Reviewerly/exHarmony huggingface dataset:
Approximation of SHAP Values for Randomized Tree Ensembles

Classification and regression trees offer straightforward methods of attributing importance values to input features, either globally or for a single prediction. Conditional feature contributions (CFCs) yield local, case-by-case explanations of a prediction by following the decision path and attributing changes in the expected output of the model to each feature along the path. However, CFCs suffer from a potential bias which depends on the distance from the root of a tree. The by now immensely popular alternative, SHapley Additive exPlanation (SHAP) values appear to mitigate this bias but are computationally much more expensive. Here we contribute a thorough, empirical comparison of the explanations computed by both methods on a set of 164 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. For random forests and boosted trees, we find extremely high similarities and correlations of both local and global SHAP values and CFC scores, leading to very similar rankings and interpretations. Unsurprisingly, these insights extend to the fidelity of using global feature importance scores as a proxy for the predictive power associated with each feature.
Monitoring and Model Analysis of Vocal Performance Teaching Environment Using Cluster Analysis from the Perspective of Core Literacy

To cultivate students' artistic quality, enhance their vocal music quality, and prepare them to make great contributions to the innovation and development of my country's vocal music art is the main goal of opening vocal music performance major in colleges and universities. With the advancement of technology and the demands of talent development, the vocal music teaching methodology for the vocal music performance major in colleges and universities must be continuously enhanced. Otherwise, there will be an issue of disconnect between teaching style and talent development, which will harm both the development of high-quality vocal music talents and the innovation and growth of vocal music performance majors in colleges and universities. The vocal music performance major at colleges and universities should actively support the reform and innovation of the vocal music teaching mode in order to extend students' knowledge, develop their all-around ability, and provide a strong foundation for vocal music performance, to develop students' all-encompassing musical abilities. This research suggests a design strategy for the monitoring and model optimization of the teaching environment for vocal performance majors from the standpoint of core literacy. To increase the efficiency and objectivity of course instruction, cluster analysis aids students in categorising and searching for vocal music performance main repertoire as well as using collaborative filtering recommendations to locate their own vocal music performance. The simulation test analysis is completed lastly. The method has a certain accuracy, which is 7.59% higher than the conventional algorithm, according to the simulation findings. In addition to significantly increasing student interest in studying vocal music performance courses, we further reform and innovation of the teaching method for these courses at colleges and universities can also strengthen students' understanding of various repertoire styles and significantly enhance their musical literacy.
Macroscopic properties of buyer–seller networks in online marketplaces

Online marketplaces are the main engines of legal and illegal e-commerce, yet their empirical properties are poorly understood due to the absence of large-scale data. We analyze two comprehensive datasets containing 245M transactions (16B USD) that took place on online marketplaces between 2010 and 2021, covering 28 dark web marketplaces, i.e. unregulated markets whose main currency is Bitcoin, and 144 product markets of one popular regulated e-commerce platform. We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology. Specifically, we find remarkable regularities in the distributions of transaction amounts, number of transactions, interevent times, and time between first and last transactions. We show that buyer behavior is affected by the memory of past interactions and use this insight to propose a model of network formation reproducing our main empirical observations. Our findings have implications for understanding market power on online marketplaces as well as intermarketplace competition, and provide empirical foundation for theoretical economic models of online marketplaces.
Competitive Learning for Unsupervised Anomaly Detection in Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) are expected to have a profound impact on the quality of experience in future smart cities. Anomaly detection is an imperative for urban ITS applications to alleviate vulnerabilities that may cause accidents and fatal causalities. Previously proposed anomaly detection methods mostly require prior knowledge and domain specific training and/or optimization procedures. Therefore, in this work, we propose Competitive Learning based Anomaly Detection (CLAD) as a generic end-to-end approach for unsupervised anomaly detection using Auto Regressive Integrated Moving Average (ARIMA) forecasting model, data imaging and Centroid Neural Networks (CentNNs). Utilizing multi-dimensional time-series data obtained from diverse sensory measurements in the DIGINET-PS smart city infrastructure of TU Berlin, we compare performance of CLAD with unsupervised competitive learning as well as deep learning based anomaly detection techniques. Experimental results show that proposed approach results in higher detection accuracy and precision compared to other methods when multiple degrees of anomalies are considered.
Does financial globalization contribute to financial development in developing countries? Evidence from Africa

