Reviewerly/exHarmony huggingface dataset:
Kecheng Jie

Graphical Abstract “A key experience in my education was a visit to Prof. Andrew I. Cooper's group at the University of Liverpool as an exchange PhD student where I started to learn about porous materials and crystal engineering and then got obsessed… The biggest scientific advance of the last decade was artificial intelligence.” Find out more about Kecheng Jie in his Introducing… Profile.
Probing Historical Image Contexts: Enhancing Visual Archive Retrieval through Computer Vision

This study examines the longstanding need and challenge of providing contextual analysis of historical images stored in digital visual archives and the accessibility of retrieving contextual information from these historical archives. Contextual analysis is essential for disciplines such as history and art history, as it allows for the contextualization of artwork and historical sources with historical narratives, which, in turn, enhances understanding of the artistic or political expression in the contents of cultural products. To address this challenge, a novel approach is proposed utilizing computer vision to trace the circulation and dissemination of historical photographs in their original contexts. This method involves first using YOLO v7 to crop historical images from pictorial magazines, then training machine learning models on the cropped printed images plus another large dataset of original historical photographs, and comparing the similarity of images between the datasets of printed images and original photographs. To ensure accuracy of image similarities between the two subsets with distinct image qualities, an ensemble of three machine learning models—Vision Transformer, EfficientNetv2, and Swin Transformer—was developed. Through this system, contexts in the circulation of historical photographs were discovered and new insights regarding the editing strategies of propaganda magazines in East Asia during WWII were uncovered. These outcomes offer supporting evidence for previous research in the history and art historical disciplines, and demonstrate the potential of computer vision for uncovering new information from digital visual archives. Our model achieves a 77.8% top-15 retrieval accuracy on our evaluation dataset.
Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study

Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we examine existing evaluation measures of fairness in recommender systems. Specifically, we focus solely on exposure-based fairness measures of individual items that aim to quantify the disparity in how individual items are recommended to users, separate from item relevance to users. We gather all such measures and we critically analyse their theoretical properties. We identify a series of limitations in each of them, which collectively may render the affected measures hard or impossible to interpret, to compute, or to use for comparing recommendations. We resolve these limitations by redefining or correcting the affected measures, or we argue why certain limitations cannot be resolved. We further perform a comprehensive empirical analysis of both the original and our corrected versions of these fairness measures, using real-world and synthetic datasets. Our analysis provides novel insights into the relationship between measures based on different fairness concepts, and different levels of measure sensitivity and strictness. We conclude with practical suggestions of which fairness measures should be used and when. Our code is publicly available. To our knowledge, this is the first critical comparison of individual item fairness measures in recommender systems.
Advancing Image Object Detection: Enhanced Feature Pyramid Network and Gradient Density Loss for Improved Performance

In the era of artificial intelligence, the significance of images and videos as intuitive conveyors of information cannot be overstated. Computer vision techniques rooted in deep learning have revolutionized our ability to autonomously and accurately identify objects within visual media, making them a focal point of contemporary research. This study addresses the pivotal role of image object detection, particularly in the contexts of autonomous driving and security surveillance, by presenting an in-depth exploration of this field with a focus on enhancing the feature pyramid network. One of the key challenges in existing object detection methodologies lies in mitigating information loss caused by multi-scale feature fusion. To tackle this issue, we propose the enhanced feature pyramid, which adeptly amalgamates features extracted across different scales. This strategic enhancement effectively curbs information attrition across various layers, thereby strengthening the feature extraction capabilities of the foundational network. Furthermore, we confront the issue of excessive classification loss in image object detection tasks by introducing the gradient density loss function, designed to mitigate classification discrepancies. Empirical results unequivocally demonstrate the efficacy of our approach in enhancing the detection of multi-scale objects within images. When evaluated across benchmark datasets, including MS COCO 2017, MS COCO 2014, Pascal VOC 2007, and Pascal VOC 2012, our method achieves impressive average precision scores of 39.4%, 42.0%, 51.5%, and 49.9%, respectively. This performance clearly outperforms alternative state-of-the-art methods in the field. This research not only contributes to the evolving landscape of computer vision and object detection but also has practical implications for a wide range of applications, aligning with the transformative trends in the automotive industry and security technologies.
The Adoption of Digital Technologies for Sharing Information on Agriculture Among Farmers

