Reviewerly/exHarmony huggingface dataset:
Effects of using augmented reality on students’ learning

This article presents the results of a systematic review of the literature regarding the effects of using Augmented Reality (AR) on the learning of science students (Natural Sciences and Biology). The PRISMA methodology was used. Ten articles were selected from indexed journals, in a period of time between 2010 and 2022, after applying the inclusion and exclusion criteria. The main inclusion criteria were: only articles with students from Basic and Secondary Education (equivalent to ISCED 2 and 3) and that report results in students’ learning. The main exclusion criteria were: duplicate articles and those that did not present a teaching strategy associated with the use of AR. The databases consulted were Scopus, ScienceDirect, Springer Link, EBSCO and Web of Science. Eight out of ten of the selected articles used an experimental methodology. The results indicate that AR had positive effects on students’ learning and motivation, as well as on other variables such as visuospatial skills and student involvement in tasks.
Gold endowment and unloading along pathway for giant gold mineralization: Insights from spatiotemporal variations of in-situ pyrite geochemistry and gold fineness from the Jiaodong gold deposits, north China Craton

Over 5000 tonnes of gold were accumulated in the Jiaodong Peninsula at the early Cretaceous, the sources and critical ore-forming processes of which are still controversial. Here, we conducted comprehensive in-situ textural, elemental and isotopic analyses on pyrite and gold grains from the different mineralization styles, hydrothermal stages and depths for three representative gold deposits (Linglong, Xiadian and Jiangjiayao) from the northwestern Jiaodong Peninsula, with the aim to exactly constrain the sources and ore-forming processes. Three groups of δ34Spyrite were identified in the studied deposits, of which the Group 1 shows stable and low δ34S (4.8–7.6‰) throughout the hydrothermal evolution while the Group 2 has elevated δ34S (6.0–12.8‰) coupled with increasing As and Au concentrations. The Group 3 shows moderate δ34S (5.2–9.8‰) accompanied by high Al and Si concentrations. The Group 1 suggests a low-δ34S primary auriferous fluid derived from a mantle-related source, whereas the Group 2 indicates the primary auriferous fluid leaching additional S, As and Au from the Precambrian metasedimentary rocks along pathway. The Group 3 is petrographically coupled with numerous silicate inclusions, indicating enhanced fluid-rock interaction between the auriferous fluids and the granitic wallrcoks. Gold fineness decreases from the early to the late hydrothermal stages, combined with increasing deposition of polymetallic sulfides, suggesting an increase of pH resulting in efficient Au deposition. Gold fineness and Bi concentrations in pyrite decrease with shallowing, suggesting that cooling affected Au deposition as well. Bismuth-Te-S minerals were identified in the deep depths, combined with the well correlated Bi and Ag, implying that Bi-Te complexes might play a role in transporting Au and Ag at high-temperature conditions. The above results corroborate that multiple sources and ore-forming processes were responsible for the giant gold mineralization, of which the gold endowment from Precambrian wallrocks needs to be further concerned.
Generating Explainable Product Comparisons for Online Shopping

An essential part of making shopping purchase decisions is to compare and contrast products based on key differentiating features, but doing this manually can be overwhelming. Prior methods offer limited product comparison capabilities, e.g., via pre-defined common attributes that may be difficult to understand, or irrelevant to a particular product or user. Automatically generating an informative, natural-sounding, and factually consistent comparative text for multiple product and attribute types is a challenging research problem. We describe HCPC (Human Centered Product Comparison), to tackle two kinds of comparisons for online shopping: (i) product-specific, to describe and compare products based on their key attributes; and (ii) attribute-specific comparisons, to compare similar products on a specific attribute. To ensure that comparison text is faithful to the input product data, we introduce a novel multi-decoder, multi-task generative language model. One decoder generates product comparison text, and a second one generates supportive, explanatory text in the form of product attribute names and values. The second task imitates a copy mechanism, improving the comparison generator, and its output is used to justify the factual accuracy of the generated comparison text, by training a factual consistency model to detect and correct errors in the generated comparative text. We release a new dataset (https://registry.opendata.aws/) of ~15K human generated sentences, comparing products on one or more attributes (the first such data we know of for product comparison). We demonstrate on this data that HCPC significantly outperforms strong baselines, by ~10% using automatic metrics, and ~5% using human evaluation.
An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages

Most click models focus on user behaviors towards a single list. However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block style instead of a single list, which requires different assumptions to model user behaviors more accurately. There exist click models for multi-block pages in desktop contexts, but they cannot be directly applied to mobile scenarios due to different interaction manners, result types and especially multi-block presentation styles. In particular, multi-block mobile pages can normally be decomposed into interleavings of basic vertical blocks and horizontal blocks, thus resulting in typically F-shape forms. To mitigate gaps between desktop and mobile contexts for multi-block pages, we conduct a user eye-tracking study, and identify users' sequential browsing, block skip and comparison patterns on F-shape pages. These findings lead to the design of a novel F-shape Click Model (FSCM), which serves as a general solution to multi-block mobile pages. Firstly, we construct a Directed Acyclic Graph (DAG) for each page, where each item is regarded as a vertex and each edge indicates the user's possible examination flow. Secondly, we propose DAG-structured GRUs and a comparison module to model users' sequential (sequential browsing, block skip) and non-sequential (comparison) behaviors respectively. Finally, we combine GRU states and comparison patterns to perform user click predictions. Experiments show that FSCM outperforms baseline models.
On the big data processing algorithms for finding frequent sequences

Abstract Sequential pattern mining algorithms extract trendy sequence appearances inside ordered transactional datasets such as market basket datasets. There is a lack of research employing big data processing techniques to locate frequent sequences on large‐scale datasets. Furthermore, there is a need for optimized sequential pattern mining algorithms that run on ordered one‐dimensional sequences. We also observe a lack of sequential pattern search studies in the literature, where the focus is centered around multi‐dimensional data sequences. Existing approaches that deal with ordered one‐dimensional datasets suffer from scalability issues as the amount of data to be analyzed is enormous. This research investigates the big data processing techniques used to find frequent sequences in large‐scale datasets. It also proposes a scalable sequence pattern mining algorithm called Sequential Pattern Acquisition by Reducing Search Space (SPARSS) designed for distributed data processing systems that efficiently handle large datasets containing sequential one‐element data. It introduces a prototype implementation of SPARSS and provides information on the SPARSS's memory and time requirements, which were calculated as part of experimental studies on a real‐world dataset. The results confirm our expectations and demonstrate SPARSS's superior scalability and run‐time efficiency compared to other distributed algorithms.
Self-Supervised Group Graph Collaborative Filtering for Group Recommendation

Nowadays, it is more and more convenient for people to participate in group activities. Therefore, providing some recommendations to groups of individuals is indispensable. Group recommendation is the task of suggesting items or events for a group of users in social networks or online communities. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, which has few or no historical directly interacted items. Existing group recommendation methods mostly adopt attention-based preference aggregation strategies to capture group preferences. However, these models either ignore the complex high-order interactions between groups, users and items or greatly reduce the efficiency by introducing complex data structures. Moreover, occasional group recommendation suffers from the problem of data sparsity due to the lack of historical group-item interactions. In this work, we focus on addressing the aforementioned challenges and propose a novel group recommendation model called Self-Supervised Group Graph Collaborative Filtering (SGGCF). The goal of the model is capturing the high-order interactions between users, items and groups and alleviating the data sparsity issue in an efficient way. First, we explicitly model the complex relationships as a unified user-centered heterogeneous graph and devise a base group recommendation model. Second, we explore self-supervised learning on the graph with two kinds of contrastive learning module to capture the implicit relations between groups and items. At last, we treat the proposed contrastive learning loss as supplementary and apply a multi-task strategy to jointly train the BPR loss and the proposed contrastive learning loss. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed model in comparison to the state-of-the-art baselines.
A Turning Point for Cyber Insurance

Technologists who understand and measure cyber risk can motivate policyholders to improve security.
Digital filtering of ghost signal in phase measuring deflectometry

