Reviewerly/exHarmony huggingface dataset:
Sememe-Based Lexical Knowledge Representation Learning

Abstract Linguistic and commonsense knowledge bases describe knowledge in formal and structural languages. Such knowledge can be easily leveraged in modern natural language processing systems. In this chapter, we introduce one typical kind of linguistic knowledge (sememe knowledge) and a sememe knowledge base named HowNet. In linguistics, sememes are defined as the minimum indivisible units of meaning. We first briefly introduce the basic concepts of sememe and HowNet. Next, we introduce how to model the sememe knowledge using neural networks. Taking a step further, we introduce sememe-guided knowledge applications, including incorporating sememe knowledge into compositionality modeling, language modeling, and recurrent neural networks. Finally, we discuss sememe knowledge acquisition for automatically constructing sememe knowledge bases and representative real-world applications of HowNet.
Cross‐modal person re‐identification based on deep attention hash learning

Summary Person re‐identification based on text description is a critical task in modern security systems. However, existing methods primarily focus on performance and overlook the crucial aspect of retrieval efficiency. In this article, we propose a novel two‐stage multimodal re‐discovery algorithm called DCH‐ReID, which leverages attention hashing. First, we introduce a chunked mapping hash learning method that effectively mitigates confusion between hash codes. Second, we propose a hash learning approach based on the channel attention mechanism, assigning higher binary bit weights to important body parts. Finally, to balance retrieval performance and time efficiency, we present a two‐stage retrieval scheme. Through extensive experiments on the CUHK‐PEDES benchmark dataset, we validate that our proposed DCH‐ReID algorithm exhibits superior efficiency and higher accuracy compared to current mainstream text‐based pedestrian re‐identification algorithms.
Leakage in restless quantum gate calibration

Quantum computers require high-fidelity quantum gates. These gates are obtained by routine calibration tasks that eat into the availability of cloud-based devices. Restless circuit execution speeds up characterization and calibration by foregoing qubit reset between circuits. Postprocessing the measured data recovers the desired signal. However, since the qubits are not reset, leakage---typically present at the beginning of the calibration---may cause issues. Here we develop a simulator of restless circuit execution based on a Markov chain to study the effect of leakage. In the context of error-amplifying single-qubit gates sequences, we show that restless calibration tolerates up to 0.5% of leakage, which is large compared to the ${10}^{\ensuremath{-}4}$ gate fidelity of modern single-qubit gates. Furthermore, we show that restless circuit execution with leaky gates reduces by 33% the sensitivity of the ORBIT cost function developed by J. Kelly et al. which is typically used in closed-loop optimal control [Phys. Rev. Lett. 112, 240504 (2014)]. Our results are obtained with standard qubit state discrimination showing that restless circuit execution is resilient against misclassified noncomputational states. In summary, the restless method is sufficiently robust against leakage in both standard and closed-loop optimal control gate calibration to provided accurate results.
Evolution of Popularity and Multiaspectual Comparison of Widely Used Web Development Frameworks

Since the emergence of the first web development frameworks at the turn of the 21st century, many of them have made a name for themselves and rose to wide popularity only to be later ousted by newer frameworks and sometimes even fall to obscurity. In our paper, we would like to depict the last fifteen years of the changing popularity of web development frameworks by observing the number of newly created repositories in open-source projects hosted at GitHub that were based on them as well as the number of questions posted on Stack Overflow regarding the respective frameworks. We analyze the correspondence between these two indicators and put them in the context of the popularity of the programming languages that the respective frameworks are based on, as measured by the TIOBE Programming Community index, in an effort to check whether one source could be used to forecast another.
Electric power training simulation platform based on virtual reality technology

Most of the conventional electric power training simulation platforms are designed based on the structural principle of BP neural network. Their software performance is limited and the platform test pass rate is low, which cannot provide good support for electric power training. To solve this problem, a new electric power training simulation platform is designed by introducing virtual reality technology. The platform adopts B/S hardware architecture, which provides basic guarantee for the stable operation of hardware equipment. Through immersive power training environment modeling, power operation drill module design, and building platform training scene library based on virtual reality technology, the platform software design was completed. According to the test results, after the application of the new platform, the test pass rate of each project has reached more than 95%, which is highly feasible.
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment

Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, such as nodes or edges, that the target GNN relies upon for making predictions. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph, which can preserve original predictions. However, an inductive bias is deep-rooted in this framework: Several subgraphs can result in the same or similar outputs as the original graphs. Consequently, they have the danger of providing spurious explanations and failing to provide consistent explanations. Applying them to explain weakly performed GNNs would further amplify these issues. To address this problem, we theoretically examine the predictions of GNNs from the causality perspective. Two typical reasons for spurious explanations are identified: confounding effect of latent variables like distribution shift and causal factors distinct from the original input. Observing that both confounding effects and diverse causal rationales are encoded in internal representations, we propose a new explanation framework with an auxiliary alignment loss, which is theoretically proven to be optimizing a more faithful explanation objective intrinsically. Concretely for this alignment loss, a set of different perspectives are explored: anchor-based alignment, distributional alignment based on Gaussian mixture models, mutual-information-based alignment, and so on. A comprehensive study is conducted both on the effectiveness of this new framework in terms of explanation faithfulness/consistency and on the advantages of these variants. For our codes, please refer to the following URL link: https://github.com/TianxiangZhao/GraphNNExplanation
On the structure of matroids arising from the gain graphs

The gain graphs are essential generalizations of the signed graphs. They are indeed directed graphs whose edges are labeled by the elements of a group rather than just ±1. In this paper, we prove the existence of matroids arising from these graphs. Further, we study the structure of the obtained matroids as well as their relation with the generalized Hamming weights, the regularity of the ideal of circuits, and the codes.
An Overview of Explainable Artificial Intelligence in the Industry 4.0 Context

Artificial intelligence (AI) has gained significant evolution in recent years that, if properly harnessed, may meet or exceed expectations in a wide range of application fields. However, because Machine Learning (ML) models have a black-box structure, end users frequently seek explanations for the predictions made by these learning models. Through tools, approaches, and algorithms, Explainable Artificial Intelligence (XAI) gives descriptions of black-box models to better understand the models’ behaviour and underlying decision-making mechanisms. The AI development in companies enables them to participate in Industry 4.0. The need to inform users of transparent algorithms has given rise to the research field of XAI. This paper provides a brief overview and introduction to the subject of XAI while highlighting why this topic is generating more and more attention in many sectors, such as industry.
An uncertainty estimator method based on the application of feature density to classify mammograms for breast cancer detection

Abstract In the area of medical imaging, one of the factors that can negatively influence the performance of prediction algorithms is the limited number of observations for each class within a labeled dataset. Usually, in order to increase the samples, a second set of unlabeled images is used. However, this set adds two new problems (i) finding patient observations with different pathologies than those observed in the labeled data set and (ii) finding images belonging to a different distribution from the dataset used in the model training process. This way, merging datasets from different sources can have an adverse effect on the distribution of features. Encountering this type of data (better known as out-of-distribution data) within the deployment environments may also lead to varying degrees of performance degradation as can be seen in the different experimental results obtained. In this research, a study of the behavior of Feature Density is made, as a mathematical model for the estimation of predictive uncertainty in supervised classification algorithms, in order to improve the behavior when out-of-distribution data are presented in the dataset. The Feature Density method is based on the estimation of feature density by means of histogram calculation (or Probability Density Function). The advantage of this method over the baseline approach (Mahalanobis distance) is that it does not assume a Gaussian-type distribution of sample characteristics and serves to estimate the uncertainty. This work focuses on the binary classification of mammography X-ray images from three different datasets simulating the condition of a different degree of contamination with out-of-distribution sample. According to the obtained results, the performance of the proposed method depends directly on the architecture of the implemented neural network.
Small object detection based on hierarchical attention mechanism and multi‐scale separable detection

