Reviewerly/exHarmony huggingface dataset:
Methodological background of electronic (digital) library collection studies

Before defining the essence and features of the electronic library collection, it is necessary to remove the issues related to the concept of an electronic document and a document as a more general concept and collection-building element. The regulatory documents – the Law on Information, Informatization and Information Protection, the Law on Librarianship, the Law on mandatory copies of documents, as well as state standards for information, library, bibliographic activities – make the significant foundation for the theory and practice. Based on their analysis, the author suggests to consider information or an object that corresponds to the profile of a library to be a potential document of this library collection. The document that belongs to the library collection is a real document. Within this definition, the focus is transferred to the specific nature of individual library, correspondingly, each library would give its own definition of the document. Thus, the concept of “electronic document of the library collection” has to be built on the basis of the document concept. The former defines the wider concept of “electronic library collection” interpreted as “the ordered collection of electronic documents”. This concept series is sufficient for the electronic library collection science, therefore all other innovations – “information object”, “information unit”, “electronic library object”, “information resource” are to be questioned or rejected. Information as a reality is disembodied, it cannot be detected in its pure form, hence many terms formed from it (information retrieval system, information transmission, information processing, storage of information, etc.) are inappropriate and should be replaced by that based on the root of the word “document”.
Sum criteria for the task of testing the independence of random numbers sequences

Random and pseudo-random number generators (RNGs) were initially used to solve numerical integration problems (the Monte Carlo method). Currently, the RNGs are used in cryptography and simulation modeling. The latter one typically uses RNGs based on computer algorithms and programs. This article presents a method aimed at testing the independence of random numbers sequences (RNSs). The method is based on the sums properties of independent random variables. Algorithms based on this method operate fast. Here not only the instant statistics including correlation coefficients are analyzed, but also the properties of empirical functions of RNSs distributed sums. In this article, the analysis is limited only to the case of uniformly distributed RNSs. The calculations performed prove the high selective efficiency of the proposed criteria, which allows to reliably distinguish between dependent and independent RNSs. Due to the high operation speed, the proposed algorithms and criteria can be used for testing very long RNSs (especially in Big Data tasks).
Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation

Clinicians prescribe antibiotics by looking at the patient’s health record with an experienced eye. However, the therapy might be rendered futile if the patient has drug resistance. Determining drug resistance requires time-consuming laboratory-level testing while applying clinicians’ heuristics in an automated way is difficult due to the categorical or binary medical events that constitute health records. In this paper, we propose a novel framework for rapid clinical intervention by viewing health records as graphs whose nodes are mapped from medical events and edges as correspondence between events in given a time window. A novel graph-based model is then proposed to extract informative features and yield automated drug resistance analysis from those high-dimensional and scarce graphs. The proposed method integrates multi-task learning into a common feature extracting graph encoder for simultaneous analyses of multiple drugs as well as stabilizing learning. On a massive dataset comprising over 110,000 patients with urinary tract infections, we verify the proposed method is capable of attaining superior performance on the drug resistance prediction problem. Furthermore, automated drug recommendations resemblant to laboratory-level testing can also be made based on the model resistance analysis.
Graph Convolution Synthetic Transformer for Chronic Kidney Disease Onset Prediction

Effective disease prediction based on electronic health records (EHR) is an important topic in health informatics. The current methods usually use common deep-learning models for disease prediction. However, it is difficult to fully learn the graphic encounter structure of EHR to improve prediction performance. Moreover, in prediction tasks, chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and complex comorbidities. Therefore, we propose a CKD onset prediction model called Graph Convolution Synthetic Transformer (GCST) based on EHR, using Fusion Attention Mechanism to solve these challenges. By modifying Transformer, GCST uses Factorized Dense Attention and Medical Local Attention to learn global and local attention, generating Synthetic Attention to learn the potential encounter structure and meaningful medical knowledge of EHR. In addition, we also propose a transfer learning strategy based on sample weighted correction to guide the prediction of GCST in specific low-resource EHR. We conduct sufficient experiments on three datasets to test the performance of GCST. Experiments show that GCST has significant improvement over state-of-the-art models.
Physician Entrepreneurs and AI Technology: An In-depth Study of Knowledge, Competence, Adoption, and Sustainability in the GCC Region

