Reviewerly/exHarmony huggingface dataset:
Spatially Consistent Representation Learning

Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image.In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods. Code is available at https://github.com/kakaobrain/scrl.
MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model. MCUa model consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.
Probabilistic solar irradiance forecasting based on XGBoost

Solar energy has received increasing attention as renewable clean energy in recent years. Power grid operators and researchers widely value probabilistic solar irradiance forecasting because it can provide uncertainty measurement for future PV production. This paper proposes a probabilistic prediction model of solar irradiance based on XGBoost. Specifically, after data preprocessing, historical data is utilized for training a point prediction model based on XGBoost. Since XGBoost is obtained by minimizing the residuals of successive iterations of multiple trees, when predicting solar irradiance at a certain time in the future, these trees can generate multiple predicted values of irradiance iteratively. Finally, the kernel density estimation method is applied to transform the above prediction results in probability prediction intervals under different confidence levels. Experimental results on public data sets show that this method has better accuracy than other benchmark algorithms. The experiment also shows that the method proposed in this paper requires less training time and simple parameter adjustment, which is very suitable for application in engineering practice.
Detail-Preserving Transformer for Light Field Image Super-resolution

Recently, numerous algorithms have been developed to tackle the problem of light field super-resolution (LFSR), i.e., super-resolving low-resolution light fields to gain high-resolution views. Despite delivering encouraging results, these approaches are all convolution-based, and are naturally weak in global relation modeling of sub-aperture images necessarily to characterize the inherent structure of light fields. In this paper, we put forth a novel formulation built upon Transformers, by treating LFSR as a sequence-to-sequence reconstruction task. In particular, our model regards sub-aperture images of each vertical or horizontal angular view as a sequence, and establishes long-range geometric dependencies within each sequence via a spatial-angular locally-enhanced self-attention layer, which maintains the locality of each sub-aperture image as well. Additionally, to better recover image details, we propose a detail-preserving Transformer (termed as DPT), by leveraging gradient maps of light field to guide the sequence learning. DPT consists of two branches, with each associated with a Transformer for learning from an original or gradient image sequence. The two branches are finally fused to obtain comprehensive feature representations for reconstruction. Evaluations are conducted on a number of light field datasets, including real-world scenes and synthetic data. The proposed method achieves superior performance comparing with other state-of-the-art schemes. Our code is publicly available at: https://github.com/BITszwang/DPT.
CraterLake

Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due to its prohibitive overheads, about 10,000X over unencrypted computation. Recent FHE accelerators have made strides to bridge this performance gap. Unfortunately, prior accelerators only work well for simple programs, but become inefficient for complex programs, which bring additional costs and challenges.
https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

This work proposes a new metaheuristic algorithm: a fixed-step average and subtraction-based optimizer (FS-ASBO).This algorithm is the improved version of the average and subtraction-based optimizer (ASBO).There are several improvements related to the original ASBO.First, the proposed algorithm replaces the randomized step size in the guided movement with the fixed step size.Second, the proposed algorithm adds an exploration mechanism after the guided movement in every iteration when the new candidate fails to find a better solution.This proposed algorithm is then implemented into a simulation to evaluate its performance.Through simulation, the proposed algorithm is challenged to solve theoretical optimization problems and real-world optimization problems.The 23 well-known benchmark functions represent the theoretical optimization problem.Meanwhile, the housing optimization problem represents the real-world one.In the simulation, the proposed algorithm is compared with particle swarm optimization (PSO), marine predator algorithm (MPA), Komodo mlipir algorithm (KMA), static Komodo algorithm (SKA), and ASBO.The result shows that this proposed algorithm is competitive to solve theoretical problem and superior to solve real-world problem.The proposed algorithm outperforms all sparing algorithms in solving seven functions.In housing optimization problem, it creates 12%, 10%, 8%, 11%, and 10% better total gross profit than the ASBO, PSO, MPA, KMA, and SKA.
Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System