We introduce a method of geometric screen modification to remove ghost reflections commonly observed in deflectometry optical testing. The proposed method modifies the optical layout and illumination source area to bypass the generation of reflected rays from the undesired surface. The layout flexibility of deflectometry allows us to design specific system layouts that avoid the generation of interrupting secondary rays. The proposed method is supported by optical raytrace simulations, and experimental results are demonstrated with convex and concave lens case studies. Finally, the limitations of the digital masking method are discussed.
Complaints Concerning Electoral Fraud and Administrative Inaccuracies: A Study of Complaints About the Parliamentary Elections in Sweden Between 2010 and 2018

In this study we examine complaints concerning electoral fraud and administrative inaccuracies in an old, stable democracy, namely Sweden. We include complaints about the Swedish parliamentary elections between 2010 and 2018. The aim is to examine why voters complain about elections with a mix of qualitative and quantitative data. The results indicate that elections in Sweden are broadly well run and that Sweden has a robust election system. However, the analysis of the complaints show that many of them relate to the handling of advance votes, and that several human errors (administrative inaccuracies) occurred in handling advance votes. The mishandling of advance votes can increase the risk of reducing citizens' confidence in the election process and, by extension, it can pose a risk of magnifying the perceived decline in the legitimacy of the democratic system.
Distant supervision for relation extraction with hierarchical attention-based networks

Distant supervision employs external knowledge bases to automatically label corpora. The labeled sentences in a corpus are usually packaged and trained for relation extraction using a multi-instance learning paradigm. The automated distant supervision inevitably introduces label noises. Previous studies that used sentence-level attention mechanisms to de-noise neither considered correlation among sentences in a bag nor correlation among bags. As a result, a large amount of effective supervision information is lost, which will affect the performance of learned relation extraction models. Moreover, these methods ignore the lack of feature information in the few-sentence bags (especially the one-sentence bags). To address these issues, this paper proposes hierarchical attention-based networks that can de-noise at both sentence and bag levels. In the calculation of bag representation, we provide weights to sentence representations using sentence-level attention that considers correlations among sentences in each bag. Then, we employ bag-level attention to combine the similar bags by considering their correlations, which can enhance the feature of target bags with poor feature information, and to provide properer weights in the calculation of bag group representation. Both sentence-level attention and bag-level attention can make full use of supervised information to improve model performance. The proposed method was compared with nine state-of-the-art methods on the New York Times datasets and Google IISc Distant Supervision dataset, respectively, whose experimental results show its conspicuous advantages in relation extraction tasks.
Cross-Attention Based Multi-Resolution Feature Fusion Model for Self-Supervised Cervical OCT Image Classification

Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive results in natural image analysis, into the classification task of cervical OCT images. Our work aims to develop a computer-aided diagnosis (CADx) approach based on a self-supervised ViT-based model to classify cervical OCT images effectively. We leverage masked autoencoders (MAE) to perform self-supervised pre-training on cervical OCT images, so the proposed classification model has a better transfer learning ability. In the fine-tuning process, the ViT-based classification model extracts multi-scale features from OCT images of different resolutions and fuses them with the cross-attention module. The ten-fold cross-validation results on an OCT image dataset from a multi-center clinical study of 733 patients in China indicate that our model achieved an AUC value of 0.9963 ± 0.0069 with a 95.89 ± 3.30% sensitivity and 98.23 ± 1.36 % specificity, outperforming some state-of-the-art classification models based on Transformers and convolutional neural networks (CNNs) in the binary classification task of detecting high-risk cervical diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Furthermore, our model with the cross-shaped voting strategy achieved a sensitivity of 92.06% and specificity of 95.56% on an external validation dataset containing 288 three-dimensional (3D) OCT volumes from 118 Chinese patients in a different new hospital. This result met or exceeded the average of four medical experts who have used OCT for over one year. In addition to promising classification performance, our model has a remarkable ability to detect and visualize local lesions using the attention map of the standard ViT model, providing good interpretability for gynecologists to locate and diagnose possible cervical diseases.
HuaBaseChain: An Extensible Blockchain With High Performance

