Reviewerly/exHarmony huggingface dataset:
INVESTIGATION OF THE HEMPEL PARADOX WITH CLASSES IN BIG DATA OF SOCIO-ECONOMIC SYSTEMS

The methods of self-learning and learning with an artificial intelligence teacher are being investigated. The research is carried out in the interests of solving the problems of countering money laundering and terrorist financing on incomplete, heterogeneous big data of socio-economic systems. The N-scheme of the Markov algorithm with a quantitative measure of induction is synthesized. A method is proposed for recording the number of relationships (interactions) of big data objects in the database of artificial intelligence. The theoretical provisions of Hempel’s paradox with classes are considered. The Hempel paradox is evaluated in heterogeneous big data. The Markov occurrence diagram for Hempel’s paradox with classes is synthesized. The Hempel paradox with classes is investigated under conditions of incompleteness and heterogeneity of the data sample. A method of inductive self-learning of artificial intelligence in the N-scheme of the Markov algorithm with a quantitative measure of induction is proposed. The method of using the associator m for training artificial intelligence in the N-scheme of the Markov algorithm is presented. A method of category, predetermined, inductive, teacher-directed self-study is proposed. It is concluded that self-learning methods have a limited potential for mathematical expressiveness. The reason for the low expressiveness is the inability to correctly operate with classes and categories, as well as the limitations of the possibility of logical predestination of truth, taking into account the heterogeneous, anthropogenic nature of objects and morphisms. The study of methods of teaching without a teacher revealed the scientific task of overcoming the Hempel paradox for classes of objects. It is indicated that the proposed method of categorically predetermined, inductive, teacher-directed self-learning actually shows that artificial intelligence a priori must be trained by a teacher, in other words, it cannot correctly self-study without it.
Analyzing the Impact of Lossy Data Reduction on Volume Rendering of Cosmology Data

Cosmology simulations are among some of the largest simulations currently run on supercomputers, generating terabytes to petabytes of data for each run. Consequently, scien-tists are seeking to reduce the amount of storage needed while preserving enough quality for analysis and visualization of the data. One of the most commonly used visualization techniques for cosmology simulations is volume rendering. Here, we investigate how different types of lossy error-bound compression algorithms affect the quality of volume-rendered images generated from reconstructed datasets. We also compute a number of image quality assessment metrics to determine which ones are the most effective at identifying artifacts in the visualizations.
Sustainable Energy-based Cryptocurrency Mining

With the growing environmental concerns in energy-intensive cryptocurrency mining, it is crucial to investigate how renewable energy sources can be used to mitigate the negative impacts caused by cryptocurrency mining. This paper systematically analyzes whether cryptocurrency mining using sustainable energy would be a viable business model in the United States. It is found that using a 3.5MW hydropower system to supply energy to 1000 units of Bitcoin Miner S19 and 21 units of ECV18000 FreeCooler is superior both financially and economically over using conventional fossil fuel systems. The results and analysis indicate that the sustainable energy-based cryptocurrency mining has a high potential to promote an environmentally friendly and cost-effective paradigm for mining cryptocurrencies.
A Decentralized Crowdfunding Solution on top of the Ethereum Blockchain

In today’s tech world, there are several crowdfunding platforms that enable both investors and organizations with a smooth flow of transactions, creating a huge hype in the market. However, security concerns and a fear of fraud are the biggest blow suffered by users on such networks. The history of crowdfunding platforms demonstrates the massive frauds that the public has been subjected to in recent years. This paper provides a completely decentralized method for creating a crowdfunding platform over the Ethereum Blockchain in the proposed system. A lot of attention has been attracted by Blockchain technology as a result of the rise of cryptocurrencies and decentralized apps. A common paradigm among them is the newly emerging Blockchain-based crowdfunding, which eliminates centralized cloud servers and uses smart contracts to fulfill each and every transaction. Blockchain, a promising decentralized paradigm, can be used to provide technological advancements like decentralization and accountability in addition to fixing the problems with current crowdfunding methods. The fundamental objective of the solution is to encourage accountability, transparency, and traceability for all stakeholders so that they always have complete control over their investments.
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics

Traditional numerical approaches have been successfully used to model mechanical behavior of heterogeneous materials (composites, multicomponent alloys, and polycrystals) widely used in industrial applications. However, these methods require a fine mesh resulting in computationally expensive and time-consuming calculations. The physics-informed deep-learning based super-resolution framework (PhySRNet) introduced in this paper is aimed at overcoming this computational challenge. PhySRNet enables reconstruction of high-resolution solution fields from their low-resolution counterparts without requiring labeled data, thereby allowing researchers to run their numerical simulations on a coarse mesh. Through an illustrative example, we demonstrate that the super-resolved fields match the accuracy of an advanced numerical solver running at 400 times the coarse mesh resolution and satisfy the (highly nonlinear) governing laws. The approach opens the door to applying machine learning and traditional numerical approaches in tandem to reduce computational complexity and accelerate scientific discovery and engineering design.
Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach

The traffic video data has become a critical factor in confining the state of traffic congestion due to the recent advancements in computer vision. This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a Deep convolutional neural network. At first, the video data is transformed into an imagery data set; then, the vehicle detection is performed using the You Only Look Once algorithm. A color-coded scheme has been adopted to transform the imagery dataset into a binary image dataset. These binary images are fed to a Deep Convolutional Neural Network. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%.
Power Analysis Side Channel Attacks and Countermeasures for the Internet of Things

With the explosion in the number of internet of things (IoT) devices in recent years, the security of these devices has become an area of major concerns across the globe. Encryption is an essential means of protecting data in IoT devices, but cryptographic implementations are vulnerable to attacks as well. These vulnerabilities can allow attackers to completely bypass or significantly weaken the theoretical strength of the encryption algorithms. Resistance to cryptanalysis alone is not adequate for building a secure cryptosystem in practice. Power analysis side-channel attacks have emerged as a very effective method of discovering cryptographic keys by monitoring the power consumption of devices. Power consumption of the chips can reveal information about the cryptographic operations being performed or the data being processed. Numerous countermeasures against power analysis attacks have been discussed in the literature. However, all these countermeasures introduce various tradeoffs, such as increased power consumption, decreased performance, and increased space requirements for additional hardware. In addition to strong resistance to power analysis attacks and simplicity of implementation, developers need to evaluate the countermeasures by taking into consideration these tradeoffs while designing and implementing the IoT, which are often operated in heterogenous and highly resource constraint environments. This paper explores the state-of-the art power analysis attacks and their countermeasures and analyze the limitations of these countermeasures based on the constraints present in IoT devices.
Artificial Neural Networks and Data Science

Artificial neural networks (ANNs) consist of a family of techniques that are commonly employed to recognize and interpret patterns in big data that are used in prediction, clustering, classification, and identification of other previously unknown data patterns. This article describes foundational concepts that relate to ANNs, including an understanding of how ANNs are linked to biological concepts and the underlying ANN families. The article includes an explanation of common ANN methods, architecture/hyperparameter determination for initializing ANNs, and current research directions. The article concludes with a discussion on the need for algorithmic transparency and repeatability of research.
Performance Evaluation for Infrared Face Recognition using Convolutional Neural Network

Infrared image has a potential to be integrated with face recognition system because the ability cannot be affected by the illumination. Thermal camera can capture image in low light condition, but a low information and dept texture quality still a weakness in a problem of infrared face recognition. Convolution Neural Network can extract more depth the features with the convolution layers. Convolution layers are needed to extract the feature from the infrared image with a low information. The combination of Convolution Neural Network with Haar Cascade can improve the result of evaluation rate. In this work, we present a comparison from several transfer learning model with ERA6-Net as our proposed model. ERA6-Net with a simply layer show superiority in the evaluation model in several parameters with the highest score in accuracy up to 95%.
Spread: Decentralized Model Aggregation for Scalable Federated Learning

Federated learning (FL) is a new distributed machine learning paradigm that enables machine learning on edge devices. One unique feature of FL is that edge devices belong to individuals; and since they are not "owned" by the FL coordinator, but can be "federated" instead, there can potentially be a huge number of edge devices. In the current distributed ML architecture, the parameter server (PS) architecture, model aggregation is centralized. When facing a large number of edge devices, the centralized model aggregation becomes the bottleneck and fundamentally restricts system scalability.
Language Diversity in Scholarly Publishing

&lt;em&gt; Data Processing and production: Jamie Diprose &lt;/em&gt; &lt;em&gt; Cross-posted from the COKI blog. &lt;/em&gt; There is a lot of lip service paid to the idea of diversity in scholarly publishing and often diversity of language is used as an example.
Visual Analytics for Systematic Reviews According to PRISMA

Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.
Ensemble Learning Methods of Adversarial Attacks and Defenses in Computer Vision: Recent Progress

Artificial intelligence (AI) has developed rapidly in recent decades and is widely used in many fields, such as natural language processing, voice recognition, and especially computer vision (CV). However, the endogenous security problems brought by the AI model itself, leading to the emergence of adversarial examples (AEs), which can fool the AI models and cause a serious impact on the classification. In recent years, researches show that ensemble learning methods are effective both in generating or detecting AEs. By integrating to generate AEs, the attackers can implement stronger and good transferability attacks to the target models. On the other hand, ensemble learning methods can also be used in defenses that can improve the robustness against AEs. In this paper, we focus on the ensemble learning methods in the CV field, and first introduce the classic adversarial attack and defense technologies. Then, we survey the ensemble learning methods in the adversarial environment and divide them into three types of frameworks (i.e., parallel, sequential, and hybrid). To the best of our knowledge, we are the first to analyze the recent proposed attacks and defenses in the adversarial environment from the perspective of these ensemble frameworks. Additionally, we summarize the advantages and disadvantages of these ensemble methods and frameworks. In the end, we give some suggestions for using ensemble frameworks, and we put forward several opinions from the aspects of attacks, defenses, and evaluations for future research directions in this field.
Rotatable Zero Knowledge Sets

Key Transparency (KT) systems allow end-to-end encrypted service providers (messaging, calls, etc.) to maintain an auditable directory of their users' public keys, producing proofs that all participants have a consistent view of those keys, and allowing each user to check updates to their own keys. KT has lately received a lot of attention, in particular its privacy preserving variants, which also ensure that users and auditors do not learn anything beyond what is necessary to use the service and keep the service provider accountable. Abstractly, the problem of building such systems reduces to constructing so-called append-only Zero-Knowledge Sets (aZKS). Unfortunately, existing aZKS (and KT) solutions do not allow to adequately restore the privacy guarantees after a server compromise, a form of Post-Compromise Security (PCS), while maintaining the auditability properties. In this work we address this concern through the formalization of an extension of aZKS called Rotatable ZKS ( $${{\textsf{RZKS}}}$$ ). In addition to providing PCS, our notion of $${{\textsf{RZKS}}}$$ has several other attractive features, such as a stronger (extractable) soundness notion, and the ability for a communication party with out-of-date data to efficiently "catch up" to the current epoch while ensuring that the server did not erase any of the past data. Of independent interest, we also introduce a new primitive called a Rotatable Verifiable Random Function (VRF), and show how to build $${{\textsf{RZKS}}}$$ in a modular fashion from a rotatable VRF, ordered accumulator, and append-only vector commitment schemes.
A novel method to calibrate the rotation axis of a line-structured light 3-dimensional measurement system

Rotation axis calibration is the key to line-structured light 3-dimensional (3D) measurement. The traditional calibration method based on circle fitting only uses the position information of the calibration target and ignores the orientation information, which is easily affected by noise. This paper proposes a new calibration method combining the orientation and position information to improve the calibration accuracy. Compared with the circle fitting method, the added orientation information makes the description of rotation more complete. On this basis, an improved extrinsic parameters estimation method is proposed to obtain more accurate position and orientation information of the target, which further improves the calibration accuracy. The experiment results show that, the radius error is reduced by about 90% and the standard deviation of radius error is reduced by about 40% compared with the traditional circle fitting method.
Defending Adversarial Examples by Negative Correlation Ensemble

The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return incorrect predictions by introducing carefully designed perturbations. Obviously, adversarial examples bring great security risks to the real-world applications of deep learning. Recently, some defence approaches against adversarial examples have been proposed. However, the performance of these approaches are still limited. In this paper, we propose a new ensemble defence approach named the Negative Correlation Ensemble (NCEn), which achieves competitive results by making each member of the ensemble negatively correlated in gradient direction and gradient magnitude. NCEn can reduce the transferability of the adversarial samples among the members in ensemble. Extensive experiments have been conducted, and the results demonstrate that NCEn could improve the adversarial robustness of ensembles effectively.
PoetryBERT: Pre-training with Sememe Knowledge for Classical Chinese Poetry

