Reviewerly/exHarmony huggingface dataset:
Digital Education Technology and Innovation Ability: Reforming the Teaching Model for Postgraduates in Physical Education

With the fast-paced development of digital education technology, the transformation and upgrading of the teaching model for physical education major has ushered in an opportunity.The original classroom teaching mode was based on the real world, resulting in problems such as single teaching process, insufficient teaching resources and inability to update the teaching environment in time.On this basis, digital education technology and physical education classroom teaching were integrated in this paper, and then the connotation and characteristics of digital education technology were analyzed; this paper constructed a new model of digital education technology empowering the teaching of physical education major from four aspects: classroom teaching process, teaching subjects, teaching resources and teaching environment; it also put forward current practical pathway to enhance students' innovation ability by means of digital education technology.
MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems

When judging the quality of a computational system for a pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in the multi-label paradigm, they are being widely adopted to diagnostics and digital therapeutics. Metrics are usually borrowed from machine learning literature, and the current consensus is to report results on a diverse set of metrics. It is infeasible to compare efficacy of computational systems which have been evaluated on different sets of metrics. From a diagnostic utility standpoint, the current metrics themselves are far from perfect, often biased by prevalence of negative samples or other statistical factors and importantly, they are designed to evaluate general purpose machine learning tasks. In this paper we outline the various parameters that are important in constructing a clinical metric aligned with diagnostic practice, and demonstrate their incompatibility with existing metrics. We propose a new metric, MedTric that takes into account several factors that are of clinical importance. MedTric is built from the ground up keeping in mind the unique context of computational diagnostics and the principle of risk minimization, penalizing missed diagnosis more harshly than over-diagnosis. MedTric is a unified metric for medical or pathological screening system evaluation. We compare this metric against other widely used metrics and demonstrate how our system outperforms them in key areas of medical relevance.
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning

Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information.In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics.Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations.This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries.Code and human evaluation results will be publicly available at https://github.com/EthanC111/factuality_summarization.
COFFEE: A Contrastive Oracle-Free Framework for Event Extraction

Event extraction is a complex task that involves extracting events from unstructured text.Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios.In this study, we consider a more realistic task setting, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given, without any oracle information including event type, event ontology, or trigger word.To address this task, we propose a new framework, COF-FEE.This framework extracts events solely based on the document context, without referring to any oracle information.In particular, COFFEE introduces a contrastive selection model to refine the generated triggers and handle multi-event instances.Our proposed COF-FEE outperforms state-of-the-art approaches in the oracle-free setting of the event extraction task, as evaluated on two public variants of the ACE05 benchmark.The code used in our study has been made publicly available 1 .
Hand Gesture Recognition System for Recognizing of Indonesian Sign Language Number Using Attentional Convolutional Network

Hand gestures are a form of communication widely used in everyday life. In addition, hand gestures are one of the interactions that can carry out in a non-contact manner that a computer can understand by recognizing the meaning of the movement from the image. However, hand gesture recognition cannot acknowledge directly by a computer; the computer requires artificial intelligence to recognize existing objects. For these problems, this research conducts a hand gesture recognition system for Numbers Sign of Indonesian Sign Language (SIBI) recognition using the Attentional Convolutional Network (ACN) method. Based on the results of the tests, the system can recognize number gestures indicated by a prediction accuracy value of 87.5% for non-real-time testing and accuracy value of 71.25% for real-time testing.
Fibrosis detection and quantification in whole slide images through deep learning

We present a new AI-based method for the quantification of liver fibrosis in tissue sections stained with Picro Sirius Red which highlights collagen. The method segments and quantifies collagen, a marker of the fibrotic response, through a deep learning model trained on 20 whole-slide images. The results show a Dice score > 90% compared to manual annotations, demonstrating its potential aid during diagnosis. Furthermore, our approach can be extended to other staining protocols.
Optimization and Evaluation of Spoken English CAF Based on Artificial Intelligence and Corpus

