Reviewerly/exHarmony huggingface dataset:
Differential evolution based on network structure for feature selection

With the accumulation of high-dimensional data, feature selection (FS) plays a significant role in tasks such as classification and clustering. Recently, many evolutionary algorithms have been developed to address FS problems from single-objective or multiobjective perspectives. However, those algorithms always ignore the complex relationships among features containing abundant available information to adjust the search direction, resulting in poor performance. To address this limitation, we propose to explore the relationship patterns hidden in feature space using the techniques of network science, and based on the exploration, we develop a new differential evolution (DE) and a new multiobjective DE (MODE) for single-objective and multiobjective FS problems, respectively. In particular, we construct a feature network to represent the complex relationships between features and propose to explore the relational patterns from the perspective of the network. After that, the features with rich information are highlighted as high-degree nodes, and some features are grouped together as clusters in the feature network. Motivated by these observations, we further propose to improve the search processes of DE and MODE and produce two network-based DEs, namely, NetG-DE and NetG-MODE. Experimental results over several datasets demonstrate that the proposed NetG-DE and NetG-MODE outperform the baseline.
ADMit: Improving NER in automotive domain with domain adversarial training and multi-task learning

AI virtual assistants are steadily expanding their way into multiple domains such as healthcare, hospitality, and telecommunication. However, such virtual assistants still suffer from a limited level of distinctive characteristics of domain-specific knowledge. In this paper, we improve the Named Entity Recognition (NER) ability by facilitating (i) the assistants’ understanding of both general and domain-specific terms by building new datasets in the automotive domain and using adversarial training and (ii) NER’s robustness against spacing errors using multi-task learning. We verify the proposed method in both Korean and English NER datasets and the experimental results demonstrate that the proposed NER model outperforms the baseline model in both general and automotive domains. The proposed method improves automotive NER performance by 1.85 points and 1.13 points in English and Korean, respectively. Furthermore, we validate proposed NER model on the in-house question answering system where queries are taken from real users.
Automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis

Automatic modulation recognition (AMR) of radar signals plays a critical role in electronic reconnaissance. Current AMR algorithms are mainly based on convolutional neural networks (CNN), which can learn the feature hierarchy by establishing high-level features from low-level features. However, for time–frequency analysis-based methods, distinct low-level features in the time–frequency spectrum can already reflect modulation characteristics. Thus, this study develops a novel approach based on low-level shape descriptors via histograms of oriented gradients (HOG) and support vector machine (SVM). Comparison studies with classic CNN-based methods have also been done to reveal the superiority of the designed approach. Experimental results demonstrate that the HOG-SVM approach has a more efficient performance. To further enhance the classification precision under low signal-to-noise ratios, an improved principal component analysis denoising algorithm is developed to improve signal quality under intense noise background. Experiments based on simulated and measured signals demonstrate that the proposed algorithm can accurately distinguish signals under intense noise environments.
River Green Land and Its Influence on Urban Economy, Leisure Development, Ecological Protection, and the Well-Being of the Elderly

The purpose of this study is to analyze whether the green space generated by river water engineering can promote urban development and the well-being of the elderly in high-risk environments. Firstly, quantitative research methods were used to analyze 750 valid questionnaires, and IBM SPSS Statistics 26.0 statistical software was used for data inspection. We continued to adopt the qualitative research method and collect the opinions of nine respondents according to the results of the questionnaire analysis. The data were then integrated, categorized, summarized, compared, and finally examined by multivariate analysis. The river green land has rich ecological and natural landscape resources and spacious leisure space, creating a comfortable leisure and living environment. This can increase job opportunities, promote community and economic development, and create safe leisure conditions. It can also improve people’s willingness for leisure, increase opportunities for interpersonal communication, and improve the quality of life and happiness of the elderly. If we should strengthen the soil structure of the river, pay attention to ecological conservation, and reduce exhaust gas and noise pollution to provide a more complete leisure space and enhance the contribution of river green space to rural areas and lives of the local elderly.
Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics

Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models. However, established methods for performing hold-out validation do not assess the consistency of the mistakes that the AI model makes during hold-out validation. Here, we show that in addition to standard methods, an enhanced technique for performing hold-out validation-that also assesses the consistency of the sample-wise mistakes made by the learning algorithm-can assist in the evaluation and design of reliable and predictable AI models. The technique can be applied to the validation of any supervised learning classification application, and we demonstrate the use of the technique on a variety of example biomedical diagnostic applications, which help illustrate the importance of producing reliable AI models. The validation software created is made publicly available, assisting anyone developing AI models for any supervised classification application in the creation of more reliable and predictable technologies.
Lp-norm-based successive denoising approach for hyperspectral images

Hyperspectral images (HSI) are essentially a data cube containing hundreds of stacked 2-D images collected over a set of adjacent spectral bands with the idea of obtaining a complete spectral information for each pixel in a scene. However, multiple factors contribute to noise, which manifests as grainy textures, dead pixels and stripes in the acquired images, and is classically modelled as a mixture of Gaussian and impulse noise statistics. Denoising therefore forms an important pre-processing task to most HSI applications. In this paper, we propose a novel ℓp norm-based successive denoising strategy for the removal of mixed Gaussian-impulse noise in HSI. The image formation model is split into two different parts. The proposed method is a Bayesian-derived variational model designed to handle the removal of Gaussian and impulse noise in a successive manner, and is solved using the half-quadratic split approach. Comparison with various existing HSI denoising techniques, both qualitatively and quantitatively, suggest the superior denoising ability of the proposed method compared to the rest.
Politeness markers in emails of non-native English speaking university students

The use of computer-mediated communication including emails has become pervasive in academic contexts as a result of the COVID-19 pandemic. What seems to be significant but simply overlooked by students is meeting politeness netiquettes while sending emails. To this end, the current study investigated the extent to which non-native English speaking university students adjust the level of politeness in their response emails written in English to that of the emails received from an American professor. To collect data, four versions of an academic email message with different levels of politeness were prepared in advance. The emails either included or excluded verbal and structural politeness markers and asked for the participants’ demographic information and their reason for participation in the study. Then, 73 university students enrolled in a general English course were selected and divided randomly into four groups each of which received one version of the email message from the professor. The results of the data analysis on the participants’ response emails, based on accommodation theory (Giles 1973) as a theoretical framework, revealed that they did not accommodate either verbal or structural politeness cues in emails. Besides, the participants’ knowledge of the politeness etiquettes in the academic email genre seemed inadequate. Finally, the article provides some pedagogical implications for course designers, materials developers, and instructors to devise some plans to raise students’ awareness of email politeness etiquettes and for students to be aware of the significance of meeting politeness principles in their academic emails.
A note on the Assmus–Mattson theorem for some binary codes II

Let C be a four-weight binary code, which has all one vector. Furthermore, we assume that C supports t-designs for all weights obtained from the Assmus–Mattson theorem. We previously showed that $$t\le 5$$ . In the present paper, we show an analogue of this result in the cases of five and six-weight codes.
Dynamic depth estimation of weakly textured objects based on light field speckle projection and adaptive step length of optical flow method

Repetitive textures in the depth estimation of weakly textured surfaces lead to numerous errors. The existing technology does not provide a complete method for light-field depth estimation in weak-texture scenes. Therefore we developed an algorithm that estimates the cost volume using speckle stereo correspondences. The subpixel-matching result is obtained using the adaptive label step length displacement theorem. The proposed method can adaptively select an appropriate step length for the sub-pixel phase-shift theorem without depending on the dataset category. The build cost volume can be adapted for weakly textured scenes. A common dataset was used to evaluate the effectiveness of the optical flow method for the adaptive selection of step length. Finally, high-precision step gauge blocks with a step difference of 10 mm were used to verify the depth-direction errors, which were less than 0.35 mm.
Text Generation Benefits Learning: a Meta-Analytic Review

