Reviewerly/exHarmony huggingface dataset:
Learning the Explainable Semantic Relations via Unified Graph Topic-Disentangled Neural Networks

Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCNs) can effectively learn node representations via aggregating neighbors based on the relation graph. However, despite a few exceptions, most of the previous work in this line does not consider the topical semantics underlying the edges, making the node representations less effective and the learned relation between nodes hard to explain. For instance, the current GNNs make us usually don’t know what is the reason for the connection of network nodes, such as the specific research topics cited in this article and the concerns among friends on social platforms. Some methods have begun to explore the extraction of relation semantics in recent related literature, but existing studies generally face two bottlenecks, i.e., either being unable to explain the mined latent relations to ensure their reasonableness and independence, or demanding the textual content of edges which is unavailable in most real-world datasets. Actually, these two issues are both crucial in practical use. In our work, we propose a novel Topic-Disentangled Graph Neural Network (TDG) to address the above two issues at the same time, which explores the relation topics from the perspective of node contents. We design an optimized graph topic module to handle node features to construct independent and explainable semantic subspaces, then the reasonable relation topics that correspond to these subspaces are assigned to each graph relation via a neighborhood routing mechanism. Our proposed model can be easily combined with related graph tasks to form an end-to-end model, to avoid the risk of deviation between node representation space and task space. To evaluate the efficiency of our model, sufficient node-related tasks are conducted on three public datasets in the experimental section. The results show the obvious superiority of TDG compared with the state-of-the-art models.
Mutual mentor: Online contrastive distillation network for general continual learning

The goal of General Continual Learning (GCL) is to preserve learned knowledge and learn new knowledge with constant memory from infinite data stream where task boundaries are blurry. Distilling the model’s response of reserved samples between the old and new models is an effective way to achieve promising performance on GCL. However, it accumulates the inherent old model’s response bias and is not robust to model changes. To this end, we propose a Mutual Mentor General Continual Learning (MMGCL) framework to tackle these problems, which explores a training process in which the student and teacher models mentor each other. Concretely, the student model consolidates the learned knowledge by respectively aligning the relation and adaptive responses with those of the teacher model while the teacher model updates its parameters by integrating the parameters of the student model to accumulate new knowledge. To further improve the effectiveness of the mutual mentor, we integrate the inter-instance knowledge to optimize the outputs of the teacher model, which can not only supervise the student model but also indirectly optimize the teacher model. Extensive experiments on six benchmark datasets demonstrate that our MMGCL significantly outperforms state-of-the-art approaches under diverse continual learning settings with various buffer sizes.
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends

Abstract Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state‐of‐the‐art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher‐level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation.
Well-graded polytomous knowledge structures

Heller (2021) and Stefanutti et al. (2020) provided the mathematical foundation for the generalization of knowledge structure theory (KST) to polytomous items. Based on their works, the well-gradedness can be extended to polytomous knowledge structures. We propose the concepts of discriminative polytomous knowledge structure and well-graded polytomous knowledge structure. Then we show that every well-graded polytomous knowledge structure is discriminative. The basis of any polytomous knowledge space is formed by the collection of all the atoms. We discuss the sufficient and necessary conditions of polytomous knowledge structures to be well-graded polytomous knowledge spaces. Moreover, we provide an example to illustrate that a well-graded polytomous knowledge space is not necessarily a polytomous closure space.
Determining the Optimal Number of Clusters using Silhouette Score as a Data Mining Technique

The identification of the same objects is very important in determining the similarity between different objects. Nowadays, there are several techniques that allow us to divide objects into different groups that differ from one to another. In order to have the best separation between the clusters, it is required that the optimal determination of the number of clusters of a corpus be made in advance. In our research, the Silhouette score technique was used in order to make the optimal determination of this number of clusters. The application of such a technique was done through the Python language, and a corpus of unstructured job vacancy data was used. After determining the optimal number, at the end we present these clusters and the similarity between them, this presentation will be done in the form of a graph in a suitable format.
System of Project Management at a Medical Hub as an Instrument for Implementation of Open Innovation

