Reviewerly/exHarmony huggingface dataset:
Air Target Threat Assessment and Prediction Based on Improved GM(1,1) Model

Due to non-dynamic and non-continuous problems, traditional air target threat assessment methods will result in low assessment accuracy and high threat risk. To this end, a novel threat assessment algorithm named IEICF-GM(1,1) (Improved Error and Initial Conduction and Feedback-GM(1,1)) is proposed in this paper. Firstly, based on their attribute information, the discrete threats of opposing targets that attack our detection sensors can be received. Secondly, the GM(1,1) model is employed to predict target threats, additionally, the correction factor is introduced to correct the inherent error of the background value in this model. Thirdly, the initial conditions of GM(1,1) model are optimized according to the threat's importance at the historical moment. Finally, the residual sequences between sensor measurement and target state prediction are fitted by using Fourier series method, and then the fitted results are feedbacked to the improved GM(1,1) model. Simulation results show that our algorithm has higher assessment accuracy, predicting dynamic and continuous target threats in the future.
BIRM: Blockchain Based Integrity References Management

Maintaining the integrity of programs is crucial for ensuring the security of the platform they operate on. The integrity of the programs on a trusted computing platform is measured and reported to a challenger by utilizing a Trusted Platform Module integrated on the platform. The challenger then judges whether the programs maintain their integrity by comparing the measurements with the integrity references. Therefore, managing the integrity references and ensuring their correctness is of utmost importance. There is currently no system where a challenger can obtain all integrity references directly. A challenger typically has to obtain the integrity references from each platform with “good” measurements. This paper presents BIRM, a consortium Blockchain-based Integrity References Management system. BIRM enforces access controls for publishing integrity references. The integrity reference files in BIRM use a unified syntax based on the JSON Web Signature specification and are stored separately in a distributed storage network. Only identity and storage index tuples are recorded in the blockchain to reduce its load. The blocks in BIRM are organized as a Merkle Mountain Range. When an inquiry is made, BIRM not only returns the requested identity and storage index tuple directly from the key-value database but also generates proof data by parsing block data to ensure the tuple's correctness. BIRM supports multiple tuple inquiries in a single request, using multiple paths in the Merkle Mountain Range tree. The amount of proof data and proof generation and checks are logarithmic in complexity relative to the total number of blocks.
The Impact of Carbon Emission on Global Warming Based on LSTM Model and PCA

Climate related topic has become a big topic in recent years. Frequent occurrence of extreme phenomenon has made people to are about the climate issue throughout the world, especially about the global warming. This study uses LSTM model to predict the temperature anomaly in the next 100 years and makes PCA to analyze the main contributor to global warming. The temperature anomaly this article used as the dependent variable will stay in a relatively stable stage and the highest anomaly is 1.24℃ while the lowest is 0.25℃. Among carbon dioxide emission, forest fires and volcanic eruption, the main factor that contributes to global warming id CO2. At end of the article, some practical suggestions are put forward to reduce carbon emission in normal life. This article aims to present a basic knowledge of global warming and climate change, and provide people with some measures to implement low carbon.
Analisis Sentimen Masyarakat Pengguna Media Sosial Twitter Terhadap Motogp Mandalika Lombok Menggunakan Metode Bidirectional Encoder Representation From Transformers (BERT)

The MotoGP One race in West Nusa Tenggara Lombok, Mandalika which was held on March 18 2022, received many responses or reactions from the public on social media, especially Twitter. There are those who agree and disagree about the holding of MotoGP in Mandalika, to find out the responses of the people who agree or disagree is needed that can process tweets data using the sentiment analysis method. The use of BERT (Bidirectional Encoder Representations from Transformers) for sentiment analysis produces a bidirectional language model that can understand the context of all words from a sentence. The dataset used goes through preprocessing stages such as case folding, data cleaning, tokenization, normalization, and removal of stopwords before sentiment analysis is carried out. This study uses several hyperparameters, namely a batch size of 32, the optimizer uses Adam with a learning rate of 3e-6 or 0.000003, and an epoch of 25. The evaluation results of the model obtain an accuracy of 55%. Precision for positive by 56%, neutral by 59%, and negative by 44%. Recall for positive is 74%, neutral is 29%, and negative is 54%. F1-score for positive is 64%, neutral is 38%, and negative is 48%.
Robust Parallel Covariance Intersection Fusion Estimators for Uncertain AR signal Systems

