Reviewerly/exHarmony huggingface dataset:
tqix.pis: A toolbox for quantum dynamics simulation of spin ensembles in Dicke basis

We introduce tqix.pis, a library of tqix, for quantum dynamics simulation of spin ensembles. The library emulates a dynamic process by a quantum circuit, including initializing a quantum state, executing quantum operators, and measuring the final state. It utilizes collective processes in spin ensembles to reduce the dimension from exponentially to quadratically with the number of particles, i.e., the quantum state spans in Dicke basis. It also facilitates the simulation time with multi-core processors and Graphics Processing Units. The library is thus applicable for the simulation of ensembles of large number of particles that have collective properties. Various phenomena, such as spin squeezing, variational quantum squeezing, quantum phase transition, and many-body quantum dynamics, can be simulated using the library. Program Title: : A toolbox for quantum dynamics simulation of spin ensembles in Dicke basis. CPC Library link to program files: https://doi.org/10.17632/wxvncw5jkv.2 Licensing provisions: GPLv3 Programming language: Python (≥3.6) External routines: NumPy, SciPy, SymPy, Matplotlib, Sklearn, pyTorch (for GPUs users). Nature of problem: is a library in , an open-source program providing some convenient tools for quantum measurement, quantum tomography, quantum metrology, and others. The library allows executing quantum circuits of spin ensembles in Dicke basis assisting from multi-core processors and Graphics Processing Units (GPUs). Solution method: Execute quantum circuits with a large number of particles. It generates a collective quantum register (collective state) by an ensemble of spin-1/2 particles under the collective process, evolves the state under applied quantum gates (collective operators), and provides measurement results on the Dicke basis. Additional comments including restrictions and unusual features: Note for Installation: Download the source code and run: Or install directly from PyPI, run: (Note: conda may be used to install the external pyTorch library.) Official website: https://vqisinfo.wixsite.com/tqix API website: https://tqix-developers.readthedocs.io/en/latest/index.html PyPI: https://pypi.org/project/tqix/
Federated Zero-Shot Industrial Fault Diagnosis With Cloud-Shared Semantic Knowledge Base

Recently, a considerable literature has grown up around the few-sample fault diagnosis task, in which few samples of fault data are available for model training. The lack of fault samples usually causes significant degradation of diagnosis performance, which is referred as few-sample problem. Many federated learning-based fault diagnosis methods are proposed as a popular strategy to solve this problem. However, the vast majority of these algorithms assume that few-sample categories of the local client are well-sampled in some clients. In this sense, they are unable to cope with faults that have rare or even no sample in all clients, namely, global unseen faults. To diagnosis global unseen fault categories, a federated zero-shot fault diagnosis framework is proposed in this article. First, we propose a general approach for building a semantic knowledge base, which provides auxiliary discriminant descriptions of different faults. Second, a bidirectional alignment network is designed using two coupled variational autoencoders, enabling the fusion of data and attributes and allowing attribute descriptions to assist in fault diagnosis tasks. Third, a cloud–edge collaboration model aggregation strategy is developed, which utilizes a generative replay mechanism and integrates the knowledge of each client, thus, enhancing the generalization and generation ability of the global model. Experiments conducted on the thermal power plant group illustrate the feasibility and effectiveness of the proposed framework to categorize local and global unseen classes.
Pruning Coherent Integrated Photonic Neural Networks

