Reviewerly/exHarmony huggingface dataset:
An Explainable Feature Selection Approach for Fair Machine Learning

As machine learning (ML) algorithms are extensively adopted in various fields to make decisions of importance to human beings and our society, the fairness issue in algorithm decision-making has been widely studied. To mitigate unfairness in ML, many techniques have been proposed, including pre-processing, in-processing, and post-processing approaches. In this work, we propose an explainable feature selection (ExFS) method to improve the fairness of ML by recursively eliminating features that contribute to unfairness based on the feature attribution explanations of the model’s predictions. To validate the effectiveness of our proposed ExFS method, we compare our approach with other fairness-aware feature selection methods on several commonly used datasets. The experimental results show that ExFS can effectively improve fairness by recursively dropping some features that contribute to unfairness. The ExFS method generally outperforms the compared filter-based feature selection methods in terms of fairness and achieves comparable results to the compared wrapper-based feature selection methods. In addition, our method can provide explanations for the rationale underlying this fairness-aware feature selection mechanism.
Delineation of Prostate Boundary from Medical Images via a Mathematical Formula-Based Hybrid Algorithm

The precise extraction of the contour of prostate on transrectal ultrasound (TRUS) is crucial for the diagnosis and treatment of prostate tumor. Due to the relatively low signal-to-noise ratio (SNR) of TRUS images and the potential of imaging artifacts, accurate contouring of the prostate from TRUS images has been a challenging task. This paper proposes four strategies to achieve higher precision of segmentation on TRUS images. Firstly, a modified principal curve-based algorithm is used to obtain the data sequence, with a small amount of prior point information adopted for coarse initialization. Secondly, an evolution neural network is devised to find an optimal network. Thirdly, a fractional-order-based network is trained with the data sequence as input, resulting in a decreased model error and increased precision. Finally, the parameters of a fractional-order-based neural network were utilized to construct an interpretable and smooth mathematical equation of the organ border. The Dice similarity coefficient (DSC), Jaccard similarity coefficient (OMG), and accuracy (ACC) of model outputs against ground-truths were 95.9 ± 2.3%, 94.9 ± 2.4%, and 95.3 ± 2.2%, respectively. The results of our method outperform several popular state-of-the-art segmentation methods.
Efficient Reinforcement Learning Using State-Action Uncertainty with Multiple Heads

In reinforcement learning, an agent learns optimal actions for achieving a task by maximizing rewards in an environment. During learning, the agent decides its action for exploration or exploitation at each time. In exploration, the agent searches for a new experience defined by the state, action, reward, and next state. In exploitation, on the other hand, the agent tries to maximize the rewards based on the experiences. Exploration-exploitation trade-off is an important issue in reinforcement learning. In previous work, this trade-off is achieved based on how uncertain what the agent already learns about the environment. While only simple criteria for this uncertainty are explored in the literature, this paper evaluates more uncertainty criteria for efficient reinforcement learning. Our novel uncertainty criteria uses agent’s multiple decisions at the same time. In addition, we also propose to employ the advantage of the multiple decisions for bridging the gap between exploration and exploitation by our novel exploitation mode. Extensive experiments verify the effectiveness of our approaches for efficient learning. We also show the learning efficiencies of different learning strategies in order to know which strategy is better for each task.
An Efficient Approach for Improving the Recall of Rough Abstract Retrieval in Scientific Claim Verification

Scientific claim verification can help the researchers easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Because there are a huge amount of papers in the corpus, most of the existing scientific claim verification solutions are always in a two-stage manner that first roughly detects a set of candidate related papers by some naïve but fast methods such as some similarity measures, and then utilizes the large but relatively slow deep neural models for accurate classification. To improve the recall of the overall system by improving the recall of the rough abstract retrieval stage, we propose an approach that also utilizes the neural classification model for the rough retrieval stage. To improve the scalability of the proposal, we propose a distillation-based method to obtain a lightweight model for the rough retrieval stage. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.
2RDA: Representation and Relation Distillation with Data Augmentation

Large pre-trained language models have demonstrated superior performance in natural language processing tasks. However, the massive number of parameters and slow inference speed make it challenging to deploy them on resource-constrained devices. Existing knowledge distillation methods use the point-to-point approach to transfer the knowledge, which restrains the ability to learn the higher-level semantic knowledge from the teacher network. In this paper, we propose Representation and Relation Distillation with Data Augmentation(2RDA), a novel knowledge distillation framework. Unlike previous methods, 2RDA introduces an improved contrastive distillation loss function for data augmentation to solve the problem that data augmentation during the fine-tuning of downstream tasks may lead to the misclassification of positive and negative sample pairs for contrastive learning. Additionally, we guide the student model to obtain structural knowledge by distilling the relational knowledge between samples from a mini-batch through distance loss. 2RDA achieves excellent results and surpasses the state-of-the-art model compression methods on the GLUE benchmark, demonstrating the effectiveness of our approach.
Cross-Domain Transformer with Adaptive Thresholding for Domain Adaptive Semantic Segmentation

