Reviewerly/exHarmony huggingface dataset:
Feature-based POI grouping with transformer for next point of interest recommendation

With the increasing prevalence of location-based services, Point of Interest (POI) recommendation has become an active research topic. While Graph Neural Networks (GNNs) have been widely used in POI recommendation models, they suffer from computational efficiency limitations when the graph structure is large. In this paper, we propose a new next POI recommendation model, which is backboned by a lightweight, feature-based POI grouping (FPG) method and a Transformer network. A unique feature of the proposed model is it uses the FPG method, which divides POIs into multiple groups based on their geographical and popularity features and analyze the similarity among the users’ preferences on the groups. By using the FPG method rather than graph-based structures, the proposed model largely reduces the computational cost in making next POI recommendation. The POI embeddings generated by the FPG method are then fed into a Transformer to generate the recommendation result. We test the proposed model on three real-world datasets and conduct comprehensive comparison studies to validate the performance of the model. The experiment results show that the proposed model has superior computational efficiency while preserving sufficient next POI recommendation accuracy. Key findings and critical implications from the experiment result and the mechanistic design of the model are also discussed in detail.
Improving semantic segmentation with knowledge reasoning network

Most methods cannot segment the semantic regions accurately due to the lack of global-level supervision or guidance of external knowledge. To overcome this limitation, we propose a Knowledge Reasoning Network (KRNet) that consists of two crucial modules: (1) a prior knowledge mapping module that incorporates external knowledge by graph convolutional network to guide learning semantic representations and (2) a knowledge reasoning module that correlates these representations with a graph built on the external knowledge and explores their interactions via the knowledge reasoning. In the prior knowledge mapping module, we adopt an algorithm to mine knowledge from an external large-scale relational modeling dataset. In the knowledge reasoning module, we adopt an iterative mechanism to perform knowledge reasoning and explore the interaction between features. Reasoning makes the spatial distribution of categories more significant. We establish state-of-the-art results on Cityscapes and ADE20K datasets, which demonstrates the effectiveness of our methods on semantic segmentation.
Online attentive kernel-based temporal difference learning

Kernel-based reinforcement learning has received increasing attention because it requires less prior knowledge linear approximation and neural networks. Online kernel-based updating, however, is hindered by the challenge of catastrophic forgetting or interference. Sparse representation is a key method to address this issue, but existing methods fail to satisfy four criteria: learnability, nonprior, nontruncation, and explicitness. In this paper, we present an attentive kernel-based value function approximation as a learnable, nonprior, nontruncated, and explicit sparse representation. We propose the online attentive kernel-based temporal difference (OAKTD) algorithm, which employs two-timescale optimization, and provide a convergence analysis for our proposed algorithm. Experimental results show that OAKTD outperforms online kernel-based TD learning algorithms, and the TD learning algorithm with Tile Coding on classical tasks, i.e., Mountain Car, Acrobot, CartPole and Puddle World.
Defending edge computing based metaverse AI against adversarial attacks

The metaverse, as an impressive technology, can achieve outstanding performance in many fields. With the development of deep neural networks (DNNs), more and more metaverse artificial intelligence (AI) applications are emerging, which have low tolerance for delay. The edge computing based metaverse AI can overcome this problem. However, these AI applications bring new challenges of high reliability, especially DNNs are subject to the security risk of adversarial attacks that generate small and imperceptible noise and cause classifier to make predictions with a high error probability. To meet the reliability requirements of AI applications, in this paper, we propose a security mechanism against adversarial attacks in edge computing based metaverse AI applications. This mechanism utilizes a modified ResNet model that defends against adversarial attacks. We deploy it on the edge cloud to preprocess the data uploaded by metaverse AI applications. In order to achieve a better model performance, we use multiple residual network blocks to build this neural network model on the basis of autoencoder. To assist the model to produce defensively high-quality images, we utilize several convolution layers with various step sizes to process multi-scale images and design to use multiple types of samples with different properties together for the model training. We evaluate our method with experiments on MNIST, CIFAR-10, and CIFAR-100 datasets, and against five attacks such as FGSM and DeepFool attacks. We show that our method achieves 96% defence success rate in MNIST and significantly outperforms existing denoising methods in terms of residual network block, multi-scale image, and training methods.
An evaluation strategy to select and discard sampling preprocessing methods for imbalanced datasets: A focus on classification models