Abstract Learning can be made more efficient when learners generate the to-be-learned text contents instead of passively receiving them. A multi-level meta-analysis was conducted to provide an overall estimate of the text generation effect’s magnitude and to identify theoretically and practically relevant moderators. Overall, generation interventions improved learning with texts compared to reading them (Hedges’ g = .41). This benefit was not attributable to time-on-task and was found across several learning conditions and settings (e.g., narratives and expository texts, multiple generation, and learning assessment tasks). The meta-analysis further suggests that generation benefits learning most strongly if the cognitive processes stimulated by the generation task complement those processes already stimulated by the text. In sum, the findings suggest that text generation can be suitable for educational applications especially if certain conditions are observed.
Incremental class learning using variational autoencoders with similarity learning

Abstract Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated catastrophic forgetting in fully connected networks, with some earlier work exploring activation functions and learning algorithms. Applications of neural networks have been extended to include similarity learning. Understanding how similarity learning loss functions would be affected by catastrophic forgetting is of significant interest. Our research investigates catastrophic forgetting for four well-known similarity-based loss functions during incremental class learning. The loss functions are Angular, Contrastive, Center, and Triplet loss. Our results show that the catastrophic forgetting rate differs across loss functions on multiple datasets. The Angular loss was least affected, followed by Contrastive, Triplet loss, and Center loss with good mining techniques. We implemented three existing incremental learning techniques, iCaRL, EWC, and EBLL. We further proposed a novel technique using Variational Autoencoders (VAEs) to generate representation as exemplars passed through the network’s intermediate layers. Our method outperformed three existing state-of-the-art techniques. We show that one does not require stored images (exemplars) for incremental learning with similarity learning. The generated representations from VAEs help preserve regions of the embedding space used by prior knowledge so that new knowledge does not “overwrite” it.
Central bank digital currencies: Digital Yuan and its role in Chinese digital economy development

Under the impact of financial technology and digital currency, “central bank digital currency” has become an international hotspot in recent years and is also one of the current research and development priorities of China’s central bank. Preparations for China’s digital RMB began as early as 2014, and in 2017 China’s central bank clearly announced that it would fully issue the digital currency. The launch of the digital RMB is a major reform and innovation in the historical development of China’s currency. China’s issuance of a central bankdigital currency will help enhance the status of the People’s Bank, strengthen the effectiveness of monetary policy, improve macro-prudential management capabilities, and promote cross-border RMB payments, etc. Up to now, China has started pilot tests in some cities, such as Shenzhen, Xian, Chengdu Suzhou. Currently, China’s digital RMB has been tested for the public, with features such as unlimited legal compensation, moderate anonymity, national statutory, dual offline payment, no cost of transaction and centralized management. With the rapid development of scientific information technology and the intensification of international competition, the digitalization of currency has become an irreversible trend. The purpose of this study is to identify the practice of digital Yuan and its role in Chinese digital economy development. The discussion tasks of this paper can be summarized as follows. Clarify the definition of digital Yuan; assess the operational model of digital Yuan; evaluate the operating framework of digital Yuan; identify the features and merits of digital Yuan application. This research adopts case study of China CBDC and literature analysis methods to analyzed the concept, characteristics and design principles of digital RMB. Then it investigates the role of digital RMB for the development of China’s digital economy, which will provide an important basis for understanding and advancing the domestic research on the central bank’s digital currency. For example, digital Yuan plays a key role in facilitating data productivity, driving digitalization of public payment scenarios and opening a healthy digital economy system.
More is Better: English Language Statistics are Biased Toward Addition

Abstract We have evolved to become who we are, at least in part, due to our general drive to create new things and ideas. When seeking to improve our creations, ideas, or situations, we systematically overlook opportunities to perform subtractive changes. For example, when tasked with giving feedback on an academic paper, reviewers will tend to suggest additional explanations and analyses rather than delete existing ones. Here, we show that this addition bias is systematically reflected in English language statistics along several distinct dimensions. First, we show that words associated with an increase in quantity or number (e.g., add, addition, more, most ) are more frequent than words associated with a decrease in quantity or number (e.g., subtract, subtraction, less, least ). Second, we show that in binomial expressions, addition‐related words are mentioned first, that is, add and subtract rather than subtract and add . Third, we show that the distributional semantics of verbs of change, such as to improve and to transform , overlap more with the distributional semantics of add/increase than subtract/decrease , which suggests that change verbs are implicitly biased toward addition. Fourth, addition‐related words have more positive connotations than subtraction‐related words. Fifth, we demonstrate that state‐of‐the‐art large language models, such as the Generative Pre‐trained Transformer (GPT‐3), are also biased toward addition. We discuss the implications of our results for research on cognitive biases and decision‐making.
Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery

Recently, with the digitalization of medicine, the utilization of real-world medical data collected from clinical sites has been attracting attention. In this study, quantum computing was applied to a linear non-Gaussian acyclic model to discover causal relationships from real-world medical data alone. Specifically, the independence measure of DirectLiNGAM, a causal discovery algorithm, was calculated using the quantum kernel and its accuracy on real-world medical data was verified. When DirectLiNGAM with the quantum kernel (qLiNGAM) was applied to real-world medical data, a case was confirmed in which the causal structure could be correctly estimated when the amount of data was small, which was not possible with existing methods. Furthermore, qLiNGAM was implemented on real quantum hardware in an experiment using IBMQ. It is suggested that qLiNGAM may be able to discover new medical knowledge and contribute to the solution of medical problems, even when only a small amount of data is available.
Convergence analysis of butterfly optimization algorithm

Convergence analysis of any random search algorithm helps to verify whether and how quickly the algorithm guarantees convergence to the point of interest. Butterfly optimization algorithm (BOA) is a popular population-based stochastic optimizer introduced by mimicking the foraging behaviors of butterflies in nature. In this paper, we have developed the Markov chain model of the BOA and analyzed the convergence behavior of the algorithm. The Markov chain model of the BOA is constituted where the population sequence generated by the algorithm is found to be a finite homogenous Markov chain and the defined population state set is found to be reducible. Convergence analysis of the algorithm is performed mathematically using the Markov chain model of the algorithm with the help of global convergence theorem which is based on a random search algorithm satisfying two subtle conditions. The butterfly algorithm has been found to satisfy the conditions for the global convergence theorem to enact, whereof it guarantees the global convergence of the BOA. We have also tried to show experimentally that the convergence of the algorithm does not always have considerable impact on rate of convergence as it is influenced by various other factors. Moreover, the convergence of BOA has been compared with several state-of-the-art algorithms experimentally. Further, the effects of the parameters, namely sensory modality and power exponent on the performance of BOA, have been studied.
Prototype-guided Instance matching for multiple pedestrian tracking

Multiple pedestrian tracking is a critical yet challenging to understand human behavior. Although existing tracking methods have achieved great advances through adopting the tracking-by-detection paradigm, they give insufficient consideration on how to improve the instance matching capability in the cluttered and crowded environment. Motivated by our observations on tracking persons that the similarity of the same target is larger than that of other persons along sequence frames, in this work, we propose a novel Prototype-guided Instance Matching (PIM) method to boost the performance of multiple pedestrian tracking task. The whole PIM framework mainly consists of a detector net, an appearance embedding module, and a self-adaptive prototype dictionary. Given the current frame as the input sample, we first adopt a classic detector to select a set of positive candidates, and their corresponding multi-scale convolutional features are then fed into the appearance embedding module for obtaining the robust instance-wise feature representation. We perform the prototype-guided instance matching optimization between the learned instance-wise feature embeddings and the prototype dictionary, where its aim is to enlarge the separability of different instances and make the distance between the same instances more compact. Here object detection and appearance embedding are jointly optimized in an end-to-end fashion. Furthermore, we maintain a self-adaptive prototype dictionary to store instance-wise prototypes, which can be adaptively updated to adapt the appearance variations of all tracked targets over time. The instance matching and prototype dictionary update are modeled into an interactive optimization process so that they can be gradually improved for each other. Comprehensive evaluations on three benchmark datasets (i.e., MOT16, MOT17, and MOT20) demonstrate the effectiveness of our proposed PIM when compared with the state-of-the-art methods for the multiple pedestrian tracking task.