Reviewerly/exHarmony huggingface dataset:
CKGE: Improving Distance Based Knowledge Graph Embedding via Contrastive Learning

Knowledge graph embedding (KGE) is critical in various downstream applications as it represents entities and relations in a knowledge graph as low-dimensional vectors. The embeddings of the entities and relations denote their semantics on the knowledge graph, which affects the effectiveness of the model. Recently, distance-based (DB) models have demonstrated great explanatory power in KGE. However, most existing DB models focus solely on single triples to independently optimize the scoring function, disregarding the interconnections among different triples. To address this issue, we propose CKGE, a novel contrastive learning approach that enhances the performance of DB models while remaining versatile enough to apply to different DB models. Specifically, CKGE improves the alignment and uniformity of DB models, meaning that the embedding of the same semantic entities should remain close under different relations, and embeddings for random entities should scatter on the hypersphere. Additionally, we present a supervised contrastive learning approach to optimize in-batch negative methods, thereby improving the learning of semantic entities. Extensive experiments on four benchmark datasets demonstrate that CKGE yields significant improvements in link prediction, especially for large-scale datasets such as ogbl-wikikg2.
CD-BNN: Causal Discovery with Bayesian Neural Network

Causal discovery involves learning Directed Acyclic Graphs (DAGs) from observational data and has widespread applications in various fields. Recent advancements in the structural equation model (SEM) have successfully applied continuous optimization techniques to causal discovery. These methods introduce acyclicity constraints to tackle the challenge of exploring the exponentially large search space that arises as the number of graph nodes increases. However, these methods often rely on point estimates that fail to fully account for the inherent uncertainty present in the data. This limitation can lead to inaccurate causal graph inference. In this paper, we propose a novel method for causal discovery with Bayesian Neural Networks (CD-BNN). CD-BNN incorporates a Bayesian Neural Network to explicitly model and quantify uncertainty in the data while reducing the influence of noise through model averaging. Moreover, we explore the extraction of the final DAG from the BNN using partial derivatives. We conduct a comprehensive set of experiments on both real-world and synthetic data to evaluate the performance of our approach. The results demonstrate that our proposed method surpasses related baselines in accurately identifying causal graphs, particularly when faced with data uncertainty.
Multimodal Learning for Automatic Summarization: A Survey

With the widespread availability of multiple data sources, such as image, audio-video, and text data, automatic summarization of multimodal data is becoming an important technology in decision support. This paper presents a comprehensive survey and summary of the main articles in the field of multimodal summarization techniques in recent years. Firstly, we define multimodal summarization and briefly describe the development process. Then, we survey existing techniques and their applicability in different domains. Additionally, we provide an analysis of their results and discuss the insights of those approaches, along with the challenges and future research directions. Based on our study, we found that the encoder-decoder approach is currently the best approach for automated summarization. In the future, we believe that the applications of multimodal summarization could develop rapidly in many different fields, particularly in medicine. In our case studies, we demonstrate that multimodal learning is a promising research direction for providing timely and accurate summarizations compared to unimodal approaches.
Machine Unlearning Methodology Based on Stochastic Teacher Network

The rise of the phenomenon of the “right to be forgotten” has prompted research on machine unlearning, which grants data owners the right to actively withdraw data that has been used for model training and requires the elimination of the contribution of that data to the model. A simple method to achieve this is to use the remaining data to retrain the model, but this is not acceptable for other data owners who continue to participate in training. Existing machine unlearning methods have been found to be ineffective in quickly removing knowledge from deep learning models. This paper proposes using a stochastic network as a teacher to expedite the mitigation of the influence caused by forgotten data on the model. We performed experiments on three datasets, and the findings demonstrate that our approach can efficiently mitigate the influence of target data on the model within a single epoch. This allows for a one-time erasure and reconstruction of the model, and the reconstructed model achieves the same performance as the retrained model.
Multi-Interest Aware Graph Convolution Network for Social Recommendation

Social recommendation endeavors to harness users’ social connections to enhance recommender systems. Graph Neural Network (GNN) has gained traction for its robust capacity in managing graph data. However, previous social recommendation works have failed to fully consider the crucial role of user distinct interests, which hinders their ability to accurately model complex user preferences and negatively affected the modeling of social influence. To tackle this challenge, we introduce a multi-interest approach to GNN-based social recommendation. Specifically, we firstly utilize a dynamic routing algorithm to cluster user interests from the items they have interacted with. Subsequently, we use a similarity-weighted GCN operation to capture user relationships within the social network. Finally, we use the aggregate the multiple interest representations for prediction. Our comprehensive experiments underscore the consistent superiority of our model compared with state-of-the-art competitors on real-world datasets.
Fair Re-Ranking Recommendation Based on Debiased Multi-graph Representations

