Reviewerly/exHarmony huggingface dataset:
Analysis of Meta-Cosmos Empowering Basic English Teaching and Learning

Metaverse is a virtual, interactive digital environment that provides students with a new learning scenario through technologies such as virtual reality, augmented reality and artificial intelligence. In order to provide a more attractive and effective English learning environment, researchers have begun to introduce meta-universe technology into English language teaching. In meta-universe-enabled basic English teaching, students can interact with the virtual world through immersive learning experiences that simulate real-life scenarios and improve their language skills. Metaverse technology can also provide students with personalized learning paths and resources. In addition, social interaction in the metaverse environment promotes the development of students' intercultural communication and cooperation skills. However, metaverse-enabled basic English language teaching also faces some challenges, such as the cost of technological equipment, teacher training and technical support, etc. Future research needs to further explore how to maximize the effect of metaverse technology in English language teaching.
Developing and Testing Audio Data Processing Modules in Python to Connect to and Data Be Scored by ASS Cloud Server

Automatic Speech Scoring (ASS) system developed on a basis of automatic speech recognition (ASR) technology is a powerful computer-assistant tool for oral test scoring. However, due to the limits of high equipment costs and high-tech operating costs of a local ASS, ASS cloud services have become the first choice of most oral English teachers and learners. The purpose of this paper is to develop and test modules in Python to preprocess the audio data, connect to the cloud server, and convert JSON data format into common Excel form. 1056 pieces of audio data were collected from test-takers’ read-aloud task of CEST-4 (College English Speaking Test band 4)) and six variables (i.e., “pronunciation”, “fluency”, “integrity”, “speed”, “duration”, and “overall”) were defined. After analyzing the data of the test results, it is found that the oral test score is mostly affected by the “pronunciation” and “integrity”, and the accuracy of pronunciation is the strongest predictor of oral performance. The modules and functions are helpful for teachers and students to use in daily oral test/practice, and these modules can also be employed in other second language oral test scored by ASS cloud sever, like oral Chinese test. Our results can provide reference and guidance for future oral research and teaching.
A Cross-Modal Hash Retrieval Method with Fused Triples

Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved. However, the existing cross-modal hash retrieval methods still need to effectively utilize the dataset’s supervisory information and the lack of similarity expression ability. This means that the label information needs to be maximized, and the potential semantic relationship between two modalities cannot be fully explored, thus affecting the judgment of semantic similarity between two modalities. To address these problems, this paper proposes Tri-CMH, a cross-modal hash retrieval method with fused triples, which is an end-to-end modeling framework consisting of two parts: feature extraction and hash learning. Firstly, the multi-modal data are preprocessing into the form of triple groups. The data supervision matrix is constructed so that the samples with labels and their meanings are aggregated together. In contrast, the samples with labels and their opposite meanings are separated, thus avoiding the problem of the under-utilization of supervisory information in the data set and achieving the effect of efficiently utilizing the global supervisory information. Meanwhile, the loss function of the hash learning part is optimized by considering the Hamming distance loss, single-modality internal loss, cross-modality loss, and quantization loss to explicitly constrain semantically similar hash codes and semantically dissimilar hash codes and to improve the model’s ability to judge cross-modality semantic similarity. The method is trained and tested on the IAPR-TC12, MIRFLICKR-25K, and NUS-WIDE datasets, and the experimental evaluation criteria are mAP and PR curve, and the experimental results show the effectiveness and practicality of the method.
Deep attention sampling hashing for efficient image retrieval

Hashing has received broad attention in large-scale image retrieval due to its appealing efficiency in computation and storage. Particularly, with the drawn of deep learning, much efforts have been directed towards using deep neural networks to learn feature representations and hash codes simultaneously, and the developed deep hashing methods have shown superior performance over conventional hashing methods. In this paper, we propose Deep Attention Sampling Hashing (DASH), a novel deep hashing method that yields high-quality hash codes to enable efficient image retrieval. Specifically, we employ two sub-networks in DASH, i.e., a master branch and a part branch, to capture global structure features and discriminative feature representations, respectively. Furthermore, we develop an Attention Sampler Module (ASM), which consists of an Object Region Extraction (ORE) block and an Informative Patch Generation (IPG) block, to yield richer informative image patches. The ORE block provides a well-designed multi-scale attentional fusion mechanism to highlight and extract the significant regions of images, and the IPG block employs a direction-specific shift mechanism to generate desired image patches with discriminative details. Both blocks could be seamlessly integrated into various convolutional neural network (CNN) architectures. Subsequently, we conduct knowledge distillation optimization to transfer the details learned by the part branch into the master branch to guide hash code learning. In addition, we design a Weibull quantization loss to minimize the information loss caused by binary quantization. The experimental results on three benchmark datasets demonstrate the effectiveness of the proposed DASH with respect to different evaluation metrics.
Proof of retrievability with flexible designated verification for cloud storage

