Reviewerly/exHarmony huggingface dataset:
Anomaly Detection of Metallurgical Energy Data Based on iForest-AE

With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data. Aiming at the problem that the training data are contaminated with anomalies in autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposes a data anomaly detection method that combines an isolated forest (iForest) and autoencoder algorithm. In this method (iForest-AE), the iForest algorithm was used to calculate the anomaly score of energy data, and the data with a lower anomaly score were selected for model training. After the test data passed through the autoencoder trained by normal data, the data whose reconstruction error was larger than the threshold were determined as an anomaly. Experiment results on the electricity consumption dataset showed that the iForest-AE method achieved an F1 score of 0.981, which outperformed other detection methods, and a significant advantage in anomaly detection.
On the decisional Diffie–Hellman problem for class group actions on oriented elliptic curves

Abstract We show how the Weil pairing can be used to evaluate the assigned characters of an imaginary quadratic order $${\mathcal {O}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> </mml:math> in an unknown ideal class $$[{\mathfrak {a}}] \in {{\,\textrm{cl}\,}}({\mathcal {O}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>a</mml:mi> <mml:mo>]</mml:mo> <mml:mo>∈</mml:mo> <mml:mrow> <mml:mspace /> <mml:mtext>cl</mml:mtext> <mml:mspace /> </mml:mrow> <mml:mo>(</mml:mo> <mml:mi>O</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> that connects two given $${\mathcal {O}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> </mml:math> -oriented elliptic curves $$(E, \iota )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>E</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ι</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and $$(E', \iota ') = [{\mathfrak {a}}](E, \iota )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>E</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mo>,</mml:mo> <mml:msup> <mml:mi>ι</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>a</mml:mi> <mml:mo>]</mml:mo> </mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>E</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ι</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . When specialized to ordinary elliptic curves over finite fields, our method is conceptually simpler and often somewhat faster than a recent approach due to Castryck, Sotáková and Vercauteren, who rely on the Tate pairing instead. The main implication of our work is that it breaks the decisional Diffie–Hellman problem for practically all oriented elliptic curves that are acted upon by an even-order class group. It can also be used to better handle the worst cases in Wesolowski’s recent reduction from the vectorization problem for oriented elliptic curves to the endomorphism ring problem, leading to a method that always works in sub-exponential time.
Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48\%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis.
The Lie Derivative for Measuring Learned Equivariance

Equivariance guarantees that a model's predictions capture key symmetries in data. When an image is translated or rotated, an equivariant model's representation of that image will translate or rotate accordingly. The success of convolutional neural networks has historically been tied to translation equivariance directly encoded in their architecture. The rising success of vision transformers, which have no explicit architectural bias towards equivariance, challenges this narrative and suggests that augmentations and training data might also play a significant role in their performance. In order to better understand the role of equivariance in recent vision models, we introduce the Lie derivative, a method for measuring equivariance with strong mathematical foundations and minimal hyperparameters. Using the Lie derivative, we study the equivariance properties of hundreds of pretrained models, spanning CNNs, transformers, and Mixer architectures. The scale of our analysis allows us to separate the impact of architecture from other factors like model size or training method. Surprisingly, we find that many violations of equivariance can be linked to spatial aliasing in ubiquitous network layers, such as pointwise non-linearities, and that as models get larger and more accurate they tend to display more equivariance, regardless of architecture. For example, transformers can be more equivariant than convolutional neural networks after training.
Multi-Label Feature Selection with Conditional Mutual Information

Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. To deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare it with the other eight multi-label feature selection algorithms on ten datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than other existing algorithms.
A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering

Neural networks have demonstrated their usefulness for solving complex regression problems in circumstances where alternative methods do not provide satisfactory results. Finding a good neural network model is a time-consuming task that involves searching through a complex multidimensional hyperparameter and weight space in order to find the values that provide optimal convergence. We propose a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. The method consists of a modified, hybrid variant of the Imperialist Competitive Algorithm (ICA). We analyze multiple strategies for initialization, assimilation, revolution, and competition, in order to find the combination of ICA steps that provides optimal convergence and enhance the algorithm by incorporating a backpropagation step in the ICA loop, which, together with a self-adaptive hyperparameter adjustment strategy, significantly improves on the original algorithm. The resulting hybrid method is used to optimize a neural network to solve a complex problem in the field of chemical engineering: the synthesis and swelling behavior of the semi- and interpenetrated multicomponent crosslinked structures of hydrogels, with the goal of predicting the yield in a crosslinked polymer and the swelling degree based on several reaction-related input parameters. We show that our approach has better performance than other biologically inspired optimization algorithms and generates regression models capable of making predictions that are better correlated with the desired outputs.
A New Low-Rank Structurally Incoherent Algorithm for Robust Image Feature Extraction