The goal of unsupervised domain adaptive semantic segmentation (UDA-SS) is to learn a model using annotated data from the source domain and generate accurate dense predictions for the unlabeled target domain. UDA methods based on Transformer utilize self-attention mechanism to learn features within source and target domains. However, in the presence of significant distribution shift between the two domains, the noisy pseudo-labels could hinder the model’s adaptation to the target domain. In this work, we proposed to incorporate self-attention and cross-domain attention to learn domain-invariant features. Specifically, we design a weight-sharing multi-branch cross-domain Transformer, where the cross-domain branch is used to align domains at the feature level with the aid of cross-domain attention. Moreover, we introduce an adaptive thresholding strategy for pseudo-label selection, which dynamically adjusts the proportion of pseudo-labels that are used in training based on the model’s adaptation status. Our approach guarantees the reliability of the pseudo labels while allowing more target domain samples to contribute to model training. Extensive experiments show that our proposed method consistently outperforms the baseline and achieves competitive results on GTA5 $$\rightarrow $$ Cityscapes, Synthia $$\rightarrow $$ Cityscapes, and Cityscapes $$\rightarrow $$ ACDC benchmark.
Understanding and evaluating harms of AI-generated image captions in political images

The use of AI-generated image captions has been increasing. Scholars of disability studies have long studied accessibility and AI issues concerning technology bias, focusing on image captions and tags. However, less attention has been paid to the individuals and social groups depicted in images and captioned using AI. Further research is needed to understand the underlying representational harms that could affect these social groups. This paper investigates the potential representational harms to social groups depicted in images. There is a high risk of harming certain social groups, either by stereotypical descriptions or erasing their identities from the caption, which could affect the understandings, beliefs, and attitudes that people hold about these specific groups. For the purpose of this article, 1,000 images with human-annotated captions were collected from news agencies “politics” sections. Microsoft's Azure Cloud Services was used to generate AI-generated captions with the December 2021 public version. The pattern observed from the politically salient images gathered and their captions highlight the tendency of the model used to generate more generic descriptions, which may potentially harm misrepresented social groups. Consequently, a balance between those harms needs to be struck, which is intertwined with the trade-off between generating generic vs. specific descriptions. The decision to generate generic descriptions, being extra cautious not to use stereotypes, erases and demeans excluded and already underrepresented social groups, while the decision to generate specific descriptions stereotypes social groups as well as reifies them. The appropriate trade-off is, therefore, crucial, especially when examining politically salient images.
Examining the Effect of Narrative Features and Thematic Music in an Audio-Based Exergame

Audio-based exergames are beneficial in that they allow users to exercise in an eyes-free and hands-free environment. In this study, we explored two audio-based exergame elements, narrative features and thematic music, that can impact exercise amount (step count and duration) and exercise enjoyment. We conducted a two-week long between-subjects study with 43 young adults and 43 middle-aged adults using SPORTIFY, an audio-based exergame. Our experimental results showed that (1) Using narrative features had a significant main effect on exercise amount and exercise enjoyment both for young adults and middle-aged adults. (2) Using thematic music had no significant main impact on exercise amount and exercise enjoyment both for young adults and middle-aged adults. (3) A significant interaction effect for exercise amount was observed in middle-aged adults, whereas a significant interaction effect for exercise enjoyment was observed in young adults.
Mining players’ experience in computer games: Immersion affects flow but not presence

The aim of this study was to investigate whether different levels of immersion while playing a computer game affect the participant's experiences of flow and presence. Understanding how different levels of immersion influence the experiences of flow and presence can shed light on the intricate interplay between these constructs and provide valuable insights into the factors that contribute to engaging and immersive gameplay. The independent variable, immersion, was manipulated in three conditions (high, moderate, and low) in a between-subject design within the video game Minecraft. Participants were asked to complete 15 minutes of gameplay and then fill out the questionnaires concerning flow and presence. The experiment was conducted remotely on a video-sharing platform. Bayesian analysis revealed an effect of immersion level on flow, while no evidence of an effect was found for the experience of presence. This study provides evidence in favor of a relation between flow and immersion while supporting a presumed double dissociation of immersion from presence. Future research using a Bayesian approach is encouraged to build further knowledge on this research topic.
Current Trends in the Study of Emotional Burnout Phenomenon