Coherent integrated photonic neural networks (IPNNs) are increasingly being explored for rapidly growing artificial intelligence applications. However, the principal roadblocks in the scalability of IPNNs are their large area footprint and high tuning power consumption during both training and inferencing. In deep neural networks (DNNs), software techniques to prune redundant weights are often utilized to reduce resource (e.g., memory, computation, and power) overheads. However, due to the complex nature in which the software weights are mapped to the building blocks of IPNNs, prior efforts to apply existing pruning approaches to IPNNs have been ineffective. We present CHAMP and LTPrune, two novel hardware-aware pruning techniques for IPNNs. Using a case study of three IPNNs with different footprints, we show that both these methods can prune more than 99% of the phase angles (which are similar to the weight parameters in DNNs). We also analyze the performance of the pruned IPNNs under phase uncertainties and present a comparative analysis of the two methods to enable advanced hardware-software-assisted design-optimization techniques for IPNNs. To expedite pruning, we also propose HybridPrune, where CHAMP and LTPrune are used in conjunction to obtain similar network sparsity as standalone-LTPrune but with up to 78.3% fewer retraining epochs.
An Efficient Graph Convolution Network for Skeleton-Based Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition has evolved as a prominent topic of computer vision research due to its vast applications in human-computer interaction, robotics, and other domains. Although there are numerous related recognition studies, the State-Of-The-Art (SOTA) methods are overparametrized. Specifically, the number of model parameters is quite large, which results in high computational costs. This work, referring to Songs ResGCN, designs an efficient and lightweight graph convolutional network, named ResGCNeXt. ResGCNeXt learns rich features from skeleton information and achieves high accuracy with less number of model parameters. First, three data preprocessing strategies according to motion analysis are designed to provide sufficient features for the recognition model. Then, an efficient graph convolutional network structure combining bottleneck and group convolution is designed to reduce the number of model parameters without loss of accuracy. Furthermore, an attention block called SENet-Part attention (SEPA) is added to improve channel and spatial feature learning. This study is validated on two benchmark datasets, and the experimental results show that ResGCNeXt provides competitive performance, especially in significantly reducing the number of model parameters. Compared to HAN-2S, which is one of the best SOTA methods, our method has half model parameters and a 0.3% higher recognition rate.
Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation

The data of recommendation systems typically only contain the purchased item as positive data and other un-purchased items as unlabeled data. To train a good recommendation model, in addition to the known positive information, we also need high-quality negative information. Capturing negative signals in positive and unlabeled data is challenging for recommendation systems. Most studies have used specific data and proposed negative sampling methods suitable to the data characteristics. Existing negative sampling strategies cannot automatically select suitable approaches for different data. However, this one-size-fits-all strategy often makes potential positive samples considered as negative, or truly negative samples considered as potential positive samples and recommend to users. In this way, it will not only turn down the recommendation result, but even also have an adverse effect. Accordingly, we propose a novel negative sampling model, Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation (RHNSR), which can combine multiple sampling strategies and dynamically adjust the proportions used by different sampling strategies. In addition, ensemble learning, which integrates various model sampling strategies for obtaining an improved solution, was applied to RHNSR. Extensive experiments were conducted on three real-world recommendation datasets, and the experimental results indicated that the proposed model significantly outperformed state-of-the-art baseline models and revealed significant improvements in precision and hit ratio (49.02% and 37.41%, respectively).
Negativity spreads faster: A large-scale multilingual twitter analysis on the role of sentiment in political communication

Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agendas aiming to influence voter behaviour. In this paper, we attempt to analyse tweets of politicians from three European countries and explore the virality of their tweets. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament in Greece, Spain and the United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times, and highlights interesting differences between political parties as well as between politicians and the general population.
A Systematic and Bibliometric Review on Face Recognition: Convolutional Neural Network

CNN (Convolution Neural Network) has boosted computer vision research by delivering outstanding outcomes in a variety of domains, including voice recognition, activity recognition, classification as well as segmentation of images, video analysis, as well as object recognition. In addition, the availability of cheap technology and massive volumes of data has created new possibilities for research for CNN. Alternative transfer functions, regularization, parameter optimization, including architectural enhancements, are a few inspiring ideas that have been researched for the advancement of CNN. Additionally, attaining architectural advancements leads to a significant increase in the deep CNN’s capability. This paper has a primary focus on a depth study of CNN (convolution neural network) on its activation functions and architecture for convolution neural networks. Furthermore, the bibliometric analysis has been done by using Scopus and Web of Science databases processed in VOSviewer software, by which researchers have ideas towards this domain. The findings of this paper will contribute to knowledge and practice of researchers working in this field by increasing their understanding of convolution neural networks.
Waterfall: Salto Collazo. Tokenomics

This article describes the core principles of the economic policy integrated into the Waterfall DAG (Directed Acyclic Graph) based system design. The main aim is to create a favorable environment incentivizing the positive behavior of each network participant and the system as a whole. Economic leverages ensure general equilibrium, to provide an optimal data replication ratio and affordable transaction fees.
Design of Hybrid Authentication Protocol for High Secure Applications in Cloud Environments

