Reviewerly/exHarmony huggingface dataset:
Emotion Analysis of Ideological and Political Education Using a GRU Deep Neural Network

Theoretical research into the emotional attributes of ideological and political education can improve our ability to understand human emotion and solve socio-emotional problems. To that end, this study undertook an analysis of emotion in ideological and political education by integrating a gate recurrent unit (GRU) with an attention mechanism. Based on the good results achieved by BERT in the downstream network, we use the long focusing attention mechanism assisted by two-way GRU to extract relevant information and global information of ideological and political education and emotion analysis, respectively. The two kinds of information complement each other, and the accuracy of emotion information can be further improved by combining neural network model. Finally, the validity and domain adaptability of the model were verified using several publicly available, fine-grained emotion datasets.
Single image based 3D human pose estimation via uncertainty learning

In monocular image scenes, 3D human pose estimation exhibits inherent ambiguity due to the loss of depth information and occlusions. Simply regressing body joints with high uncertainties will lead to model overfitting and poor generalization. In this paper, we propose an uncertainty-based framework to jointly learn 3D human poses and the uncertainty of each joint. Our proposed joint estimation framework aims to mitigate the adverse effects of training samples with high uncertainties and facilitate the training procedure. To be specific, we model each body joint as a Laplace distribution for uncertainty representation. Since visual joints often exhibit low uncertainties while occluded ones have high uncertainties, we develop an adaptive scaling factor, named the uncertainty-aware scaling factor, to ease the network optimization in accordance with the joint uncertainties. By doing so, our network is able to converge faster and significantly reduce the adverse effects caused by those ambiguous joints. Furthermore, we present an uncertainty-aware graph convolutional network by exploiting the learned joint uncertainties and the relationships among joints to refine the initial joint localization. Extensive experiments on single-person (Human3.6M) and multi-person (MuCo-3DHP & MuPoTS-3D) 3D human pose estimation datasets demonstrate the effectiveness of our method.
Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

The trajectory planning of multiple unmanned aerial vehicles (UAVs) is the core of efficient UAV mission execution.Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods.However, multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment.Therefore, a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance, trajectory time, trajectory threat, and trajectory coordination distance costs of UAVs.The NSGA-III algorithm, which overcomes the problems of traditional trajectory planning, is used to solve the model.This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm.Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm, thereby addressing different actual needs.
Facilitating database tuning with hyper-parameter optimization

Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database community. This is embodied with a number of systems featuring advanced tuning capabilities being developed. However, it remains a challenge to select the best solution for database configuration tuning, considering the large body of algorithm choices. In addition, beyond the applications on database systems, we could find more potential algorithms designed for configuration tuning. To this end, this paper provides a comprehensive evaluation of configuration tuning techniques from a broader perspective, hoping to better benefit the database community. In particular, we summarize three key modules of database configuration tuning systems and conduct extensive ablation studies using various challenging cases. Our evaluation demonstrates that the hyper-parameter optimization algorithms can be borrowed to further enhance the database configuration tuning. Moreover, we identify the best algorithm choices for different modules. Beyond the comprehensive evaluations, we offer an efficient and unified database configuration tuning benchmark via surrogates that reduces the evaluation cost to a minimum, allowing for extensive runs and analysis of new techniques.
CirC: Compiler infrastructure for proof systems, software verification, and more

Cryptographic tools like proof systems, multi-party computation, and fully homomorphic encryption are usually applied to computations expressed as systems of arithmetic constraints. In practice, this means that these applications rely on compilers from high-level programming languages (like C) to such constraints. This compilation task is challenging, but not entirely new: the software verification community has a rich literature on compiling programs to logical constraints (like SAT or SMT). In this work, we show that building shared compiler infrastructure for compiling to constraint representations is possible, because these representations share a common abstraction: stateless, non-uniform, non-deterministic computations that we call existentially quantified circuits, or EQCs. Moreover, we show that this shared infrastructure is useful, because it allows compilers for proof systems to benefit from decades of work on constraint compilation techniques for software verification. To make our approach concrete we create CirC, an infrastructure for building compilers to EQCs. CirC makes it easy to compile to new EQCs: we build support for three, R1CS (used for proof systems), SMT (used for verification and bug-finding), and ILP (used for optimization), in $\approx 2000$ LOC. It’s also easy to extend CirC to support new source languages: we build a feature-complete compiler for a cryptographic language in one week and $\approx 900$ LOC, whereas the reference compiler for the same language took years to write, comprises $\approx 24000$ LOC, and produces worse-performing output than our compiler. Finally, CirC enables novel applications that combine multiple EQCs. For example, we build the first pipeline that (1) automatically identifies bugs in programs, then (2) automatically constructs cryptographic proofs of the bugs’ existence.
SecFloat: Accurate Floating-Point meets Secure 2-Party Computation