English is the most widely used language in the world, and the pronunciation of its spoken language is equally important.The traditional methods are not high in complexity, accuracy and fluency (CAF) for spoken English recognition.Therefore, it is very important to use AI and corpus to optimize and evaluate spoken English CAF.This paper aims to study the optimization and evaluation of spoken English CAF using AI and corpus, and proposes to use the Hidden Markov (HMM) model and convolutional neural network (CNN) model in the field of AI to optimize and evaluate spoken English CAF.By selecting a variety of English voices from the BNC corpus for model training and testing, and selecting the complexity, accuracy, fluency and harmonic average of the CNN model recognition as evaluation indicators, the HMM model's recognition spectrogram is added up and analyzed.In the experimental test, it was found that when the number of frames is 210, the indicators of the CNN model have been greatly improved, so the number of frames selected for the test in this paper is 210.The results show that the A value obtained by the HMM model test is about 85%, the CNN model is 67%, and the traditional SVM model is only 35%.The HMM model is tested with a C value of about 60%, the CNN model is 65%, and the traditional model is only 45%.The F-value obtained from the test of the HMM model is about 83%, the CNN model is 67%, and the traditional model is 46%.In contrast, the HMM model has higher recognition accuracy for spoken English, and the recognition results are more fluent.However, the CNN model can recognize spoken English with higher complexity, and both the CNN model and the HMM model can improve the CAF optimization effect of spoken English.
Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models

Event sequences are widely available across application domains and there is a long history of models for representing and analyzing such datasets. Summary Markov models are a recent addition to the literature that help identify the subset of event types that influence event types of interest to a user. In this paper, we introduce logical summary Markov models, which are a family of models for event sequences that enable interpretable predictions through logical rules that relate historical predicates to the probability of observing an event type at any arbitrary position in the sequence. We illustrate their connection to prior parametric summary Markov models as well as probabilistic logic programs, and propose new models from this family along with efficient greedy search algorithms for learning them from data. The proposed models outperform relevant baselines on most datasets in an empirical investigation on a probabilistic prediction task. We also compare the number of influencers that various logical summary Markov models learn on real-world datasets, and conduct a brief exploratory qualitative study to gauge the promise of such symbolic models around guiding large language models for predicting societal events.
Abstraction of Nondeterministic Situation Calculus Action Theories

We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language. We assume that we have both an abstract and a concrete nondeterministic basic action theory, and a refinement mapping which specifies how abstract actions, decomposed into agent actions and environment reactions, are implemented by concrete ConGolog programs. This new setting supports strategic reasoning and strategy synthesis, by allowing us to quantify separately on agent actions and environment reactions. We show that if the agent has a (strong FOND) plan/strategy to achieve a goal/complete a task at the abstract level, and it can always execute the nondeterministic abstract actions to completion at the concrete level, then there exist a refinement of it that is a (strong FOND) plan/strategy to achieve the refinement of the goal/task at the concrete level.
FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks

Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require more training data, which is often scarce in real-world applications due to privacy regulations (e.g., GDPR). Federated graph learning (FGL) enables multiple clients to train a GNN collaboratively without sharing their local data. However, existing FGL methods mainly focus on homogeneous GNNs or knowledge graph embeddings; few have considered heterogeneous graphs and HGNNs. In federated heterogeneous graph learning, clients may have private graph schemas. Conventional FL/FGL methods attempting to define a global HGNN model would violate schema privacy. To address these challenges, we propose FedHGN, a novel and general FGL framework for HGNNs. FedHGN adopts schema-weight decoupling to enable schema-agnostic knowledge sharing and employs coefficients alignment to stabilize the training process and improve HGNN performance. With better privacy preservation, FedHGN consistently outperforms local training and conventional FL methods on three widely adopted heterogeneous graph datasets with varying client numbers. The code is available at https://github.com/cynricfu/FedHGN.
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention

Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that their induced attentions are less robust and generalizable against noisy graphs due to lack of direct supervision. In this paper, we present a new framework which utilizes the tool of causality to provide a powerful supervision signal for the learning process of attention functions. Specifically, we estimate the direct causal effect of attention to the final prediction, and then maximize such effect to guide attention attending to more meaningful neighbors. Our method can serve as a plug-and-play module for any canonical attention-based GNNs in an end-to-end fashion. Extensive experiments on a wide range of benchmark datasets illustrated that, by directly supervising attention functions, the model is able to converge faster with a clearer decision boundary, and thus yields better performances.
A Novel Learnable Interpolation Approach for Scale-Arbitrary Image Super-Resolution