The successful application of graph neural networks in recommendation scenarios causes serious exposure of sensitive information of users. Research shows that social bias such as sexism and ageism are prevalent in recommendations, and the use of multi-graph information even makes it worse. Existing fair recommendation algorithms only concentrate on users’ sensitive attributes in user-item graph, failing to fully remove those attributes from multiple graphs. In addition, merely hiding sensitive information is not enough, there is still a gap in recommendation utility for different user groups. In this work, we propose a novel fair re-ranking recommendation model based on debiased multi-graph representations, which contains three functional layers. Multi-graph embedding layer iteratively propagates and aggregates both topological and interactive information on multiple graphs. Attribute hiding layer uses generative adversarial networks to hide user sensitive information and thus debias users’ representations. Fair ranking layer adopts a re-ranking strategy with our proposed unfairness metric to further optimize the final recommendation list. Extensive experiments on real-world datasets demonstrate the performance of our proposed model in both recommendation utility and fairness, outperforming state-of-the-art models.
A Novel Adaptive Distribution Distance-Based Feature Selection Method for Video Traffic Identification

The rapid proliferation of video applications in recent years has triggered an unprecedented surge in Internet video traffic, which in turn has presented substantial challenges for effective network management. However, existing methods for extracting features from video traffic primarily focus on conventional traffic attributes, resulting in suboptimal identification accuracy. Furthermore, the challenge of handling high-dimensional data is a common hurdle in video traffic identification, necessitating a robust approach to select the most pertinent features crucial for accurate identification. Despite the abundance of studies utilizing feature selection to enhance identification performance, there exists a notable lack of research that addresses the quantification of feature distributions with small or no overlap. This study proposes, firstly, the extraction of features relevant to videos, thereby assembling an expansive feature repertoire. Secondly, in the pursuit of forming an effective subset of features, the current research introduces the adaptive distribution distance-based feature selection (ADDFS) methodology. Using the Wasserstein distance metric to quantify the differences between feature distributions. To gauge the efficacy of this proposal, a dataset comprising video traffic from various platforms within a campus network environment was collected, and a series of experiments were conducted using these datasets. The experimental results indicate that the proposed method can achieve highly accurate identification performance for video traffic.
Boosting Adversarial Attacks with Improved Sign Method

Deep neural networks (DNNs) can be susceptible to adversarial examples, which involve adding imperceptible perturbations to benign images in order to deceive the models. Although many current adversarial attack methods achieve nearly 100% success rates in white-box attacks, their effectiveness often diminishes when targeting black-box models due to limited transferability. The transfer-based attack technique involves choosing a surrogate model that closely resembles the target model. By exploiting the shared decision boundaries among different models, this method generates highly transferable adversarial examples. Most attack methods change the gradient using the Sign Method (SM) and add small perturbation to the original image. Although SM is simple and effective, it only extracts the sign of the gradient unit, ignoring the size of the gradient value, and resulting in an inaccurate update direction in the iterative process. In our research, we introduce an innovative approach called the Improved Sign Method (ISM). This strategy involves assigning weights to gradients, aiming to mitigate this concern and thereby enhance the efficacy of black-box attacks in terms of transferability. The effectiveness of our proposed Improved Sign Method (ISM) is strongly validated through a series of comprehensive experiments. These results unequivocally demonstrate its capability to significantly enhance the transferability of adversarial examples within black-box attack scenarios. Furthermore, ISM seamlessly integrates with the Fast Gradient Sign Attack Method (FGSM) family, making it applicable across various scenarios. Notably, the computational overhead associated with this integration is practically negligible. Moreover, by incorporating with other advanced attack methods, the performance of black-box attack has been significantly enhanced.
A Knowledge-Enhanced Inferential Network for Cross-Modality Multi-hop VQA