In this work, the federated learning mechanism is introduced into the deep learning of medical models in Internet of Things (IoT)-based healthcare system. Cryptographic primitives, including masks and homomorphic encryption, are applied for further protecting local models, so as to prevent the adversary from inferring private medical data by various attacks such as model reconstruction attack or model inversion attack, etc. The qualities of the datasets owned by different participants are considered as the main factor for measuring the contribution rate of the local model to the global model in each training epoch, instead of the size of datasets commonly used in deep learning. A dropout-tolerable scheme is proposed in which the process of federated learning would not be terminated if the number of online clients is not less than a preset threshold. Through the analysis of the security, it shows that the proposed scheme satisfies data privacy. Computation cost and communication cost are also analyzed theoretically. Finally, skin lesion classification using training images provided by the HAM10000 medical dataset is set as an example of healthcare applications. Experimental results show that compared with existing schemes, the proposed scheme obtained promising results while ensuring privacy preserving.
Wireless Communications for Data Security: Efficiency Assessment of Cybersecurity Industry—A Promising Application for UAVs

The design of cooperative applications combining several unmanned aerial and aquatic vehicles is now possible thanks to the considerable advancements in wireless communication technology and the low production costs for small, unmanned vehicles. For example, the information delivered over the air instead of inside an optical fiber causes it to be far simpler for an eavesdropper to intercept and improperly change the information. This article thoroughly analyzes the cybersecurity industry’s efficiency in addressing the rapidly expanding requirement to incorporate compelling security features into wireless communication systems. In this research, we used a combination of DEA window analysis with the Malmquist index approach to assess the efficiency of the cybersecurity industry. We used input and output factors utilizing financial data from 2017–2020 sources from a US market. It was found that U1—Synopsys and U9—Fortinet exhibited the best performances when relating Malmquist and DEA window analysis. By evaluating ten big companies in the cybersecurity industry, we indicate that U2—Palo Alto Networks and U6—BlackBerry Ltd. companies needed significant improvements and that four other companies were generally more efficient. The findings of this study provide decision-makers a clear image and it will be the first study to evaluate and predict the performance of cyber security organizations, providing a valuable reference for future research.
The Negation of Basic Probability Assignment

In many cases, we obtain information using various methods in order to make better decisions.The everything in nature and society has its negative, the negation of negation has significant meaning.Considering the problem from two aspects, we can get more accurate information.However, in most cases, the information of negation is ignored.Hence, the negation provides a new view to obtain information.However, existing negation method mainly apply to probability distribution.How to get the negation of basic probability assignment (BPA) in Dempster-Shafer (D-S) theory is still an open issue.The paper proposed the new negation method of BPA.Besides, some numerical examples are given to this approach for better understanding.Moreover, in order to demonstrate the efficiency of the proposed method, the paper compared the changes of uncertainty between original and negation by using some uncertain measurement methods.Finally, practical application is used to be discussed the application of proposed method.
The Impact of Financial Sector Development and Sophistication on Sustainable Economic Growth

The drivers of economic growth and development are among the most important issues explored by economic theory. Sustainability of economic development was previously linked by various economic schools of thought to natural resources (agriculture, land, minerals, metals etc.), labor force (including skills, productivity, and education), entrepreneurship or technology and innovation. Capital was later introduced by classical economic theory as the key element. Without significant capital accumulation, all other production factors remain idle. The value added of the production process is a result of the existence, the accessibility and the cost of capital. Therefore, the development and the sophistication of the financial sector has gradually become very important for any nation interested in sustainable growth. This paper investigates the impact of financial sector development, sophistication and performance on economic growth based on a panel regression methodology. We found statistically significant results that confirm the importance of this connection and that are very consistent with economic theory and previous relevant articles and studies.
Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization

Adam is a popular variant of stochastic gradient descent for finding a local minimizer of a function. In the constant stepsize regime, assuming that the objective function is differentiable and nonconvex, we establish the convergence in the long run of the iterates to a stationary point under a stability condition. The key ingredient is the introduction of a continuous-time version of Adam, under the form of a nonautonomous ordinary differential equation. This continuous-time system is a relevant approximation of the Adam iterates, in the sense that the interpolated Adam process converges weakly toward the solution to the ODE. The existence and the uniqueness of the solution are established. We further show the convergence of the solution toward the critical points of the objective function and quantify its convergence rate under a Łojasiewicz assumption. Then, we introduce a novel decreasing stepsize version of Adam. Under mild assumptions, it is shown that the iterates are almost surely bounded and converge almost surely to critical points of the objective function. Finally, we analyze the fluctuations of the algorithm by means of a conditional central limit theorem.
Clustering‐based real‐time anomaly detection—A breakthrough in big data technologies

Abstract Off late, the ever increasing usage of a connected Internet‐of‐Things devices has consequently augmented the volume of real‐time network data with high velocity. At the same time, threats on networks become inevitable; hence, identifying anomalies in real time network data has become crucial. To date, most of the existing anomaly detection approaches focus mainly on machine learning techniques for batch processing. Meanwhile, detection approaches which focus on the real‐time analytics somehow deficient in its detection accuracy while consuming higher memory and longer execution time. As such, this paper proposes a novel framework which focuses on real‐time anomaly detection based on big data technologies. In addition, this paper has also developed streaming sliding window local outlier factor coreset clustering algorithms (SSWLOFCC), which was then implemented into the framework. The proposed framework that comprises BroIDS, Flume, Kafka, Spark streaming, SparkMLlib, Matplot and HBase was evaluated to substantiate its efficacy, particularly in terms of accuracy, memory consumption, and execution time. The evaluation is done by performing critical comparative analysis using existing approaches, such as K‐means, hierarchical density‐based spatial clustering of applications with noise (HDBSCAN), isolation forest, spectral clustering and agglomerative clustering. Moreover, Adjusted Rand Index and memory profiler package were used for the evaluation of the proposed framework against the existing approaches. The outcome of the evaluation has substantially proven the efficacy of the proposed framework with a much higher accuracy rate of 96.51% when compared to other algorithms. Besides, the proposed framework also outperformed the existing algorithms in terms of lesser memory consumption and execution time. Ultimately the proposed solution enable analysts to precisely track and detect anomalies in real time.
Improving Abstractive Document Summarization with Salient Information Modeling

Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information.To tackle the above difficulties, we propose a Transformerbased encoder-decoder framework with two novel extensions for abstractive document summarization.Specifically, (1) to encode the documents comprehensively, we design a focus-attention mechanism and incorporate it into the encoder.This mechanism models a Gaussian focal bias on attention scores to enhance the perception of local context, which contributes to producing salient and informative summaries.(2) To distinguish salient information precisely, we design an independent saliency-selection network which manages the information flow from encoder to decoder.This network effectively reduces the influences of secondary information on the generated summaries.Experimental results on the popular CNN/Daily Mail benchmark demonstrate that our model outperforms other state-of-the-art baselines on the ROUGE metrics.
Federated Tensor Mining for Secure Industrial Internet of Things

In a vertical industry alliance, Internet of Things (IoT) deployed in different smart factories are similar. For example, most automobile manufacturers have the similar assembly lines and IoT surveillance systems. It is common to observe the industrial knowledge using deep learning and data mining methods based on the IoT data. However, some knowledge is not easy to be mined from only one factory's data because the samples are still few. If multiple factories within an alliance can gather their data together, more knowledge could be mined. However, the key concern of these factories is the data security. Existing matrix-based methods can guarantee the data security inside a factory but do not allow the data sharing among factories, and thus their mining performance is poor due to lack of correlation. To address this concern, in this article we propose the novel federated tensor mining (FTM) framework to federate multisource data together for tensor-based mining while guaranteeing the security. The key contribution of FTM is that every factory only needs to share its ciphertext data for security issue, and these ciphertexts are adequate for tensor-based knowledge mining due to its homomorphic attribution. Real-data-driven simulations demonstrate that FTM not only mines the same knowledge compared with the plaintext mining, but also is enabled to defend the attacks from distributed eavesdroppers and centralized hackers. In our typical experiment, compared with the matrix-based privacy-preserving compressive sensing (PPCS), FTM increases up to 24% on mining accuracy.
Automated Essay Scoring with Discourse-Aware Neural Models

Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering.Neural networks offer an alternative to feature engineering, but they typically require more annotated data.This paper explores network structures, contextualized embeddings and pre-training strategies aimed at capturing discourse characteristics of essays.Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available.
Deep Variational Matrix Factorization with Knowledge Embedding for Recommendation System

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this article, we have proposed a deep learning based fully Bayesian treatment recommendation framework, DVMF, which has high-quality performance and ability to integrate any kinds of side information handily and efficiently. In DVMF, the variational inference technique and the reparameterization tricks are introduced to make DVMF possible to be optimized by the stochastic gradient-based methods, in addition, two novel deep neural networks have been constructed to infer the hyper-parameters of the distributions of latent factors from the knowledge of user and item, which are represented as low-dimensional real-valued vectors retaining primary features. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments.
Combining polar harmonic transforms and 2D compound chaotic map for distinguishable and robust color image zero-watermarking algorithm

Although zero-watermarking can provide an effective and distortion-free scheme for image copyright protection, its robustness and discriminability do not meet expectations in existing methods. Some cannot resist effectively geometric attacks, others do not consider the discriminability and equalization. For that reason, this paper proposes a robust and distinguishable color image zero-watermarking algorithm based on polar harmonic transforms (PHTs) and compound chaotic map. In the proposed algorithm, firstly three PHTs moments of an image are computed simultaneously and accurate moments are selected for the robustness. Then, content-based binary feature sequence is acquired by judging the relation between magnitudes of adjacent moments for the discriminability. Finally, compound chaotic map is employed to encrypt copyright logo for ensuring security and scramble binary feature sequence for improving the equalization. Experimental results show that the proposed zero-watermarking algorithm has good equalization and discriminability, and an advantage in robustness compared with other zero-watermarking and traditional watermarking.
Data fusion for the measurement of potentially toxic elements in soil using portable spectrometers

Soil contamination posed by potentially toxic elements is becoming more serious under continuously development of industrialization and the abuse of fertilizers and pesticides. The investigation of soil potentially toxic elements is therefore urgently needed to ensure human and other organisms’ health. In this study, we investigated the feasibility of the separate and combined use of portable X-ray fluorescence (pXRF) and visible near-infrared reflectance (vis-NIR) sensors for measuring eight potentially toxic elements in soil. Low-level fusion was achieved by the direct combination of the pXRF and vis-NIR spectra; middle-level fusion was achieved by the combination of selected bands of the pXRF and vis-NIR spectra using the Boruta feature selection algorithm; and high-level fusion was conducted by outer-product analysis (OPA) and Granger–Ramanathan averaging (GRA). The estimation accuracy for the eight considered elements were in the following order: Zn > Cu > Ni > Cr > As > Cd > Pb > Hg. The measurement for Cu and Zn could be achieved by pXRF spectra alone with Lin’s concordance correlation coefficient (LCCC) values of 0.96 and 0.98, and ratio of performance to interquartile distance (RPIQ) values of 2.36 and 2.69, respectively. The measurement of Ni had the highest model performance for high-level fusion GRA with LCCC of 0.89 and RPIQ of 3.42. The measurements of Cr using middle- and high-level fusion were similar, with LCCC of 0.86 and RPIQ of 2.97. The best estimation accuracy for As, Cd, and Pb were obtained by high-level fusion using OPA, with LCCC >0.72 and RPIQ >1.2. However, Hg measurement by these techniques failed, having an unacceptable performance of LCCC <0.20 and RPIQ <0.75. These results confirm the effectiveness of using portable spectrometers to determine the contents of several potentially toxic elements in soils.