The use of information and communication technologies for agricultural communication has become dominant among farmers across the globe. However, literature shows some differential usage of these technologies among farmers. While research has utilised various theories to study the adoption and acceptance of technologies, it seems no single theory has been found to reliably explain the usage of ICTs for agricultural communication. This study sought to provide a comparative analysis of various theories in their applications in the use of ICTs for agricultural communication. This study developed an integrated model based on these theories with the variables extracted from the livelihoods framework and the agricultural knowledge and innovation systems. The study then recommended further tests on the validity of this model to determine the reliability of the model across cultures.
The Use of Mobile Technology Tools and Apps to Support Students' Learning Goals

A broad range of information and communication technology (ICT) tools and services, including mobile applications (apps), are being deployed for on-demand learning in developed societies like the United States of America (USA). Some of the apps found to be in use for learning in one university in the USA were Quizlett, Mathway, SAT Prep, and Blackboard. The old concept of teacher-led learning classrooms has changed. The teacher is no longer the “know it all” provider of information to “blank slates” (students) but rather, the literature informs us of the teacher as coach and facilitator of instruction in technologically equipped classrooms. The literature points to the fact that mobile technologies are being used in classrooms in developed world regions such as the USA, but we know very little about developing world regions, including Nigeria. The study reported in this chapter helps to address the need and it investigated students' use of smart phones and applications (apps) to support their learning goals.
Mapping the Mine: Combining Portable X-ray Fluorescence, Spectroradiometry, UAV, and Sentinel-2 Images to Identify Contaminated Soils—Application to the Mostardeira Mine (Portugal)

Old and abandoned mines are testimonials of ancient industrial activities, and as such, they are able to convey environmental concerns. A multidisciplinary approach combining ground measurements—i.e., portable X-ray fluorescence and optical spectroradiometry—with airborne multispectral images—i.e., Sentinel-2 and unmanned aerial vehicles—was conducted to define a baseline for the characterization of areas that constitute environmental burdens. The Mostardeira mine, an old copper mine located in the Portuguese Ossa-Morena Zone, was selected as a case study. The results reveal that the soils have toxic metals—e.g., As (mean = 1239 ppm) and Cu (mean = 435 ppm)—above the defined health thresholds. The spectroradiometry provided insights into the soil characterization using data from the vis-NIR spectral region, allowing us to distinguish agricultural soil, mine waste, and bare soils. The spectra obtained are comparable with the USGS soil spectra standards, namely Clinozoisite Epidote HS299, Hematitic Alt. Tuff CU91-223, and Sand DWO-3-DEL2ar1 no oil. The airborne images considered through the lens of principal components analysis and supervised and unsupervised machine learning techniques (random forest and K-means) are found to be effective tools in creating cartographic representations of the contaminated soils. The collected data are used to construct a baseline for characterizing these environmentally challenging areas, whereas the methodological approach is revealed to be successful for tackling the posed environmental problems, allowing us to map the old mine environment passives.
Optimal parameter configurations for sequential optimization of the variational quantum eigensolver

The variational quantum eigensolver (VQE) is a hybrid algorithm to find the minimum eigenvalue/vector of the given Hamiltonian by optimizing a parameterized quantum circuit (PQC) using a classical computer. Sequential optimization methods, which are often used in quantum circuit tensor networks, are popular for optimizing the parameterized gates of PQCs. In this paper, we focus on the case where the components to be optimized are single-qubit gates, in which the analytic optimization of a single-qubit gate is sequentially performed. The analytical solution is given by diagonalization of a matrix whose elements are computed from the expectation values of observables specified by a set of predetermined parameters, which we refer to as the parameter configurations. In this paper, we first show that the optimization accuracy significantly depends on the choice of parameter configurations owing to the statistical errors in the expectation values. We then identify a metric that quantifies the optimization accuracy of a parameter configuration for all possible statistical errors, named configuration overhead/cost or C-cost. We theoretically provide the lower bound of C-cost and show that, for the minimum size of parameter configurations, the lower bound is achieved if and only if the parameter configuration satisfies the so-called equiangular line condition. Finally, we provide numerical experiments demonstrating that the optimal parameter configuration exhibits the best result in several VQE problems. We hope that this general statistical methodology will enhance the efficacy of sequential optimization of PQCs for solving practical problems with near-term quantum devices.
Person-Specific Template Matching Using a Dynamic Time Warping Step-Count Algorithm for Multiple Walking Activities