Abstract The ability of modern detectors to detect small targets is still an unresolved topic compared to their capability of detecting medium and large targets in the field of object detection. Accurately detecting and identifying small objects in the real‐world scenario suffer from sub‐optimal performance due to various factors such as small target size, complex background, variability in illumination, occlusions, and target distortion. Here, a small object detection method for complex traffic scenarios named deformable local and global attention (DLGADet) is proposed, which seamlessly merges the ability of hierarchical attention mechanisms (HAMs) with the versatility of deformable multi‐scale feature fusion, effectively improving recognition and detection performance. First, DLGADet introduces the combination of multi‐scale separable detection and multi‐scale feature fusion mechanism to obtain richer contextual information for feature fusion while solving the misalignment problem of classification and localisation tasks. Second, a deformation feature extraction module (DFEM) is designed to address the deformation of objects. Finally, a HAM combining global and local attention mechanisms is designed to obtain discriminative features from complex backgrounds. Extensive experiments on three datasets demonstrate the effectiveness of the proposed methods. Code is available at https://github.com/ACAMPUS/DLGADet
Generalized partially bent functions, generalized perfect arrays, and cocyclic Butson matrices

Abstract In a recent survey, Schmidt compiled equivalences between generalized bent functions, group invariant Butson Hadamard matrices, and abelian splitting relative difference sets. We establish a broader network of equivalences by considering Butson matrices that are cocyclic rather than strictly group invariant. This result has several applications; for example, to the construction of Boolean functions whose expansions are generalized partially bent functions, including cases where no bent function can exist.
A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal

Eliminating mixed noise from images is a challenging task because accurately describing the attenuation of noise distribution is difficult. However, most existing algorithms for mixed noise removal solely rely on the local information of the image and neglect the global information, resulting in suboptimal denoising performance when dealing with complex mixed noise. In this paper, we propose a nested UNet based on multi-scale feature extraction (MSNUNet) for mixed noise removal. In MSNUNet, we introduce a U-shaped subnetwork called MSU-Subnet for multi-scale feature extraction. These multi-scale features contain abundant local and global features, aiding the model in estimating noise more accurately and improving its robustness. Furthermore, we introduce a multi-scale feature fusion channel attention module (MSCAM) to effectively aggregate feature information from different scales while preserving intricate image texture details. Our experimental results demonstrate that MSNUNet achieves leading performance in terms of quality metrics and the visual appearance of images.
Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia

Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia.
Statistical inference of integer valued time series based on signed thinning operator of symbolic generalized power series in random environment

A kind of integer autoregressive model of random coefficients in random environment based on Signed thinning operator of signed generalized power series thinning operator is studied statistically. Conditional maximum likelihood estimators of model parameters are presented. The simulation results show that the model is effective in solving the parameter estimation problem of negative integer-valued time series in random environment.
Data augmentation strategies to improve text classification: a use case in smart cities

Text classification is a very common and important task in Natural Language Processing. In many domains and real-world settings, a few labeled instances are the only resource available to train classifiers. Models trained on small datasets tend to overfit and produce inaccurate results – Data augmentation (DA) techniques come as an alternative to minimize this problem. DA generates synthetic instances that can be fed to the classification algorithm during training. In this article, we explore a variety of DA methods, including back translation, paraphrasing, and text generation. We assess the impact of the DA methods over simulated low-data scenarios using well-known public datasets in English with classifiers built fine-tuning BERT models. We describe the means to adapt these DA methods to augment a small Portuguese dataset containing tweets labeled with smart city dimensions (e.g., transportation, energy, water, etc.). Our experiments showed that some classes were noticeably improved by DA – with an improvement of 43% in terms of F1 compared to the baseline with no augmentation. In a qualitative analysis, we observed that the DA methods were able to preserve the label but failed to preserve the semantics in some cases and that generative models were able to produce high-quality synthetic instances.
Social Protections and the COVID-19 Pandemic in Latin America

The COVID-19 pandemic unleashed a socioeconomic catastrophe on the region with the highest rate of inequality in the world, Latin America. Never before have social protections (especially in public health) become so important and so demanded by large sections of society. A study of the social protection systems of some of the most important Latin American countries (Argentina, Brazil, Chile, and Uruguay) in the light of the observed ideopolitical alternation between progressive and conservative governments reveals how the sociopolitical environment gave rise to a neoconservative cycle that, in combination with the COVID-19 pandemic, resulted in the decay of these systems and the recognition of their importance. A pandemia da COVID-19 desencadeou uma catástrofe socioeconômica na região com o maior índice de desigualdade do mundo, a América Latina. Nunca antes as proteções sociais (especialmente da saúde pública) se tornaram tão importantes e tão exigidas por grandes setores da sociedade. Um estudo dos sistemas de proteção social de alguns dos mais importantes países latino-americanos (Argentina, Brasil, Chile e Uruguai) à luz da alternância ideopolítica observada entre governos progressistas e conservadores revela como o ambiente sociopolítico deu origem a um ciclo neoconservador que, em combinação com a pandemia de COVID-19, resultou na decadência desses sistemas e no reconhecimento de sua importância.
Entanglement‐Enhanced Quantum Ranging in Near‐Earth Spacetime

