Reviewerly/exHarmony huggingface dataset:
Self-supervised learning for heterogeneous graph via structure information based on metapath

Graph neural networks (GNNs) are the dominant paradigm for modeling and handling graph structural data by learning universal node representation. The traditional way of training GNNs depends on a great many labeled data, which is time-consuming and money-consuming. In some special scenes, it is even unavailable and impracticable. Self-supervised representation learning, which can generate labels by graph structural data itself, is a potential approach to tackle this problem. And turning to research self-supervised learning problems for heterogeneous graphs is more challenging than dealing with homogeneous graphs, there are fewer studies about it as well. In this paper, we propose a SElf-supervised learning method for heterogeneous graph via Structure Information based on Metapath (SESIM). Firstly, the pseudo-labels are constructed to train pretext tasks, using data itself and avoiding time-consuming manual labeling. Afterward, we use traditional graph neural networks to aggregate node features, obtaining the node embeddings. And then, the primary task and pretext tasks are designed by these node embeddings. The pretext tasks, i.e., jump numbers prediction between nodes in each metapath, can improve the representation ability of the primary task. Moreover, predicting jump numbers in each metapath can effectively utilize graph structural information, which is the essential property of nodes. Therefore, SESIM deepens the understanding of models for graph structure. At last, we train the primary task and pretext tasks jointly and balance the contributions of pretext tasks for the primary task. The key advantage of our proposed model is that we research self-supervised learning for the heterogeneous graph to address the time-consuming and money-consuming problem of obtaining labels. And we design a novel pretext task, i.e., jump numbers prediction in each metapath, via graph structural information based on the metapath. Empirical results validate the performance of the SESIM method and demonstrate that this method can improve the representation ability of traditional neural networks on link prediction tasks and node classification tasks.
The error-correcting pair for TGRS codes

Twisted generalized Reed-Solomon codes are the generalization of generalized Reed-Solomon codes, due to their good algebraic structure and applications in cryptography, which have been widely studied in recent years. As a general algebraic decoding method for linear codes, the error-correcting pair exists in many classical linear codes. In this paper, we extend the error-correcting pair to twisted generalized Reed-Solomon codes. For twisted generalized Reed-Solomon codes with twist 1 or 2, we constructively show that the existence of the error-correcting pair. Moreover, we introduce the corresponding decoding procedure of twisted generalized Reed-Solomon codes by ℓ-error-correcting pairs and give several examples.
Bayesian composite quantile regression for the single-index model

By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model. The posterior distributions for the unknown parameters are derived, and the Markov chain Monte Carlo sampling algorithms are also given. The proposed method is illustrated by three simulation examples and a real dataset.
Multimodal Natural Language Explanation Generation for Visual Question Answering Based on Multiple Reference Data

As deep learning research continues to advance, interpretability is becoming as important as model performance. Conducting interpretability studies to understand the decision-making processes of deep learning models can improve performance and provide valuable insights for humans. The interpretability of visual question answering (VQA), a crucial task for human–computer interaction, has garnered the attention of researchers due to its wide range of applications. The generation of natural language explanations for VQA that humans can better understand has gradually supplanted heatmap representations as the mainstream focus in the field. Humans typically answer questions by first identifying the primary objects in an image and then referring to various information sources, both within and beyond the image, including prior knowledge. However, previous studies have only considered input images, resulting in insufficient information that can lead to incorrect answers and implausible explanations. To address this issue, we introduce multiple references in addition to the input image. Specifically, we propose a multimodal model that generates natural language explanations for VQA. We introduce outside knowledge using the input image and question and incorporate object information into the model through an object detection module. By increasing the information available during the model generation process, we significantly improve VQA accuracy and the reliability of the generated explanations. Moreover, we employ a simple and effective feature fusion joint vector to combine information from multiple modalities while maximizing information preservation. Qualitative and quantitative evaluation experiments demonstrate that the proposed method can generate more reliable explanations than state-of-the-art methods while maintaining answering accuracy.
A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization

Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective and fact-based summary. It is faster and less computationally intensive than abstract summarization techniques. Learning cross-sentence relationships is crucial for extractive text summarization. However, most of the language models currently in use process text data sequentially, which makes it difficult to capture such inter-sentence relations, especially in long documents. This paper proposes an extractive summarization model based on the graph neural network (GNN) to address this problem. The model effectively represents cross-sentence relationships using a graph-structured document representation. In addition to sentence nodes, we introduce two nodes with different granularity in the graph structure, words and topics, which bring different levels of semantic information. The node representations are updated by the graph attention network (GAT). The final summary is obtained using the binary classification of the sentence nodes. Our text summarization method was demonstrated to be highly effective, as supported by the results of our experiments on the CNN/DM and NYT datasets. To be specific, our approach outperformed baseline models of the same type in terms of ROUGE scores on both datasets, indicating the potential of our proposed model for enhancing text summarization tasks.
The Impact of Augmented Reality (AR) on the Academic Performance of High School Students

New technologies currently play a fundamental role in the educational context. As a result, augmented reality (AR) has recently gained a presence in educational centres. However, this educational technology has not been explored in depth at the secondary education level. Therefore, this research aims to analyse the impact of augmented reality on the academic performance of secondary education students, considering gender and the students’ attitudes toward this technology. In this mixed-method research based on convenient sampling, 321 students aged 14 to 17 years from the same secondary education school were assigned to an experimental group (n = 159) and a control group (n = 162). The control group used a traditional methodology in a slide-based learning environment, while the experimental group worked with an AR mobile application (ComputAR) designed with the same concepts. The data collection instruments used comprised a pre-test/post-test in both groups and semi-structured interviews in the experimental group. The results showed that the students who used augmented reality achieved better grades, highlighting the potential benefits of integrating this technology into the teaching process. No significant differences were observed regarding the gender of the students. In conclusion, this study provides findings that encourage the use of augmented reality in secondary schools.
A Fuzzy-Based Emotion Detection Method to Classify the Relevance of Pleasant/Unpleasant Emotions Posted by Users in Reviews of Service Facilities

Many sentiment analysis methods have been proposed recently to evaluate, through the Web, the perceptions of users and their satisfaction with the use of products and services; these approaches have been applied in various fields in which it is necessary to evaluate, for example, the degree of appreciation of a product or a service or political orientations or emotional states following an event or the occurrence of a phenomenon. On the other hand, these methods are based on natural language processing models needed to capture information hidden in comments, which generally require a high computational cost which can affect their performance; for this reason, review-collecting providers prefer to synthetically evaluate user satisfaction by considering a score on a numerical scale entered by users. To overcome this criticality, we propose an emotion detection method based on a light fuzzy-based document classification model to capture the relevance of pleasant and unpleasant emotions expressed by users in their reviews of service facilities. This method is implemented in a geo-computational framework and tested to evaluate the satisfaction of customers of theater venues located in the municipality of Naples (Italy). A fuzzy-based approach is used to classify user satisfaction according to the relevance of the emotional categories of pleasant and unpleasant. We show that our emotion detection method refines service feature pleasure assessments expressed on scales by users in their reviews.
Behavior of Science and Engineering Students to Digital Reading: Educational Disruption and Beyond

This paper investigates the impact of digital reading during educational disruption on science and engineering students’ learning experience. Before the pandemic, some studies explored whether university students preferred using printed or digital resources for their academic readings. Amidst the pandemic, online learning became essential. Several studies showed students’ preference for printed text. This paper extends a pilot study that was conducted during the first COVID-19 wave in China. A survey consisting of Likert questions and open questions was designed using MS-Forms. The survey was shared with the science and engineering students in Years 2–4 (Levels 1–3) of their study at SWJTU-Leeds Joint School, Southwest Jiaotong University in Chengdu, China. This covered students from four undergraduate programs: Civil Engineering with Transport, Electronic and Electrical Engineering, Mechanical Engineering, and Computer Science. In total, 223 students participated in this study. The survey was anonymous and was made available to students for a month. The participation rate is nearly 27%. Findings indicate that the behavior of science and engineering students toward digital reading was different than other majors, and it is generally favorable. The necessity for online learning during educational disruption has encouraged some students to develop their digital reading skills.
Explanation matters: An experimental study on explainable AI