Blockchain has been extensively used on the Internet of Things (IoT). However, several problems exist in current blockchain systems that limit their usability in IoT networks. First, Proof-of-Work (PoW) consensus most used in current blockchain systems generates excessive energy consumption. Second, the single digital token system limits the development of blockchain in multiple IoT applications. Finally, the large amount of data generated by IoT devices and stored in blockchains has huge storage requirements. To deal with these problems, we propose HuaBaseChain—an extensible blockchain with high performance. HuaBaseChain has three features. First, HuaBaseChain uses a novel Proof-of-Participation (PoP) consensus to reduce energy consumption. Second, HuaBaseChain extends the single digital token model to a multidimensional digital token model, enabling more varied applications that can be tailored to the needs of IoT device managers. Third, HuaBaseChain devises an underlying Merkle forest data structure to reduce storage requirements. HuaBaseChain is suited for IoT applications that contain a variety of IoT devices and generate massive amounts of data. We demonstrate the efficiency of HuaBaseChain on IoT data sets and the significantly larger Bitcoin data set. Our experiments show that HuaBaseChain runs efficiently, with significantly reduced energy consumption, greater efficiency, lower storage requirements, and higher query speed.
Disentangled Negative Sampling for Collaborative Filtering

Negative sampling is essential for implicit collaborative filtering to generate negative samples from massive unlabeled data. Unlike existing strategies that consider items as a whole when selecting negative items, we argue that normally user interactions are mainly driven by some relevant, but not all, factors of items, leading to a new direction of negative sampling. In this paper, we introduce a novel disentangled negative sampling (DENS) method. We first disentangle the relevant and irrelevant factors of positive and negative items using a hierarchical gating module. Next, we design a factor-aware sampling strategy to identify the best negative samples by contrasting the relevant factors while keeping irrelevant factors similar. To ensure the credibility of the disentanglement, we propose to adopt contrastive learning and introduce four pairwise contrastive tasks, which enable to learn better disentangled representations of the relevant and irrelevant factors and remove the dependency on ground truth. Extensive experiments on five real-world datasets demonstrate the superiority of DENS against several state-of-the-art competitors, achieving over 7% improvement over the strongest baseline in terms of [email protected] and [email protected] Our code is publically available at https://github.com/Riwei-HEU/DENS .
Dependency-aware Self-training for Entity Alignment

Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominate current EA research but still suffer from their reliance on labelled mappings. To solve this problem, a few works have explored boosting the training of EA models with self-training, which adds confidently predicted mappings into the training data iteratively. Though the effectiveness of self-training can be glimpsed in some specific settings, we still have very limited knowledge about it. One reason is the existing works concentrate on devising EA models and only treat self-training as an auxiliary tool. To fill this knowledge gap, we change the perspective to self-training to shed light on it. In addition, the existing self-training strategies have limited impact because they introduce either much False Positive noise or a low quantity of True Positive pseudo mappings. To improve self-training for EA, we propose exploiting the dependencies between entities, a particularity of EA, to suppress the noise without hurting the recall of True Positive mappings. Through extensive experiments, we show that the introduction of dependency makes the self-training strategy for EA reach a new level. The value of self-training in alleviating the reliance on annotation is actually much higher than what has been realised. Furthermore, we suggest future study on smart data annotation to break the ceiling of EA performance.
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark

Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel ([email protected] of 36.6% on Few-Shot). We then establish a strong baseline that scores a [email protected] of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task. Datasets and codes are released at https://github.com/HITsz-TMG/Hansel.
Generalizing Graph Neural Network across Graphs and Time

Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. Meanwhile, following a message passing framework, graphneural network (GNN) is an inductive and powerful graph representation tool. We further explore inductive GNN from more specific perspectives: (1) generalizing GNN across graphs, in which we tackle with the problem of semi-supervised node classification across graphs; (2) generalizing GNN across time, in which we mainly solve the problem of temporal link prediction; (3) generalizing GNN across tasks; (4) generalizing GNN across locations.