We build a library SecFloat for secure 2-party computation (2PC) of 32-bit single-precision floating-point operations and math functions. The existing functionalities used in cryptographic works are imprecise and the precise functionalities used in standard libraries are not crypto-friendly, i.e., they use operations that are cheap on CPUs but have exorbitant cost in 2PC. SecFloat bridges this gap with its novel crypto-friendly precise functionalities. Compared to the prior cryptographic libraries, SecFloat is up to six orders of magnitude more precise and up to two orders of magnitude more efficient. Furthermore, against a precise 2PC baseline, SecFloat is three orders of magnitude more efficient. The high precision of SecFloat leads to the first accurate implementation of secure inference. All prior works on secure inference of deep neural networks rely on ad hoc float-to-fixed converters. We evaluate a model where the fixed-point approximations used in privacy-preserving machine learning completely fail and floating-point is necessary. Thus, emphasizing the need for libraries like SecFloat.
Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)

Earthquake is a major hazard to humans, buildings, and infrastructure. Early warning systems should detect an earthquake and issue a warning with earthquake information such as location, magnitude, and depth. Earthquake detection from raw waveform data using deep learning models such as graph neural networks (GNN) is becoming an important research area. The multilayered structure of the GNN with a number of epochs takes more training time. It is also hard to train the model with saturating nonlinearities. The batch normalization technique is applied to each mini-batch to reduce epochs in training and obtain a steady distribution of activation values. It improves model training and prediction accuracy. This study proposes a deep learning model batch normalization graph convolutional neural network (BNGCNN) for early earthquake detection. It consists of two main components: CNN and GNN. Input to the CNN model is multi-station and three-component waveform data with magnitude ≥3.0 were collected from January 2000 to January 2015 for Southern California. The extracted features of CNN are appended with location information and input to GNN model for earthquake detection. After hyperparameter tuning of the BNGCNN, when testing and evaluating the model on the Southern California dataset, our method shows promising results to the baseline model GNN by obtaining a low error rate to predict the magnitude, depth, and location of an earthquake.
Review on Energy Application Using Blockchain Technology With an Introductions in the Pricing Infrastructure

With the rapid transformation of the energy sector towards modern power systems represented by smart grids (SGs), microgrids (MG), and distributed generation, blockchain (BC) technology has shown the capability for solving security, privacy, and reliability challenges that hinder progress. Currently, the energy structure is forming a decentralized system that prioritizes customer satisfaction. BC technology undertakes power network stockholders in a secure energy market, transparent transactions, and fair competition and offers promising energy solutions. This paper is a comprehensive review of energy applications using BC integration. Firstly, we introduce the drivers of BC leverage that make it a potentially important component of the power network. Following that, we provide background information on BC and its application in areas other than the energy sector. Subsequently, we discuss studies and sort potential energy applications from various recent papers and surveys that have already adopted BC technology in the energy sector. Then, we summarize the pricing infrastructure for applying BC in the energy sector and identify the requirements to build it. Finally, energy security and privacy challenges based on BC are highlighted, along with potential drawbacks and concerns related to the pricing infrastructure.
On the generation of realistic synthetic petrographic datasets using a style-based GAN

Deep learning architectures have transformed data analytics in geosciences, complementing traditional approaches to geological problems. Although deep learning applications in geosciences show encouraging signs, their potential remains untapped due to limited data availability and the required in-depth knowledge to provide a high-quality labeled dataset. We approached these issues by developing a novel style-based deep generative adversarial network (GAN) model, PetroGAN, to create the first realistic synthetic petrographic datasets across different rock types. PetroGAN adopts the architecture of StyleGAN2 with adaptive discriminator augmentation (ADA) to allow robust replication of statistical and esthetical characteristics and improve the internal variance of petrographic data. In this study, the training dataset consists of > 10,000 thin section images both under plane- and cross-polarized lights. Here, using our proposed novel approach, the model reached a state-of-the-art Fréchet Inception Distance (FID) score of 12.49 for petrographic images. We further observed that the FID values vary with lithology type and image resolution. The generated images were validated through a survey where the participants have various backgrounds and level of expertise in geosciences. The survey established that even a subject matter expert observed the generated images were indistinguishable from real images. This study highlights that GANs are a powerful method for generating realistic synthetic data in geosciences. Moreover, they are a future tool for image self-labeling, reducing the effort in producing big, high-quality labeled geoscience datasets. Furthermore, our study shows that PetroGAN can be applied to other geoscience datasets, opening new research horizons in the application of deep learning to various fields in geosciences, particularly with the presence of limited datasets.
Cyber-security and risky behaviors in a developing country context: a Pakistani perspective

