Reviewerly/exHarmony huggingface dataset:
A two level ensemble classification approach to forecast bitcoin prices

Purpose Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model. Design/methodology/approach In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm. Findings The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively. Originality/value In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.
PICF-LDA: a topic enhanced LDA with probability incremental correction factor for Web API service clustering

Abstract Web API is a popular way to organize network services in cloud computing environment. However, it is a challenge to find an appropriate service for the requestor from massive Web API services. Service clustering can improve the efficiency of service discovery for its ability of reducing search space. Latent Dirichlet Allocation (LDA) is the most frequently used topic model in service clustering. To further improve the topic representation ability of LDA, we propose a new variant model of LDA with probability incremental correction factor (PICF-LDA) to generate the high-quality service representation vectors (SRVs) for Web API services. We first compute the words’ topic contribution degree (TCD) in the service description text by its context weight and part-of-speech (POS) weight. Then the probability incremental correction factor (PICF) for a word is designed based on TCD and the word’s maximum topic probability value. PICF is used to correct the probability distributions in SRVs. Experiments show that PICF-LDA has a better performance than LDA, the variant LDA models and other state-of-the-art topic models in service clustering.
Hypertension and Obesity: Risk Factors for Thyroid Disease

Thyroid disease instances have rapidly increased in the past few decades; however, the cause of the disease remains unclear. Understanding the pathogenesis of thyroid disease will potentially reduce morbidity and mortality rates. Currently, the identified risk factors from existing studies are controversial as they were determined through qualitative analysis and were not further confirmed by quantitative implementations. Association rule mining, as a subset of data mining techniques, is dedicated to revealing underlying correlations among multiple attributes from a complex heterogeneous dataset, making it suitable for thyroid disease pathogenesis identification. This study adopts two association rule mining algorithms (i.e., Apriori and FP-Growth Tree) to identify risk factors correlated with thyroid disease. Extensive experiments were conducted to reach impartial findings with respect to knowledge discovery through two independent digital health datasets. The findings confirmed that gender, hypertension, and obesity are positively related to thyroid disease development. The history of I131 treatment and Triiodothyronine level can be potential factors for evaluating subsequent thyroid disease.
Comparative Research on the Game Behavior of the Participants in the Traditional Supply Chain Finance and the Supply Chain Finance on the Blockchain

The authors took the financing warehouse in supply chain finance as an example, used the game between capital providers (banks and their entrusted logistics supervision enterprises) and capital demanders (core enterprises of supply chain, upstream suppliers, and downstream dealers) as the research object, and constructed the income matrices, respectively, and the Nash equilibrium of pure decision and mixed decision was calculated. The authors compared the game behavior of the participants in the traditional supply chain finance and the supply chain finance on the blockchain and the difference of the mixed decision Nash equilibrium whether blockchain rewards and punishment were added. When the rewards and punishment were added to encourage the transaction information placing in the blockchain, the Nash equilibrium point would be further away from the origin point, and the capital provider and the capital demander would choose to cooperate with greater probability. When the cost of the blockchain is gradually reduced, the two sides of the game choose to place the transaction information in the chain, which can improve the cooperation of the participants of the supply chain finance, and they can get more profit.
End-to-End Insulator String Defect Detection in a Complex Background Based on a Deep Learning Model

Normal power line insulators ensure the safe transmission of electricity. The defects of the insulator reduce the insulation, which may lead to the failure of power transmission systems. As unmanned aerial vehicles (UAVs) have developed rapidly, it is possible for workers to take and upload aerial images of insulators. Proposing a technology to detect insulator defects with high accuracy in a short time can be of great value. The existing methods suffer from complex backgrounds so that they have to locate and extract the insulators at first. Some of them make detection relative to some specific conditions such as angle, brightness, and object scale. This study aims to make end-to-end detections using aerial images of insulators, giving the locations of insulators and defects at the same time while overcoming the disadvantages mentioned above. A DEtection TRansformer (DETR) having an encoder–decoder architecture adopts convolutional neural network (CNN) as the backbone network, applies a self-attention mechanism for computing, and utilizes object queries instead of a hand-crafted process to give the direct predictions. We modified this for insulator detection in complex aerial images. Based on the dataset we constructed, our model can get 97.97 in mean average precision when setting the threshold of intersection over union at 0.5, which is better than Cascade R-CNN and YOLOv5. The inference speed of our model can reach 25 frames per second, which is qualified for actual use. Experimental results demonstrate that our model meets the robustness and accuracy requirements for insulator defect detection.
On Entanglement-Assisted Multistatic Radar Techniques

Entanglement-based quantum sensors have much better sensitivity than corresponding classical sensors in a noisy and lossy regime. In our recent paper, we showed that the entanglement-assisted (EA) joint monostatic-bistatic quantum radar performs much better than conventional radars. Here, we propose an entanglement-assisted (EA) multistatic radar that significantly outperforms EA bistatic, coherent state-based quantum, and classical radars. The proposed EA multistatic radar employs multiple entangled transmitters performing transmit-side optical phase conjugation, multiple coherent detection-based receivers serving as EA detectors, and a joint detector.
Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples

Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big data analysis using statistics and machine learning algorithms. However, as the use of big data increases, problems also occur. One of them is a zero-inflated problem in structured data preprocessed from big data. Most count values are zeros because a specific word is found in only some documents. In particular, since most of the patent data are in the form of a text document, they are more affected by the zero-inflated problem. To solve this problem, we propose a generation of synthetic samples using statistical inference and tree structure. Using patent document and simulation data, we verify the performance and validity of our proposed method. In this paper, we focus on patent keyword analysis as text big data analysis, and we encounter the zero-inflated problem just like other text data.
An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma

Routine examination of entire histological slides at cellular resolution poses a significant if not insurmountable challenge to human observers. However, high-resolution data such as the cellular distribution of proteins in tissues, e.g., those obtained following immunochemical staining, are highly desirable. Our present study extends the applicability of the PathoFusion framework to the cellular level. We illustrate our approach using the detection of CD276 immunoreactive cells in glioblastoma as an example. Following automatic identification by means of PathoFusion's bifocal convolutional neural network (BCNN) model, individual cells are automatically profiled and counted. Only discriminable cells selected through data filtering and thresholding were segmented for cell-level analysis. Subsequently, we converted the detection signals into the corresponding heatmaps visualizing the distribution of the detected cells in entire whole-slide images of adjacent H&E-stained sections using the Discrete Wavelet Transform (DWT). Our results demonstrate that PathoFusion is capable of autonomously detecting and counting individual immunochemically labelled cells with a high prediction performance of 0.992 AUC and 97.7% accuracy. The data can be used for whole-slide cross-modality analyses, e.g., relationships between immunochemical signals and anaplastic histological features. PathoFusion has the potential to be applied to additional problems that seek to correlate heterogeneous data streams and to serve as a clinically applicable, weakly supervised system for histological image analyses in (neuro)pathology.
Progressive sampling surrogate-assisted particle swarm optimization for large-scale expensive optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have performed well on low- and medium-scale expensive optimization problems (EOPs). However, with the dimensionality increasing, existing SAEAs have trouble getting reliable surrogates for solving large-scale EOPs. In this paper, we propose a progressive sampling surrogate-assisted particle swarm optimization (PS-SAPSO) to efficiently solve the large-scale EOPs from the perspective of data collection and model training. For the data collection, a progressive sampling strategy with restart operation is proposed to collect the new sample solutions during the evolution process for training the radial basis function network (RBFN) surrogate. Specifically, a social learning particle swarm optimization is employed to generate the new sample solutions under the control of a progressively varying stop criterion. For the model training, a dynamic tunning strategy is proposed to obtain a reliable RBFN by adaptively adjusting the hyperparameter setting during the evolution process. The experimental result shows that PS-SAPSO can achieve competitive or better performance compared with four state-of-the-art SAEAs on widely used benchmark functions. Moreover, ablation experiments are conducted to show the effectiveness of the components of the PS-SAPSO algorithm.
High performance evolutionary computation with tensor-based acceleration

Optimization methods, including evolutionary algorithms, need increasing efficiency in order to solve more and more complex problems in the shortest possible time. Apart from tuning the structure of those algorithms and adapting them to the given problem, the best way to speed up computations is introducing more parallelism, either at the hardware level (by using accelerators), or at the architecture level (by using multiple compute nodes). In this paper, we propose a method for expressing evolutionary computations, based on the tensor computational model. The presented approach enables cross-platform hardware acceleration on CPUs, GPUs and TPUs. To validate the new method, we contribute an open, extensible evolutionary framework, with support for distributed, accelerated execution in heterogeneous environments. Finally, we demonstrate results of the conducted tests that confirm the efficiency of the proposed approach, also in comparison to other existing frameworks.
Evolutionary feature selection

Feature selection is an approach to selecting the best set of features from a feature pool. Its goal is to increase the performance of the machine learning model by providing sufficient information while avoiding redundant or irrelevant features. Due to the high dimensionality of data in practical problems, solutions ranging from genetic algorithms to reinforcement learning have been recently tried to solve this task. In this work, we propose a novel feature selection architecture that uses metaheuristic techniques combined with evolutionary algorithms and chaos theory to select the best features for a model. It uses the concept of evolution, which guides the algorithm to the best path and a chaotic map function to create new random subsets of features. The backbone of this algorithm, mutation and crossover operator, is inspired by genetic algorithms. It uses these methods to increase the exploration and exploitation strategies for the search space. We tested the proposed method on 10 datasets using different machine learning models and achieved significant improvement on each dataset compared to other methods in the literature.
Hard problems are easier for success-based parameter control

Recent works showed that simple success-based rules for self-adjusting parameters in evolutionary algorithms (EAs) can match or outperform the best fixed parameters on discrete problems. Non-elitism in a (1, λ) EA combined with a self-adjusting of spring population size λ outperforms common EAs on the multimodal Cliff problem. However, it was shown that this only holds if the success rate λ that governs self-adjustment is small enough. Otherwise, even on OneMax, the self-adjusting (1, λ) EA stagnates on an easy slope, where frequent successes drive down the of spring population size.
Cyber deterrence with imperfect attribution and unverifiable signaling