This paper focuses on cross-modality multi-hop visual questions, which require multi-hop reasoning over different sources of knowledge from multiple modalities, such as image and text. Due to the lack of cross-modality reasoning ability, it is difficult for the traditional model to make correct predictions. To solve this problem, we propose a new knowledge-enhanced inferential framework. We first build a reasoning graph to capture the topological relations between the objects in the given image and the logical relations of entities corresponding to these objects. To align the visual objects and textual entities, we design a cross-modality retriever with the help of an external multimodal knowledge graph. Based on the logical and topological relations on the graph, we can derive the answer by decomposing a complex multi-hop question into a series of attention-based reasoning steps. The result of the previous step acts as the context of the next step. By linking the results of all steps, we can form an evidence chain to the answer. Extensive experiments conducted on the popular KVQA dataset demonstrate the effectiveness of our approach.
An Extractive Automatic Summarization Method for Chinese Long Text

The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods utilize deep learning technology to treat automatic summarization as a binary classification task. However, the effectiveness of automatic summarization for Chinese long text is limited by the maximum input length of the model, and it requires a large amount of training data. This paper proposes an unsupervised extractive automatic summarization method which solves the long text encoding problem by incorporating contextual semantics into sentence-level encoding. Firstly, we obtain the semantic representation of sentences by using the RoBERTa model. Secondly, we propose an improved k-Means algorithm to cluster sentence representations. By defining sparse and dense clusters, we improve the accuracy of summary sentence selection while preserving maximum semantic information from the original text. Experimental results on the CAIL2020 dataset show that our method outperforms baselines by 6.64/7.68/7.14% respectively on ROUGE-1/2/L. Moreover, we further enhance the automatic summarization results by 4.5/5.36/3.24% by adding domain rules tailored to the dataset’s characteristics.
Effi-Emp: An AI Based Approach Towards Positive Empathic Expressions

Recently due to the advent of the Web, the ubiquity of search engines, and the widespread use of social media, the modern world has been witnessing technologies that can mimic human behavior. With the rise of Internet usage among youth and the prevalence of cyberbullying, mental health has become a global issue. It is one of the great convictions that newer technologies can also make a valuable contribution to creating an efficient and empathic model which will depend less on computation power and provide the same output as bigger models. This paper presents a novel approach to address a fundamental mental health aspect by leveraging knowledge distillation to generate positive and highly empathic feedback. The proposed method “Effi-Emp”, is a hybrid approach that utilizes transformers and attention layers to generate empathic dialogues which closely mimic human dialogues. Sentence coherence and rate of change during sentence generation have been given special attention so that the model generates high-quality empathic sentences. By utilizing the last layer of the model Knowledge Distillation was achieved with about 20% improved run time in comparison with the State-of-Art model. Automatic and baseline evaluation methods demonstrate that the proposed model outperforms all other models by a decent margin.
Heterogeneous Line Graph Neural Network for Link Prediction

Heterogeneous network link prediction is an important network information mining problem. Existing link prediction methods for heterogeneous networks typically require predefined meta-paths with prior knowledge. To address the problem, we propose a new model, named Heterogeneous Line Graph Neural Network (HLGNN), in this paper. Firstly, we design a line graph transformation module to encapsulate node features and transform the heterogeneous network into a heterogeneous line graph. Then, we propose an intra-type aggregation component to collect the same type of edges. As we have aggregated node information in each type, we design an inter-layer aggregation to combine messages from multiple node types. Finally, we put the aggregation results into a multilayer perceptron to achieve link prediction. The experimental results show that, compared to the state-of-the-art baselines, the proposed method achieves superior performance.
Rethinking the Evaluation of Deep Neural Network Robustness

Evaluating the robustness of deep neural networks (DNNs) is crucial for ensuring the reliability and security of machine learning systems. Prior approaches quantify the probability of a DNN being compromised under a specified constraint. Despite their utility, these techniques suffer from low efficiency and effectiveness in evaluating the robustness of DNNs. The paper presents a promising evaluation approach, named typeII-EvaA, for accurately and efficiently assessing the robustness of DNNs against adversarial attacks. The typeII-EvaA overcomes the limitations of existing evaluation methods by devising several new assessment methods, called typeII-AssMs, which use attack success rate (ASR) constraints to minimize perturbation magnitudes. Additionally, we introduce a more effective human imperceptibility metric, CIEDE2000, which aligns with the human vision system to probe almost all human-imperceptible areas for obtaining the most threatening adversarial examples. Extensive experimental results corroborate that typeII-EvaA has practical implications for improving the security of DNN-based systems. And typeII-AssMs can achieve 100% ASR against various defense mechanisms. Our intention is for the typeII-EvaA to serve as a benchmark for future efforts toward developing robust DNNs that can withstand adversarial examples.
Analyzing the Convergence of Federated Learning with Biased Client Participation