Deep convolutional neural networks (CNNs) have achieved unprecedented success in single image super-resolution over the past few years. Meanwhile, there is an increasing demand for single image super-resolution with arbitrary scale factors in real-world scenarios. Many approaches adopt scale-specific multi-path learning to cope with multi-scale super-resolution with a single network. However, these methods require a large number of parameters. To achieve a better balance between the reconstruction quality and parameter amounts, we proposes a learnable interpolation method that leverages the advantages of neural networks and interpolation methods to tackle the scale-arbitrary super-resolution task. The scale factor is treated as a function parameter for generating the kernel weights for the learnable interpolation. We demonstrate that the learnable interpolation builds a bridge between neural networks and traditional interpolation methods. Experiments show that the proposed learnable interpolation requires much fewer parameters and outperforms state-of-the-art super-resolution methods.
Capturing the Long-Distance Dependency in the Control Flow Graph via Structural-Guided Attention for Bug Localization

To alleviate the burden of software maintenance, bug localization, which aims to automatically locate the buggy source files based on the bug report, has drawn significant attention in the software mining community. Recent studies indicate that the program structure in source code carries more semantics reflecting the program behavior, which is beneficial for bug localization. Benefiting from the rich structural information in the Control Flow Graph (CFG), CFG-based bug localization methods have achieved the state-of-the-art performance. Existing CFG-based methods extract the semantic feature from the CFG via the graph neural network. However, the step-wise feature propagation in the graph neural network suffers from the problem of information loss when the propagation distance is long, while the long-distance dependency is rather common in the CFG. In this paper, we argue that the long-distance dependency is crucial for feature extraction from the CFG, and propose a novel bug localization model named sgAttention. In sgAttention, a particularly designed structural-guided attention is employed to globally capture the information in the CFG, where features of irrelevant nodes are masked for each node to facilitate better feature extraction from the CFG. Experimental results on four widely-used open-source software projects indicate that sgAttention averagely improves the state-of-the-art bug localization methods by 32.9\% and 29.2\% and the state-of-the-art pre-trained models by 5.8\% and 4.9\% in terms of MAP and MRR, respectively.
The Parameterized Complexity of Finding Concise Local Explanations

We consider the computational problem of finding a smallest local explanation (anchor) for classifying a given feature vector (example) by a black-box model. After showing that the problem is NP-hard in general, we study various natural restrictions of the problem in terms of problem parameters to see whether these restrictions make the problem fixed-parameter tractable or not. We draw a detailed and systematic complexity landscape for combinations of parameters, including the size of the anchor, the size of the anchor's coverage, and parameters that capture structural aspects of the problem instance, including rank-width, twin-width, and maximum difference.
Can I Really Do That? Verification of Meta-Operators via Stackelberg Planning

Macro-operators are a common reformulation method in planning that adds high-level operators corresponding to a fixed sequence of primitive operators. We introduce meta-operators, which allow using different sequences of actions in each state. We show how to automatically verify whether a meta-operator is valid, i.e., the represented behavior is always doable. This can be checked at once for all instantiations of the meta-operator and all reachable states via a compilation into Stackelberg planning, a form of adversarial planning. Our results show that meta-operators learned for multiple domains can often express useful high-level behaviors very compactly, improving planners' performance.
IMF: Integrating Matched Features Using Attentive Logit in Knowledge Distillation

