difficulty converging quickly due to the sparse reward problem in a large area of sea. To overcome the dilemma, we propose a double broad reinforcement learning based on hindsight experience replay (DBRL-HER) for the collision avoidance system of USVs to improve the efficiency and accuracy of decision-making. The algorithm decouples the two steps of target action selection and target Q value calculation to form the double broad reinforcement learning method and then adopts hindsight experience replay to allow the agent to learn from the experience of failure in order to greatly improve the sample utilization efficiency. Through training in a grid environment, the collision avoidance success rate of the proposed algorithm was found to be 31.9 percentage points higher than that in the deep Q network (DQN) and 24.4 percentage points higher than that in BRL. A Unity 3D simulation platform with high fidelity was also designed to simulate the movement of USVs. An experiment on the platform fully verified the effectiveness of the proposed algorithm.
Virtual Clusters: Isolated, Containerized HPC Environments in Kubernetes

Today, Cloud and HPC workloads tend to use different approaches for managing resources. However, as more and more applications require a mixture of both high-performance and data processing computation, convergence of Cloud and HPC resource management is becoming a necessity. Cloud-oriented resource management strives to share physical resources across applications to improve infrastructure efficiency. On the other hand, the HPC community prefers to rely on job queueing mechanisms to coordinate among tasks, favoring dedicated use of physical resources by each application. In this paper, we design a combined Slurm-Kubernetes system that is able to run unmodified HPC workloads under Kubernetes, alongside other, non-HPC applications. First, we containerize the whole HPC execution environment into a virtual cluster, giving each user a private HPC context, with common libraries and utilities built-in, like the Slurm job scheduler. Second, we design a custom Slurm-Kubernetes protocol that allows Slurm to dynamically request resources from Kubernetes. Essentially, in our system the Slurm controller delegates placement and scheduling decisions to Kubernetes, thus establishing a centralized resource management endpoint for all available resources. Third, our custom Kubernetes scheduler applies different placement policies depending on the workload type. We evaluate the performance of our system compared to a native Slurm-based HPC cluster and demonstrate its ability to allow the joint execution of applications with seemingly conflicting requirements on the same infrastructure with minimal interference.
Web Service Anti-patterns Detection Using CNN with Varying Sequence Padding Size

Today, many IT companies use service-oriented architecture (SOA) as an effective architectural method for building their systems. Like many other complex structures, a service-based system (SBS) can be modified to meet new user needs. Continuous improvement of service-based systems to meet user needs will reduce the quality of software development leading to problems called Anti-patterns in web services. An anti-pattern is generally chosen as a good solution to the problem posed; however, it brings more liabilities than benefits. Anti-patterns inhibit the sustainability and perception of software systems. Therefore, there is an increasing need for the prediction of anti-patterns in the early stages of software design to enhance software quality in terms of maintenance costs, execution costs and memory consumption. In this paper, we analyse the effectiveness of convolutional neural network (CNN) with distinct padding sizes in the detection of web service anti-patterns. We conclude that CNN with two hidden layers is performing better with a mean accuracy of 97.58. SMOTE performs best among the data sampling techniques. We also inferred that the word embedding technique with a sequence padding size as 200 performed best with a mean accuracy of 97.76%.
KDC: New Dataset for Kannada Document Categorization

In recent years, multilingual texts are blooming in the digital world. Analysis of these multilingual texts for various computational intelligence tasks is vital. To analyze and automate these various language tasks, resources are obligatory. Hence, there is a huge demand for the creation of regional language resources. Exclusively, processing of Indian regional languages for computational linguistic tasks demands the prodigious resources. Here, the new dataset for Kannada document classification is presented. Formerly, an exhaustive review on the various contributions of the researchers for Indian regional languages is discussed in Sect. 1. Further, the characteristics and the challenges faced in creation of the new Kannada document dataset for categorization are explained. Later, the validation results of the new dataset are presented by experimenting the various machine learning algorithms on the dataset. At last, results of various predominantly used classifiers are discussed.
Joint Blind Estimation of Multidomain Parameters for Multiple Frequency Hopping Signals