In recent years, the usage of artificial intelligence (AI) has grown significantly, and its implementation is now widespread across all industries worldwide. In terms of finances, the business sector is aware of the importance of AI in healthcare. Physician entrepreneurship is a familiar idea that has developed over time. Doctors who are also entrepreneurs have broadened their areas of expertise and taken on more primary clinical duties. This study aims to assess the knowledge, competency, adaptability, and sustainability of artificial intelligence (AI) technology among physician entrepreneurs in GCC nations. This study will focus on how AI technology is used in the healthcare industry.A sample of 220 respondents will be used in this study's survey. Out of the 220 participants, 152 males (69.09%) and68 females (30.9%) were included in the study. Based on the results of the structural equation model, competence, adaptability, and sustainability contribute to artificial intelligence and have a significant positive impact. After analyzing the interview responses, it is clear that incorporating AI into a company demands a considerable investment in employees, technology, and software. This study focused on how well physician entrepreneurs understand, use, and maintain artificial intelligence regarding knowledge, competency, adaptability, and sustainability. However, comparative research was conducted only for developed countries with similar or different settings.
Semi-supervised Classification Based on Graph Convolution Encoder Representations from BERT

Attention-based models have attracted crazy enthusiasm both in natural language processing and graph processing. We propose a novel model called Graph Encoder Representations from Transformers (GERT). Inspired by the similar distribution between vertices in graphs and words in natural language, GERT utilizes the equivalent of sentences-vertices obtained from truncated random walks to learn the local information of vertices. Then, GERT combines the strengths of local information learned from random walks and long-distance dependence obtained from transformer encoder models to represent latent features. Compared to other transformer models, the advantages of GERT include extracting local and global information, being suitable for homogeneous and heterogeneous networks, and possessing stronger strengths in extracting latent features. On top of GERT, we integrate convolution to extract information from the local neighbors and obtain another novel model Graph Convolution Encoder Representations from Transformers (GCERT). We demonstrate the effectiveness of proposed models on six networks DBLP, BlogCatalog, CiteSeerX, CoRE, Flickr, and PubMed. Evaluation results show that our models improve $$F_1$$ scores of current state-of-the-art methods up to $$10\%$$ .
Pessimistic Adversarially Regularized Learning for Graph Embedding

Autoencoder frameworks have been effectively employed for graph embedding, resulting in successful analysis of graph in low-dimensional space. Recently, generative models (GANs), which learn data distribution of the adversarial method have been increasingly applied to graph autoencoders (GAEs). Despite the effectiveness of current research, many GAEs lack the ability to provide instantaneous feedback and ensure stable updates within the GAN component. In particular, the MiniMax Multi-Agent Deep Deterministic Policy Gradient (M3DDPG) has demonstrated that using a 1-step gradient descent can enhance the performance, which can also be leveraged to train the encoder to further improve the adaptability of graph embedding. Motivated by this, we propose the Pessimistic Graph Autoencoder (PGAE), and its variational version Pessimistic Variational Graph Autoencoder (PVGAE). These methods reduce the output probability of the discriminator module through pessimistic parameters which make the feature distribution generated by encoder restore maximally the actual distribution of the original graph. Furthermore, we employ graph embedding to reconstruct the original graph information and constrain the generation of embedding vectors to preserve topological structure and node content of the original graph. Our approaches yield competitive results in node clustering and node classification tasks, outperforming numerous state-of-the-art graph autoencoders across three benchmark datasets.
Multi-level Transformer for Cancer Outcome Prediction in Large-Scale Claims Data