This study used panel data from 33 African nations during a period from 2005-2019 to examine the relationship between financial globalization and financial development. Regressions with Driscoll-Kraay and panel-corrected standard errors, which account for cross-sectional dependence, were employed to analyze the link. The main findings of this study reveal the existence of a U-shaped relationship between financial globalization and financial development in Africa, suggesting that a lower level of financial globalization appears to have a negative influence on financial development. However, above a certain point, a growth in financial globalization is accompanied by an increase in financial development. In other words, countries with a lower (higher) level of financial globalization have experienced negative (positive) effects on their financial development. Trade openness, financial freedom, and investment freedom have also been found to be strongly and positively related to financial development in Africa. Based on the findings, African countries are advised to consider the appropriate timing prior to fully opening their capital accounts to international players in order to enjoy the benefits of financial globalization.
Rule-based models via the axiomatic fuzzy set clustering and their granular aggregation

Rule-based models have become a popular way to represent and analyze the main knowledge residing in data because of the increasing complexity and uncertainty. For extracting semantically sound rules in a multi-view perspective, a novel rule-based model combined with the axiomatic fuzzy set (AFS) algorithm is developed in distributed systems. Using the idea of the AFS algorithm, several clusters and accompanying fuzzy descriptions are formed in terms of data distribution; thereby, the input of rules exhibits well-defined semantics by the logic compound of the predefined linguistic terms. The output of the rule is approximated by the Takagi–Sugeno (T–S) model, in which an extended weighted least squares method is designed to optimize the parameter vectors simultaneously. In virtue of the diversity of these individual results, a granular aggregation procedure incorporates the weighted principle of justifiable granularity to summarize all the local results into compact and meaningful descriptors (information granules). Five experiments considering synthetic and publicly available datasets are carried out to demonstrate the performance of the proposed approach. In addition, the proposed approach is also shown effective in an application involving the credit dataset.
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning

Humans tend to decompose a sentence into different parts like sth do sth at someplace and then fill each part with certain content. Inspired by this, we follow the principle of modular design to propose a novel image captioner: learning to Collocate Visual-Linguistic Neural Modules (CVLNM). Unlike the widely used neural module networks in VQA, where the language (i.e., question) is fully observable, the task of collocating visual-linguistic modules is more challenging. This is because the language is only partially observable, for which we need to dynamically collocate the modules during the process of image captioning. To sum up, we make the following technical contributions to design and train our CVLNM: (1) distinguishable module design—four modules in the encoder including one linguistic module for function words and three visual modules for different content words (i.e., noun, adjective, and verb) and another linguistic one in the decoder for commonsense reasoning, (2) a self-attention based module controller for robustifying the visual reasoning, (3) a part-of-speech based syntax loss imposed on the module controller for further regularizing the training of our CVLNM. Extensive experiments on the MS-COCO dataset show that our CVLNM is more effective, e.g., achieving a new state-of-the-art 129.5 CIDEr-D, and more robust, e.g., being less likely to overfit to dataset bias and suffering less when fewer training samples are available. Codes are available at https://github.com/GCYZSL/CVLMN .
Distributed Logistics Resources Allocation with Blockchain, Smart Contract, and Edge Computing