This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, NL). The StepMatchDTWBA algorithm used dynamic time warping (DTW) barycentre averaging to create personalised templates for representative steps, accounting for individual walking variations. DTW was then used to measure the similarity between the template and accelerometer epoch. The StepMatchDTWBA algorithm had an average root-mean-square error of 2 steps for healthy gaits and 12 steps for simulated pathological gaits over a distance of about 10 m (GAITRite walkway) and one flight of stairs. It outperformed benchmark algorithms for the simulated pathological population, showcasing the potential for improved accuracy in personalised step counting for pathological populations. The StepMatchDTWBA algorithm represents a significant advancement in accurate step counting for both healthy and pathological populations. This development holds promise for creating more precise and personalised activity monitoring systems, benefiting various health and wellness applications.
Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition

Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is a stacked encoded model, combining artificial intelligence (AI) with the Internet of Things (IoT), which refines feature extraction and classification to overcome these challenges. We leverage a lightweight backbone model for preliminary feature extraction and use stacked autoencoders to further refine these features. Our approach harnesses the scalability of big data, showing notable improvement in accuracy, precision, recall, F1-score, and complexity analysis. Our model's effectiveness is demonstrated through testing on the ArSL2018 benchmark dataset, showcasing superior performance compared to state-of-the-art approaches. Additional validation through an ablation study with pre-trained convolutional neural network (CNN) models affirms our model's efficacy across all evaluation metrics. Our work paves the way for the sustainable development of high-performing, IoT-based sign-language-recognition applications.
Development of a Virtual Reality Memory Maze Learning System for Application in Social Science Education

Virtual reality (VR) is a 3D space created by computer graphics technology and immersive devices. In addition to providing a new visual experience, users can also interact with others in the virtual world to address any of their needs. In recent years, the application of virtual reality in the field of education has attracted the attention of more and more people. This study combined virtual reality and concepts of spatial memory in wayfinding and memory palace to develop a VR memory maze learning system based on the seven large-scale expedition voyages of Zheng He (1405–1433) in the early Ming Dynasty of imperial China. The objective is to improve memory and efficiency in learning social science by correlating spatial information and organizational skills in the virtual environment. A teaching experiment has been conducted to explore its impacts on learning effectiveness, learning motivation, and cognitive load, as well as learners’ technology acceptance of the VR system. The analysis results indicate that using the VR system for learning social science can improve learning effectiveness; it can also increase learning motivation and reduce cognitive load. The questionnaire results of the technology acceptance model analysis show that most learners were satisfied with various aspects of this system.
Construction of an Online Cloud Platform for Zhuang Speech Recognition and Translation with Edge-Computing-Based Deep Learning Algorithm

The Zhuang ethnic minority in China possesses its own ethnic language and no ethnic script. Cultural exchange and transmission encounter hurdles as the Zhuang rely exclusively on oral communication. An online cloud-based platform was required to enhance linguistic communication. First, a database of 200 h of annotated Zhuang speech was created by collecting standard Zhuang speeches and improving database quality by removing transcription inconsistencies and text normalization. Second, SAformerNet, a more efficient and accurate transformer-based automatic speech recognition (ASR) network, is achieved by inserting additional downsampling modules. Subsequently, a Neural Machine Translation (NMT) model for translating Zhuang into other languages is constructed by fine-tuning the BART model and corpus filtering strategy. Finally, for the network’s responsiveness to real-world needs, edge-computing techniques are applied to relieve network bandwidth pressure. An edge-computing private cloud system based on FPGA acceleration is proposed to improve model operation efficiency. Experiments show that the most critical metric of the system, model accuracy, is above 93%, and inference time is reduced by 29%. The computational delay for multi-head self-attention (MHSA) and feed-forward network (FFN) modules has been reduced by 7.1 and 1.9 times, respectively, and terminal response time is accelerated by 20% on average. Generally, the scheme provides a prototype tool for small-scale Zhuang remote natural language tasks in mountainous areas.
Software tools for creating and analyzing a text data bank of short electronic messages from social network users