Globalization activates qualitative changes in multiple economic entities and requires the development of alternative forms of business organization. At present, one of the most promising development tracks is represented by the implementation of network structures, aimed at achieving common goals and obtaining a win-win outcome via joint effort. Business structures that invite dynamic and consistent transformations on a wide scale prove to be able to successfully compete in the market. In this regard, a project management system at a medical hub serves as a vital tool for implementation of open innovation. Participation in the medical hub allows coordinating intentions and establishing aligned communication between all stakeholders, suppliers and private institutions. In modern sectors of the economy, a developing hub becomes a unique structure, because it unites the contributions of the most important healthcare specialists in a single framework. This research examines the structure of healthcare business process models, and scrutinizes the communication between suppliers, partners and consumers of medical services. It also defines the main directions and outlines strategic goals. Assessment of performance of a project management system at a medical hub proves to be the issue of particular relevance, due to the fact that its tasks should be primarily aimed at increasing the share of successful projects and implementing only those ones that comply with the strategy. Based on the latter, a model for the project management system at a medical hub was designed. As a result, the authors developed an assessment mechanism for innovative projects using SNA methods that align with intra-communication interactions (transactions) between the participants in a medical hub. The conducted research allows concluding that in the current era of cutting-edge technologies, the project management system should be considered the most effective management tool for coordinating the actions of a corporate structure at a medical hub.
AI, IoMT and Blockchain in Healthcare

The healthcare industry is adopting new technologies such as AI, IoMT, and blockchain to enhance patient outcomes, reduce costs, and improve operational efficiencies. These technologies can revolutionize healthcare by facilitating personalized patient-focused care, improving clinical outcomes, and reducing expenses. However, the implementation of these technologies requires collaboration between healthcare providers, technology companies, and regulatory bodies to ensure patient privacy and data security. This study explores the role of AI, IoMT, and blockchain in public healthcare and their current applications, obstacles, and future research areas. It emphasizes the advantages that these technologies bring to the IoT and the difficulties involved in their implementation.
A Vulnerability Assessment Framework for Privacy-preserving Record Linkage

The linkage of records to identify common entities across multiple data sources has gained increasing interest over the last few decades. In the absence of unique entity identifiers, quasi-identifying attributes such as personal names and addresses are generally used to link records. Due to privacy concerns that arise when such sensitive information is used, privacy-preserving record linkage (PPRL) methods have been proposed to link records without revealing any sensitive or confidential information about these records. Popular PPRL methods such as Bloom filter encoding, however, are known to be susceptible to various privacy attacks. Therefore, a systematic analysis of the privacy risks associated with sensitive databases as well as PPRL methods used in linkage projects is of great importance. In this article we present a novel framework to assess the vulnerabilities of sensitive databases and existing PPRL encoding methods. We discuss five types of vulnerabilities: frequency, length, co-occurrence, similarity, and similarity neighborhood, of both plaintext and encoded values that an adversary can exploit in order to reidentify sensitive plaintext values from encoded data. In an experimental evaluation we assess the vulnerabilities of two databases using five existing PPRL encoding methods. This evaluation shows that our proposed framework can be used in real-world linkage applications to assess the vulnerabilities associated with sensitive databases to be linked, as well as with PPRL encoding methods.
Evaluating Explainable Artificial Intelligence Methods Based on Feature Elimination: A Functionality-Grounded Approach

Although predictions based on machine learning are reaching unprecedented levels of accuracy, understanding the underlying mechanisms of a machine learning model is far from trivial. Therefore, explaining machine learning outcomes is gaining more interest with an increasing need to understand, trust, justify, and improve both the predictions and the prediction process. This, in turn, necessitates providing mechanisms to evaluate explainability methods as well as to measure their ability to fulfill their designated tasks. In this paper, we introduce a technique to extract the most important features from a data perspective. We propose metrics to quantify the ability of an explainability method to convey and communicate the underlying concepts available in the data. Furthermore, we evaluate the ability of an eXplainable Artificial Intelligence (XAI) method to reason about the reliance of a Machine Learning (ML) model on the extracted features. Through experiments, we further, prove that our approach enables differentiating explainability methods independent of the underlying experimental settings. The proposed metrics can be used to functionally evaluate the extent to which an explainability method is able to extract the patterns discovered by a machine learning model. Our approach provides a means to quantitatively differentiate global explainability methods in order to deepen user trust not only in the predictions generated but also in their explanations.
Dynamic-Pix2Pix: Medical image segmentation by injecting noise to cGAN for modeling input and target domain joint distributions with limited training data