This work proposes a strategy to help in selecting a synthetic sampling algorithm that enhances the figures of merit of imbalanced pattern recognition models. Three imbalance quantitative structure-activity relationship (QSAR) datasets were used to highlight the efficiency of the present approach. This approach evaluates the pattern recognition algorithms' figures of merit using an experimental design organization. The factor analysis was performed using each figure of merits individually and simultaneously with the Derringer and Suich desirability function. Here, the aim is to better understand the influence of synthetic sampling on pattern recognition models' figure of merits to develop a strategy to choose the sample that may lead to predictions with enhanced overall figures of metric and discard the sample that prejudices the models. Three undersampling (regular downsampling, undersampling based on clustering and Tomek links), three oversampling (regular upsampling, SMOTE, and ADASYN), and two hybrid sampling (SMOTE-TL and SPIDER) methods were used to balance the datasets to build the models. Due to these datasets’ non-Gaussian characteristics proven by the multivariate Shapiro‒Wilk test, the classification models were based on support vector machine with radial base function, C5.0, artificial neural networks, extreme gradient boosting, and random forest algorithms. For these datasets, it was observed that oversampling methods tend to increase the sensitivity and accuracy while undersampling increases the accuracy and specificity. Hybrid methods tend to improve all the figures of merit. However, it is harder to correctly balance the samples between the classes, especially when few variables for a sample are available. Comparison with the original manuscript data results showed that proper sampling preprocessing can enhance the figure of merits of imbalanced datasets.
Accelerating wargaming reinforcement learning by dynamic multi-demonstrator ensemble

Deep Reinforcement Learning (DRL) has become a promising technique to deal with tough wargaming decision-making problems. However, DRL suffers an inherent problem of low learning efficiency and it often requires massive cost of training steps, which may be alleviated with expert demonstrations in wargaming domains. Most learning methods with demonstrations generally treat the demonstration data from different expert demonstrators without distinction. Besides, a more appropriate and effective mechanism is highly needed to control sampling balance of expert-generated demonstration samples and agent-generated interaction ones. To tackle the two issues, this work proposes an improved approach to leverage expert demonstrations to further accelerate DRL. It innovatively extracts inherent diversity in multiple demonstrators by pre-training agents individually from multiple demonstration sources, thereby producing a strong and initial ensemble model. In addition, a novel technique to evaluate the learning importance of each demonstrator is designed to dynamically tune sampling ratios of learning data in a more adaptive and effective manner. Through the evaluation on several classic game tasks and a typical wargaming scenario, our method shows superior performance over several state-of-the-art methods and significantly raises DRL’s efficiency for typical wargaming decision-making applications.
Detecting abnormal behavior of automatic test equipment using autoencoder with event log data

The utilization of automatic test equipment (ATE) in a wafer test is crucial because it helps maintain optimal temperatures and simultaneously provides electrical power for multiple chips. Ensuring the proper functioning of ATE is essential to avoid productivity losses, such as extended wafer testing times and potential malfunctions. Although previous research has investigated the detection of ATE malfunctions using yields, probe cards, and electrical characteristic data, these approaches exhibit limitations in data characteristics and are not suitable for continuous monitoring. This study introduces a novel approach for detecting abnormal behavior within ATE by using event log data and an autoencoder. Autoencoders have demonstrated efficacy in identifying abnormal behavior, making them apt for detecting such behavior within ATE using event log data. To develop this approach, data analysts and wafer test engineers collaborated to establish a practical methodology that encapsulates the operational characteristics of ATE. This methodology comprises four distinct steps. The proposed approach was assessed using simulated event log data and was proven effective in detecting abnormal behavior within ATE. Furthermore, the approach was applied to real-world ATE data from a semiconductor company, identifying abnormal temperature control and monitoring behavior. This detection can lead to reduced wafer test times and contribute to environmental protection efforts.
“To comply or to react, that is the question:” the roles of humanness versus eeriness of AI-powered virtual influencers, loneliness, and threats to human identities in AI-driven digital transformation