Abstract Explainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations.
Fighting Corruption in Developing Countries to Meet the Challenge of Human Capital Development: Evidence from Sub-Saharan African Countries

This paper provides both theoretical and empirical evidence on the need to consider institutions in human capital development in Sub-Saharan African (SSA) countries. In a model of human capital accumulation with public expenditure where we include a corruption parameter, it is found that when a bureaucrat or any other agent takes away part of the amount allocated to education or health, he reduces the quantity and quality of public services. Moreover, an estimation with the generalized moment method in system on panel data of 35 countries covering the period 1996–2018, the results show on the one hand that corruption manages to reduce the output of education and the average duration of studies and life expectancy of citizens through its negative effect on public spending in the education and health sector. On the other hand, corruption has a direct and negative impact on the performance of education. Indeed, it creates a lack of motivation in learning and in plans to pursue higher education because the return is very low, as is access to health and education services. In addition, corruption also distorts the allocation of public expenditure in favor of military, transport, mining, energy, and fuel expenditure as opposed to health and education expenditure. The effectiveness of governments in combating corruption is fundamental to human capital accumulation in Sub-Saharan African countries.
SiamADT: Siamese Attention and Deformable Features Fusion Network for Visual Object Tracking

To date, existing Siamese-based trackers have achieved excellent performance. However, in some complex scenarios, using deep convolutional layers alone can not effectively capture powerful representative features. To solve this problem, we propose a Siamese Attention and Deformable features fusion network for visual object Tracking (SiamADT). The proposed SiamADT consists of three modules: a Siamese attention network module for attention feature extraction, a deformable features fusion module, and a classification-regression module for bounding box prediction. Our framework uses ResNet-50 as the backbone for anchor-free tracking. Without tricky anchor hyperparameters tuning and manual intervention, SiamADT is more flexible and versatile. We conduct extensive experiments on four challenging benchmark datasets. The results demonstrate that SiamADT achieves competitive performance among state-of-the-art methods, with real-time speed—30 frames per second.
Blockchain based resource governance for decentralized web environments

Decentralization initiatives such as Solid and ActivityPub aim to give data owners more control over their data and to level the playing field by enabling small companies and individuals to gain access to data, thus stimulating innovation. However, these initiatives typically employ access control mechanisms that cannot verify compliance with usage conditions after access has been granted to others. In this paper, we extend the state of the art by proposing a resource governance conceptual framework, entitled ReGov, that facilitates usage control in decentralized web environments. We subsequently demonstrate how our framework can be instantiated by combining blockchain and trusted execution environments. Through blockchain technologies, we record policies expressing the usage conditions associated with resources and monitor their compliance. Our instantiation employs trusted execution environments to enforce said policies, inside data consumers' devices.} We evaluate the framework instantiation through a detailed analysis of requirements derived from a data market motivating scenario, as well as an assessment of the security, privacy, and affordability aspects of our proposal.
OSGNN: Original graph and Subgraph aggregated Graph Neural Network

Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN.
Efficient short read mapping to a pangenome that is represented by a graph of ED strings