This paper is concerned with robust parallel covariance intersection fusion estimation problem for AR systems with uncertain noise variances. By the state space method, the original system is converted into a multi-model system. According to the mini-max robust estimation principle, the local robust estimators are presented. And the PCI fusion estimators are presented according to the PCI fusion algorithm. The robustness and the robust accuracies of them are proved. A simulation verifies the correctness and effectiveness of the proposed results.
ContRE: A Complementary Measure for Robustness Evaluation of Deep Networks via Contrastive Examples

Training images with data transformations, e.g., crops, shifts, rotations and color distortions, have been suggested as contrastive examples to evaluate the robustness of deep neural networks against data noises [1]. In this work, we propose a practical framework ContRE (which is the meaning of “against” in French) that uses Contrastive examples for DNN Robustness Estimation. Specifically, ContRE follows the assumption in [2], [3] that robust DNN models with good generalization performance are capable of extracting a consistent set of features and making consistent predictions from the same image under varying data transformations. Incorporating with a set of randomized strategies for well-designed data transformations over the training set, ContREadopts classification errors and Fisher ratios on the generated contrastive examples to assess and analyze the robustness of DNN models, which correlates to the models’ generalization performance. To show the effectiveness and efficiency of ContRE, extensive experiments have been done using various DNN models, e.g., ResNet, VGGNet, DenseNet, EfficientNet, etc., on three open source benchmark datasets, i.e., CIFAR-10, CIFAR-100, and ImageNet, with thorough ablation studies and applicability analyses. Our experiment results confirm that ❨1❩ behaviors of deep models on contrastive examples are strongly correlated to what on the testing set, and ❨2❩ the robustness that ContRE calculates is a robust measure of generalization performance complementing to the testing set in various settings. Codes is to be publicly available.
Enhancing Robustness and Generalization in Deep Learning Models for Image Processing

In recent years, deep learning models have demonstrated remarkable success in various image processing tasks, ranging from object recognition to medical image analysis. However, their performance often degrades in the presence of unseen data or adversarial attacks, highlighting the need for enhancing robustness and generalization. This paper explores innovative approaches to address these challenges, aiming to improve the reliability and applicability of deep learning models in real-world scenarios. The first section of the paper delves into the importance of robustness and generalization in deep learning models for image processing tasks. It discusses the implications of model vulnerabilities, such as overfitting to training data and susceptibility to adversarial perturbations, on the reliability of model predictions. [1] Through a comprehensive review of existing literature, various factors influencing robustness and generalization are identified, including dataset diversity, model architecture, regularization techniques, and adversarial training methods. The paper proposes novel methodologies to enhance the robustness and generalization capabilities of deep learning models. One key approach involves the integration of diverse training data sources, including synthetic data augmentation and domain adaptation techniques, to expose the model to a wider range of scenarios and improve its ability to generalize to unseen data. Additionally, advanced regularization techniques, such as dropout and batch normalization, are explored to mitigate overfitting and improve model generalization. The paper investigates the effectiveness of adversarial training strategies in enhancing model robustness against adversarial attacks. By incorporating adversarially generated examples during training, deep learning models can learn to better resist perturbations and maintain performance under adversarial conditions. Moreover, the paper explores the potential of incorporating uncertainty estimation methods, such as Bayesian neural networks and Monte Carlo dropout, to quantify model uncertainty and improve robustness in uncertain environments. This paper presents a comprehensive investigation into enhancing robustness and generalization in deep learning models for image processing tasks. By addressing key challenges such as overfitting, dataset bias, and adversarial vulnerabilities, the proposed methodologies offer promising avenues for improving the reliability and applicability of deep learning models in real-world scenarios.
RESPON SISWA TERHADAP PEMBELAJARAN BERKELOMPOK PADA PEMBELAJARAN JARAK JAUH ONLINE

The COVID-19 pandemic has resulted in learning having turned into online distance learning. The learning is dominated by independent learning, even though the ability to socialize and work in teams is needed at this time. Collaborating is one of the skills needed in the 21st century. Students' opinions differ, because the rapid development of education or internet access does not guarantee the quality of education and student satisfaction. Based on this explanation, the purpose of this study was to determine student responses regarding group learning in online distance learning. This research is a descriptive research using survey method. The instrument used is a questionnaire sheet. The number of respondents who filled out the questionnaire was 484 students. Questionnaire data were analyzed by describing the findings obtained which were described through descriptive statistics. The results showed that students gave a positive response to implementing group learning during online distance learning, for the following reasons: the task was carried out more easily; can get help when experiencing difficulties; can discuss by expressing opinions; can share ideas with group members, etc. However, there are also some weaknesses in this learning, such as the role of group members; domination that works unevenly; talk a lot, etc.
Klasifikasi Data Bantuan Sosial pada Desa Sindangpano dengan Menggunakan Algoritma K-Nearest Neighbor