Knowledge distillation (KD) is an effective method for transferring the knowledge of a teacher model to a student model, that aims to improve the latter's performance efficiently. Although generic knowledge distillation methods such as softmax representation distillation and intermediate feature matching have demonstrated improvements with various tasks, only marginal improvements are shown in student networks due to their limited model capacity. In this work, to address the student model's limitation, we propose a novel flexible KD framework, Integrating Matched Features using Attentive Logit in Knowledge Distillation (IMF). Our approach introduces an intermediate feature distiller (IFD) to improve the overall performance of the student model by directly distilling the teacher's knowledge into branches of student models. The generated output of IFD, which is trained by the teacher model, is effectively combined by attentive logit. We use only a few blocks of the student and the trained IFD during inference, requiring an equal or less number of parameters. Through extensive experiments, we demonstrate that IMF consistently outperforms other state-of-the-art methods with a large margin over the various datasets in different tasks without extra computation.
G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training. But in real-life, models are of ten applied on data with new classes, which can lead to massive misclassification and thus significantly degrade performance. Hence, developing open-set classification methods is crucial to determine if a given sample belongs to a known class. Existing methods for open-set node classification generally use transductive learning with part or all of the features of real unseen class nodes to help with open-set classification. In this paper, we propose a novel generative open-set node classification method, i.e., G2Pxy, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and external unknown proxies are generated via mixup to efficiently anticipate the distribution of novel classes. Using the generated proxies, a closed-set classifier can be transformed into an open-set one, by augmenting it with an extra proxy classifier. Under the constraints of both cross entropy loss and complement entropy loss, G2Pxy achieves superior effectiveness for unknown class detection and known class classification, which is validated by experiments on bench mark graph datasets. Moreover, G2Pxy does not have specific requirement on the GNN architecture and shows good generalizations.
TITAN : Task-oriented Dialogues with Mixed-Initiative Interactions

In multi-domain task-oriented dialogue systems, users proactively propose a series of domain-specific requests that can often be under-or over-specified, sometimes with ambiguous and cross-domain demands. System-sided initiative would be necessary to identify certain situations and appropriately interact with users to resolve them. However, most existing task-oriented dialogue systems fail to consider such mixed-initiative interaction strategies, performing low efficiency and poor collaboration ability in human-computer conversation. In this paper, we construct a multi-domain task-oriented dialogue dataset with mixed-initiative strategies named TITAN from the large-scale dialogue corpus MultiWOZ 2.1. It contains a total of 1,800 human-human conversations where the system can either ask clarification questions actively or provides relevant information to address failure situations and implicit user requests. We report the results of several baseline models on system response generation and dialogue act prediction to assess the performance of SOTA methods on TITAN. These models can capture mixed-initiative dialogue acts, while remaining the deficiency to actively generate implicit requests and accurately provide alternative information, suggesting ample room for improvement in future studies.
Meaning of “Taboo Association” in Bugis Community of Tanah Pilih Village, Banyuasin, South Sumatra

This study examines the importance of taboo meaning in the Bugis community in Tanah Pilih Village, Banyuasin Regency, South Sumatra. The purpose of this study is to describe the importance of the meaning of taboo associations in the Bugis community of Tanah Pilih Village, Banyuasin Regency. The method used in this study is a qualitative method, ethnographic approach. Data was collected from six informants. Data collection techniques used include interviews, observation, recording, and documentation. Data analysis techniques include data reduction, data presentation, and drawing conclusions. The subjects of this study were individuals who had a deep understanding of taboos in the village of Tanah Pilih. The results of the study revealed fifteen taboo association meanings. Of the six informants, five members of the spark plug community still believed in and adhered to the taboos, while one informant did not believe in the taboos. Taboos are meant to be associated with the goal of educating children, ensuring that their behavior and attitudes remain appropriate, and promoting the ethical values ​​of respect and decency. Tabu is the first choice of Bugis parents to proactively combat the negative effects of globalization on their children.
ROADMAP FOR THE DEVELOPMENT OF SUSTAINABLE STEAM ENVIRONMENTAT MATH SCHOOL – VARNA

The aim of this article is to analyse existing STEM practices at High School of Mathematics-Varn and international practices in the area and current technological and social trends and to try to synthesize a roadmap for sustainable development of STEAM practices. The roadmap given examines milestones, indicators of success and assessment of progress, current status and perspective for the development of sustainable STEAM infrastructure, available and future partnerships with institutions, organizations and associations. It is extremely important to achieve a sustainable STEAM environment to pay attention to the qualifications of teachers and their motivation to participate in STEAM activities.