&lt;p style="text-align: justify;"&gt;The article presents a review of current trends in empirical research of the phenomenon of emotional burnout over the last 5 years, which were on display in the ScienceDirect catalogue. As a result of the analysis three topical directions of burnout research were identified. The first one is connected with a role of emotional intelligence in a burnout occurrence. It was established that a person's understanding of his/her own and other people's emotions and managing them can help to overcome and prevent emotional burnout. The second direction investigates the role of emotional work in a burnout. It was found that a person's ability to direct his/her emotions and to use different strategies of their expression can help to reduce burnout, and "psychological flexibility" provides an opportunity to prevent the onset of emotional burnout. The third direction is related to a person's social identity. The link between a person's social identity and a reduction of burnout is demonstrated, the role of psychological connection to the group and social identity strengthening in the reduction of emotional burnout is shown. The growing interest in the role and place of a person's identity in burnout is especially emphasized in the review. The article presents perspectives for further research of a burnout phenomenon.&lt;/p&gt;
Development and Training: The Deployment of Web-based Vaccination Queue system at Military Resort Command 052/Wijayakrama

Implementing the COVID-19 vaccination queue system by the 052/Wijayakrama Military Resort Command in Tangerang is a form of community service carried out by Multimedia Nusantara University (UMN). This system was created to simplify the vaccination process and reduce the waiting time for vaccination participants. This website-based queuing system is designed with the Laravel framework using the rapid application development model and functionality testing using black box testing. The results of functionality testing of this queuing system indicate that the system can function properly and meet the needs to simplify the vaccination process. More than 1,000 people have used this queuing system, making it easy for the public to get the COVID-19 vaccination. Applying the COVID-19 vaccination queue system by the 052/Wijayakrama Military Resort Command in Tangerang is an example of a technology application to support government programs tackling the COVID-19 pandemic. This system has made it easy for people to get vaccinated and has helped the government to organize and manage the vaccination process better.
Hierarchical Diachronic Embedding of Knowledge Graph Combined with Fragmentary Information Filtering

Knowledge graph(KG) embedding is often used in link prediction, triplet classification, and knowledge graph completion(KGC), which is critical to knowledge relation extraction and recommendation algorithm design. The previous works in KG embedding have made excellent achievements in KGC, but they ignore fragment information and the hierarchy of temporal information in KG. The hierarchy of temporal information indicates that different dimensions of information such as year, month, and day have different degrees of influence on the fact information. Fragmentary information is usually weakly or uncorrelated with the main information, reducing KG embedding learning efficiency. To solve these problems, we propose two methods: Firstly, we propose word frequency filtering to filter fragment information. Secondly, we propose hierarchical diachronic embedding for temporal KG, which provides the characteristics of an entity at different levels of time. We add the consideration of fragmentation filtering and hierarchical temporal embedding to the static KG embedding model SimplE, which results in a novelty model for KGC–HDF-ASimplE(Hierarchical Diachronic Embedding of Knowledge Graph with Fragmentary Information Filtering for Average SimplE). Experimental results manifest that the HDF-ASimplE model is superior to the previous works on KGC, and the evaluation parameters of KGC are increased by 14% on average, Hit@10 to 81.7%. It proves the effectiveness of our method.
Supervised Attention Using Homophily in Graph Neural Networks

Graph neural networks have become the standard approach for dealing with learning problems on graphs. Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great success to different tasks. In the GAT model, each node assigns an importance score to its neighbors using an attention mechanism. However, similar to other graph neural networks, GATs aggregate messages from nodes that belong to different classes, and therefore produce node representations that are not well separated with respect to the different classes, which might hurt their performance. In this work, to alleviate this problem, we propose a new technique that can be incorporated into any graph attention model to encourage higher attention scores between nodes that share the same class label. We evaluate the proposed method on several node classification datasets demonstrating increased performance over standard baseline models.
LTNI-FGML: Federated Graph Machine Learning on Long-Tailed and Non-IID Data via Logit Calibration

