Reviewerly/exHarmony huggingface dataset:
Adaptive Heterogeneous Graph Contrastive Clustering with Multi-similarity

With the proliferation of interactive systems, heterogeneous graph clustering has become an important research topic in the field of unsupervised learning. However, the existing methods generally have one or more of the following problems: 1) they fail to fully mine the similarity between nodes in heterogeneous graphs; 2) they cannot effectively deal with heterogeneous graphs without node attribute information; 3) the predicted labels generated during model iterations are not used as guidance information for subsequent iterations. To address the above problems, we propose an Adaptive Heterogeneous graph Contrastive clustering with Multi-Similarity (AHCMS) model. The model adaptively learns a high-level representation containing specific semantic information through a feature extraction module and an attention mechanism. Secondly, the feature enhancement module is used to extract the consistency information between different meta-paths from two aspects of attribute information and topology structure, so as to encourage the adjacent nodes of different meta-paths to be as similar as possible and reduce the dependence on the attribute information. Moreover, the topological similarity contained in the semantic information is fully explored in the high-order proximity module, making the high-level representation more discriminative. In addition, AHCMS also introduces a self-supervised clustering mechanism to guide the high-level representation to become clustering task-oriented representations. Extensive experimental results on four heterogeneous datasets show that the model’s clustering performance consistently outperforms most baseline methods.
Fine-Grained Knowledge Enhancement for Empathetic Dialogue Generation

An engaging dialogue system is supposed to generate empathetic responses, which requires a cognitive understanding of users’ situations and an affective perception of their emotions. Most of the existing work only focuses on modeling the latter, while neglecting the importance of the former. Despite some efforts to enhance chatbots’ empathy in both cognition and affection, limited cognition conditions and inaccessible fine-grained information still impair the effectiveness of empathy modeling. To address this issue, we propose a novel fine-grained knowledge-enhanced empathetic dialogue generation model KEEM. We first explore strategies to filter fine-grained commonsense and emotional knowledge and leverage knowledge to construct cognitive and affective context graphs. And we learn corresponding context representations from the two knowledge-enhanced context graphs. Then we encode the raw dialogue context to learn the original cognitive and affective representations and fuse them with the knowledge-enhanced representations in cognition and affection. Finally, we feed the two fused representations into a decoder to produce empathetic replies. Extensive experiments conducted on the benchmark dataset EMPATHETICDIALOGUES verify the effectiveness of our model in comparison with several competitive models.
Multidimensional Adaptative kNN over Tracking Outliers (Makoto)

This paper presents an approach to detect outliers present in a data set, also called aberration. These outliers often cause problems to the learning algorithms by deviating their behavior, which makes them less efficient. It is therefore necessary to identify and remove them during the cleaning data step before the learning process. For this purpose, a method that detects if data is an outlier from its k nearest neighbors is proposed for multidimensional data sets. In order to make the method more accurate, the number of k nearest neighbors chosen is adaptive for each class present in the data set, and each neighbor has a different weight in the decision, depending on their respective proximity. The proposed method is called Makoto for Multidimensional Adaptative kNN Over Tracking Outliers. The effectiveness of this method is compared with four other known methods based on different principles: LOF (Local Outlier Factor), Isolation forest, One Class SVM and Inter Quartile Range (IQR). Thus, on the basis of 406 synthetic data sets and 17 real data sets with distinct characteristics, the Makoto method appears to be more efficient.
BBAC: Blockchain-Based Access Control Scheme for EHRs with Data Sharing Support

With the advancement of modern medicine, human whole genome sequencing technology has become more and more efficient and accurate. Genomic data are characterized by large data volume, privacy, and ease of being tampered with. Genomic data are usually stored in different data centers, and it is not easy to share the data. We propose a blockchain-based dual-verifiable cloud storage solution BBAC with the features of traceability and non-comparability of blockchain. First, we use homomorphic encryption technology to encrypt data and upload it to the cloud to ensure transmission security and data privacy protection. At the same time, the aggregated ciphertext and uploader information is stored on the blockchain to avoid the risk of data tampering by illegal users effectively and enable the traceability of malicious users, realizing double verification of data integrity in the cloud. Security analysis proves BBAC is more secure and reliable than similar schemes.
Joint Embedding of Local Structures and Evolutionary Patterns for Temporal Link Prediction