A pangenome represents many diverse genome sequences of the same species. In order to cope with small variations as well as structural variations, recent research focused on the development of graph-based models of pangenomes. Mapping is the process of finding the original location of a DNA read in a reference sequence, typically a genome. Using a pangenome instead of a (linear) reference genome can, e.g. reduce mapping bias, the tendency to incorrectly map sequences that differ from the reference genome. Mapping reads to a graph, however, is more complex and needs more resources than mapping to a reference genome. Reducing the complexity of the graph by encoding simple variations like SNPs in a simple way can accelerate read mapping and reduce the memory requirements at the same time.We introduce graphs based on elastic-degenerate strings (ED strings, EDS) and the linearized form of these EDS graphs as a new representation for pangenomes. In this representation, small variations are encoded directly in the sequence. Structural variations are encoded in a graph structure. This reduces the size of the representation in comparison to sequence graphs. In the linearized form, mapping techniques that are known from ordinary strings can be applied with appropriate adjustments. Since most variations are expressed directly in the sequence, the mapping process rarely has to take edges of the EDS graph into account. We developed a prototypical software tool GED-MAP that uses this representation together with a minimizer index to map short reads to the pangenome. Our experiments show that the new method works on a whole human genome scale, taking structural variants properly into account. The advantage of GED-MAP, compared with other pangenomic short read mappers, is that the new representation allows for a simple indexing method. This makes GED-MAP fast and memory efficient.Sources are available at: https://github.com/thomas-buechler-ulm/gedmap.
EEG signature of breaks in embodiment in VR

The brain mechanism of embodiment in a virtual body has grown a scientific interest recently, with a particular focus on providing optimal virtual reality (VR) experiences. Disruptions from an embodied state to a less- or non-embodied state, denominated Breaks in Embodiment (BiE), are however rarely studied despite their importance for designing interactions in VR. Here we use electroencephalography (EEG) to monitor the brain's reaction to a BiE, and investigate how this reaction depends on previous embodiment conditions. The experimental protocol consisted of two sequential steps; an induction step where participants were either embodied or non-embodied in an avatar, and a monitoring step where, in some cases, participants saw the avatar's hand move while their hand remained still. Our results show the occurrence of error-related potentials linked to observation of the BiE event in the monitoring step. Importantly, this EEG signature shows amplified potentials following the non-embodied condition, which is indicative of an accumulation of errors across steps. These results provide neurophysiological indications on how progressive disruptions impact the expectation of embodiment for a virtual body.
Active learning for ordinal classification on incomplete data

Existing active learning algorithms typically assume that the data provided are complete. Nonetheless, data with missing values are common in real-world applications, and active learning on incomplete data is less studied. This paper studies the problem of active learning for ordinal classification on incomplete data. Although cutting-edge imputation methods can be used to impute the missing values before commencing active learning, inaccurately imputed instances are unavoidable and may degrade the ordinal classifier’s performance once labeled. Therefore, the crucial question in this work is how to reduce the negative impact of imprecisely filled instances on active learning. First, to avoid selecting filled instances with high imputation imprecision, we propose penalizing the query selection with a novel imputation uncertainty measure that combines a feature-level imputation uncertainty and a knowledge-level imputation uncertainty. Second, to mitigate the adverse influence of potentially labeled imprecisely imputed instances, we suggest using a diversity-based uncertainty sampling strategy to select query instances in specified candidate instance regions. Extensive experiments on nine public ordinal classification datasets with varying value missing rates show that the proposed approach outperforms several baseline methods.
Person re-identification based on improved attention mechanism and global pooling method

Deep network has become a new favorite for person re-identification (Re-ID), whose research focus is how to effectively extract the discriminative feature representation for pedestrians. In the paper, we propose a novel Re-ID network named as improved ReIDNet (iReIDNet), which can effectively extract the local and global multi-granular feature representations of pedestrians by a well-designed spatial feature transform and coordinate attention (SFTCA) mechanism together with improved global pooling (IGP) method. SFTCA utilizes channel adaptability and spatial location to infer a 2D attention map and can help iReIDNet to focus on the salient information contained in pedestrian images. IGP makes iReIDNet capture more effectively the global information of the whole human body. Besides, to boost the recognition accuracy, we develop a weighted joint loss to guide the training of iReIDNet. Comprehensive experiments demonstrate the availability and superiority of iReIDNet over other Re-ID methods. The code is available at https://github.com/XuRuyu66/ iReIDNet.