Cyber-security behavior research is scant with even scarce studies carried out in developing countries. We examine the cyber-security and risky Internet behaviors of undergraduate students from Pakistan, taking into account the diversity of these students in terms of demographics, socioeconomic status, and the digital divide. Data were collected using a survey questionnaire. A total of 294 students belonging to six different cities of Pakistan were surveyed employing multistage stratified sampling in face-to-face interaction. The results indicated significant differences of cyber-security posture in terms of gender, age and digital divide variables. The profiles of students based on cyber-security and risky Internet behaviors indicate three groups with a majority of them falling into group that exhibits more risk-averse yet low cyber-security behavior. Moreover, proactive cyber-security awareness behavior has a positive impact on high risk-averse behavior. The implications of the findings are studied in terms of providing customized training and awareness. The future directions are laid out for further explorations in terms of cultural differences within and cross-country contexts.
Label correlations variation for robust multi-label feature selection

Numerous high-dimension multi-label data are produced, leading to the imperative need to design excellent multi-label feature selection methods. It is of paramount importance to exploit label correlations in previous methods. However, there exist two unsolved issues in most of existing methods. First, most of existing methods explore label correlations based on the original label space with redundant and irrelevant label information. Second, previous methods either consider second-order label correlations or high-order label correlations, in fact, both two types of label correlations are significant and complementary for capturing label information. To this end, this paper proposes a robust multi-label feature selection method with both two types of label correlations. To start with, we introduce the self-expression model to consider the high-order label correlations, additionally, the l2,1-norm is imposed onto the self-expression coefficient matrix to eliminate redundant and noisy information. Furthermore, we employ a label-level regularizer to achieve pairwise label correlations. Finally, an optimization scheme with convergence proof is designed to deal with the objective function. Multiple experimental analysis results on fourteen multi-label data sets manifest that the classification performance of the proposed method outperforms other baselines.
Zircon classification from cathodoluminescence images using deep learning

• An automated zircon classification from cathodoluminescence images. • Deep learning models using over 4000 cathodoluminescence images of igneous, metamorphic, and hydrothermal zircons. • The model facilitates quick and nearly error-free zircon type distributions. Zircon is a widely-used heavy mineral in geochronological and geochemical research because it can extract important information to understand the history and genesis of rocks. Zircon has various types, and an accurate examination of zircon type is a prerequisite procedure before further analysis. Cathodoluminescence (CL) imaging is one of the most reliable ways to classify zircons. However, current CL image examination is conducted by manual work, which is time-consuming, bias-prone, and requires expertise. An automated and bias-free method for zircon classification is absent but necessary. To this end, deep convolutional neural networks (DCNNs) and transfer learning are applied in this study to classify the common types of zircons, i.e., igneous, metamorphic, and hydrothermal zircons. An atlas with over 4000 CL images of these three types of zircons is created, and three DCNNs are trained using these images. The results of this study indicate that the DCNNs can distinguish hydrothermal zircons from other zircons, as indicated by the highest accuracy of 100%. Although similar textures in igneous and metamorphic zircons pose great challenges for zircon classification, the DCNNs successfully classify 95% igneous and 92% metamorphic zircons. This study demonstrates the high accuracy of DCNNs in zircon classification and presents the great potentiality of deep learning techniques in numerous geoscientific disciplines.
A New Reliable System For Managing Virtual Cloud Network