With numerous applications in cloud storage, Proof of Retrievability (PoR) is an efficient solution for verification and retrieval of big data on a remote server. Existing PoR schemes can support two mutually exclusive verification modes, namely either private verifiability or public verifiability, depending on whether the outsourced data is verifiable by the data owner (user) only or by everyone. In order to allow the user to flexibly designate the capability of verification, we propose a novel framework for PoR schemes that enables fine-grained control of remote data verification for cloud storage. Our idea is enabling the user to dynamically release some tokens (verification keys) to one or more third parties (designated verifiers), such that only the user and the designated verifiers can verify and retrieve the outsourced data from the server. We formalize this notion as PoR with Flexible Designated Verification (PoR-FDV), and define an extended adversarial model for PoR-FDV based on Juel et al.'s PoR model. Under the new framework, we propose a generic, simple and elegant construction of PoR-FDV from public verifiable PoR and identity-based KEM/DEM, which then gives birth to a concrete and efficient PoR-FDV scheme. Compared to existing PoR schemes, PoR-FDV provides higher flexibility and scalability, as well as many useful features.
Sweet Gradient matters: Designing consistent and efficient estimator for Zero-shot Architecture Search

Zero-shot Neural Architecture Search has garnered attention due to its training-free nature and rapid search speed. However, existing zero-shot estimators commonly suffer from low consistency, which hampers their practicality. In this work, we theoretically analyze that network generalization and convergence are highly correlated with Sweet Gradient of Parameter, i.e., the number of parameters whose gradient absolute values are within a certain interval. Empirical results indicate that Sweet Gradient of Parameter brings a higher consistency than the overall number of parameters. Additionally, we demonstrate a positive correlation between the network depth and the proportion of parameters with sweet gradients in each layer. Based on the analysis, we propose a training-free method to find the Sweet Gradient interval and obtain an estimator, named Sweetimator. Furthermore, Sweet Gradient can be an effective and general approach to promote the consistency of zero-shot estimators. Experiments show that Sweetimator and Sweet-enhanced estimators have significant consistency improvement in multiple benchmarks. Our method achieves state-of-the-art performance with 256x speedup in NAS-Bench-201 and maintains high competitiveness in DARTS, MobileNet, and Transformer search spaces. The source code is available at https://github.com/xingxing-123/SweetGradient.
Boosting domain generalization by domain-aware knowledge distillation

Deep neural networks often suffer performance degradation when the testing data distribution differs significantly from the training data distribution. To address this problem, most domain generalization (DG) approaches focus on learning domain-invariant features from multiple source domains. However, enforcing model invariance among different domains can also result in information that is not shared across training domains being discarded, even though it may still be relevant to unseen domains. We argue that a generalized model should be discriminative towards comprehensive features across different domains. To avoid unexpected information loss, we propose a two-stage learning scheme, named Domain-aware Knowledge Distillation (DAKD). In the first stage, we pre-train a parameter-efficient multi-expert model, where each expert is trained to be responsible for a specific source domain and preserve the domain-specific features. In the second stage, following the traditional knowledge distillation formulation, we train a new student network assisted by the pre-trained experts, where the prediction of the student for a certain domain is regularized by the soft output of the corresponding expert. A comprehensive ablation study and analysis highlight that our distilled model can preserve more source-domain-specific features, and obtain higher accuracy on unseen domains compared to its counterpart without distillation. We discuss the relationship between the generalization risk bound theory and our method. The upper bound is reduced by mitigating source-target domain discrepancy and reducing risk on source domains. Experiments demonstrate that our method provides state-of-the-art performance on PACS and other more challenging datasets such as Office-Home and DomainNet. The source code is available at https://github.com/ZZQ321/DAKD.git
On learning distribution alignment for video-based visible-infrared person re-identification