Accurate blind estimation of multidomain parameters(spatial, time, and frequency parameters) for multiple frequency hopping signals intercepted in the battlefield is a key link to support jamming, information recovery and other tasks, and has always been a hot and difficult issue in the field of electronic warfare reconnaissance. In this paper, an efficient blind estimation algorithm based on polarization sensitive array(PSA) signal processing and time-frequency(TF) analysis is proposed. The basic idea is as follows: firstly, the frequency, duration, 2D-DOA and polarization parameters of each hop of the intercepted multiple frequency hopping signals are estimated; Then, the stability of the spatial parameters is used to realize the “hop” sorting of each FH signal and the frequency sorting of each signal; Finally, according to the theoretical characterization of time domain parameters of FH signals and the hop sorting results, the calculation method of hop rate and hop time of each FH signal is given. The novel algorithm proposed in this paper does not need parameter pairing and can be applied to the under determined conditions. Theoretically, only 5 orthogonal dipole pair antennas are required to realize efficient and accurate estimation of hop rate, hop timing, frequency set, 2D-DOA and polarization parameters of multiple (more than 5) FH signals intercepted in battlefield. Numerical examples are carried out to show the performance of the proposed algorithm.
On Machine Learning Knowledge Representation in the Form of Partially Unitary Operator. Knowledge Generalizing Operator

A new form of ML knowledge representation with high generalization power is developed and implemented numerically. Initial 𝐼𝑁 attributes and 𝑂𝑈𝑇 class label are transformed into the corresponding Hilbert spaces by considering localized wavefunctions. A partially unitary operator optimally converting a state from 𝐼𝑁 Hilbert space into 𝑂𝑈𝑇 Hilbert space is then built from an optimization problem of transferring maximal possible probability from 𝐼𝑁 to 𝑂𝑈𝑇, this leads to the formulation of a new algebraic problem. Constructed Knowledge Generalizing Operator 𝑼 can be considered as a 𝐼𝑁 to 𝑂𝑈𝑇 quantum channel; it is a partially unitary rectangular matrix of the dimension dim(𝑂𝑈𝑇)×dim(𝐼𝑁) transforming operators as Aᴼᵁᵀ= 𝑼 Aᴵᴺ 𝑼† . Whereas only operator 𝑼 projections squared are observable ⟨𝑂𝑈𝑇|𝑼|𝐼𝑁⟩² (probabilities), the fundamental equation is formulated for the operator 𝑼 itself. This is the reason of high generalizing power of the approach; the situation is the same as for the Schrödinger equation: we can only measure ψ² , but the equation is written for ψ itself.
Hybrid Deep Learning CNN-Bidirectional LSTM and Manhattan Distance for Japanese Automated Short Answer Grading

This paper discusses the development of an Automatic Essay Grading System (SIMPLE-O) designed using hybrid CNN and Bidirectional LSTM and Manhattan Distance for Japanese language course essay grading. The most stable and best model is trained using hyperparameters with kernel sizes of 5, filters or CNN outputs of 64, a pool size of 4, Bidirectional LSTM units of 50, and a batch size of 64. The deep learning model is trained using the Adam optimizer with a learning rate of 0.001, an epoch of 25, and using an L1 regularization of 0.01. The average error obtained is 29%.
Connecting Design to Code

How can developers use what they know about design to more effectively understand code? This article explores the ways in which the brain helps developers understand code and how this process can be facilitated through tools and processes that help understand code in terms of its design.
Density-based Outlier Detection in Multi-dimensional Datasets

Density-based outlier detection is one of the hot issues in data mining.A point is determined as outlier on basis of the density of points near them.The existing density-based detection algorithms have high time complexity, in order to reduce the time complexity, a new outlier detection algorithm DODMD (Density-based Outlier Detection in Multidimensional Datasets) is proposed.Firstly, on the basis of ZH-tree, the concept of micro-cluster is introduced.Each leaf node is regarded as a micro-cluster, and the micro-cluster is calculated to achieve the purpose of batch filtering.In order to obtain n sets of approximate outliers quickly, a greedy method is used to calculate the boundary of LOF and mark the minimum value as .Secondly, the outliers can filtered out by , the real outliers are calculated, and then the result set is updated to make the boundary closer.Finally, the accuracy and efficiency of DODMD algorithm are verified on real dataset and synthetic dataset respectively.
Techniques for Privacy-Preserving Data Aggregation in an Untrusted Distributed Environment

