Reviewerly/exHarmony huggingface dataset:
Co-Evolving Graph Reasoning Network for Emotion-Cause Pair Extraction

Emotion-Cause Pair Extraction (ECPE) aims to extract all emotion clauses and their corresponding cause clauses from a document. Existing approaches tackle this task through multi-task learning (MTL) framework in which the two subtasks provide indicative clues for ECPE. However, the previous MTL framework considers only one round of multi-task reasoning and ignores the reverse feedbacks from ECPE to the subtasks. Besides, its multi-task reasoning only relies on semantics-level interactions, which cannot capture the explicit dependencies, and both the encoder sharing and multi-task hidden states concatenations can hardly capture the causalities. To solve these issues, we first put forward a new MTL framework based on Co-evolving Reasoning. It (1) models the bidirectional feedbacks between ECPE and its subtasks; (2) allows the three tasks to evolve together and prompt each other recurrently; (3) integrates prediction-level interactions to capture explicit dependencies. Then we propose a novel multi-task relational graph (MRG) to sufficiently exploit the causal relations. Finally, we propose a Co-evolving Graph Reasoning Network (CGR-Net) that implements our MTL framework and conducts Co-evolving Reasoning on MRG. Experimental results show that our model achieves new state-of-the-art performance, and further analysis confirms the advantages of our method.
A Passage Retrieval Transformer-Based Re-Ranking Model for Truthful Consumer Health Search

Searching for online information is nowadays a critical task in a scenario characterized by information overload and misinformation. To address these issues, it is necessary to provide users with both topically relevant and truthful information. Re-ranking is a strategy often used in Information Retrieval (IR) to consider multiple dimensions of relevance. However, re-rankers often analyze the full text of documents to obtain an overall relevance score at the re-ranking stage, which can lead to sub-optimal results. Some recent Transformer-based re-rankers actually consider text passages rather than the entire document, but focus only on topical relevance. Transformers are also being used in non-IR solutions to identify information truthfulness, but just to perform a binary classification task. Therefore, in this article, we propose an IR model based on re-ranking that focuses on suitably identified text passages from documents for retrieving both topically relevant and truthful information. This approach significantly reduces the noise introduced by query-unrelated content in long documents and allows us to evaluate the document’s truthfulness against it, enabling more effective retrieval. We tested the effectiveness of the proposed solution in the context of the Consumer Health Search task, considering publicly available datasets. Our results show that the proposed approach statistically outperforms full-text retrieval models in the context of multidimensional relevance, such as those based on aggregation, and monodimensional relevance Transformer-based re-rankers, such as BERT-based re-rankers.
Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection

Anomaly detection aims at detecting examples that do not conform to normal behavior. Increasingly, anomaly detection is being approached from a semi-supervised perspective where active learning is employed to acquire a small number of strategically selected labels. However, because anomalies are not always well-understood events, the user may be uncertain about how to label certain instances. Thus, one can relax this request and allow the user to provide soft labels (i.e., probabilistic labels) that represent their belief that a queried example is anomalous. These labels are naturally noisy due to the user's inherent uncertainty in the label and the fact that people are known to be bad at providing well-calibrated probability instances. To cope with these challenges, we propose to exploit a Gaussian Process to learn from actively acquired soft labels in the context of anomaly detection. This enables leveraging information about nearby examples to smooth out possible noise. Empirically, we compare our proposed approach to several baselines on 21 datasets and show that it outperforms them in the majority of experiments.
PEACE: Cross-Platform Hate Speech Detection - A Causality-Guided Framework

Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics. Due to the different policies of the platforms, different groups of people express hate in different ways. Furthermore, due to the lack of labeled data in some platforms it becomes challenging to build hate speech detection models. To this end, we revisit if we can learn a generalizable hate speech detection model for the cross platform setting, where we train the model on the data from one (source) platform and generalize the model across multiple (target) platforms. Existing generalization models rely on linguistic cues or auxiliary information, making them biased towards certain tags or certain kinds of words (e.g., abusive words) on the source platform and thus not applicable to the target platforms. Inspired by social and psychological theories, we endeavor to explore if there exist inherent causal cues that can be leveraged to learn generalizable representations for detecting hate speech across these distribution shifts. To this end, we propose a causality-guided framework, PEACE, that identifies and leverages two intrinsic causal cues omnipresent in hateful content: the overall sentiment and the aggression in the text. We conduct extensive experiments across multiple platforms (representing the distribution shift) showing if causal cues can help cross-platform generalization.
PDF-VQA: A New Dataset for Real-World VQA on PDF Documents

Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions. We proposed a new document-based VQA dataset, PDF-VQA, to comprehensively examine the document understanding from various aspects, including document element recognition, document layout structural understanding as well as contextual understanding and key information extraction. Our PDF-VQA dataset extends the current scale of document understanding that limits on the single document page to the new scale that asks questions over the full document of multiple pages. We also propose a new graph-based VQA model that explicitly integrates the spatial and hierarchically structural relationships between different document elements to boost the document structural understanding. The performances are compared with several baselines over different question types and tasks (The full dataset is released in https://github.com/adlnlp/pdfvqa ).
DegUIL: Degree-Aware Graph Neural Networks for Long-Tailed User Identity Linkage

User identity linkage (UIL), matching accounts of a person on different social networks, is a fundamental task in cross-network data mining. Recent works have achieved promising results by exploiting graph neural networks (GNNs) to capture network structure. However, they rarely analyze the realistic node-level bottlenecks that hinder UIL's performance. First, node degrees in a graph vary widely and are long-tailed. A significant fraction of tail nodes with small degrees are underrepresented due to limited structural information, degrading linkage performance seriously. The second bottleneck usually overlooked is super head nodes. It is commonly accepted that head nodes perform well. However, we find that some of them with super high degrees also have difficulty aligning counterparts, due to noise introduced by the randomness of following friends in real-world social graphs. In pursuit of learning ideal representations for these two groups of nodes, this paper proposes a degree-aware model named DegUIL to narrow the degree gap. To this end, our model complements missing neighborhoods for tail nodes and discards redundant structural information for super head nodes in embeddings respectively. Specifically, the neighboring bias is predicted and corrected locally by two modules, which are trained using the knowledge from structurally adequate head nodes. As a result, ideal neighborhoods are obtained for meaningful aggregation in GNNs. Extensive experiments demonstrate the superiority of our model. Our data and code can be found at https://github.com/Longmeix/DegUIL .
Citizen involvement in COVID-19 contact tracing with digital tools: a qualitative study to explore citizens’ perspectives and needs

Contact tracing (CT) is a key strategy when dealing with outbreaks of infectious diseases such as COVID-19. The scale of the COVID-19 pandemic has often left public health professionals (PHPs), who are responsible for the execution of CT, unable to keep up with the rapid and largescale spread of the virus. To enhance or support its execution, and potentially lower the workload for PHPs, citizens may be more actively involved in CT-tasks that are commonly executed by PHPs (referred to as 'self-led CT'). There is limited insight into citizens' perspectives on and needs for self-led CT for COVID-19. This study aims to explore the perspectives and needs of Dutch citizens on taking more responsibilities in the execution of CT for COVID-19, potentially through the use of digital tools.An exploratory qualitative study was performed, in which online semi-structured interviews were conducted. Questions were based on the Reasoned Action Approach and Health Belief Model. Interviews were audio-recorded and transcribed verbatim. A thematic analysis was conducted to identify citizens' perspectives and needs to participate in self-led CT.We conducted 27 interviews with Dutch citizens. Seven main themes were identified from the interviews: 1) 'Citizens' perspectives on self-led CT are influenced by prior experiences with regular CT', 2) 'Citizens' felt responsibilities and the perceived responsibilities of the PHS in CT shape their perspectives on self-led CT', 3) 'Anticipated impacts of self-led CT on the CT-process', 4) 'Citizens' attitude towards the application of self-led CT depends on their own perceived skills and the willingness and skills of others', 5) 'Shame and social stigma may hamper participation in self-led CT', 6) 'Concerns about privacy and data security: a barrier for self-led CT', and 7) 'Citizens' perspectives and anticipated needs for the implementation and application of self-led CT in practice'.Most interviewees hold a positive attitude towards self-led CT and using digital tools for this purpose. However, their intention for self-led CT may depend on various factors, such as prior experiences with regular CT, and their perceived self-efficacy to participate. Perspectives and needs of citizens should be considered for the future implementation of self-led CT in practice.
Human pose estimation and action recognition for fitness movements

Human pose estimation and action recognition have important applications in the areas of security, medicine and sports. In this work, we propose an improved YOLOv7-Pose algorithm to solve the problem of human posture and action recognition for fitness movements in various scenarios. For the algorithm based on YOLOv7-Pose, we add the function of classification to the original network. And we introduce Coordinate Attention to the network to improve the model's ability to identify important features of human skeletal joints and action classifications. The improved ConvNeXt network structure is introduced to replace the CBS convolution kernel of the original model to improve the accuracy of human key point detection and action classification of the model. We optimise the spatial pyramid pool structure, which can reduce the loss function and accelerate the convergence rate of the model. We adopt EIOU as the regression function of the target detection frame to improve the accuracy of the coordinate regression. Experiments show that the improved YOLOv7-Pose has an mAP of 95.9% on a homemade test set of fitness actions, which is 5.4% higher than HRNet, with a 4.2% improvement over the original YOLOv7 algorithm, suggesting that the accuracy of action recognition and key point estimation has significantly improved.
Visualizations for universal deep-feature representations: survey and taxonomy

