Reviewerly/exHarmony huggingface dataset:
Using AI Chatbot for Math Tutoring

Aim. The aim of this paper is to present the results of a pilot validation of an artificial intelligence chatbot for mathematics tutoring. The subject of the analysis is the behaviour of the students in the course, in particular their approach to solving problems without external motivation through scoring or other assessment. Methods. The analysis of student behaviour was carried out on the basis of records in the application database. The subject of the analysis was not only the pupils' responses, but also the time taken to produce them and the overall trajectory through the course. Results. The results of the analysis show that students who choose to join the course, work intensively in the lessons. Most pupils solve more than one basic task. A significant proportion of pupils also use the prepared help and instructional videos. The analysis of the solution time allowed identification of a group of pupils who do not even read the problems and only confirm the results. Conclusion. The pilot validation confirmed that the chat-bot format is close to the learners, and they are able to use the courses created in this environment. Solution time analysis suggests that this parameter can be used to analyse learner behaviour and system control. Further analyses in this area are needed.
The Negative Transfer Effect on the Neural Machine Translation of Egyptian Arabic Adjuncts into English: The Case of Google Translate

Parallel corpora for low-resource Arabic dialects and English are limited and small-scale, and most neural machine translation models, including Google Translate, rely mainly on parallel corpora of standard Arabic and English to train for dialectal Arabic translation. A model well trained to translate to and from standard Arabic is believed to efficiently translate dialectal Arabic, given their similarities. This study demonstrates the impact of not using large-scale, dialect-specific parallel corpora by quantitatively and qualitatively analyzing the performance of Google Translate in translating Egyptian Arabic adjuncts. Compared to human reference translation, Google Translate achieved a low BLEU score of 14.69. Qualitative analysis showed that reliance on standard Arabic parallel corpora caused a negative transfer problem manifested in the literal translation of idiomatic adjuncts, the misinterpretation of dialectal adjuncts as main clause constituents, the translation of dialectal adjuncts after orthographically similar standard Arabic words, and the use of standard Arabic common lexical meanings to translate dialect-specific adjuncts. This study’s findings will be relevant for researchers interested in dialectal Arabic neural machine translation and has implications for investment in the development of large-scale, dialect-specific corpora to better process the peculiarities of Arabic dialects and reduce the effect of negative transfer from standard Arabic.
A Reinforcement Learning Method of Solving Markov Decision Processes: An Adaptive Exploration Model Based on Temporal Difference Error

Traditional backward recursion methods face a fundamental challenge in solving Markov Decision Processes (MDP), where there exists a contradiction between the need for knowledge of optimal expected payoffs and the inability to acquire such knowledge during the decision-making process. To address this challenge and strike a reasonable balance between exploration and exploitation in the decision process, this paper proposes a novel model known as Temporal Error-based Adaptive Exploration (TEAE). Leveraging reinforcement learning techniques, TEAE overcomes the limitations of traditional MDP solving methods. TEAE exhibits dynamic adjustment of exploration probabilities based on the agent’s performance, on the one hand. On the other hand, TEAE approximates the optimal expected payoff function for subprocesses after specific states and times by integrating deep convolutional neural networks to minimize the temporal difference error between the dual networks. Furthermore, the paper extends TEAE to DQN-PER and DDQN-PER methods, resulting in DQN-PER-TEAE and DDQN-PER-TEAE variants, which not only demonstrate the generality and compatibility of the TEAE model with existing reinforcement learning techniques but also validate the practicality and applicability of the proposed approach in a broader MDP reinforcement learning context. To further validate the effectiveness of TEAE, the paper conducts a comprehensive evaluation using multiple metrics, compares its performance with other MDP reinforcement learning methods, and conducts case studies. Ultimately, simulation results and case analyses consistently indicate that TEAE exhibits higher efficiency, highlighting its potential in driving advancements in the field.
Gradient Correction for White-Box Adversarial Attacks