In order to solve the problem in which structurally incoherent low-rank non-negative matrix decomposition (SILR-NMF) algorithms only consider the non-negativity of the data and do not consider the manifold distribution of high-dimensional space data, a new structurally incoherent low rank two-dimensional local discriminant graph embedding (SILR-2DLDGE) is proposed in this paper. The algorithm consists of the following three parts. Firstly, it is vital to keep the intrinsic relationship between data points. By the token, we introduced the graph embedding (GE) framework to preserve locality information. Secondly, the algorithm alleviates the impact of noise and corruption uses the L1 norm as a constraint by low-rank learning. Finally, the algorithm improves the discriminant ability by encrypting the structurally incoherent parts of the data. In the meantime, we capture the theoretical basis of the algorithm and analyze the computational cost and convergence. The experimental results and discussions on several image databases show that the proposed algorithm is more effective than the SILR-NMF algorithm.
Arabic authorship attribution on Twitter: what is really matters?

Recently, authorship attribution (AA) of online social networks texts has gained more attention. However, since 2015, when the first work that addressed the AA of Arabic tweets was published, we found that nothing much has been done after that. Thus, the current paper presents an extensive study that investigates the effects of various factors on the AA of Arabic short-texts, especially tweets. This led to a proposed architecture in which the AA accuracy is examined depending on the size of the training dataset, the number of classes covered, the text processing techniques applied, the methods used for both feature selection and extraction, and finally, the classifier implemented. As a result, we performed 792 different tests. The highest accuracy recorded is 97.4%, and it is among the best results published so far.
Modified three-term conjugate gradient algorithm and its applications in image restoration

In image restoration, the goal is often to bring back a high-quality version of an image from a lower-quality copy of it. In this article, we will investigate one kind of recovery issue, namely recovering photos that have been blurred by noise in digital photographs (sometimes known as "salt and pepper" noise). When subjected to noise at varying frequencies and intensities (30,50,70,90). In this paper, we used the conjugate gradient algorithm to Restorative images and remove noise from them, we developed the conjugate gradient algorithm with three limits using the conjugate condition of Dai and Liao, where the new algorithm achieved the conditions for descent and global convergence under some assumptions. According to the results of the numerical analysis, the recently created approach is unequivocally superior to both the fletcher and reeves (FR) method and the fletcher and reeves three-term (TTFR) metod. Use the structural similarity index measure (SSIM), which is used to measure image quality and the higher its value, the better the result. The original image was compared with all the noisy images and each according to the percentage of noise as well as the images processed with the four methods.
Cryptography based on retina information

The security of message information has drawn more attention nowadays, so; cryptography has been used extensively. This research aims to generate secured cipher keys from retina information to increase the level of security. The proposed technique utilizes cryptography based on retina information. The main contribution is the original procedure used to generate three types of keys in one system from the retina vessel's end position and improve the technique of three systems, each with one key. The distances between the center of the diagonals of the retina image and the retina vessel's end (diagonal center-end (DCE)) represent the first key. The distances between the center of the radius of the retina and the retina vessel's end (radius center-end (RCE)) represent the second key. While the diagonal-radius center and the retina vessel's end (diagonal-radius center-end (DRCE)) represent the third key. The results illustrate the process's validity and applicability. Also, improve the time required to decrypt the cipher-text by a brute force attack (BFA) from (4.358e+139) year in the compared technique to (1.3074e+140) year for retina3. The BFA time will increase with increasing the number of retina vessels, as in retina1, 2, and 3, which have 24, 53, and 103 retina vessels.
FedMed-ATL: Misaligned Unpaired Cross-Modality Neuroimage Synthesis via Affine Transform Loss

The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases. However, collecting the full set of well-aligned and paired data is impractical, since the practical difficulties may include high cost, long time acquisition, image corruption, and privacy issues. Previously, the misaligned unpaired neuroimaging data (termed as MUD) are generally treated as noisy labels. However, such a noisy label-based method fails to accomplish well when misaligned data occurs distortions severely. For example, the angle of rotation is different. In this paper, we propose a novel federated self-supervised learning (FedMed) for brain image synthesis. An affine transform loss (ATL) was formulated to make use of severely distorted images without violating privacy legislation for the hospital. We then introduce a new data augmentation procedure for self-supervised training and fed it into three auxiliary heads, namely auxiliary rotation, auxiliary translation, and auxiliary scaling heads. The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms. The proposed method also reduces the demand for deformable registration while encouraging to leverage the misaligned and unpaired data. Experimental results verify the outstanding performance of our learning paradigm compared to other state-of-the-art approaches.
DeepGuard: Backdoor Attack Detection and Identification Schemes in Privacy-Preserving Deep Neural Networks