Abstract In data science and content-based retrieval, we find many domain-specific techniques that employ a data processing pipeline with two fundamental steps. First, data entities are represented by some visualizations, while in the second step, the visualizations are used with a machine learning model to extract deep features. Deep convolutional neural networks (DCNN) became the standard and reliable choice. The purpose of using DCNN is either a specific classification task or just a deep feature representation of visual data for additional processing (e.g., similarity search). Whereas the deep feature extraction is a domain-agnostic step in the pipeline (inference of an arbitrary visual input), the visualization design itself is domain-dependent and ad hoc for every use case. In this paper, we survey and analyze many instances of data visualizations used with deep learning models (mostly DCNN) for domain-specific tasks. Based on the analysis, we synthesize a taxonomy that provides a systematic overview of visualization techniques suitable for usage with the models. The aim of the taxonomy is to enable the future generalization of the visualization design process to become completely domain-agnostic, leading to the automation of the entire feature extraction pipeline. As the ultimate goal, such an automated pipeline could lead to universal deep feature data representations for content-based retrieval.
Cognitive Assessment Based on Skeleton-Based Action Recognition: Ball-Drop Case

Human action recognition aims at extracting features on top of human skeletons and estimating human pose. It has received increasing attention in recent years. However, existing methods capture only the action information while in a real world application such as cognitive assessment, we need to measure the executive functioning that helps psychiatrists to identify some mental disease such as Alzheimer, Schizophrenia and ADHD. In this paper, we propose a skeleton-based action recognition named Mind-In-Action (MIA) for cognitive assessment. MIA integrates a pose estimator to extract the human body joints and then automatically measures the executive functioning employing the distance and elbow angle calculation. Three score functions were designed to measure the executive functioning: the accuracy score, the rhythm score and the functioning score. We evaluate our model on two different datasets and show that our approach significantly outperforms the existing methods.
A Closer Look at the Security Risks in the Rust Ecosystem

Rust is an emerging programming language designed for the development of systems software. To facilitate the reuse of Rust code, crates.io , as a central package registry of the Rust ecosystem, hosts thousands of third-party Rust packages. The openness of crates.io enables the growth of the Rust ecosystem but comes with security risks by severe security advisories. Although Rust guarantees a software program to be safe via programming language features and strict compile-time checking, the unsafe keyword in Rust allows developers to bypass compiler safety checks for certain regions of code. Prior studies empirically investigate the memory safety and concurrency bugs in the Rust ecosystem, as well as the usage of unsafe keywords in practice. Nonetheless, the literature lacks a systematic investigation of the security risks in the Rust ecosystem. In this article, we perform a comprehensive investigation into the security risks present in the Rust ecosystem, asking “what are the characteristics of the vulnerabilities, what are the characteristics of the vulnerable packages, and how are the vulnerabilities fixed in practice?”. To facilitate the study, we first compile a dataset of 433 vulnerabilities, 300 vulnerable code repositories, and 218 vulnerability fix commits in the Rust ecosystem, spanning over 7 years. With the dataset, we characterize the types, life spans, and evolution of the disclosed vulnerabilities. We then characterize the popularity, categorization, and vulnerability density of the vulnerable Rust packages, as well as their versions and code regions affected by the disclosed vulnerabilities. Finally, we characterize the complexity of vulnerability fixes and localities of corresponding code changes, and inspect how practitioners fix vulnerabilities in Rust packages with various localities. We find that memory safety and concurrency issues account for nearly two thirds of the vulnerabilities in the Rust ecosystem. It takes over 2 years for the vulnerabilities to become publicly disclosed, and one-third of the vulnerabilities have no fixes committed before their disclosure. In terms of vulnerability density, we observe a continuous upward trend at the package level over time, but a decreasing trend at the code level since August 2020. In the vulnerable Rust packages, the vulnerable code tends to be localized at the file level, and contains statistically significantly more unsafe functions and blocks than the rest of the code. More popular packages tend to have more vulnerabilities, while the less popular packages suffer from vulnerabilities for more versions. The vulnerability fix commits tend to be localized to a limited number of lines of code. Developers tend to address vulnerable safe functions by adding safe functions or lines to them, vulnerable unsafe blocks by removing them, and vulnerable unsafe functions by modifying unsafe trait implementations. Based on our findings, we discuss implications, provide recommendations for software practitioners, and outline directions for future research.
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams

Existing methods for explainable artificial intelligence (XAI), including popular feature importance measures such as SAGE, are mostly restricted to the batch learning scenario. However, machine learning is often applied in dynamic environments, where data arrives continuously and learning must be done in an online manner. Therefore, we propose iSAGE, a time- and memory-efficient incrementalization of SAGE, which is able to react to changes in the model as well as to drift in the data-generating process. We further provide efficient feature removal methods that break (interventional) and retain (observational) feature dependencies. Moreover, we formally analyze our explanation method to show that iSAGE adheres to similar theoretical properties as SAGE. Finally, we evaluate our approach in a thorough experimental analysis based on well-established data sets and data streams with concept drift.
Opportunities and Risks of the “Metaverse” for Environmental Sustainability

The Metaverse is a shared digital space that has the potential to revolutionize the development of the internet. It combines the physical and virtual worlds into one, allowing people to engage and explore the virtual environment together. Moreover, the use of Metaverse technology can aid in environmental preservation by helping us recognize the shortcomings of our current model and the impact it has on the environment. This study examines how the Metaverse can contribute to environmental protection and sustainability, as well as its potential negative impacts. Finally, it explores the role that the Metaverse is likely to play in the future.
Learning Locally Interpretable Rule Ensemble

This paper proposes a new framework for learning a rule ensemble model that is both accurate and interpretable. A rule ensemble is an interpretable model based on the linear combination of weighted rules. In practice, we often face the trade-off between the accuracy and interpretability of rule ensembles. That is, a rule ensemble needs to include a sufficiently large number of weighted rules to maintain its accuracy, which harms its interpretability for human users. To avoid this trade-off and learn an interpretable rule ensemble without degrading accuracy, we introduce a new concept of interpretability, named local interpretability, which is evaluated by the total number of rules necessary to express individual predictions made by the model, rather than to express the model itself. Then, we propose a regularizer that promotes local interpretability and develop an efficient algorithm for learning a rule ensemble with the proposed regularizer by coordinate descent with local search. Experimental results demonstrated that our method learns rule ensembles that can explain individual predictions with fewer rules than the existing methods, including RuleFit, while maintaining comparable accuracy.
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity

Interpretable machine learning has demonstrated impressive performance while preserving explainability. In particular, neural additive models (NAM) offer the interpretability to the black-box deep learning and achieve state-of-the-art accuracy among the large family of generalized additive models. In order to empower NAM with feature selection and improve the generalization, we propose the sparse neural additive models (SNAM) that employ the group sparsity regularization (e.g. Group LASSO), where each feature is learned by a sub-network whose trainable parameters are clustered as a group. We study the theoretical properties for SNAM with novel techniques to tackle the non-parametric truth, thus extending from classical sparse linear models such as the LASSO, which only works on the parametric truth. Specifically, we show that SNAM with subgradient and proximal gradient descents provably converges to zero training loss as $$t\rightarrow \infty $$ , and that the estimation error of SNAM vanishes asymptotically as $$n\rightarrow \infty $$ . We also prove that SNAM, similar to LASSO, can have exact support recovery, i.e. perfect feature selection, with appropriate regularization. Moreover, we show that the SNAM can generalize well and preserve the ‘identifiability’, recovering each feature’s effect. We validate our theories via extensive experiments and further testify to the good accuracy and efficiency of SNAM (Appendix can be found at https://arxiv.org/abs/2202.12482 .).
Multi-label Image Classification with Multi-scale Global-Local Semantic Graph Network

With the development of deep learning techniques, multi-label image classification tasks have achieved good performance. Recently, graph convolutional network has been proved to be an effective way to explore the labels dependencies. However, due to the complexity of label semantic relations, the static dependencies obtained by existing methods cannot consider the overall characteristics of an image and accurately locate the target region. Therefore, we propose the Multi-scale Global-local Semantic Graph Network (MGSGN) for multi-label image classification, which mainly includes three important parts. First, the multi-scale feature reconstruction aggregates complementary information at different levels in CNN through cross-layer attention, which can effectively identify target categories of different sizes. We then design a channel dual-branch cross-attention module to explore the correlation between global information and local features in multi-scale features, which using the way of adaptive cross-fusion to locate the target area more accurately. Moreover, we propose the multi-perspective weighted cosine measure in multi-perspective dynamic semantic representation module to construct content-based label dependencies for each image to dynamically construct a semantic relationship graph. Extensive experiments on the two public datasets have verified that the classification performance of our model is better than many state-of-the-art methods.