Motivated by the asymmetric information inherent to cyberwarfare, we examine a game of deterrence between an attacker and a defender in which the defender can signal its retaliatory capability but can only imperfectly attribute an attack. We show that there are equilibria in which the defender sends noisy signals to increase its expected payoff. In some equilibria, the defender can use signaling to deter an attacker and increase its payoff. In a different and somewhat counter-intuitive equilibrium, the defender can increase its expected payoff through signaling by luring the attacker to attack more.
SimpleCut: A simple and strong 2D model for multi-person pose estimation

This article proposes a simple and efficient multi-person pose estimation model which follows a bottom-up approach and is based on a few deconvolutional layers added on a U-net lookalike ResNet featuremap. SimpleCut contains four independent modules: joints module, coordinates (coords) module, main-joint pairing module, and other-joints pairing module. The joints module builds a score on joints of each individual on the image, whereas the coords module encodes the location of those joints, and both the pairing modules generate image-conditioned pairing of the joints on a small scale. The pairing modules help set up the proposals into a variable number of consistent body part configurations by an optimization strategy that efficiently brings significant speed-up factors. We demonstrated that simultaneously inferring these bottom-up representations of detection and association encode global context sufficiently well to allow a greedy parse to attain high-quality results with low computational cost. SimpleCut evaluated on three publicly available large-scale dataset benchmarks such as ms-coco, lspet, and mpii human pose dataset.
Rich global feature guided network for monocular depth estimation

Monocular depth estimation is a classical but challenging task in the field of computer vision. In recent years, Convolutional Neural Network (CNN) based models have been developed to estimate high-quality depth map from a single image. Most recently, some Transformer based models have led to great improvements. All the researchers are looking for a better way to handle the global processing of information which is crucial for depth relation inference but of high computational complexity. In this paper, we take advantage of both the Transformer and CNN then propose a novel network architecture, called Rich Global Feature Guided Network (RGFN), with which rich global features are extracted from both encoder and decoder. The framework of the RGFN is the typical encoder-decoder for dense prediction. A hierarchical transformer is implemented as the encoder to capture multi-scale contextual information and model long-range dependencies. In the decoder, the Large Kernel Convolution Attention (LKCA) is adopted to extract global features from different scales and guide the network to recover fine depth maps from low spatial resolution feature maps progressively. What's more, we apply the depth-specific data augmentation method, Vertical CutDepth, to boost the performance. Experimental results on both the indoor and outdoor datasets demonstrate the superiority of the RGFN compared to other state-of-the-art models. Compared with the most recent method AdaBins, RGFN improves the RMSE score by 4.66% on the KITTI dataset and 4.67% on the NYU Depth v2 dataset.
Adaptive Experience Sampling for Motion Planning Using the Generator-Critic Framework

Sampling-based motion planners are widely used for motion planning with high- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dof</small> robots. These planners generally rely on a uniform distribution to explore the search space. Recent work has explored learning biased sampling distributions to improve the time efficiency of these planners. However, learning such distributions is challenging, since there is no direct connection between the choice of distributions and the performance of the downstream planner. To alleviate this challenge, this letter proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">apes</small> , a framework that learns sampling distributions optimized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly</i> for the planner's performance. This is done using a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">critic</i> , which serves as a differentiable surrogate objective modeling the planner's performance – thus allowing gradients to circumvent the non-differentiable planner. Leveraging the differentiability of the critic, we train a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generator</i> , which outputs sampling distributions optimized for the given problem instance. We evaluate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">apes</small> on a series of realistic and challenging high- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dof</small> manipulation problems in simulation. Our experimental results demonstrate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">apes</small> can learn high-quality distributions that improve planning performance more than other biased sampling baselines.
Film Effect Optimization by Deep Learning and Virtual Reality Technology in New Media Environment

Today, new media technology has widely penetrated art forms such as film and television, which has changed the way of visual expression in the new media environment. To better solve the problems of weak immersion, poor interaction, and low degree of simulation, the present work uses deep learning technology and virtual reality (VR) technology to optimize the film playing effect. Firstly, the optimized extremum median filter algorithm is used to optimize the "burr" phenomenon and a low compression ratio of the single video image. Secondly, the Generative Adversarial Network (GAN) in deep learning technology is used to enhance the data of the single video image. Finally, the decision tree algorithm and hierarchical clustering algorithm are used for the color enhancement of VR images. The experimental results show that the contrast of a single-frame image optimized by this system is 4.21, the entropy is 8.66, and the noise ratio is 145.1, which shows that this method can effectively adjust the contrast parameters to prevent the loss of details and reduce the dazzling intensity. The quality and diversity of the specific types of images generated by the proposed GAN are improved compared with the current mainstream GAN method with supervision, which is in line with the subjective evaluation results of human beings. The Frechet Inception Distance value is also significantly improved compared with Self-Attention Generative Adversarial Network. It shows that the sample generated by the proposed method has precise details and rich texture features. The proposed scheme provides a reference for optimizing the interactivity, immersion, and simulation of VR film.