Reviewerly/exHarmony huggingface dataset:
Coarse-grained privileged learning for classification

Privileged information, a form of prior knowledge, can significantly enhance traditional machine learning performance through a novel paradigm known as learning using privileged information (LUPI). Although effective, current studies on LUPI require a distinct piece of privileged information per input, and these fine-grained priors are difficult to collect in practice. To this end, this paper proposes a brand new problem of learning with class-wise privileged information, where instances within the same class share identical privileged information. As far as we know, this problem has not yet been explored. We build a support vector machine with coarse-grained class-wise priors (CGSVM+) and put forward a novel and reliable augmenting strategy to solve it. In addition, two datasets are collected from nature reserves in Xinjiang, China, along with their class-wise privileged information annotated by professionals. Extensive experiments demonstrate the effectiveness of CGSVM+, with the best average accuracy of 80.16% (94.70%) and the best average F-score of 79.87% (94.57%) on the plant (animal) datasets.
Experimental study on autonomous docking and hook-locking control for unmanned surface vehicle platforms

The challenge in autonomous docking and hook locking of multiple unmanned surface navigation platforms is to design an appropriate autonomous docking controller and ensure that the electric control hook can iteratively dock with the passive vessel. The current docking control solution for unmanned surface platforms needs to design an automatic tracking–generated trajectory and visual guidance docking system, capable of detecting the connection status after the hook-lock action to ensure a successful connection. However, most of the docking control designs for unmanned surface platforms pay more attention to the first-time success rate but pay less attention to the failures caused by complex and intelligent docking devices or environmental interference during docking. This article proposes a control strategy for autonomous docking and hook locking of two unmanned surface platforms. It guides the active vessel to the side of the passive vessel under the task requirements and triggers the visual docking control algorithm to complete the mechanical connection of the two joints. This method employs an iterative detection mechanism to improve the automatic hook and lock capability of both the active and passive vessels. The indoor pool and outdoor lake experiments demonstrate that the proposed method can successfully perform automatic iterative docking and hook locking, even in the presence of wave disturbance, showcasing the effectiveness of the proposed method.
A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data

Abstract We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set of conditions. Second, we show that our model can correctly identify question‐level clusters asymptotically, and the parameters of interest that measure the proficiency of examinees in solving certain questions can be estimated at a rate (up to a log term). Third, we present a tractable sampling algorithm to obtain valid posterior samples from our proposed model. Compared to the existing methods, our model manages to reveal the multi‐dimensionality of the examinees' proficiency level in handling different types of questions parsimoniously by imposing a nested clustering structure. The proposed model is evaluated via a series of simulations as well as apply it to an English proficiency assessment data set. This data analysis example nicely illustrates how our model can be used by test makers to distinguish different types of students and aid in the design of future tests.
Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition

In the era of internet and big data, recommender systems are necessary to filter out useless information. Collaborative filtering (CF) is one of the most successful technique used in recommender systems, this technique suffers from a large number of users and items, or the scalability issue. In this paper, a new hybrid method based on K-means clustering (KM) and Singular Value Decomposition (SVD) which uses evolutionary algorithms is proposed to deal with scalability issue. On the one hand, KM optimized by Particle Swarm Optimization (PSO) and denoising by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster users and reduce the number of comparisons between the target user and other users. On the other hand, SVD optimized by Genetic Algorithm (GA) is used to reduce the number of items. The proposed method is assessed on three standard datasets and the results are compared with basic CF and other extended versions that use clustering algorithms, evolutionary algorithms and dimensionality reduction techniques. The results show that our proposed method performed better than other methods in terms of precision, recall and MAE, and the scalability problem was improved by reducing the time complexity. Also, the combined clustering method was optimized in terms of Davies-Bouldin and the Dunn's index compared to the basic clustering methods.
Investigating the Effects of Career Education Programs on High School Students’ Career Development Competencies in Korea