Classical Chinese poetry has a history of thousands of years and is a precious cultural heritage of humankind. Compared with the modern Chinese corpus, it is irrecoverable and specially organized, making it difficult to be learned by existing pre-trained language models. Besides, with the thousands of years of development, many words in classical Chinese poetry have changed their meanings or been out of use today, which further limiting the capability of existing pre-trained models to learn the semantics of classical Chinese poetry. To address these challenges, we construct a large-scale sememe knowledge graph of classical Chinese Poetry (SKG-Poetry), which connects the vocabularies in classical Chinese poetry and modern Chinese. By extracting the sememe knowledge from classical Chinese poetry, our model PoetryBERT not only enlarges the irrecoverable pre-training corpus but also enriches the semantics of the vocabularies in classical Chinese poetry, which enables PoetryBERT to be successfully used in downstream tasks. Specifically, we evaluate our model in two tasks in the field of Chinese classical poetry, which are poetry theme classification and poetry-modern Chinese translation. Extensive experiments are conducted on the two tasks to show the effectiveness of sememe knowledge based pre-training model.
Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval

Deep learning-based image retrieval has been a strongly emphasized area in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic information of the image but also can manage large-scale image retrieval tasks scalably. In this chapter, we propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning to perform large-scale synthetic aperture radar (SAR) image search tasks. Moreover, a training method for the DNNs induced by contrastive learning that does not require any labeling procedure is introduced. This can facilitate the tractability of large-scale datasets with relative ease. Finally, we demonstrate the performance of the proposed method by conducting experiments on the polarimetric SAR image datasets.
A Novel Defect Detection Method for Insulators of Power Transmission Line Based on YOLOv5

Insulators are widely used in power transmission lines. They need to go through defect detection before being used on site. The traditional method is through manual detection, which is inefficient and costly. This paper proposes a novel defect detection method based on YOLOv5 for insulators of power transmission line. First, in order to solve the problem of dataset richness, the insulator defect dataset is enhanced and annotated. Then the data in the defect dataset is converted into a unified format and input into YOLOv5 network for training. The optimal defect detection YOLOv5 network model is obtained by adjusting the learning rate and optimizing the network structure. Finally, the YOLOv5 network model has been compared with SSD, YOLOv4, and faster RCNN network model. The experimental results show that the defect detection method based on YOLOv5 has faster detection speed, higher detection accuracy in defect detection for insulators of power transmission line than SSD, YOLOv4, and faster RCNN network model.
Leveraging Blockchain Technology Centric Tools in Enhancing the Security Safeguards of Documents Storage

The most widely used method for exchanging data worldwide is the Internet. Numerous cloud service providers support this sharing by letting customers store and share data over the Internet. However, cloud service providers have consistently failed to guarantee data's 100% security regarding privacy. Data breaches, piracy, and hacking attacks have threatened cloud providers' security mechanisms. However, customer data should be 100% secure because it may contain private information that should only be accessible to the owner and an intended audience. As a result, making this system more secure is critical to maintaining data privacy and cloud provider trust. We present a system that incorporates cloud-based data security into the Blockchain. It permits clients to store information in the cloud and gives a specific access control component that guarantees information security. Users can share data with others with permission by sharing the document's link with the intended user. The owner will have access to logs of all operations performed with the document at any time. This will guarantee the data's true ownership and privacy. A third party or individual will be permitted to access the document with prior authorization. This will increase cloud storage's security and reduce data breaches and attacks.
A Novel Feature Selection Based Text Classification Using Multi-layer ELM

Deep learning architectures used for text classification are becoming increasingly prevalent. However, the existing deep architectures have flaws such as slow speed, long training times, and the local minimum problem. Multi-layer Extreme Learning Machine has overcome these problems by avoiding backpropagation and thus saves a significant amount of training time, ensures global optimal, and can handle a vast quantity of data. The most important characteristic of Multi-layer ELM is its feature space (FS), which allows the input features to be linearly separated without using any kernel techniques. The architecture of Multi-layer ELM and its technique of feature mapping are examined in this research with the help of a novel feature selection technique termed as Correlation-based Feature Selection (CORFS). Empirical results of the proposed feature selection technique are compared with state-of-the-art techniques. Different classification algorithms are extensively tested on Multi-layer ELM feature space and on TFIDF vector space to demonstrate the efficiency of the feature mapping technique. Results of the experiment revealed that the proposed feature selection technique is better than the conventional feature selection techniques, and the feature space of Multi-layer ELM outperforms TFIDF.