Differential Privacy (DP) conceptualizes a system for sharing information about a cluster of individuals in a dataset and protecting their privacy at the same time. DP, characterized by parameters (ϵ, δ), provides mathematical assurances on privacy and hence is a recognized approach for privacy-preserving data analysis and aggregation. DP data transformation mechanisms add randomness to the data to achieve differential privacy, therefore adversely impacting the utility of the data resulting in a trade-off between utility and privacy.
Three-wavelength digital photoelasticity

A three-wavelength photoelasticity method is proposed to simplify the optical setup and speed up data acquisition. By recording six intensity images with circularly polarized illuminations of three close wavelengths, the phase retardation and corresponding inner stress can be computed accurately with a correspondingly developed computational algorithm. Since the mechanical rotations of wave plates and polarizers required by classic photoelasticity techniques are avoided, the data acquisition of this proposed method is very speedy, and measurement of a dynamic sample can be achieved with a very simple and compact optical setup. Besides theoretical analyses, numerical and experimental evidences are also used to confirm the feasibility of this suggested three-wavelength digital photoelasticity method.
New Interactive Architecture between Energy Monitoring Equipment and Power System

The rapid development of new energy has a great impact on the traditional power system. In order to ensure the stability and security of the power grid, the power system will monitor and regulate the power generation and grid connection of energy. However, compared with traditional energy, new energy has the characteristics of wide distribution, unstable power generation and small power generation, so it is more necessary to strengthen the monitoring of new energy and invest more monitoring equipment, Then, the access of a large number of monitoring equipment has caused great pressure on the power system, and the existing power system architecture has increasingly failed to meet the access requirements of new energy monitoring equipment. Therefore, the core of this paper is to propose a new power system architecture scheme, a new monitoring equipment access mode, and a new information interaction processing method to solve the problems encountered by the power system in the access of new energy monitoring equipment.
Doing More With Moiré Pattern Detection in Digital Photos

Detecting moiré patterns in digital photographs is meaningful as it provides priors towards image quality evaluation and demoiréing tasks. In this paper, we present a simple yet efficient framework to extract moiré edge maps from images with moiré patterns. The framework includes a strategy for training triplet (natural image, moiré layer, and their synthetic mixture) generation, and a Moiré Pattern Detection Neural Network (MoireDet) for moiré edge map estimation. This strategy ensures consistent pixel-level alignments during training, accommodating characteristics of a diverse set of camera-captured screen images and real-world moiré patterns from natural images. The design of three encoders in MoireDet exploits both high-level contextual and low-level structural features of various moiré patterns. Through comprehensive experiments, we demonstrate the advantages of MoireDet: better identification precision of moiré images on two datasets, and a marked improvement over state-of-the-art demoiréing methods.
Latency-Oriented Secure Wireless Federated Learning: A Channel-Sharing Approach With Artificial Jamming

As a promising framework for distributed machine learning (ML), wireless federated learning (FL) faces the threat of eavesdropping attacks when a trained ML model is sent over a radio channel. To address this threat, we propose channel-sharing-based artificial jamming to increase the secrecy throughput of FL clients (FCs). Specifically, when an FC performs local model training, a selected device such as a sensor node (SN) not involved in the FL opportunistically accesses the FC’s channel to transmit its sensing data. In return, when the FC sends its locally trained model to the FL server (FLS), the selected SN provides artificial jamming to increase the FC’s secrecy throughput. Considering multiple FCs and SNs, we first consider a given pairing of FCs and SNs and optimize the local training time, the model uploading time, and the transmit-power of the FCs to minimize the total latency of FL training. After proving the convexity of this optimization problem, we propose an efficient algorithm to derive the semi-analytical solution. Then, we further investigate the pairing of the FCs and the SNs to minimize a system-wise cost reflecting both energy consumption and latency. The resulting problem is a bicriteria pairing problem, and we propose an efficient algorithm to compute the optimal pairing solution. Numerical results demonstrate the efficiency and performance advantage of our proposed channel-sharing-based approach with artificial jamming in comparison with different benchmark schemes.
A Feasibility Study of In-Situ Damage Visualization in Basalt-Fiber Reinforced Polymers with Open-Source Digital Volume Correlation