Virtual cloud network (VCN) usage is popular today among large and small organizations due to its safety and money-saving.Moreover, it makes all resources in the company work as one unit.VCN also facilitates sharing of files and applications without effort.However, cloud providers face many issues in managing the VCN on cloud computing including these issues: Power consumption, network failures, and data availability.These issues often occur due to overloaded and unbalanced load tasks.In this paper, we propose a new automatic system to manage VCN for executing the workflow.The new system called Multi-User Hybrid Scheduling (MUSH) can solve running issues and save power during workflow execution.It consists of three phases: Initialization, virtual machine allocation, and task scheduling algorithms.The MUSH system focuses on the execution of the workflow with deadline constraints.Moreover, it considers the utilization of virtual machines.The new system can save makespan and increase the throughput of the execution operation.
EHMIN: Efficient approach of list based high-utility pattern mining with negative unit profits

High-utility pattern mining is an important sub-literature in the data mining literature. This literature discusses the discovery of useful pattern information from large databases by considering not only supports of patterns but also profits and quantities of items. This literature has the potential to be applied to various problems in the real world, so many methods for the improvement of the algorithm performance have been studied. Moreover, there have also been attempts to extend the flexibility of this literature. The traditional approaches in this literature considered the positive unit profits of items in a given database only. However, this literature can take extended flexibility into account by considering negative as well as positive unit profits of the items. In this paper, we suggest an efficient approach for mining high-utility patterns with negative unit profits. Moreover, the experimental performance tests, which are performed on various real and synthetic datasets in this paper, show that the proposed algorithm has a better performance than the state-of-the-art methods in this literature in terms of the runtime, memory usage, and scalability.
Hybrid methodology for analysis of structured and unstructured data to support decision-making in public security

This work proposes a hybrid methodology that enables the integration of structured and unstructured data to support the decision-making process in public security contexts. The proposed methodology facilitates classification and prediction of crime in a given region, making it possible to identify actions to improve public security based on the results. The integration of the data takes place in two main steps: (1) loading and analyzing structured data made available by government agencies; and (2) absorbing, classifying, and analyzing unstructured data from digital platforms such as Twitter, Where I Was Robbed, and CityCop. In this way, it becomes possible to transform these unstructured data into structured data to be incorporated into a historical database on which algorithms can act to classify, measure, and predict crime. To illustrate the applicability of this methodology, we conducted a study in the city of Recife, Brazil. Structured and unstructured data were gathered in order to conduct a neighborhood classification analysis of crime hot spots. Based on that analysis, we conducted a series of actions intended to bring improvements to the region by the local police. We obtained an increase in the algorithms’ accuracy rate of 80%, indicating that public security organizations can base their actions on the results of the proposed methodology.
XAI for intrusion detection system: comparing explanations based on global and local scope

Intrusion Detection System is a device or software in the field of cybersecurity that has become an essential tool in computer networks to provide a secured network environment. Machine Learning based IDS offers a self-learning solution and provides better performance when compared to traditional IDS. As the predictive performance of IDS is based on conflicting criteria, the underlying algorithms are becoming more complex and hence, less transparent. Explainable Artificial Intelligence is a set of frameworks that help to develop interpretable and inclusive machine learning models. In this paper, we use Permutation Importance, SHapley Additive exPlanation, Local Interpretable Model-Agnostic Explanation algorithms, Contextual Importance and Utility algorithms, covering both global and local scope of explanation to IDSs on Random Forest, eXtreme Gradient Boosting and Light Gradient Boosting machine learning models along with a comparison of explanations in terms of accuracy, consistency and stability. This comparison can help cyber security personnel to have a better understanding of the predictions of cyber-attacks in the network traffic. A case study focusing on DoS attack variants shows some useful insights on the impact of features in prediction performance.
Intuitionistic fuzzy twin support vector machines for imbalanced data

The classification of imbalanced datasets is one of the main challenges in machine learning techniques. Support vector machine (SVM), which generates a model biased within the majority class, usually has bad performance on the minority class because this class may be considered incorrectly as noises. Moreover, datasets often include noises and outliers, and SVM cannot effectively deal with those datasets. In this paper, to defeat the aforementioned challenges, we propose intuitionistic fuzzy twin support vector machines for imbalanced data (IFTSVM-ID). The proposed method can easily handle imbalanced datasets in the presence of noises and outliers. A reasonable weighting strategy is offered to deal with imbalanced classes, and a margin-based technique is assigned to reduce the impact of noise and outliers. We formulate the linear and non-linear kernel functions to find two non-parallel hyperplanes. One real-world and thirty-two imbalanced datasets are selected to validate the performance of IFTSVM-ID. The Friedman test and the bootstrap technique with 95% confidence interval are applied to quantify the results statistically. The experimental results show that our proposed method has much better performance in comparison with other similar techniques.