Deep neural networks (DNNs) play key roles in various artificial intelligence applications such as image classification and object recognition. However, a growing number of studies have shown that there exist adversarial examples in DNNs, which are almost imperceptibly different from the original samples but can greatly change the output of DNNs. Recently, many white-box attack algorithms have been proposed, and most of the algorithms concentrate on how to make the best use of gradients per iteration to improve adversarial performance. In this article, we focus on the properties of the widely used activation function, rectified linear unit (ReLU), and find that there exist two phenomena (i.e., wrong blocking and over transmission) misguiding the calculation of gradients for ReLU during backpropagation. Both issues enlarge the difference between the predicted changes of the loss function from gradients and corresponding actual changes and misguide the optimized direction, which results in larger perturbations. Therefore, we propose a universal gradient correction adversarial example generation method, called ADV-ReLU, to enhance the performance of gradient-based white-box attack algorithms such as fast gradient signed method (FGSM), iterative FGSM (I-FGSM), momentum I-FGSM (MI-FGSM), and variance tuning MI-FGSM (VMI-FGSM). Through backpropagation, our approach calculates the gradient of the loss function with respect to the network input, maps the values to scores, and selects a part of them to update the misguided gradients. Comprehensive experimental results on ImageNet and CIFAR10 demonstrate that our ADV-ReLU can be easily integrated into many state-of-the-art gradient-based white-box attack algorithms, as well as transferred to black-box attacks, to further decrease perturbations measured in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\ell _2}$</tex-math> </inline-formula> -norm.
Determining the location of the Bayan Obo rare earth elements mineralization body by the transfer learning method

Bayan Obo is the largest rare earth element (REE) deposit in the world. The occurrence of REE is closely related to the dolomite in this area. Dolomite serves as the mother rock of REE mineralization and the ore body. How to accurately locate and characterize dolomite is the key to determining the distribution of REE and estimating its reserves. A large amount of geophysical work has been conducted in this area, including a dense seismic array, various electromagnetic methods, gravity and aeromagnetic surveys, as well as numerous petrophysical property measurements. To fully leverage the results obtained by these geophysical methods and develop an understanding of the physical property structure, a multisource geophysical data fusion technology is developed. First, various physical property profiles obtained from inversion on the same profile are converted into images with identical resolution and dimension. Then, an image adaptive feature extraction technique based on transfer learning is used to extract features of different scales from multisource images. Subsequently, the fusion image is reconstructed based on the local nearest neighbor weighted average feature fusion rule to obtain the final fusion result. This aids in identifying the spatial appearance pattern of the target for detection. Given the physical characteristics of the mineralized dolomite, which has high density, high resistivity, and high magnetic susceptibility, its location and shape can be defined in the fusion image. The results indicate that the occurrence depth of dolomite can extend up to 1500 m and the dolomite has a southward tilt as one of its primary structural characteristics. The predicted range of dolomite distribution is consistent with the formation range revealed by drilling, making it a reliable basis for predicting the distribution of rare earth ore bodies.
BERT-Based Approaches to Identifying Malicious URLs

Malicious uniform resource locators (URLs) are prevalent in cyberattacks, particularly in phishing attempts aimed at stealing sensitive information or distributing malware. Therefore, it is of paramount importance to accurately detect malicious URLs. Prior research has explored the use of deep-learning models to identify malicious URLs, using the segmentation of URL strings into character-level or word-level tokens, and embedding and employing trained models to differentiate between URLs. In this study, a bidirectional encoder representation from a transformers-based (BERT) model was devised to tokenize URL strings, employing its self-attention mechanism to enhance the understanding of correlations among tokens. Subsequently, a classifier was employed to determine whether a given URL was malicious. In evaluating the proposed methods, three different types of public datasets were utilized: a dataset consisting solely of URL strings from Kaggle, a dataset containing only URL features from GitHub, and a dataset including both types of data from the University of New Brunswick, namely, ISCX 2016. The proposed system achieved accuracy rates of 98.78%, 96.71%, and 99.98% on the three datasets, respectively. Additionally, experiments were conducted on two datasets from different domains—the Internet of Things (IoT) and Domain Name System over HTTPS (DoH)—to demonstrate the versatility of the proposed model.
Artificial Intelligence in Tourism