This work analyses damage formation within the bulk of basalt fiber-reinforced polymers (BFRP) by means of open-source Digital Volume Correlation (DVC). Volumetric image data were obtained from conventional in-situ X-Ray computed micro-tomography (µCT) of samples loaded in tension. The open-source image registration toolkit Elastix was employed to obtain full 3D displacement fields from the image data. We assessed the accuracy of the DVC results using the method of manufactured solution and showed that the approach followed here can detect deformation with a magnitude in the order of a fiber diameter which in the present case is 17 µm. The beneficial influence of regularization on DVC results is presented on the manufactured solution as well as on real in-situ tensile testing CT data of a BFRP sample. Results of the correlation showed that conventional µCT equipment in combination with DVC can be used to detect defects which could previously only be visualized using synchrotron facilities or destructive methods.
Preserving Transparency and Integrity of Elections Utilising Blockchain Technology

Digital voting is increasingly important in both established and emerging democracies. Some of the advantages of digital voting are faster vote count and tabulation; accurate results; increased voters’ participation and convenience; and effective handling of complex electoral system formats that require laborious counting procedures. However, transparency, credibility, and integrity concerns, as well as the limited possibility of recount, usually make traditional digital voting systems unpopular. Digital voting using blockchain technology, however, is safe, transparent, and immutable, which makes it a suitable choice for future decentralized voting systems. In particular, the Ethereum blockchain is proposed as an appropriate platform for the backbone of an e-voting system due to its widespread use, transparency, consistency and provision of smart contracts. Initial piloting on the implementation of a blockchain-based voting framework in Jordan shows promising results on its transparency and integrity by incorporating a space for representatives and observers to monitor the election procedure and results as an additional measure to ensure its efficiency and reliability. The uptake of the proposed system calls for further debate and dialogue amongst governments and people, especially in developing countries where democracy is still in its infancy.
The Study of Feedback in Writing from College English Teachers and Artificial Intelligence Platform Based on Mixed Method Teaching.

Abstract:With the development of modern teaching technology, intelligent writing correction platform has emerged and become an effective way to supplement traditional feedback from teachers under the new situation. The main theoretical basis of this study is dynamic evaluation theory. The experimental group and the controlled group were assigned three writing tasks. The statistical analysis software SPSS was used to analyze the data results. In the study, literature review, experiment, questionnaire and interview methods were used. The literature summarizes the development status of teacher feedback and intelligent platform correction at home and abroad. The experimental method is to compare the similarities and differences between teacher feedback and intelligent platform correction in specific experiments. Through questionnaires and interviews, we can understand the attitudes of teachers and students towards the intelligent correction platform.&#x0D; &#x0D; The research results show that the two feedback methods are consistent and have obvious differences. Teacher feedback has more significant advantages in the two dimensions of text structure and content, while the intelligent writing correction platform is too general and procedural in the feedback of these two dimensions. In comparison, the feedback of the intelligent writing correction platform on vocabulary and grammar is more detailed, giving students detailed information. The combination of the two ways of feedback and the application of them to the teaching of English writing in senior high schools will have a more ideal effect, which will have a positive effect on the improvement of senior high school students' English writing level.&#x0D; Keywords: Dynamic evaluation theory; Teacher feedback; Feedback from AI writing platform; contrastive study
A Cross-Line Structured Light Scanning System Based on a Measuring Arm

The measurement system proposed in this paper, using a measuring arm and line structured light, has a wide range of applications. To improve the scanning efficiency, the system outlined in this paper uses two single-line structured lights to form crosshair structured light, which we combine with a measuring arm to form a comprehensive scanning measurement system. The calibration method of Zhengyou Zhang and a calibration board are used to complete parameter calibration of the sensors and cameras, as well as hand–eye calibration of the measuring arm. For complex curved-surface objects, this system extracts the cross-line structured light optical center location, which suffers from ambiguity. Therefore, we introduce the use of periodic control of the two line structured light sources in order to resolve the light extraction polysemy. Our experimental results indicate that the proposed system can effectively satisfy the function of crosshair structured light scanning of large, complex surfaces.