Federated Learning (FL) is a promising decentralized machine learning framework that enables a massive number of clients (e.g., smartphones) to collaboratively train a global model over the Internet without sacrificing their privacy. Though FL’s efficacy in non-convex problems is proven, its convergence amidst biased client participation lacks theoretical study. In this paper, we analyze the convergence of FedAvg on non-convex problems, which is the most renowned FL algorithm. We assume even data distribution but non-IID among clients, and elucidate the convergence rate of FedAvg in situations characterized by biased client participation. Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, demonstrating that unbiased client participation results in 11% to 50% higher test accuracy compared to extremely biased client participation.
Discovery of Emotion Implicit Causes in Products Based on Commonsense Reasoning

This paper focuses on the task of product emotion cause analysis which aims to find essential causes of certain emotions from product reviews. Current works only study the explicit causes which are some spans in the given text. However, some crucial and useful causes may be expressed vaguely. They may not mention but can be inferred from the text semantics. They can capture the deeper reasons to explain some unknown phenomena, which can well support the applications like market research and product optimization. To address this problem, we in this paper propose a new task of Emotion Implicit Cause Discovery (EICD). We develop a novel method that can deduce the implicit causes based on the contexts and commonsense knowledge. Our method first retrieves related knowledge from the large language model to construct reasoning graphs for the emotions and potential causes. We then encode the structural knowledge in the graph and infer the implicit cause by deductive reasoning. To evaluate our method, we construct a large dataset called EICDset, based on Amazon product reviews. Experiments on it demonstrate the effectiveness of our model.
Exploration of Stochastic Selection of Splitting Attributes as a Source of Inducing Diversity

One of the most important requirements for a decision forest to secure better ensemble accuracy is generating simultaneously accurate as well as diverse decision trees as base classifiers. Most of the decision forest algorithms in literature exploit either or both of the two major sources of diversity: subspacing and sampling for inducing diversity among the decision trees. Recently, a decision forest algorithm named “Stochastic Forest” introduces stochastic selection of splitting attributes in the process of inducing decision trees and reports promising results. In this paper, we explore the worthiness of “stochastic selection of splitting attributes” as a source of inducing diversity in comparison with the existing two major sources of diversity. We carry out experiments on twenty-five popular data sets that are publicly available from the UCI Machine Learning Repository. The experimental analysis demonstrates the worthiness of “stochastic selection of splitting attributes” as an effective source of diversity.
Voice Conversion with Denoising Diffusion Probabilistic GAN Models

Voice conversion is a method that allows for the transformation of speaking style while maintaining the integrity of linguistic information. There are many researchers using deep generative models for voice conversion tasks. Generative Adversarial Networks (GANs) can quickly generate high-quality samples, but the generated samples lack diversity. The samples generated by the Denoising Diffusion Probabilistic Models (DDPMs) are better than GANs in terms of mode coverage and sample diversity. But the DDPMs have high computational costs and the inference speed is slower than GANs. In order to make GANs and DDPMs more practical we proposes DiffGAN-VC, a variant of GANs and DDPMS, to achieve non-parallel many-to-many voice conversion (VC). We use large steps to achieve denoising, and also introduce a multimodal conditional GANs to model the denoising diffusion generative adversarial network. According to both objective and subjective evaluation experiments, DiffGAN-VC has been shown to achieve high voice quality on non-parallel data sets. Compared with the CycleGAN-VC method, DiffGAN-VC achieves speaker similarity, naturalness and higher sound quality.
A Fine-Grained Verification Method for Blockchain Data Based on Merkle Path Sharding

Blockchain has been widely applied these years. As its volume of data is greatly increasing, it is necessary to shard the data to utilize hardward storage efficiently. After sharding, however, the validated data are stored in different nodes and read cross-shardingly, which is inefficient because of extra communication time. To solve this problem, the authors use the Mp-tree (Merkle Path Tree) structure to replace the Merkle tree in the traditional blockchain, and then shard the blocks according to the Mp-tree architecture. The Mp-tree can validate the accuracy of data to avoid cross-sharding so as to improve the efficiency of validation. It has some advantages: first, it can reduce the volume of storage space by fine-grained slicing; second, it can improve the validation efficiency of blockchain slicing by independent validation. This study demonstrates that our design can optimize the generation efficiency in existing sharding systems, and also exploiting its independent verification feature to improve verification efficiency.