The traditional centralized logistics resources allocation method can no longer adapt to the new business model of decentralized e-commerce, requiring transaction security for all parties involved in the logistics process. Utilizing blockchain and smart contract technologies to build logistics resources allocation network foundation and edge computing technology to assist the resource-constrained transport nodes in implementing complex computation, this paper proposes a distributed logistics resources allocation chain (DLRAChain) concept and designs a DLRAChain network that supports independent decision-making, fair bidding, and secure allocation of interests for all resources allocation participants. The corresponding system models are constructed according to the different roles of DLRAChain participants. Furthermore, the logistics resources requester–provider negotiation process is formulated as a two-stage Stackelberg game. To resolve the optimization problem of the game, the iterative game algorithm (IGA) and distributed logistics resources allocation algorithm (DLRAA) are proposed. Finally, the utility of warehouse and transport nodes and reward of mobile edge computing (MEC) nodes are analyzed with experimental simulation results. The results demonstrate that the proposed models adequately address the DLRA problem, and that the proposed game and corresponding algorithms efficiently achieve the optimal strategy, saving the response time of resources allocation participants.
Personal Information Security Environment Monitoring and Law Protection Using Big Data Analysis

This article explores the causes of the issues with personal data privacy and outlines the limitations of China's current legal framework. This article makes the argument that, in the information age, self-discipline and legal protection should be combined in order to safeguard personal information safety. It also makes specific recommendations for strengthening legal protection. This research also develops a data processing platform for data safety and privacy protection while studying the technology of data safety environment monitoring and privacy protection. This work develops optimization methodologies, such as dynamic privacy budget allocation, to increase the model's speed of convergence and the calibre of the generated data. It adjusts to various privacy and timeliness needs under the assumption that the objective of selective matching of private safety will be satisfied. According to the experimental findings, this algorithm's accuracy can reach 96.27%. This method enhances the model's speed of convergence and the calibre of the data created, and it addresses the flaw that the present data fusion publishing procedure cannot withstand the attack of background information. The study's findings can serve as a starting point for future work on data security and the protection of personal information.
Literature review of the reciprocal value of artificial and human intelligence in early childhood education

This paper presents a systematic literature review of artificial intelligence (AI)-supported teaching and learning in early childhood. The focus is on human–machine cooperation in education. International evidence and associated problems with the reciprocal contributions of humans and machines are presented and discussed, as well as future horizons regarding AI research in early education. Also, the ethical implications of applying machine learning, deep learning and learning analytics in early childhood education are considered. The method adopted has five steps: identification of the research, evaluation and selection of the literature, data extraction, synthesis, and results. The results shown that AI applications still present limitations in terms of the challenges encountered in early childhood education and data privacy and protection policies.
Dealing with the unevenness: deeper insights in graph-based attack and defense

Graph Neural Networks (GNNs) have achieved state-of-the-art performance on various graph-related learning tasks. Due to the importance of safety in real-life applications, adversarial attacks and defenses on GNNs have attracted significant research attention. While the adversarial attacks successfully degrade GNNs’ performance significantly, the internal mechanisms and theoretical properties of graph-based attacks remain largely unexplored. In this paper, we develop deeper insights into graph structure attacks. Firstly, investigating the perturbations of representative attacking methods such as Metattack, we reveal that the perturbations are unevenly distributed on the graph. By analyzing empirically, we show that such perturbations shift the distribution of the training set to break the i.i.d. assumption. Although degrading GNNs’ performance successfully, such attacks lack robustness. Simply training the network on the validation set could severely degrade the attacking performance. To overcome the drawbacks, we propose a novel k-fold training strategy, leading to the Black-Box Gradient Attack algorithm. Extensive experiments are conducted to demonstrate that our proposed algorithm is able to achieve stable attacking performance without accessing the training sets. Finally, we introduce the first study to analyze the theoretical properties of graph structure attacks by verifying the existence of trade-offs when conducting graph structure attacks.
Research on Authentic Signature Identification Method Integrating Dynamic and Static Features