Deep neural networks (DNNs) have profoundly changed our lifeways in recent years. The cost of training a complicated DNN model is always overwhelming for most users with limited computation and storage resources. Consequently, an increasing number of people are considering to resort to a cloud for an outsourced DNN model training. However, the DNN models training process outsourced to the cloud faces privacy and security issues due to the semi-honest and malicious cloud environments. To preserve the privacy of the data and the parameters in DNN models during the outsourced training and to detect whether the models are injected with backdoors, this paper presents DeepGuard, a framework of privacy-preserving backdoor detection and identification in an outsourced cloud environment for multi-participant computation. In particular, we design a privacy-preserving reverse engineering algorithm for recovering the triggers and detecting the backdoor attacks among three cooperative but non-collusion servers. Moreover, we propose a backdoor identification algorithm adapting to single-label and multi-label attack detection. Finally, extensive experiments on the prevailing datasets such as MNIST, SVHN, and GTSRB confirm the effectiveness and efficiency of backdoor detection and identification in a privacy-preserving DNN model.
Software Development Data Analysis and Processing under the Internet of Things Monitoring System

In order to solve the problem of highly extensible vibration test data acquisition and analysis, the author proposes a method for software development data analysis and processing under the Internet of Things monitoring system. The software platform is mainly designed through the design of software architecture based on multitask operation, active window design, reserved API interface and hardware universal design; it ensures the strong expansibility of the software platform, so as to realize the universality of the software platform. High-level vibration data analysis software designed based on this platform, such as modal parameter identification and dynamic load identification software, can be easily redeveloped by using the existing functions and software architecture of the platform, expand software functions, realize more complex vibration data analysis and processing, reduce repetitive labor, and speed up the software development process. The results showed that: the amplitude error is less than 4%. Conclusions. The feasibility and availability of software development data under the IoT monitoring system are verified.
More or Less Unnatural: Semantic Similarity Shapes the Learnability and Cross-Linguistic Distribution of Unnatural Syncretism in Morphological Paradigms

Morphological systems often reuse the same forms in different functions, creating what is known as syncretism. While syncretism varies greatly, certain cross-linguistic tendencies are apparent. Patterns where all syncretic forms share a morphological feature value (e.g., first person, or plural number) are most common cross-linguistically, and this preference is mirrored in results from learning experiments. While this suggests a general bias towards natural (featurally homogeneous) over unnatural (featurally heterogeneous) patterns, little is yet known about gradients in learnability and distributions of different kinds of unnatural patterns. In this paper we assess apparent cross-linguistic asymmetries between different types of unnatural patterns in person-number verbal agreement paradigms and test their learnability in an artificial language learning experiment. We find that the cross-linguistic recurrence of unnatural patterns of syncretism in person-number paradigms is proportional to the amount of shared feature values (i.e., similarity) amongst the syncretic forms. Our experimental results further suggest that the learnability of syncretic patterns also mirrors the paradigm's degree of feature-value similarity. We propose that this gradient in learnability reflects a general bias towards similarity-based structure in morphological learning, which previous literature has shown to play a crucial role in word learning as well as in category and concept learning more generally. Rather than a dichotomous natural/unnatural distinction, our results thus support a more nuanced view of (un)naturalness in morphological paradigms and suggest that a preference for similarity-based structure during language learning might shape the worldwide transmission and typological distribution of patterns of syncretism.
Blockchain Applications in Education Affecting Challenges and Problems in Digital

Blockchain is thought to have a significant potential to offer a wide range of benefits for education development because of its special properties of decentralization and security. Its use in schooling is quite new. new but expanding swiftly. This paper described the standard blockchain. Briefly describe procedures and qualities. then, current blockchain applications thorough summaries of the findings in education, especially those Learning record keeper, decentralized education ecosystem, and credential issuance and management. The final topic of discussion was obstacles, both technical and otherwise. It is intended to give a thorough examination of the potential applications of blockchain. aid in the creation of new application systems and assist in the evolution of education.
Developing a Semantic Question Answering System for E-learning Environments using Linguistic Resources

The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand the questions without any doubts in meaning and how to provide the most relevant answers to the questions. In this study, a question-answering system for specific courses has been developed to support the learning environment. The research outcomes indicate that the proposed method helps to solve the problem of ambiguities in meaning through the integration of natural language processing tools and semantic resources that can help to overcome several problems related to the natural language structure. This method also helps improve the capability to understand students’ needs and, consequently, to retrieve the most suitable answers.