Learning to translate images from a source to a target domain with applications such as converting simple line drawing to oil painting has attracted significant attention. The quality of translated images is directly related to two crucial issues. First, the consistency of the output distribution with that of the target is essential. Second, the generated output should have a high correlation with the input. Conditional Generative Adversarial Networks, cGANs, are the most common models for translating images. The performance of a cGAN drops when we use a limited training dataset. In this work, we study the obstacles that prevent a U-shaped model from learning the target domain distribution from limited data by using noise as input. This study helps to increase the Pix2Pix (a form of cGAN) target distribution modeling ability from limited data with the help of dynamic neural network theory. Our model has two learning cycles. The model learns the correlation between input and ground truth in the first cycle. Then, the model's architecture is refined in the second cycle to learn the target distribution from noise input. These processes are executed in each iteration of the training procedure. Helping the cGAN learn the target distribution from noise input results in a better model generalization during the test time and allows the model to fit almost perfectly to the target domain distribution. As a result, our model surpasses the Pix2Pix model in segmenting HC18 and Montgomery's chest x-ray images. Both qualitative and Dice scores show the superiority of our model. Although our proposed method does not use thousand of additional data for pretraining, it produces comparable results for the in and out-domain generalization compared to the state-of-the-art methods.
Implications of social media technologies (SMTs) to library services

Purpose Academic libraries may become obsolete in the twenty-first century unless they begin to harness new technology and improve information and service delivery. This paper aims to examine the emergence of social media technologies (SMTs) with their implications to academic library services in developing countries in the world. It is believed that a growing number of libraries of all types are starting to use SMTs to enhance their service delivery, similar to other institutions. Design/methodology/approach The research, which was conducted on a desk, examined how the implementation of SMTs may affect library services and operations. The study emphasizes that many academic libraries in developing countries have not yet adopted and implemented SMTs despite the potential that they hold for libraries. Findings Given that there has been relatively little study linking SMTs to librarianship, this may be because there is a low degree of awareness of SMTs’ importance in libraries. This study discovered that there are other types of SMTs relevant to academic libraries including Facebook, Twitter, YouTube, Blog among others. This paper reported the implications of SMTs to library services in developing countries. Originality/value This study is the original idea from the authors and does not reflect on any copyrighted materials. The study recommended among others that sufficient training and more expertise need to be gained by academic libraries to further facilitate implementation of social networks in the libraries, and adoption of contemporary media technologies such as social media in meeting the needs of the 21st-century library patrons should be prioritized.
A lightweight method for face expression recognition based on improved MobileNetV3

Abstract Facial expression recognition plays a significant role in the application of man–machine interaction. However, existing models typically have shortcomings with numerous parameters, large model sizes, and high computational costs, which are difficult to deploy in resource‐constrained devices. This paper proposes a lightweight network based on improved MobileNetV3 to mitigate these disadvantages. Firstly, we adjust the channels in the high‐level network to reduce the number of parameters and model size, and then, the coordinate attention mechanism is introduced to the network, which enhances the attention of the network with few parameters and low computing cost. Furthermore, a complementary pooling structure is designed to improve the coordinate attention mechanism, which enables it to assist the network in extracting salient features sufficiently. In addition, the network with the joint loss consisting of the softmax loss and centre loss is trained, which can minimize the intra‐class gap and improve the classification performance. Finally, the network is trained and tested on public datasets FERPlus and RAF‐DB, with the best accuracy of 87.5% and 86.6%, respectively. The FLOPs, parameters, and the memory storage size are only 0.19GMac, 1.3 M, and 15.9 MB, respectively, which is lighter than most state‐of‐the‐art networks. Code is available at https://github.com/RIS‐LAB1/FER‐mobilenet .
FatNet: High-Resolution Kernels for Classification Using Fully Convolutional Optical Neural Networks