Since the emergence of the Covid-19 Pandemic, many people have joined affacted, one of which is people who have lost their livelihoods. In this study the data used is data on social assistance in the village of Sindangpano in 2021. The data will be processed using the data mining method the k-Nearest Neighbor (KNN) algorithm. One of the them is the emergence ofsocial jealousy between communities. In this study using data mining methods that are assisted by using the K-Nearest Neighbor (KNN) Algorithm and sopported by existing data using Knowlage Discovery Data (KDD) data mining techniques with data selection, transformation, data mining and evaluation in the distribution of social assistance in Sindangpano Village, there are often many problems because people think that the social assistance is distributed is not righ on target so that many people feel that they are not being noticed by the government. Problems that are often found are social jealousy. Among the people this is caused by a lack of information, communication and education by the government to the comunity. This teseach was made with the hope of producing a solution to a problem, so that it can solve many problems that occur in the distribution of Social Assistance in Sindangpano of Village. The results obtained from this classification trial an accuracy rate of 99.57% with a Village Fund precision of 99.50%, BPNT is 99.62%. and the recal result from the Village Fund are 99.50%, BPNT 99.62%. The result of this classification can also be seen what percentage of the community received the assistance, so that the level of balance in the number of recipients can be seen
Integrasi Teknologi Artificial Intelligence Dalam Sistem Akuntansi Modern

Artificial Intelligence (AI) has transformed the landscape of business and industries as a whole, including the field of accounting. The implementation of AI systems in accounting has brought significant changes, accelerating processes, enhancing efficiency, improving reporting accuracy, boosting analytical capabilities within accounting departments, and providing deep insights for accounting professionals. The objective is to explore the extent to which the integration of AI technology changes how organizations manage and analyze financial data, as well as providing insights for better decision-making. This research method employs a qualitative descriptive approach. Previous research literature is analyzed and explored. The research findings indicate that the integration of AI technology into accounting systems can yield operational efficiency by automating routine tasks, enhancing financial reporting accuracy through in-depth data analysis, and strengthening predictive capabilities in decision-making. The conclusion of this research is that the integration of AI technology into accounting systems has the potential to bring about transformative changes in how organizations manage finances and make decisions.
Active Scene Learning

Sketch recognition allows natural and efficient interaction in pen-based interfaces. A key obstacle to building accurate sketch recognizers has been the difficulty of creating large amounts of annotated training data. Several authors have attempted to address this issue by creating synthetic data, and by building tools that support efficient annotation. Two prominent sets of approaches stand out from the rest of the crowd. They use interim classifiers trained with a small set of labeled data to aid the labeling of the remainder of the data. The first set of approaches uses a classifier trained with a partially labeled dataset to automatically label unlabeled instances. The others, based on active learning, save annotation effort by giving priority to labeling informative data instances. The former is sub-optimal since it doesn't prioritize the order of labeling to favor informative instances, while the latter makes the strong assumption that unlabeled data comes in an already segmented form (i.e. the ink in the training data is already assembled into groups forming isolated object instances). In this paper, we propose an active learning framework that combines the strengths of these methods, while addressing their weaknesses. In particular, we propose two methods for deciding how batches of unsegmented sketch scenes should be labeled. The first method, scene-wise selection, assesses the informativeness of each drawing (sketch scene) as a whole, and asks the user to annotate all objects in the drawing. The latter, segment-wise selection, attempts more precise targeting to locate informative fragments of drawings for user labeling. We show that both selection schemes outperform random selection. Furthermore, we demonstrate that precise targeting yields superior performance. Overall, our approach allows reaching top accuracy figures with up to 30% savings in annotation cost.
PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