Link prediction tackles the prediction of missing facts in an incomplete knowledge graph (KG) and has been widely explored in reasoning and information retrieval. The vast majority of existing methods perform link prediction on static KGs, with the assumption that the relational facts are generally correct. However, some facts may not be universally valid, as they tend to evolve. Despite the prevalence of temporal knowledge graphs (TKGs) with evolving facts, the studies on such data for temporal link prediction are still far from resolved. In this paper, we propose SiepNet, a novel graph neural network for temporal link prediction, driven by local Structural Information and Evolutionary Patterns. Specifically, SiepNet captures the local structural information based on a relation-aware GNN architecture, and incorporates temporal attention to model long- and short-range historical dependencies hidden in TKGs. Moreover, SiepNet integrates local structures and evolutionary patterns to enhance the semantic representation of evolving facts in TKGs. The extensive experiments on five real-world TKG datasets demonstrate the effectiveness of our approach SiepNet in temporal link prediction, compared with the state-of-the-art methods.
Implicit Sentiment Extraction Using Structure Generation with Sentiment Instructor Prompt Template

Aspect-Category-Opinion-Sentiment quadruple extraction (ACOS) is the novel and challenging sentiment analysis task, which aims to analyze the full range of emotional causes. Existing approaches focus on solving explicit sentiment, but struggle with analyzing implicit sentiment reviews. In this paper, to address the issue, we propose SI-TS, a framework that takes implicit sentiment extraction into account. Specifically, we design target structure (TS) to capture implicit sentiment by converting sentiment elements into a structured format. Furthermore, to adaptively generate appropriate TS according to different sentiment scenarios, we design an prompt template based sentiment instructor(SI). It assists the framework in effectively extracting implicit sentiment elements from the reviews. Extensive experiments were conducted on two widely used ACOS benchmarks, and improvements in F1 values were observed. Specifically, we achieved a 1.05% and 1.28% improvement in F1 values for Laptop-ACOS and Restaurant-ACOS, respectively. Notably, significant results were achieved in extracting implicit sentiment.
Enhancing Knowledge-Aware Recommendation with Contrastive Learning

Knowledge graph serves as a side information, bringing diversity and interpretability to the recommendation. A well-developed recommender system can efficiently capture user and item characteristics, accurately reflecting user preferences. However, supervised signals with graph structure are extraordinarily sparse, and the collaborative and knowledge graphs contain irrelevant edges, exacerbating noise propagation and reducing the robustness of recommendations. To address the above issues, we propose a model for enhancing Knowledge-aware Recommendation with Contrastive Learning (KRCL), including two contrastive learning tasks and three functional modules. Specifically, we construct two views, using TransR and TATEC to optimize knowledge representations from distance and semantic aspects, respectively. After the item-side knowledge is augmented, we remove unreliable interaction edges from collaborative graph to reduce noise propagation. We then perform contrastive learning on the output node representations of different views through graph propagation. To further tap the latent interest of users, we consider users/items that exhibit similar representations as semantic neighbors, treating them as positive pairs in contrastive learning. The structural and semantic contrastive tasks are eventually integrated in a multi-task learning manner to jointly boost the recommendation performance. To validate the effectiveness of our method, we conduct extensive experiments on three benchmark datasets. Experimental results demonstrate that our KRCL significantly outperforms previous state-of-the-art baselines.
User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation

Graph neural networks have demonstrated impressive performance in the field of recommender systems. However, existing graph neural network recommendation approaches are proficient in capturing users’ mainstream interests and recommending popular items but fall short in effectively identifying users’ niche interests, thus failing to meet users’ personalized needs. To this end, this paper proposes the User-oriented Interest Representation on Knowledge Graph (UIR-KG) approach, which leverages the rich semantic information on the knowledge graph to learn users’ long-tail interest representation. UIR-KG maximizes the recommendation of long-tail items while satisfying users’ mainstream interests as much as possible. Firstly, a popular constraint-based long-tail neighbor selector is proposed, which obtains the target user’s long-tail neighbors by conducting high-order random walks on the collaborative knowledge graph and constraining item popularity. Secondly, a knowledge-enhanced hybrid attention aggregator is proposed, which conducts high-order aggregation of users’ long-tail interest representations on the collaborative knowledge graph by comprehensively combining relationship-aware attention and self-attention mechanisms. Finally, UIR-KG predicts the ratings of uninteracted items and provides Top-N recommendation results for the target user. Experimental results on real datasets show that compared with existing relevant approaches, UIR-KG can effectively improve recommendation diversity while maintaining recommendation accuracy, especially in long-tail recommendations. Code is available at https://github.com/ZZP-RS/UIR-KG
When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR Prediction