Since a large amount of data gets transferred, the security and privacy are important in cloud computing which increases the risk of data attack. Cryptography is a method of transmitting and storing data so only the intended receiver or recipient can read and process it. The original message, known as plain text, can be converted to an unintelligible version known as cipher text in cryptography. The ability to securely delete sensitive data from storage is becoming more important. In this method some important security services including authentication Protocol is provided in cloud computing. The entire architecture is divided into four modules they are security server, cloud database, hybrid authentication protocol and communication. To provide efficient security hybrid authentication protocol is utilized. In hybrid authentication protocol initially data will be registered and to this data privacy is provided using privacy module. Now to the obtained data security key is provided. After that data base functions are performed. Security server will provide security for the transmitter and received data. Cloud data base will store the data. From results it can observe that hybrid authentication protocol will improve the performance, security, scalability and reduces the errors, Delay and attacks which are obtaining from unauthorized users.
Improving the Precision of Multiparameter Estimation in the Teleportation of Qutrit under Amplitude Damping Noise

Since the initial discovery of quantum teleportation, it is devoted to transferring unknown quantum states from one party to another distant partner. However, in the scenarios of remote sensing, what people truly care about is the information carried by certain parameters. The problem of multiparameter estimation in the framework of qutrit teleportation under amplitude damping (AD) noise is studied. Particularly, two schemes are proposed to battle against AD noise and enhance the precision of multiparameter estimation by utilizing weak measurement (WM) and environment-assisted measurement (EAM). For two-phase parameters encoded in a qutrit state, the analytical formulas of the quantum Fisher information matrix (QFIM) can be obtained. The results prove that the scheme of EAM outperforms the WM one in the improvements of both independent and simultaneous estimation precision. Remarkably, the EAM scheme can completely ensure the estimation precision against the contamination by AD noise. The reason should be attributed to the fact that EAM is carried out after the AD noise. Thus, it extracts information from both the system and the environment. The findings show that the techniques of WM and EAM are helpful for remote quantum sensing and can be generalized to other qutrit-based quantum information tasks under AD decoherence.
Decoupled Control Design of Aerial Manipulation Systems for Vegetation Sampling Application

A key challenge in the use of drones for an aerial manipulation task such as cutting tree branches is the control problem, especially in the presence of an unpredictable and nonlinear environment. While prior work focused on simplifying the problem by modeling a simple interaction with branches and controlling the system with nonlinear and non-robust control schemes, the current work deals with the problem by designing novel robust nonlinear controllers for aerial manipulation systems that are appropriate for vegetation sampling. In this regard, two different potential control schemes are proposed: nonlinear disturbance observer-based control (NDOBC) and adaptive sliding mode control (ASMC). Each considers the external disturbances and unknown parameters in controller design. The proposed control scheme in both methods employs a decoupled architecture that treats the unmanned aerial vehicle and the manipulator arm of the sampler payload as separate units. In the proposed control structures, controllers are designed after comprehensively investigating the dynamics of both the aerial vehicle and the robotic arm. Each system is then controlled independently in the presence of external disturbances, unknown parameter changes, and the nonlinear coupling between the aerial vehicle and robotic arm. In addition, fully actuated and underactuated aerial platforms are examined, and their stability and controllability are compared so as to choose the most practical framework. Finally, the simulation findings verify and compare the performance and effectiveness of the proposed control strategies for a custom aerial manipulation system that has been designed and developed for field trials.
Study of Adam and Adamax Optimizers on AlexNet Architecture for Voice Biometric Authentication System

Biometric authentication has surpassed password or token authentication in significance. Even though several methods for biometric authentication systems have been developed, the Deep Learning method is considered to be significantly more efficient than the other methods, especially Convolutional Neural Network (CNN). For this paper, the CNN architecture that was evaluated is AlexNet since it is compatible with a small dataset. Considering optimization techniques are important in Deep Learning method, this research will use the proposed voice dataset to determine if Adam or AdaMax is the optimal optimizer for the AlexNet architecture. The proposed dataset consists of seven celebrity classes, with 20 audio files in each class that is collected from Google and Youtube. In improving the model's accuracy, k-fold with cross-validation approach was selected. The experiment proved that the AdaMax optimizer outperforms Adam on the proposed dataset.
An RGB-D camera-based indoor occupancy positioning system for complex and densely populated scenarios