The high school years represent a critical transitional phase during which students navigate career exploration and career decision-making, significantly shaping their lifelong personal development. Policy authorities in Korea have taken proactive measures to strengthen career education within high schools. However, the extent to which students benefit from participating in career education programs remains a subject of debate. Despite increasing interest in career education since the 2000s, empirical studies investigating the causal effects of these programs have been relatively scarce in Korea. The primary aim of this study is to causally estimate the effectiveness of career education programs offered by high schools, and examine how the impact of these activities varies based on the characteristics of participating students, particularly their career plans after high school graduation. To achieve this objective, DID and DDD models were employed, utilizing a balanced panel dataset spanning two years, from the ‘Korean Education &amp; Employment Panel II’. The findings of this study revealed the significant positive impact of career education on high school students’ career development competencies. Notably, career clubs and career experience programs were identified as effective in enhancing students’ career development competencies. Furthermore, the effects of career club and job simulation varied depending on the students’ post-graduation plans. These results underscore the importance of optimizing the educational effectiveness of career education by emphasizing students’ firsthand experiences and considering individual students’ career plans when implementing career education programs.
Camera and LiDAR Fusion for Urban Scene Reconstruction and Novel View Synthesis via Voxel-Based Neural Radiance Fields

3D reconstruction of urban scenes is an important research topic in remote sensing. Neural Radiance Fields (NeRFs) offer an efficient solution for both structure recovery and novel view synthesis. The realistic 3D urban models generated by NeRFs have potential future applications in simulation for autonomous driving, as well as in Augmented and Virtual Reality (AR/VR) experiences. Previous NeRF methods struggle with large-scale, urban environments. Due to the limited model capability of NeRF, directly applying them to urban environments may result in noticeable artifacts in synthesized images and inferior visual fidelity. To address this challenge, we propose a sparse voxel-based NeRF. First, our approach leverages LiDAR odometry to refine frame-by-frame LiDAR point cloud alignment and derive accurate initial camera pose through joint LiDAR-camera calibration. Second, we partition the space into sparse voxels and perform voxel interpolation based on 3D LiDAR point clouds, and then construct a voxel octree structure to disregard empty voxels during subsequent ray sampling in the NeRF, which can increase the rendering speed. Finally, the depth information provided by the 3D point cloud on each viewpoint image supervises our NeRF model, which is further optimized using a depth consistency loss function and a plane constraint loss function. In the real-world urban scenes, our method significantly reduces the training time to around an hour and enhances reconstruction quality with a PSNR improvement of 1–2 dB, outperforming other state-of-the-art NeRF models.
MHLDet: A Multi-Scale and High-Precision Lightweight Object Detector Based on Large Receptive Field and Attention Mechanism for Remote Sensing Images

Object detection in remote sensing images (RSIs) has become crucial in recent years. However, researchers often prioritize detecting small objects, neglecting medium- to large-sized ones. Moreover, detecting objects hidden in shadows is challenging. Additionally, most detectors have extensive parameters, leading to higher hardware costs. To address these issues, this paper proposes a multi-scale and high-precision lightweight object detector named MHLDet. Firstly, we integrated the SimAM attention mechanism into the backbone and constructed a new feature-extraction module called validity-neat feature extract (VNFE). This module captures more feature information while simultaneously reducing the number of parameters. Secondly, we propose an improved spatial pyramid pooling model, named SPPE, to integrate multi-scale feature information better, enhancing the model to detect multi-scale objects. Finally, this paper introduces the convolution aggregation crosslayer (CACL) into the network. This module can reduce the size of the feature map and enhance the ability to fuse context information, thereby obtaining a feature map with more semantic information. We performed evaluation experiments on both the SIMD dataset and the UCAS-AOD dataset. Compared to other methods, our approach achieved the highest detection accuracy. Furthermore, it reduced the number of parameters by 12.7% compared to YOLOv7-Tiny. The experimental results illustrated that our proposed method is more lightweight and exhibits superior detection accuracy compared to other lightweight models.
An overview of quantum resistance digital signatures based on hash functions