The research aims at developing an algorithm for creating and analyzing a text data bank of short electronic messages (posts) from social networks using free software tools. The scientific novelty lies in the fact that to solve such a problem, an interdisciplinary approach is used, taking into account the latest achievements of applied and mathematical linguistics and information security, with the involvement of the current regulatory framework. In the course of the work, according to the proposed graphical model, textual research material of ca. 1.5 MB was collected using the Web Scraper plug-in; a text data bank of short electronic messages was generated, converted into a CSV format suitable for further processing; a basic analysis of this data bank was carried out using PolyAnalyst free software package, which included such procedures as the extraction of terms, entities and keywords, sentiment analysis and determination of the subject matter of texts. As a result, the functionality of the created algorithm was proven, prospects for further research were identified – working with big text data and analyzing this data to find destructive content in them.
Analysis of the Effectiveness of Local Government's Overseas Export Support Office

Purpose: The purpose of this study is to prove the effectiveness of an export support policy to ensure its sustainability. This study focuses on analyzing the operational effectiveness of overseas export support offices operated by local governments for the purpose of revitalizing regional exports.&#x0D; Research design, data, and methodology: The research methodology adopted a case study approach. The Overseas Trade Center of Jeollabuk-do was chosen as the research case and is analyzed accordingly. To conduct the case analysis, methods employed include research design; validation of dependency patterns; interviews with users, practitioners, and administrators; expert group evaluations; and cross-evaluation of actual export performance.&#x0D; Results: The operation of overseas export support offices by local governments has been effective. Verification of six dependent patterns according to the research hypothesis all yielded positive results.&#x0D; Conclusions: The expert group evaluation and the comprehensive evaluation of export performance by participating companies indicate that local government overseas export support offices are indeed beneficial for small and medium-sized regional enterprises in their export efforts.
A Method Mining Correlation High Utility Itemsets with Negative Profits

High-utility itemset (HUI) mining is an important task in the data mining process. Recently, many algorithms have been proposed to mine sets of HUIs. Most algorithms only work with data sets with positive return values. However, in the real world, the set of business items often includes both positive (+) and negative (-) profit values. To solve this problem, an algorithm is needed to find HUIs with negative returns in the database.The issue is how to mine efficiently correlation high utility itemsets with negative profits. Given the transactional database D and the profit table of products, how to find the best correlation high utility itemsets on the database with items having negative profits. To address these issues, the paper proposes a COHUIs_CoHUN method to mine correlation high utility itemsets with negative profits
Research on pleasant mood in college English classroom: take Hainan Vocational University of Science and Technology as an example

With the development of society and the process of globalization, English has become one of the globally recognized languages. In higher education English teaching, how to stimulate students' interest and positive emotions, and enhance learning effectiveness, has always been a focus of teachers. Task-based teaching is an effective pedagogical approach that emphasizes tasks as the core element, encourages students to actively participate, and achieves learning objectives through task completion. In task-based teaching, students' emotional experiences and emotional states have a significant impact on learning outcomes. Therefore, this study aims to investigate pleasurable emotions in task-based teaching in college English classrooms, taking Hainan University of Science and Technology as an example, to provide valuable insights for English education teaching practices.
Proposing of Imaging Graph Neural Network with Defined Security Pattern for Improving Smart Contract Vulnerability Detection

A smart contract is a special set of protocols based on blockchain technology to implement the terms oragreements between the parties in the contract. Lots of smart contracts are built and deployed everyday. However, to ensure the safety of smart contracts is still a big challenge. Smart contracts are built to carry out transactions directly related to cryptocurrencies, therefore, the loss of security of smart contracts leads to huge financial losses. Common methods being used to check and to verify smart contract security are heavily dependent on hard rules defined by experts, leading to low detection accuracy and non-scalable, which can be bypassed by experienced attackers. In this paper, we propose to use the combination of Imaging Graph Neural Network With Defined Pattern to detect vulnerabilities in smart contracts. We construct a contract graph that shows the relationship between the main components in a smart contract. Then we extract graph features from normalized graphs, and combine graph features with defined security patterns to create combined features. Finally, we implemented normalization to gray scale image and feed it to the Convolutional Neural Network (CNN) to learn for vulnerability detection. Results show significantly improved accuracy compared to previous methods or other models. Specifically, 96,42%, 90,12%, 79% for reentrancy, timestamp dependence and infinite loop