Click-Through Rate (CTR) prediction is a core task in recommendation systems. Despite VAE-based models have shown promising accuracy performance, they are still weak in supporting cold-start CTR prediction due to limited personal interactions. To this end, this paper proposes a content-based variational CTR model, which jointly models content information and interactions behaviors in a shared probability space via variational inference. Specifically, a three-step scheme is designed to fully utilize content information for the improved ability of preference modeling toward cold-start users. First, our method adopts VAE to model user preferences from personal interactions by probabilistic distributions, instead of a fixed embedding vector for representing the user’s interest. Then, we transform content information into variational probabilistic distribution to model the implicit preferences of cold-start users. Finally, a variational alignment strategy is applied to maximize the similarity between variational preference distributions obtained from interactions behaviors and content information respectively, so that the interest of the cold user can be recovered. Besides, we adopt a self-attention mechanism to reasonably balance the importance of latent features for CTR prediction. Experiments on two public real datasets show the effectiveness of the proposed approach.
COUNSELING ON NUTRITION EDUCATION THROUGH VIDEO MEDIA AND LEAFLETS ON CHANGES IN KNOWLEDGE OF FRUIT AND VEGETABLE CONSUMPTION IN PUBLIC HEALTH STUDENTS

Fruits and vegetables are important components that the body needs to get nutritional content, where fruits and vegetables contain lots of vitamins, fiber, complex vegetable and animal proteins which are very useful for our bodies. The benefits of consuming fruits and vegetables apart from maintaining health conditions also prevent various chronic diseases that can be harmful to humans such as heart disease, cancer, obesity and other diseases. Adolescents are the most vulnerable age group in terms of consumption of vegetables and fruit, this habit will have a negative impact on health in the future. University students are a transitional group from adolescence to early adulthood. This research was conducted to look at the behavior of eating fruits and vegetables in college students. The study used cross sectional with a quantitative approach with a total of 64 peopleThe five senses that transmit knowledge to the brain are the eyes. This study aimed to determine the effect of nutrition education through video and leaflets media on fruit and vegetable consumption of changes. This study is one group pre-test post-test with the type of research pre experiment. Respondents are students of Colleges, the number of respondents as many as 64 respondents. Data were collected by questionnaire pre-test and post-test as well as the Food Frequency Questionnaire, and analyzed by Paired Samples T-test and Independent Sample T-test with alternative by Wilcoxon and Mann Whitney.
Adaptive Prototype Network with Common and Discriminative Representation Learning for Few-Shot Relation Extraction

The task of few-shot relation extraction presents a significant challenge as it requires predicting the potential relationship between two entities based on textual data using only a limited number of labeled examples for training. Recently, quite a few studies have proposed to handle this task with task-agnostic and task-specific weights, among which prototype networks have proven to achieve the best performance. However, these methods often suffer from overfitting novel relations because every task is treated equally. In this paper, we propose a novel methodology for prototype representation learning in task-adaptive scenarios, which builds on two interactive features: 1) common features are used to rectify the biased representation and obtain the relative class-centered prototype as much as possible, and 2) discriminative features help the model better distinguish similar relations by the representation learning of the entity pairs and instances. We obtain the hybrid prototype representation by combining common and discriminative features to enhance the adaptability and recognizability of few-shot relation extraction. Experimental results on FewRel dataset, under various few-shot settings, showcase the improved accuracy and generalization capabilities of our model.
Multi-grained Logical Graph Network for Reasoning-Based Machine Reading Comprehension

Machine reading comprehension (MRC) is a crucial and challenging task in natural language processing (NLP). In order to equip machines with logical reasoning abilities, the challenging logical reasoning tasks are proposed. Existing approaches use graph-based neural models based on either sentence-level or entity-level graph construction methods which designed to capture a logical structure and enable inference over it. However, sentence-level methods result in a loss of fine-grained information and difficulty in capturing implicit relationships, while entity-level methods fail to capture the overall logical structure of the text. To address these issues, we propose a multi-grained graph-based mechanism for solving logical reasoning MRC. To combine the advantages of sentence-level and entity-level information, we mine elementary discourse units (EDUs) and entities from texts to construct graph, and learn the logical-aware features through a graph network for subsequent answer prediction. Furthermore, we implement a positional embedding mechanism to enforce the positional dependence, which facilitates logical reasoning. Our experimental results demonstrate that our approach provides significant and consistent improvements via multi-grained graphs, outperforming competitive baselines on both ReClor and LogiQA benchmarks.
D-Score: A White-Box Diagnosis Score for CNNs Based on Mutation Operators