Facing the challenge of developing quantum computers, quantum-resistant digital signature algorithms have been developed based on hash functions and have received the attention of many scientists. This paper focuses on evaluating hash function-based quantum-resistant digital signature algorithms and their applicability in protecting transmitted information. Analyzing, synthesizing and comparing the strengths and limitations of the algorithms is the main research method of the paper. It also highlights the challenge of applying them in different fields such as banking, e-commerce, and government. The article is useful for those who are just starting to study quantum-resistant digital signatures, helping them get an overview of the field. It also assists regulators, governments and other organizations in making decisions and policies regarding digital signature security platforms.
Column Cyclic Rank Metric Codes and Linear Complementary Dual Rank Metric Codes

We introduce and study column cyclic rank metric (CCRM) codes over finite fields. We present natural questions for the existence and characterization of CCRM codes. We completely solve these questions only in the first nontrivial case: The case of the minimum rank distance [Formula: see text] and the number of rows [Formula: see text]. We also consider linear complementary dual rank metric (LCDRM) codes both in the setting of CCRM codes and in the general setting of rank metric codes. We also ask the natural questions for the existence and characterization of CCRM codes in the subclass of LCDRM codes. We also completely solve these questions for this subclass of LCDRM codes only in the first nontrivial case that [Formula: see text] and [Formula: see text]. Moreover we extend a characterization of linear complementary dual (LCD) codes of Massey to arbitrary rank metric codes in the general setting.
RESEARCH ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PRICE FORECASTING OF SOME COMMODITIES

The global economy is significantly impacted by changes in the price of primary commodities. As a result, both the academic and professional sectors have paid attention to price predictions for major commodities. The goal of this study is to build an artificial intelligence-based model for one-day market price predictions for important commodities like copper, crude oil, gas, and silver. The information on commodity trading was gathered between 01/2000 and 10/2019. Different models based on group method of data handling (GMDH), long short-term memory (LSTM), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were developed. Theil's U, RMSE, MAPE, MAE, R, and other performance indices were used to compare the models. The findings demonstrated that, in terms of commodity price prediction, the suggested model based on GMDH technique performs better than alternative approaches. A viable alternative for price prediction is the GMDH-based model. For economists and professionals involved in commodity price forecasting, the GMDH can be a useful tool.
Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

Abstract Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation.
On the link between generative semi-supervised learning and generative open-set recognition

This study investigates the relationship between semi-supervised learning (SSL, which is training off partially labelled datasets) and open-set recognition (OSR, which is classification with simultaneous novelty detection) under the context of generative adversarial networks (GANs). Although no previous study has formally linked SSL and OSR, their respective methods share striking similarities. Specifically, SSL-GANs and OSR-GANs require their generators to produce ’bad-looking’ samples which are used to regularise their classifier networks. We hypothesise that the definitions of bad-looking samples in SSL and OSR represents the same concept and realises the same goal. More formally, bad-looking samples lie in the complementary space, which is the area between and around the boundaries of the labelled categories within the classifier’s embedding space. By regularising a classifier with samples in the complementary space, classifiers achieve improved generalisation for SSL and also generalise the open space for OSR. To test this hypothesis, we compare a foundational SSL-GAN with the state-of-the-art OSR-GAN under the same SSL-OSR experimental conditions. Our results find that SSL-GANs achieve near identical results to OSR-GANs, proving the SSL-OSR link. Subsequently, to further this new research path, we compare several SSL-GANs various SSL-OSR setups which this first benchmark results. A combined framework of SSL-OSR certainly improves the practicality and cost-efficiency of classifier training, and so further theoretical and application studies are also discussed.
Expectations and perceptions of rural electrification: A comparison of the providers’ and beneficiaries’ cognitive maps in Rural Sumba, Indonesia