In many fields of social life, such as justice, finance, communication and so on, signatures are used for identity recognition. The increasingly convenient and extensive application of technology increases the opportunity for forged signatures. How to effectively identify a forged signature is still a challenge to be tackled by research. Offline static handwriting has a unique structure and strong interpretability, while online handwriting contains dynamic information, such as timing and pressure. Therefore, this paper proposes an authentic signature identification method, integrating dynamic and static features. The dynamic data and structural style of the signature are extracted by dot matrix pen technology, the global and local features, time and space features are fused and clearer and understandable features are applied to signature identification. At the same time, the classification of a forged signature is more detailed according to the characteristics of signature and a variety of machine learning models and a deep learning network structure are used for classification and recognition. When the number of classifications is 5, it is better to identify simple forgery signatures. When the classification number is 15, the accuracy rate is mostly about 96.7% and the highest accuracy reaches 100% on CNN. This paper focuses on feature extraction, incorporates the advantages of dynamic and static features and improves the classification accuracy of signature identification.
Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics

Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.
Application of remote sensing techniques to identify iron ore deposits in the Central Eastern Desert, Egypt: a case study at Wadi Karim and Gabal El-Hadid areas

Abstract The integration of remote sensing, spectral measurements and field verification helped in discriminating new iron occurrences hosted in the metavolcanic rocks at Wadi Karim and Gabal El-Hadid areas in the Central Eastern Desert (CED) of Egypt. The iron host rocks were carefully recognized through different image processing techniques that were applied on Landsat-8 satellite image data with the implementation of the spectral signatures of the various iron minerals and host rocks. The detected iron ore is found in the form of Banded Iron Formation (BIF), veins and lenses of varied thicknesses ranging from a few centimetres to 5 m. They are composed mainly of magnetite and hematite, as well as accessories of aluminium, iron silicates and quartz. Moreover, the structure controls on iron ore occurrences were also illustrated in the investigated areas. The alignment of the iron forms with the primary and secondary structural planes of their host rocks may indicate the origin and mode of occurrence of the explored iron ore, as contemporaneous and syngenetic deposition with the solidification of the host metavolcanics. Several remote sensing techniques including Crosta principal component analysis (CPCA), constrained energy minimization (CEM) supervised classification technique and a newly proposed Landsat-8 band ratio (band6/band2) were applied for discriminating the iron-rich localities within the studied areas. These techniques showed high efficiency and applicability in the accurate delineation of the iron-enriched areas; this was validated through the field study.
The Effect of Data Augmentation Methods on Pedestrian Object Detection

Night landscapes are a key area of monitoring and security as information in pictures caught on camera is not comprehensive. Data augmentation gives these limited datasets the most value. Considering night driving and dangerous events, it is important to achieve the better detection of people at night. This paper studies the impact of different data augmentation methods on target detection. For the image data collected at night under limited conditions, three different types of enhancement methods are used to verify whether they can promote pedestrian detection. This paper mainly explores supervised and unsupervised data augmentation methods with certain improvements, including multi-sample augmentation, unsupervised Generative Adversarial Network (GAN) augmentation and single-sample augmentation. It is concluded that the dataset obtained by the heterogeneous multi-sample augmentation method can optimize the target detection model, which can allow the mean average precision (mAP) of a night image to reach 0.76, and the improved Residual Convolutional GAN network, the unsupervised training model, can generate new samples with the same style, thus greatly expanding the dataset, so that the mean average precision reaches 0.854, and the single-sample enhancement of the deillumination can greatly improve the image clarity, helping improve the precision value by 0.116.
Youth on standby? Explaining adolescent and young adult bystanders’ intervention against online hate speech

Most adolescents and young adults frequently encounter hate speech online. Although online bystander intervention is essential to combating such hate, young bystanders may need support with initiating interventions online. Thus, to illuminate the factors of young bystanders’ intervention, we conducted a nationwide, quota-based, quantitative online survey of 1180 young adults in Germany. Among the results, perceived personal responsibility for combating online hate speech positively predicted online bystanders’ direct and indirect intervention. Moreover, frequent exposure to online hate speech was positively associated with bystander intervention, whereas, a perceived threat or low self-efficacy reduced the likelihood of intervention. Also, a greater acceptance of negative consequences and being educated about online hate speech through peers or campaigns all positively predicted some direct and indirect forms of online bystander intervention.