Convolutional neural networks (CNNs) have been widely applied in many safety-critical domains, such as autonomous driving and medical diagnosis. However, concerns have been raised with respect to the trustworthiness of these models: The standard testing method evaluates the performance of a model on a test set, while low-quality and insufficient test sets can lead to unreliable evaluation results, which can have unforeseeable consequences. Therefore, how to comprehensively evaluate CNNs and, based on the evaluation results, how to enhance their robustness are the key problems to be urgently addressed. Prior work has used mutation tests to evaluate the test sets of CNNs. However, the evaluation scores are black boxes and not explicit enough for what is being tested. In this paper, we propose a white-box diagnostic approach that uses mutation operators and image transformation to calculate the feature and attention distribution of the model and further present a diagnosis score, namely D-Score, to reflect the model’s robustness and fitness to a dataset. We also propose a D-Score based data augmentation method to enhance the CNN’s performance to translations and rescalings. Comprehensive experiments on two widely used datasets and three commonly adopted CNNs demonstrate the effectiveness of our approach.
BEHAVIOR OF ELEMENTARY CHILDREN BEFORE AND AFTER EDUCATION ABOUT THE DANGEROUS SNACKING

Elementary school age children are a group of children who are prone to buying food or snacks carelessly because elementary school age children usually only see food from its shape, color and taste without seeing whether the food is good for consumption or not. If elementary school children consume unhealthy food, it can have an impact or effect on health, for example it can cause diarrhea or other digestive system disorders, besides that it can have an effect on their growth and development because the elementary school period is a time when a child needs good and healthy nutrition. . The purpose of this Community Service (PKM) is to provide education to elementary school children to change their behavior not to snack randomly. The PKM activity was carried out at SD N 104242 Lubuk Pakam for 3 days, to see the behavior before and after being given education about the impact of snacking indiscriminately, by distributing questionnaires and observing PKM participants. The results of the PKM show that before education the majority of knowledge is lacking, namely 52.3%, bad attitudes are 70.1% and actions with frequent snacks are 59.7%, and after education most of the knowledge becomes good up to 53%, attitudes become good 68.6% and the act of buying snacks indiscriminately became infrequent up to 58.2%. From the results of this community service, it can be concluded that the behavior of elementary school children is getting better, it is recommended for teachers or parents to keep an eye on their children so they don't snack randomly.
Technical Competence Of Madrasah Administrative Personnel In Managing Web-Based Information Systems

This research aimed at finding out how administration staff did their technical competences in managing web-based information systems at State Islamic Senior High School 3 Pekanbaru. The foci of this research were 1) “how was the management of web-based information systems at State Islamic Senior High School 3 Pekanbaru?”, and 2) “how was the implementation of the technical competences of administration staff in managing web-based information systems at State Islamic Senior High School 3 Pekanbaru?”. It was qualitative descriptive research. Interview, observation, and documentation were the techniques of collecting data. The data in this research were analyzed with the steps—data collection, data reduction, data display, drawing conclusions, and verification. The object was the technical competences of administration staff in managing web-based information systems at State Islamic Senior High School 3 Pekanbaru. The subjects of this research were administration staff at State Islamic Senior High School 3 Pekanbaru. The informants in this research were the head of administration staff and 1 administration staff in the computer data center field as the main informants. The research findings indicated that, web-based information system management was carried out by system planning, system design, system implementation, system control, and system maintenance. In the management of web-based information systems at State Islamic Senior High School 3 Pekanbaru, administration staff carried out their technical competencies—working based on the task field of computer data center, and being skilled in using hardware and web-based information system software. Based on the findings obtained, it was necessary to improve work procedures, it could be with the existence of a clear standard operational procedure, in term of managing web-based information in Islamic schools. To the government, the ministry of religion in the field of Islamic school education and staff was necessary to consider the simplification of the web-based information service system based on the functions and objectives of the information system implemented for efficiency and benefit of technology in educational administration.
Students’ Experiences toward Scripted and Unscripted Role Playing in Junior High School

This study has two main research objectives, namely, to find out the students’ experiences in scripted and unscripted role play and to find out which mode of role play, scripted or unscripted, better assist them to develop speaking competence. This research took two classes of seventh grades as the participant with each class had 34 students. Mixed qualitative and quantitative method was chosen to answer the research questions. The qualitative method used the observation and questionnaire as the instrument. The quantitative method used SPSS to test difference of speaking scores between the scripted and unscripted classes. One of the findings is that the students were doing well in preparing the role play such as, building sentences and understanding the role play card. Another finding is that they experienced difficulty during the role play performance. In evaluating role play, students got some positive impact. Also, the results showed that the students who did unscripted role play got better speaking scores than students who had scripted role play did. It can be concluded, even though they face some difficulties during the performance, students had some positive impact after doing role play and unscripted role play is better than scripted role play in assisting students speaking competence.