The 17 United Nations Sustainable Development Goals (SDG) provide a global common vision for development that achieves equality and equity. SDG 7 on energy has motivated development actors worldwide to promote universal electricity access through rural electrification projects. However, implementing technological interventions in rural areas can be challenging due to differences in worldviews between implementing agencies and cultural communities in which the beneficiaries are embedded. These differences increase complexity of both technology and knowledge transfer as it involves a negotiation of meaning and an adjustment of norms. This study aims to investigate the influence of long-existing cultural practices on the knowledge transfer process for implementation of a renewable energy rural electricity project on Sumba Island in Indonesia. The development actors' expectations and the beneficiaries' perceptions are compared to explore the extent to which knowledge has been transferred. We employ a semi-quantitative method called fuzzy cognitive maps (FCM) to obtain a comparable measurement that is qualitatively explainable. This method enables us to generate a simple statistical analysis based on sixteen key informant interviews. We also generate a belief-based model to predict the electricity access outcomes if the beneficiaries remained on the current level of knowledge. The analysis revealed differences between the providers' expectations and perceptions of the beneficiaries, which helps to explain why predicted electricity access outcomes do not meet expectations. The qualitative analysis also enables us to identify barriers to achieving SDG7 on Sumba Island. We argue that the subsistence nature of beneficiaries' economic practices limits expectations of becoming business entrepreneurs who can pay for electricity tariffs.
Underwater Image Enhancement Using Hyper-Laplacian Reflectance Priors and CNN-Based Classification

The physical properties of water cause an underwater image to degrade. Enhancing damaged underwater photos is an important topic of study. Several academics have used various picture-enhancing methods and models. We have included a hyper-Laplacian reflectance priors enhancement technique for underwater images and a Convolutional Neural Network (CNN) for the classification of images. The classification is performed on the real images and the enhanced images when passed through an HLRP code.
Content-based image retrieval for industrial material images with deep learning and encoded physical properties

Abstract Industrial materials images are an important application domain for content-based image retrieval. Users need to quickly search databases for images that exhibit similar appearance, properties, and/or features to reduce analysis turnaround time and cost. The images in this study are 2D images of millimeter-scale rock samples acquired at micrometer resolution with light microscopy or extracted from 3D micro-CT scans. Labeled rock images are expensive and time-consuming to acquire and thus are typically only available in the tens of thousands. Training a high-capacity deep learning (DL) model from scratch is therefore not practicable due to data paucity. To overcome this “few-shot learning” challenge, we propose leveraging pretrained common DL models in conjunction with transfer learning. The “similarity” of industrial materials images is subjective and assessed by human experts based on both visual appearance and physical qualities. We have emulated this human-driven assessment process via a physics-informed neural network including metadata and physical measurements in the loss function. We present a novel DL architecture that combines Siamese neural networks with a loss function that integrates classification and regression terms. The networks are trained with both image and metadata similarity (classification), and with metadata prediction (regression). For efficient inference, we use a highly compressed image feature representation, computed offline once, to search the database for images similar to a query image. Numerical experiments demonstrate superior retrieval performance of our new architecture compared with other DL and custom-feature-based approaches.
Video surveillance-based fall detection system using object-level feature thresholding and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si49.svg" display="inline" id="d1e814"><mml:mrow><mml:mi>Z</mml:mi><mml:mo>−</mml:mo></mml:mrow></mml:math>numbers

A new algorithm, namely Z−numbers-based deep feature thresholding (Z−DFT) is described for handling the issue concerning uncertainty that arises while classifying various fall and no-fall events in complex scenarios in both indoor (viz., home and hospital) and outdoor (viz., construction area and road). The Z−DFT consists of four phases: (i) object detection and tracking, (ii) feature extraction, (iii) feature thresholding and rule generation, and (iv) Z−numbers-based analysis for quantifying the reliability of the detected falls. Unlike state-of-the-art methods, Z−DFT uses both OpenPose and object-level features. This enables better modeling of both indoor and outdoor falls. New object-level features considered are change in area, aspect ratio, speed variation, and change in direction of the detected objects. The detection of fall locations involves two phases, viz., probable location and specific location. The probable location corresponding to each OpenPose and object-level feature is determined based on its statistical information. The commonality of these probable locations results in the specific location which is determined by framing linguistic rules using all the features. Z−numbers computed with features further reflect the reliability of detection. The characteristic features of Z−DFT are demonstrated over eighteen real-time videos acquired from YouTube8M and UR Fall data, along with its superiority claimed over ten state-of-the-art algorithms.