Artificial intelligence in tourism activities opens a bundle of emerging applications for tourists and companies. This chapter aims to delineate the stages of the tourist journey and the usage of four types of intelligence suggested in the literature: mechanical, analytical, intuitive, and empathetic. Based on these two ideas, the authors propose a useful framework for disentangling the different types of current and future applications of AI in tourism. Each stage involves multiple suppliers with different types of AI applications, and its adoption will ultimately rely on tourist trust and, therefore, willingness to share data and the use of robotics and other AI forms. The chapter ends with some trends and reflections on the expansion of AI in tourism that pivot around these ideas: job replacement and flexible operations; mobile-centric approach; data integration and analytics; revenue management and customer interactions tension; (v) neuroscientific tools for AI in tourism.
Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. A series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.
RGB-D salient object detection via convolutional capsule network based on feature extraction and integration

Fully convolutional neural network has shown advantages in the salient object detection by using the RGB or RGB-D images. However, there is an object-part dilemma since most fully convolutional neural network inevitably leads to an incomplete segmentation of the salient object. Although the capsule network is capable of recognizing a complete object, it is highly computational demand and time consuming. In this paper, we propose a novel convolutional capsule network based on feature extraction and integration for dealing with the object-part relationship, with less computation demand. First and foremost, RGB features are extracted and integrated by using the VGG backbone and feature extraction module. Then, these features, integrating with depth images by using feature depth module, are upsampled progressively to produce a feature map. In the next step, the feature map is fed into the feature-integrated convolutional capsule network to explore the object-part relationship. The proposed capsule network extracts object-part information by using convolutional capsules with locally-connected routing and predicts the final salient map based on the deconvolutional capsules. Experimental results on four RGB-D benchmark datasets show that our proposed method outperforms 23 state-of-the-art algorithms.
Food manufacturer willingness to employ blockchain technology system under the social Co-governance framework: China's situation

The blockchain technology system has gradually come to be employed in the food supply chain system, and it has emerged that the system offers the unique function of effectively curbing counterfeiting by food manufacturers. Unlike previous research on adoption by enterprises of new technology, this paper probes into the specific evolutionary routes of game subjects from the perspective of the precondition for enterprises' willingness to employ blockchain technology on the basis of China's social co-governance framework and by establishing a tripartite evolutionary game model of food manufacturer, government and consumer. The study then tests and verifies the stability conditions of equilibrium points and the relationship between these equilibrium points and the social co-governance level through numerical simulation analysis. On the above basis, the expected market proceeds of food producers employing blockchain technology and the influence of government and consumer behavior on enterprises' selection of a behavior strategy and the level of social co-governance are analyzed. The results show that every subject selects their own behavior strategy on the basis of the balance of their respective interests, and the final stability condition of the system is independent of their initial intentions. Rather, the expected sales volume of foods employing blockchain technology, governmental behavior (e.g., supervision, casual inspection, economic punishment, and fiscal subsidies), and complaints made by consumers constitute the main factors that influence food enterprises' selection of a behavior strategy. The level of social co-governance and the behavior of both government and consumers will ultimately be accomplished by influencing enterprises' expected economic returns, and the selection of an enterprise behavior strategy internally depends on the expected economic returns from producing foods employing blockchain technology. Therefore, this paper makes relevant proposals in an attempt to assist the Chinese government to better promote and popularize the blockchain technology system among food manufacturing enterprises.
A General Framework for Circular Local Likelihood Regression

AbstractThis paper presents a general framework for the estimation of regression models with circular covariates, where the conditional distribution of the response given the covariate can be specified through a parametric model. The estimation of a conditional characteristic is carried out nonparametrically, by maximizing the circular local likelihood, and the estimator is shown to be asymptotically normal. The problem of selecting the smoothing parameter is also addressed, as well as bias and variance computation. The performance of the estimation method in practice is studied through an extensive simulation study, where we cover the cases of Gaussian, Bernoulli, Poisson and Gamma distributed responses. The generality of our approach is illustrated with several real-data examples from different fields.Keywords: Circular dataData-driven smoothing selectionLocal likelihoodNonparametric regressionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment Enabled by Large Language Models