Abstract A quantum ranging protocol to determine the distance between an observer and a target in the near‐Earth curved spacetime is proposed. Unlike quantum illumination, the quantum ranging scheme utilizes multiple quantum hypothesis testing to simultaneously determine the presence and location of the target. The interest is in how the Earth's spacetime curvature influences photon propagation as well as the performance of the quantum ranging. It is found that the maximum potential advantage of the quantum ranging strategy in the curved spacetime outperforms its flat spacetime counterpart. It is shown that the number of transmitted modes can enhance the maximum potential advantage of the quantum ranging tasks. In contrast, the maximum potential advantage of quantum ranging cannot be significantly increased by dividing the range into multiple slices in the curved spacetime.
Solar Photovoltaic Energy Production Conditions in the Urban Environment of Athens, Cairo, Granada and Vienna

This study deals with the effect of clouds and aerosols on solar photovoltaic energy in the urban environments and conditions of Athens, Cairo, Granada and Vienna, so that there is diversity in terms of cloud presence, aerosol types and irradiation levels. To this direction, satellite-based remote sensing data were used for a decade (2010–2019) from Eumetsat in conjunction with Copernicus and radiative-transfer-modelled data. Furthermore, an idealized solar energy planning scenario, making the most of photovoltaics installed on the roofs of city buildings, was investigated in order to cover the electricity needs of the pilot cities and to promote local energy security and transition.
The Linux based body falls alarm system

With the development of the Internet of Things, timely alarm and real-time monitoring of the Internet of Things have become a reality and become more and more important. Traditional remote monitoring technology of the Internet of Things has been unable to meet the rapid demand for the development of modern Internet of Things industry. In order to meet the needs of the new era, real-time, secure, intelligent and extensible monitoring terminals have become a research hotspot in the modern Internet of Things industry. This paper proposes a human fall alarm system, including the establishment of arm hardware platform, the implantation of Linux system, the development of drivers and applications, the training of deep learning model, the deployment and optimization of application programs. The human falling alarm system has high practical value in the practical application, which can realize the real-time alarm and monitoring of human falling, and effectively improve the safety and convenience of home, office and other places. At the same time, the system also has a strong scalability, according to the actual demand for flexible customization and expansion.
Combining the Back Propagation Neural Network and Particle Swarm Optimization Algorithm for Lithological Mapping in North China

Lithological mapping is a crucial tool for exploring minerals, reconstructing geological formations, and interpreting geological evolution. The study aimed to investigate the application of the back propagation neural network (BPNN) and particle swarm optimization (PSO) algorithm in lithological mapping. The study area is the Beiliutumiao map-sheet (No. K49E011021) in Inner Mongolia, China. This area was divided into two parts, with the left side used for training and the right side used for validation. Fifteen geological relevant factors, including geochemistry (1:200,000-scale) and geophysics (1:50,000-scale), were used as predictor variables. Taking one lithology as an example, the lithological binary mapping method was introduced in detail, and then the complete lithology was mapped. The model was compared with commonly used spatial data mining methods using the E-measure, S-measure, and Weighted F-measure values. In diorite testing, the accuracy and kappa of the optimized model were 92.11% and 0.81, respectively. The validation results showed that our method outperformed the traditional BPNN and weights-of-evidence approaches. In the extension of the complete lithological mapping, the accuracy, recall, and F1-score were 82.66%, 74.54%, and 0.76, respectively. Thus, the proposed method is useful for predicting the distribution of one lithology and completing the whole lithological mapping at a fine scale. In addition, the trained network can be extended to an adjacent area with similar lithological features.