There are several options for 3D visualization in Python, a few notable projects include Matplotlib (Hunter, 2007), Mayavi (Ramachandran & Varoquaux, 2011), the yt Project (Turk et al., 2010), and the Visualization Toolkit (VTK) (Schroeder, Lorensen, & Martin, 2006).However, few open-source packages are capable of handling large, spatiallyreferenced datasets and some are powerful yet have inherently complex application programming interfaces (APIs), which might create a barrier to entry for new users.Notably, VTK is a powerful scientific visualization software library, and with Python bindings, it combines the speed of C++ with the rapid prototyping of Python.Despite this, VTK code programmed in Python using the base VTK Python package is unnecessarily complicated as its API binds existing C++ calls.The PyVista Python package provides a concise, well-documented interface exposing VTK's powerful visualization backend; enabling researchers to rapidly explore large datasets, communicate their spatial findings, and facilitate reproducibility.PyVista further seeks to simplify standard mesh creation and plotting routines without compromising on the speed of the C++ VTK backend.
Significant applications of virtual reality for COVID-19 pandemic

During COVID-19 pandemic, researchers are using innovative technologies for fast-tracking the development to end this menace. Virtual Reality (VR) also offers an imperative role for fighting this pandemic, through audiovisual-based virtual communication. A brief study on Virtual Reality and its applications for the COVID-19 pandemic is carried out by employing keywords as Virtual reality or VR and COVID-19 from the databases of SCOPUS, Google Scholar, PubMed, Web of science Academia and ResearchGate. VR is beneficial for remote sites for exploring telemedicine, planning, treatment, and controlling of the infections by providing proper awareness to the people regarding this disease. VR technology develops a platform to reduce the face to face interaction of doctors with the infected COVID-19 patients. Through live video streaming, it helps to improve surveillance systems on the ongoing situation.
Quantum walks on a programmable two-dimensional 62-qubit superconducting processor

Simulating quantum walkers Quantum walks are the quantum mechanical analogs of classical random walks, describing the propagation of a quantum walker across a lattice, and find application in developing algorithms for simulating quantum many-body systems. Gong et al. used an 8-by-8 two-dimensional (2D) superconducting qubit square lattice containing 62 functional qubits to show how multiple (two) walkers traverse a 2D qubit array, interfering as they go. The authors were also able to program the paths that the walkers follow, demonstrating a Mach-Zehnder interferometer in which a single or multiple quantum walkers coherently traverse two paths before interfering and exiting at a single port. The results illustrate the potential for superconducting-based quantum processors in simulating large-scale quantum systems. Science , abg7812, this issue p. 948
Faithful and Customizable Explanations of Black Box Models

As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To this end, we propose Model Understanding through Subspace Explanations (MUSE), a novel model agnostic framework which facilitates understanding of a given black box model by explaining how it behaves in subspaces characterized by certain features of interest. Our framework provides end users (e.g., doctors) with the flexibility of customizing the model explanations by allowing them to input the features of interest. The construction of explanations is guided by a novel objective function that we propose to simultaneously optimize for fidelity to the original model, unambiguity and interpretability of the explanation. More specifically, our objective allows us to learn, with optimality guarantees, a small number of compact decision sets each of which captures the behavior of a given black box model in unambiguous, well-defined regions of the feature space. Experimental evaluation with real-world datasets and user studies demonstrate that our approach can generate customizable, highly compact, easy-to-understand, yet accurate explanations of various kinds of predictive models compared to state-of-the-art baselines.
Optimal Approximation with Sparsely Connected Deep Neural Networks

We derive fundamental lower bounds on the connectivity and the memory requirements of deep neural networks guaranteeing uniform approximation rates for arbitrary function classes in $L^2(\mathbb{R}^d)$. In other words, we establish a connection between the complexity of a function class and the complexity of deep neural networks approximating functions from this class to within a prescribed accuracy. Additionally, we prove that our lower bounds are achievable for a broad family of function classes. Specifically, all function classes that are optimally approximated by a general class of representation systems---so-called affine systems---can be approximated by deep neural networks with minimal connectivity and memory requirements. Affine systems encompass a wealth of representation systems from applied harmonic analysis such as wavelets, ridgelets, curvelets, shearlets, $\alpha$-shearlets, and, more generally, $\alpha$-molecules. Our central result elucidates a remarkable universality property of neural networks and shows that they achieve the optimum approximation properties of all affine systems combined. As a specific example, we consider the class of $\alpha^{-1}$-cartoon-like functions, which is approximated optimally by $\alpha$-shearlets. We also explain how our results can be extended to the approximation of functions on low-dimensional immersed manifolds. Finally, we present numerical experiments demonstrating that the standard stochastic gradient descent algorithm yields deep neural networks with close-to-optimal approximation rates. Moreover, these results indicate that stochastic gradient descent can learn approximations that are sparse in the representation systems optimally sparsifying the function class the network is trained on.