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">manually crafted</i> seed alignments, which are expensive to obtain. In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments. Specifically, for predicate embeddings, AutoAlign constructs a predicate-proximity-graph with the help of large language models to automatically capture the similarity between predicates across two KGs. For entity embeddings, AutoAlign first computes the entity embeddings of each KG independently using TransE, and then shifts the two KGs' entity embeddings into the same vector space by computing the similarity between entities based on their attributes. Thus, both predicate alignment and entity alignment can be done without manually crafted seed alignments. AutoAlign is not only fully automatic, but also highly effective. Experiments using real-world KGs show that AutoAlign improves the performance of entity alignment significantly compared to state-of-the-art methods. Our source code is available at ruizhang-ai/AutoAlign.
Random forest kernel for high-dimension low sample size classification

High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best possible concept from such data. In a previous work, we proposed a dissimilarity-based approach for multi-view classification, the random forest dissimilarity, that perfoms state-of-the-art results for such problems. In this work, we transpose the core principle of this approach to solving HDLSS classification problems, by using the RF similarity measure as a learned precomputed SVM kernel (RFSVM). We show that such a learned similarity measure is particularly suited and accurate for this classification context. Experiments conducted on 40 public HDLSS classification datasets, supported by rigorous statistical analyses, show that the RFSVM method outperforms existing methods for the majority of HDLSS problems and remains at the same time very competitive for low or non-HDLSS problems.
Hard Disk Posture Recognition and Grasping Based on Depth Vision

The conventional operation and maintenance of 5G computer rooms is characterized by a low level of automation, which can result in untimely replacement of hard disks and potential data loss. In order to achieve efficient and timely replacement of hard disks in 5G computer rooms, this paper proposes a novel methodology for attitude recognition of such disks using an RGB-D depth camera and quintic polynomial interpolation algorithm. This method obtains two-dimensional position information of the hard disk through RGB images, and then combines depth images to obtain the coordinate system of the three-dimensional hard disk. The precise identification of the area to grasp a hard disk is achieved through the design of the grasping process, RGB-D image preprocessing, attitude estimation, and grasping trajectory planning. Additionally, by combining hard disk attitude estimation, the robot arm can be effectively controlled to complete the grasping process of the hard disk. The experiments carried out on the visual recognition method proposed in the article have shown that it achieves high accuracy in recognizing the grasping area of a hard disk. Moreover, the robot arm grasping system based on this method has been used to replace hard disks in 5G computer rooms automatically.
IKEA - Phygital Transformation in Furniture Manufacturing Industry

IKEA was formed in a remote corner of Sweden, where it was difficult to contact potential clients in larger towns. Is currently serving over 775 million clients worldwide and has a presence in 35 countries IKEA is the best illustration of phygital transformation having the combination of Physical plus digital, i.e., merging digital experiences with physical ones. Technology is a requirement of the twenty-first century. IKEA has established a physical and digital presence using technology. IKEA mixes AR (Augmented Reality) and VR (Virtual Reality), which is a fundamental cause of the company’s success. This is the case study with a strong emphasis on sustainability development with the emerging phygital transmission of IKEA. The entire research is based on secondary data.
<i>TimeTuner</i>: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations

Deep learning (DL) approaches are being increasingly used for time-series forecasting, with many efforts devoted to designing complex DL models. Recent studies have shown that the DL success is often attributed to effective data representations, fostering the fields of feature engineering and representation learning. However, automated approaches for feature learning are typically limited with respect to incorporating prior knowledge, identifying interactions among variables, and choosing evaluation metrics to ensure that the models are reliable. To improve on these limitations, this paper contributes a novel visual analytics framework, namely TimeTuner, designed to help analysts understand how model behaviors are associated with localized correlations, stationarity, and granularity of time-series representations. The system mainly consists of the following two-stage technique: We first leverage counterfactual explanations to connect the relationships among time-series representations, multivariate features and model predictions. Next, we design multiple coordinated views including a partition-based correlation matrix and juxtaposed bivariate stripes, and provide a set of interactions that allow users to step into the transformation selection process, navigate through the feature space, and reason the model performance. We instantiate TimeTuner with two transformation methods of smoothing and sampling, and demonstrate its applicability on real-world time-series forecasting of univariate sunspots and multivariate air pollutants. Feedback from domain experts indicates that our system can help characterize time-series representations and guide the feature engineering processes.