The dynamic changes in occupants’ presence, movements and other behaviours could affect the actual operation and energy consumption of buildings. The design and operation of buildings can be adjusted to be more energy efficient by thinking over the actual distribution of occupants and application scenarios. Nevertheless, accurate, nonintrusive and applicable occupancy positioning systems, especially for complex and densely populated scenarios, have not yet been established. Herein, we propose a novel indoor positioning system based on a red green blue-depth (RGB-D) camera (CIOPS-RGBD). This system utilizes multiple RGB-D cameras to capture colour and depth images from different views. A data fusion algorithm is developed according to the result from a human pose estimation method. Then, the proposed CIOPS-RGBD system was setup and verified within a multifunction room for steady and dynamic accuracy estimation. The accuracy was verified within 10 cm in most cases. Finally, the system was tested under different application scenarios with more than 25 occupants in an 86 m 2 space. The results demonstrate that the system can provide high-quality occupancy positioning and body orientation information for these scenarios in almost real-time, providing a solid basis to improve the actual operation and design of indoor environment creation systems.
Random forest classifier and neural network for fraction identification of refuse-derived fuel images

The current paper aims at the evaluation of computer vision methods to identify RDF (Refuse-derived fuels) fractions based on images of the RDF particles. For this purpose, images of 1345 single RDF particles, with a typical size in the cm-range, were taken in bird’s eye view and assigned manually to one of six predefined material fractions. Two possible methods were tested for fraction identification: First, a classical machine learning approach consisting of feature extraction of color histograms and Haralick-textures as input for a random forest classifier and, second, a neural network approach with transfer learning. In the machine learning approach, the random forest classifier with feature extraction based on image color distribution (histograms of colors in images) and Haralick-textures (matrix of gray-value co-occurrences) achieved an accuracy of 49 %. The neural network approach is based on the Xception network, a state-of-the-art convolutional neural network with depthwise separable convolutions. The accuracy of the Xception network for recognizing RDF fractions with transfer learning is 69%, thus considerably better than with the machine learning approach and a good starting point for identifying the very inhomogeneous appearance of RDF fractions. The Xception approach was then used in a more realistic setup where the RDF particles are transported by a conveyor belt which allowed for simplified image acquisition. Here, some additional measures are needed for image recognition like edge detection performed by the Canny algorithm. The accuracy for a test data set could be increased to 71 % after using images from this setup to improve the neural network.
Algorithms for separating diffuse and beam irradiance from data over the East Asia-Pacific region: A multi-temporal-scale evaluation based on minute-level ground observations

Diffuse irradiance and direct normal irradiance are two components that are critical to both energy and ecological systems; however, these are rarely available in most ground observations, which only provide the global horizontal irradiance (GHI), leaving the separation of them from the GHI an inevitable process. Although many separation models have been developed to provide solutions, a large proportion of these has been constructed based on site-specific datasets at different time resolutions, limiting their possibilities to be adopted to other sites without previously being extensively evaluated. In this study, we evaluated a total of 80 separation algorithms with specific attention to their time resolutions, based on the finest minute-scale observations from 13 irradiation sites across six Köppen-Geiger-Photovoltaic climate classification zones in East Asia-Pacific. We found that there was a dramatic difference in the performance of hourly-scale models and that the Starke3 model gave the best performance of the minute-scale models. Further residual analyses revealed that the clearness index, solar zenith angle, and atmospheric parameters played important roles. Since data from the geostationary Himawari-8 satellite are available to provide minute-scale atmospheric parameters, there is the possibility of reducing the residuals by incorporating the information with high temporal resolutions into separation models. The results of this study will provide a more comprehensive understanding of the utilization and improvement of separation models.
Inverted Residual Fourier Transformation for Lightweight Single Image Deblurring

Single image deblurring aims to restore a sharp image by removing blurred areas in the single image. Such blurred images are not only visually unpleasant, but cause various problems in many applications like image recognition. In recent years, with the development of deep learning, neural networks are used in many single image deblurring. Especially, encoder-decoder structures are widely used for single image deblurring and successfully restore high quality images. However, FLOPs and the number of parameters tend to increase to restore a high-quality image. Thus, this paper proposes a new lightweight network (IRFTNet) based on UNet, which is a widely used basic network of encoder-decoder structures. Our proposed network has three features to improve performance and lightweight. First, a new backbone called Inverted Residual Fourier Transformation block (IRFTblock) based on inverted residual block is introduced to decrease computational complexity. Second, a new module called Lower Feature Synthesis (LFS) is introduced to efficiently transfer encoder information from lower layers to upper layers. Finally, multiple outputs structure proposed in MIMO-UNet is introduced. These improvements resulted in a 32.98dB in PSNR on the GoPro dataset, despite approximately half FLOPs and the number of parameters of DeepRFT. Further ablation studies show the effectiveness of various components in our proposed model.