Federated Graph Machine Learning (FGML) is an emerging field that combines graph neural networks and federated learning to learn from distributed graph-structured data while preserving the privacy of data owners. Dealing with non-IID data is a major challenge in the field of FGML, and existing methods have been proposed to address the negative impawct of non-IID data, but they only consider the case where the class distribution of client nodes is balanced. However, in many real-world scenarios, the class distribution of client nodes is long-tailed, which can lead to significant bias in the model. To address this issue, this paper proposes a novel framework called LTNI-FGML, which addresses the joint problem of non-IID and long-tailed data in FGML. The framework comprises two main stages. In the first stage, we introduce GraphSage combined with the Mixup model on the client side to utilize the graph structure information and achieve feature anonymization. In the second stage, we propose a hybrid mechanism that incorporates a layer-wise attention mechanism and logit adjustment to address the challenges of long-tailed and non-IID data. Finally, we validate our proposed approach on several benchmark datasets to demonstrate its effectiveness in scenarios of long-tailed and non-IID data distribution.
Multi-Granularity Contrastive Learning for Graph with Hierarchical Pooling

Graph contrastive learning is an unsupervised learning method for graph data. It aims to learn useful representations by maximizing the similarity between similar instances and minimizing the similarity between dissimilar instances. Despite the success of the existing GCL methods, they generally overlook the hierarchical structures of graphs. This structure is inherent in graph data and can facilitate the organization and management of graphs, such as social networks. Therefore, the representation results learned from previous methods often lack important hierarchical information in the graph, resulting in suboptimal performance for downstream tasks. In this paper, we propose a $$\textbf{M}$$ ulti- $$\textbf{G}$$ ranularity $$\textbf{G}$$ raph $$\textbf{C}$$ ontrastive $$\textbf{L}$$ earning ( $$\textbf{MG2CL}$$ ) framework that considers the hierarchical structures of graphs in contrastive learning. This method enables effective learning of better graph representations by combining view information at different resolutions. In addition, we add a cross-granularity contrast module to further improve the accuracy of representations. Extensive experiments are conducted on seven graph classification datasets to demonstrate the effectiveness of MG2CL in learning unsupervised graph representations.
Sequence-Based Modeling for Temporal Knowledge Graph Link Prediction

Currently, the majority of research in temporal knowledge graph link prediction focuses on completing missing facts. Nevertheless, the utilization of knowledge graphs to forecast future facts has garnered significant scholarly attention. The attainment of efficient future fact prediction for time-series data hinges primarily on an in-depth exploration of both past historical facts and concurrent facts in the present. Presently, the majority of research in this domain lacks an all-encompassing integration of temporal points and durations in factual features, thereby hindering the effective management of two distinct types of facts with varying chronologies and ultimately disregarding their latent influence on future facts. This paper introduces an advanced representation model - the Progressive Representation Graph Attention Network (PRGAN) - which harnesses the potential of Graph Convolutional Neural Network and Recurrent Neural Network. PRGAN aims to ameliorate the existing shortcomings and augment the efficacy of future event prediction through attention-based learning of progressive representations of entities and relations in time series. We evaluated our proposed method with five event datasets. Extensive experimentation revealed that, in comparison with other baseline models, the PRGAN model displayed remarkable performance and efficiency in temporal reasoning tasks, thereby demonstrating its outstanding superiority.
LogE-Net: Logic Evolution Network for Temporal Knowledge Graph Forecasting

In recent years, research on predicting future events using temporal knowledge graphs by leveraging their rich structural and historical information has just begun. Due to their interesting application potential, they have gained more attention from researchers. Existing methods mainly focus on using evolution representation learning to aggregate and propagate recent graph structural information or integrate historical memory to infer the future. However, simply considering the historical repeated facts during reasoning is insufficient. Therefore, we propose a novel model called LogE-Net (Logic Evolution network) that simultaneously considers short-term facts and logical rules. Additionally, we propose a mutual-information control mechanism based on contrastive learning to control the freedom of temporal evolution. Finally, experiments on two public datasets demonstrate that our model consistently outperforms the state-of-the-art baseline on entity prediction and relation prediction tasks.
An Untrained Neural Model for Fast and Accurate Graph Classification

Recent works have proven the feasibility of fast and accurate time series classification methods based on randomized convolutional kernels [5, 32]. Concerning graph-structured data, the majority of randomized graph neural networks are based on the Echo State Network paradigm in which single layers or the whole network present some form of recurrence [7, 8]. This paper aims to explore a simple form of a randomized graph neural network inspired by the success of randomized convolutions in the 1-dimensional domain. Our idea is pretty simple: implement a no-frills convolutional graph neural network and leave its weights untrained. Then, we aggregate the node representations with global pooling operators, obtaining an untrained graph-level representation. Since there is no training involved, computing such representation is extremely fast. We then apply a fast linear classifier to the obtained representations. We opted for LS-SVM since it is among the fastest classifiers available. We show that such a simple approach can obtain competitive predictive performance while being extremely efficient both at training and inference time.