Virtual influencers play a variety of roles in emerging media environments. To test the diffusion of AI-powered virtual influencers among social media users and to examine antecedents, mediators, and moderators relevant to compliance with and reactance to virtual influencers, data were collected using two cross-sectional surveys (∑ N = 1623). Drawing on the “Diffusion of Innovations” theory, survey data from Study 1 (N1 = 987) provide preliminary descriptive statistics about US social media users' levels of awareness of, knowledge of, exposure to, and engagement with virtual influencers. Drawing from the theoretical framework of the Uncanny Valley Hypothesis and the CASA (Computers Are Social Actors) paradigm, Study 2 examines social media users' compliance with versus reactance to AI-powered virtual influencers. Survey data from Study 2 (N2 = 636) provide inferential statistics supporting the moderated serial mediation model that proposes (1) empathy and engagement with AI-powered virtual influencers mediate the effects of perceived humanness versus eeriness of virtual influencers on social media users' behavioral intention to purchase the products recommended by the virtual influencers (serial and total mediation effects) and (2) loneliness moderates the effects of humanness versus eeriness on empathy. Drawing from the theory of Psychological Reactance, Study 2 further reports the moderation effect of social media users' trait reactance and perceived threats to one's own human identity on the relationship between perceived eeriness and compliance with versus situational reactance to virtual influencers. Theoretical contributions to CASA research and the uncanny valley literature as well as managerial implications for the AI-powered media industry and virtual influencer marketing are discussed.
A survey of text detection and recognition algorithms based on deep learning technology

Optical Character Recognition (OCR) poses a crucial challenge within the realm of computer vision research, as it plays a pivotal role in converting vast amounts of unstructured text data into structured formats to support diverse artificial intelligence applications. The OCR process encompasses two core components: text detection and text recognition. Text detection involves identifying and extracting text regions, achieved through either object detection or segmentation techniques, while text recognition focuses on accurately deciphering the content within these identified regions. In recent years, remarkable strides have been made in the domain of text recognition, primarily driven by deep learning-based models. These models eliminate the need for manual feature processing and excel in recognizing text even within complex scenes, surpassing the performance of traditional text recognition methods and subsequently emerging as the dominant approach. The objective of this paper is to present a comprehensive survey of both text detection and text recognition models. Firstly, we systematically categorize and provide an overview of existing off-the-shelf text detection methods. Subsequently, we conduct an in-depth investigation of six distinct text recognition models, taking into account their unique implementations. Additionally, we explore and analyze the principal datasets that currently prevail in the field of text detection and recognition. Furthermore, this research entails a meticulous performance comparison of various text detection algorithms on the CTW1500, TotalText, and ICDAR2015 datasets. Additionally, we evaluate and scrutinize the efficacy of mainstream text recognition algorithms on the IIIT-5K, SVT, ICDAR2013, SVT-P, CUTE80, and ICDAR2015 datasets. Finally, we conclude with a discussion on the future development and research trends concerning text detection and recognition, providing insights that can further drive progress in this crucial area.
Expansion window local alignment weighted network for fine-grained sketch-based image retrieval

Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) is a worthwhile task, which can be useful in many scenarios like recommendation systems, receiving a great deal of attention. In this study, we analyze challenges faced in FG-SBIR and propose a novel Expansion Window Local Alignment Weighted Network (EWLAW-Net). Specifically, it contains two main components: the Expansion Window Local Alignment module (EWLA) and the Local Weighted Fusion module (LWF). The EWLA module adopts an expansion window mechanism to align local features extracted from the backbone with the same semantic meaning between photos and sketches. The LWF module assigns weights to each local feature of the sketch after evaluating their importance and fuses them to calculate the similarity between the sketch and photos for retrieval. Experiments are conducted on five datasets and the results demonstrate the effectiveness of the proposed method.
Image entropy equalization: A novel preprocessing technique for image recognition tasks

Image entropy is the metric used to represent a complexity of an image. This study considers the hypothesis that image entropy differences affect machine learning algorithms' performance. This paper proposes a novel preprocessing technique, image entropy equalization, to delete the image entropy differences. The goal is to transform all images into the same entropy. Such a process is implemented by editing all images into the same histogram. Image entropy equalization is evaluated by comparing the original and equalized images in various machine learning tasks. The main advantage of image entropy equalization is to improve the AUC score for one-class autoencoder (OCAE). This result gives a new hypothesis that using image entropy equalization could improve various studies using autoencoder (AE). In addition, the proposed method shows fair results for classification and regression tasks. On the other hand, the main challenges are that the equalization process depends on a reference histogram and is affected by diverse backgrounds.
Unlocking operational excellence: A deep dive into a communication-driven multi-strategy state transition algorithm for industrial process optimization