This paper studies the matching problem of cross-modality video data from a discrete distribution alignment view. Central to this discussion is the visible-infrared person re-identification (VI-reID), a crucial feature that bolsters surveillance systems’ efficacy in monitoring individuals across diverse lighting conditions. Going beyond traditional image-to-image matching paradigms, a recent study shows that temporal information can bring richer cues to encode the pedestrian representation, improving the representation power of deep neural networks. However, this integration further complicates cross-modality data matching due to the joint processing of spatial and temporal information. This paper formulates the video data as a discrete distribution and aligns the cross-modality video representation by reducing the matching cost between the two distributions. To this end, a natural idea for aligning the videos is to reduce the divergence of distributions. Moreover, the powerful optimal transport (OT) scheme, which generates the optimal matching flows and establishes the relevance of two distributions, is also employed as a way to measure the distance of distributions. Nevertheless, we observe that endowing the OT in the advanced VI-reID feature extractor leads to a non-symmetric measurement. To mitigate this, the paper introduces a new metric, namely symmetric optimal transport (SOT), reformulating OT into a symmetric form. Thorough analyses and empirical studies affirm the superiority of the proposed SOT, which significantly outperforms the current state-of-the-art methods according to standard benchmarking evaluations.
Vehicle re-identification based on grouping aggregation attention and cross-part interaction

Vehicle re-identification (Re-ID) plays an important role in intelligent transportation systems. To solve the key problem of small inter-class difference, we propose a vehicle Re-ID network based on grouping aggregation attention and cross-part interaction (GACP), which uses a channel context local interaction attention block (CCLI) and a cross-part interaction module (CPIM) to extract global and local discriminative features, respectively. The CCLI constructs the context information of each channel and performs local interaction to realize the communication of local and long-range information, thus effectively infer the weights of channels. In addition, the CCLI further reduces the number of parameters and improves the attention effect through a grouping aggregation module and an attention enhancement constraint. The CPIM enhances the correlation between vehicle parts obtained by rigid segmentation on feature maps to mine robust local information. Extensive experiments on two popular datasets VeRi776 and VehicleID demonstrate the effectiveness of the proposed method.
ER-LAC: Span-Based Joint Entity and Relation Extraction Model with Multi-Level Lexical and Attention on Context Features

In recent years, joint entity–relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and relation classification. This framework addresses the issues of overlap and error propagation that were present in previous entity–relation extraction models. However, span-based models overlook the influence of lexical information on the semantic representation of the span and fail to consider relations with a strong intrinsic connection between span pairs. To tackle these aforementioned issues, we present a new ERE model called ER-LAC (Span-based Joint Entity and Relation Extraction Model with Multi-level Lexical and Attention on Context Features). This model is designed with multi-granularity lexical features to enhance the semantic representation of spans, and a transformer classifier is employed to capture the internal connections between span pairs, thereby improving the performance of relational classification. To demonstrate the effectiveness of the proposed model, ablation experiments were conducted on the CoNLL04 dataset. The proposed model was also compared with other models on three datasets, showcasing its computational efficiency. The results indicate that the introduced lexical features and classifier enhance the F1 score for entity extraction by 0.84% to 2.04% and improve the F1 score for relationship classification by 0.96% to 2.26% when compared to the previous state-of-the-art (SOTA) model and the baseline SpERT model, respectively.
Pretty Good Strategies for Benaloh Challenge

Abstract Benaloh challenge allows the voter to audit the encryption of her vote, and in particular to check whether the vote has been represented correctly. An interesting analysis of the mechanism has been presented by Culnane and Teague. The authors propose a natural game-theoretic model of the interaction between the voter and a corrupt, malicious encryption device. Then, they claim that there is no “natural” rational strategy for the voter to play the game. In consequence, the authorities cannot provide the voter with a sensible auditing strategy, which undermines the whole idea. Here, we claim the contrary, i.e., that there exist simple rational strategies that justify the usefulness of Benaloh challenge.
Factors related to user perceptions of artificial intelligence (AI)-based content moderation on social media

Artificial intelligence (AI)-based moderation systems have been increasingly used by social media companies to identify and remove inappropriate user-generated content (e.g., misinformation) on their platforms. Previous research on AI moderation has primarily focused on situational and technological factors in predicting users' perceptions of it, while little is known about the role of individual characteristics. To bridge this gap, this study examined whether and how familiarity, political ideology, and algorithm acceptance are related to perceptions of AI moderation. By analyzing survey data from a nationally representative panel in the United States (N = 4562), we found that individuals who were more familiar with AI moderation expressed less favorable perceptions of it. Those who identified themselves as liberals were more likely to view AI moderation positively than those who identified themselves as conservatives. The higher the algorithm acceptance, the more favorable the perception. Moreover, trust in AI moderation significantly mediated the relationship between these three individual characteristics (familiarity, political ideology, and algorithm acceptance) and perceptions. The findings enrich the current understanding of user responses to AI moderation and provide practical implications for policymakers and designers.
South African Life Sciences teachers’ pedagogical beliefs and their influence on information communication and technology integration