This paper describes the transformation of a traditional in silico classification network into an optical fully convolutional neural network with high-resolution feature maps and kernels. When using the free-space 4f system to accelerate the inference speed of neural networks, higher resolutions of feature maps and kernels can be used without the loss in frame rate. We present FatNet for the classification of images, which is more compatible with free-space acceleration than standard convolutional classifiers. It neglects the standard combination of convolutional feature extraction and classifier dense layers by performing both in one fully convolutional network. This approach takes full advantage of the parallelism in the 4f free-space system and performs fewer conversions between electronics and optics by reducing the number of channels and increasing the resolution, making this network faster in optics than off-the-shelf networks. To demonstrate the capabilities of FatNet, it was trained with the CIFAR100 dataset on GPU and the simulator of the 4f system. A comparison of the results against ResNet-18 shows 8.2 times fewer convolution operations at the cost of only 6% lower accuracy. This demonstrates that the optical implementation of FatNet results in significantly faster inference than the optical implementation of the original ResNet-18. These are promising results for the approach of training deep learning with high-resolution kernels in the direction toward the upcoming optics era.
Enriching the dialogue state tracking model with a asyntactic discourse graph

Dialogue state tracking is a key component of a task-oriented dialogue system. Recently, pretrained language models are widely used to track a dialogue state by generating or extracting values from a dialogue. These models have a shortcoming that they can explicitly encode contextual information of a dialogue. In this paper, we present a Syntactically ENriched Discourse Dialogue State Tracking model (SEND-DST) that fully leverages the discourse and syntax information of a dialogue. Our model consists of two parts: a dialogue encoding module and a slot-value extraction module. To provide a model with syntactic structures of utterances and the discourse flow of a dialogue, we devise a Syntactic Discourse (SD) graph using the dependency trees of the utterances. Experimental results show that SEND-DST achieves state-of-the-art results on multiple datasets, such as MultiWOZ 2.1, demonstrating its effectiveness.
MoCA: Incorporating domain pretraining and cross attention for textbook question answering

Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams. Compared with Visual Question Answering (VQA), TQA contains a large number of uncommon terminologies and various diagram inputs. It brings new challenges to the representation capability of language model for domain-specific spans. Also, it requires the model to take fully advantage of the complementary information of different diagram types, which pushes the multimodal fusion task to a more complex level. To tackle the above issues, we propose a novel model named MoCA, which incorporates Multi-stage domain pretraining and Cross-guided multimodal Attention for the TQA task. Firstly, we introduce a multi-stage domain pretraining module to conduct unsupervised post-pretraining with a span mask strategy and supervised pre-finetune. Especially for domain post-pretraining, we propose a heuristic generation algorithm to employ the terminology corpus. Secondly, to fully consider the rich inputs of context and diagrams, we propose a cross-guided multimodal attention mechanism to update the features of text, question diagram and instructional diagram based on a progressive strategy. Further, a dual gating mechanism is adopted to improve the model ensemble of three background retrievals. The experimental results show the superiority of our model, which outperforms the state-of-the-art methods on the validation and test split respectively. Also, ablation and comparison experiments verify the effectiveness of each module proposed in our model.
Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation

Though Chinese word segmentation (CWS) relies heavily on arithmetic power to train huge models and human work to label corpora, models and algorithms are still less accurate, especially for segmentation in a specific domain. In this study, a high-degree-of-freedom-priority candidate term boundary conflict reduction method (HFCR) is proposed to solve the problem of manually setting thresholds on segmentation based on information entropy. We quantify the uncertainty of left and right character connections of candidate terms and then arrange them in descending order for local comparisons to determine term boundaries. Dynamic numerical comparisons are adopted instead of setting a threshold manually and randomly. Experiments show that the average F1-value of CWS for Chinese geological text is higher than 95% and the F1-value for Chinese general datasets is higher than 87%. Compared with representative tokenizers and the SOTA model, our method performs better, which solves the term boundary conflict problem well and has excellent performance on single geological text segmentation without any samples or labels.