The optimization of industrial processes is crucial for enhancing operational safety, productivity, and energy efficiency. However, the increasing complexity of industrial processes poses challenges to the applicability of intelligent optimization algorithms, such as the state transition algorithm. The main challenges include limited individual diversity, the intricate task of escaping local optima, and the underutilization of solutions beyond predefined boundaries. To overcome these challenges, this study proposes a novel intelligent stochastic optimization algorithm called the communication-driven multi-strategy state transition algorithm (CDMS-STA). CDMS-STA incorporates three self-designed strategies: communication, self-learning, and cross-boundary solution processing. Specifically, the communication strategy enhances the diversity of candidate solutions by promoting information exchange and sharing among individuals. The self-learning strategy facilitates adaptive adjustments based on historical information. The cross-boundary solution processing strategy maximizes the utilization of solutions beyond defined boundaries. To evaluate the effectiveness of the proposed algorithm, extensive experiments were conducted on a numerical sample case and two chemical process cases. The experimental results demonstrate that CDMS-STA outperforms several state-of-the-art optimization methods to find the optimal solution, which also validates the potential of the proposed algorithm for practical application in industrial process optimization.
An attention-based and deep sparse priori cascade multi-view stereo network for 3D reconstruction

Recently 3D reconstruction has received extensive attention and deep research due to the continuous development of the field of computer vision. Nonetheless, the learning-based methods for 3D reconstruction are not robust enough, resulting in the reconstruction model having many occlusions and outliers. In this study, we aim to improve feature matching correlation, aggregate global contextual information, and enhance the robustness of depth estimation to improve the quality of the reconstruction. We propose an attention-based deep sparse priori cascade multi-view stereo network, ADS-MVSNet. Firstly, we propose a feature extraction module based on the attention mechanism to obtain the regions of interest in the input scene. Secondly, we propose a depth sparse prior strategy module to estimate the depth map of the input scene more accurately. It is followed by refinement of the initial depth map using a coarse-to-fine method to improve the accuracy of point cloud reconstruction. These two modules are lightweight and effective, improving the robustness of depth map estimation. We conduct experiments on three common datasets (DTU, ETH3D, Tanks & Temples) and a dataset of a real scene created by us. The experimental results show that ADS-MVSNet performs better in reconstruction quality compared to classical methods.
On the universality of product for classes of linear functions of two variables

Abstract Earlier, the author introduced the concept of a universal function and proved the existence of universal functions for classes of linear k -valued functions of two variables for k ≥ 5 . In this paper, we show that the product modulo k is a universal function for the class of linear k -valued functions of two variables if and only if k = 6l ± 1 .
Path Planning of UAV Crossing Dense Obstacle Area Based on Improved Dynamic Window Approach

With the continuous development and deepening of UAV research field, the demand for UAV navigation tasks is also increasing, and its path planning technology has gradually become an important part of UAV research and development. Aiming at the problem of path planning in local dense obstacle area of UAV, an improved dynamic window approach (DWA) is proposed. Aiming at the problem that DWA algorithm itself has fixed evaluation parameters and cannot effectively adapt to dense obstacle environment, combined with electric potential energy theory, the prediction trajectory selection mechanism is improved and the dynamic adjustment function of velocity parameters is designed. Finally, the feasibility and effectiveness of the improved DWA algorithm are proved by simulation experiments.
Key-Value Information Extraction from Full Handwritten Pages

We propose a Transformer-based approach for information extraction from digitized handwritten documents. Our approach combines, in a single model, the different steps that were so far performed by separate models: feature extraction, handwriting recognition and named entity recognition. We compare this integrated approach with traditional two-stage methods that perform handwriting recognition before named entity recognition, and present results at different levels: line, paragraph, and page. Our experiments show that attention-based models are especially interesting when applied on full pages, as they do not require any prior segmentation step. Finally, we show that they are able to learn from key-value annotations: a list of important words with their corresponding named entities. We compare our models to state-of-the-art methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform previous performances on all three datasets.
A Graphical Approach to Document Layout Analysis

Document layout analysis (DLA) is the task of detecting the distinct, semantic content within a document and correctly classifying these items into an appropriate category (e.g., text, title, figure). DLA pipelines enable users to convert documents into structured machine-readable formats that can then be used for many useful downstream tasks. Most existing state-of-the-art (SOTA) DLA models represent documents as images, discarding the rich metadata available in electronically generated PDFs. Directly leveraging this metadata, we represent each PDF page as a structured graph and frame the DLA problem as a graph segmentation and classification problem. We introduce the Graph-based Layout Analysis Model (GLAM), a lightweight graph neural network competitive with SOTA models on two challenging DLA datasets - while being an order of magnitude smaller than existing models. In particular, the 4-million parameter GLAM model outperforms the leading 140M+ parameter computer vision-based model on 5 of the 11 classes on the DocLayNet dataset. A simple ensemble of these two models achieves a new state-of-the-art on DocLayNet, increasing mAP from 76.8 to 80.8. Overall, GLAM is over 5 times more efficient than SOTA models, making GLAM a favorable engineering choice for DLA tasks.