Evidence suggests that learners actively construct knowledge and improve digital literacy when Information Communication and Technologies (ICTs) are used effectively. However, ICTs have not been exploited adequately in a science classroom. Early research argues that teachers’ pedagogical beliefs are potential barriers to delaying effective ICT integration in teaching and learning. In South Africa, research is silent regarding investigating Life Sciences teachers’ pedagogical beliefs translating into ICT integration. Hence, this study intended to close the knowledge gap and add to the body of knowledge. This study adopted a qualitative design to investigate three South African Life Sciences teachers pedagogical beliefs in Gauteng province. Qualitative data were collected from three Life Sciences teachers through lesson observations and semi-structured interviews. Teachers were observed teaching grade 11 Life Sciences classes. The video recordings were analyzed using the Teaching Dimension Observation Protocol (TDOP) to confirm findings from the questionnaire and establish how teachers integrate technology into teaching. Lastly, interviews were conducted to triangulate findings from lesson observations and questionnaires. The teachers integrated technology in ways that supported traditional beliefs. The results indicate the complexity of the relationship between pedagogical beliefs and ICT integration in practice. Teachers reflected on challenges in ICT integration that possibly impacted on their practices of ICT integration. They referred to a lack of learner access to smart devices and issues surrounding WiFi connectivity. In addition, teachers suggested that they needed training on innovative practices in ICT integration.
Application of Analytic Hierarchy Process in Prospecting-Take Zijin Mountain in Fujian as an Example

Since the development of RS and GIS technology, its application in ore prospecting has attracted much attention. There are many previous studies with remarkable achievements. In this paper, the Zijinshan mining area in Fujian Province is selected as the research object, and the Landsat 8 image is used as the basic data to carry out structural interpretation and remote sensing alteration information extraction, and then the metallogenic target area is divided by means of GIS and analytic hierarchy process, which provides reference for further prospecting.
Improving Collaborative Filtering Recommendations with Tag and Time Integration in Virtual Online Communities

In recent years, virtual online communities have experienced rapid growth. These communities enable individuals to share and manage images or websites by employing tags. A collaborative tagging system (CTS) facilitates the process by which internet users collectively organize resources. CTS offers a plethora of useful information, including tags and timestamps, which can be utilized for recommendations. A tag represents an implicit evaluation of the user’s preference for a particular resource, while timestamps indicate changes in the user’s interests over time. As the amount of information increases, it is feasible to integrate more detailed data, such as tags and timestamps, to improve the quality of personalized recommendations. The current study employs collaborative filtering (CF), which incorporates both tag and time information to enhance recommendation precision. A computational recommender system is established to generate weights and calculate similarities by incorporating tag data and time. The effectiveness of our recommendation model was evaluated by linearly merging tag and time data. In addition, the proposed CF method was validated by applying it to big data sets in the real world. To assess its performance, the size of the neighborhood was adjusted in accordance with the standard CF procedure. The experimental results indicate that our proposed method significantly improves the quality of recommendations compared to the basic CF approach.
Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs

Determining when instructor intervention is needed, based on learners’ comments and their urgency in massive open online course (MOOC) environments, is a known challenge. To solve this challenge, prior art used autonomous machine learning (ML) models. These models are described as having a "black-box" nature, and their output is incomprehensible to humans. This paper shows how to apply eXplainable Artificial Intelligence (XAI) techniques to interpret a MOOC intervention model for urgent comments detection. As comments were selected from the MOOC course and annotated using human experts, we additionally study the confidence between annotators (annotator agreement confidence), versus an estimate of the class score of making a decision via ML, to support intervention decision. Serendipitously, we show, for the first time, that XAI can be further used to support annotators creating high-quality, gold standard datasets for urgent intervention.
Holistic Graph-Based Document Representation and Management for Open Science

(Extended Abstract) While most previous research focused only on the textual content of documents, advanced support for document management in Digital Libraries, for Open Science, requires handling all aspects of a document: from structure, to content, to context. These different but inter-related aspects cannot be handled separately, and were traditionally ignored in Digital Libraries. We propose a graph-based unifying representation and handling model based on the definition of an ontology that integrates all the different perspectives and drives the document description in order to boost the effectiveness of document management. We also show how even simple algorithms can profitably use our proposed approach to return relevant and personalized outcomes in different document management tasks.