Predicting outcomes for cancer patients initiating chemotherapy is essential for care planning and offers potential to support clinical and health policy decision-making. Existing models leveraging deep learning with longitudinal healthcare data have demonstrated the benefits of Transformer-based approaches to learning temporal relationships among medical codes (e.g., diagnoses, medications, procedures). Recent applications have also recognised the benefit of including patient information such as demographics to improve predictions. However, much of the existing work has focused on Electronic Health Record (EHR) data, and applications to administrative claims data, which has a differing temporal structure to EHR, are limited. Furthermore, it is still unclear how to best encode medical data from both EHR and claims data and model it collectively in Transformer models. Motivated by the above, this work proposes a Multi-Level Transformer specifically designed for claims data (Claims-MLT) to enhance cancer outcome prediction. The model uses a dual-level structure to learn effective patient representations by considering the low-level claims item relationships and sequential patterns in patient claim histories. We also integrate patient demographic and clinical features to provide additional information to the model. We evaluate our approach on two tasks from a real-world cancer dataset containing breast and colorectal cancer patients, and demonstrate the proposed model outperforms comparative baselines.
Open Text Classification Based on Dynamic Boundary Balance

Open classification is the problem where there exist some unseen/unknown classes in the test set, i.e., these unknown/unseen classes don’t appear when the model is trained. Existing work often maps samples to high-dimensional space to make decisions, which leads to unobservable and inexplicable results. To address the issue, we shift perspectives to two-dimensional space and put forward a two-stage learning method built on the dynamic decision boundaries balance. We refer it to open classification with dynamic boundary balance (OCD2B). First, we construct a vanilla classifier via known classes with BERT model. Then, we use the prior knowledge of known classes to dynamically determine the decision boundaries between known classes and unknown classes in low-dimensional space. We propose a novel boundary loss function as a boundary balance strategy to reduce open space risk and empirical risk. Experimental results on two standard datasets show that our method achieves performance gain over existing methods, providing easily observable results. In particular, the larger the ratio of unseen classes is, the more obvious the performance advantage the model achieves.
Label-Aware Hierarchical Contrastive Domain Adaptation for Cross-Network Node Classification

Cross-network node classification (CNNC) has gained a great deal of attention recently, which aims to transfer the knowledge from a label-rich source network to accurately classify nodes for a different but related unlabeled target network. To tackle the problem of network shift, the existing CNNC algorithms combine graph neural networks (GNNs) and domain adaptation (DA) to solve the problem. However, GNNs are vulnerable to network structure noises, and the traditional DA methods mainly focus on matching the marginal distributions and cannot guarantee the alignment of the class-conditional distributions of different networks. To remedy these deficiencies, we propose a novel label-aware hierarchical contrastive domain adaptation (LHCDA) model to address CNNC. On one hand, we use multi-head graph attention network (GAT) to learn noise-resistant node embeddings. On the other hand, a label-aware hierarchical contrastive domain adaptation module is designed to align the class-conditional distributions across networks at both node-node level and node-class level. Since target labels are unavailable, we use K-means clustering to generate pseudo-labels and employ the prediction confidence to reduce the noises. Extensive experimental results on six CNNC tasks demonstrate that the proposed LHCDA model is superior than previous state-of-the-art CNNC methods.
Discriminative Graph-Level Anomaly Detection via Dual-Students-Teacher Model

Different from the current node-level anomaly detection task, the goal of graph-level anomaly detection is to find abnormal graphs that significantly differ from others in a graph set. Due to the scarcity of research on the work of graph-level anomaly detection, the detailed description of graph-level anomaly is insufficient. Furthermore, existing works focus on capturing anomalous graph information to learn better graph representations, but they ignore the importance of an effective anomaly score function for evaluating abnormal graphs. Thus, in this work, we first define anomalous graph information including node and graph property anomalies in a graph set and adopt node-level and graph-level information differences to identify them, respectively. Then, we introduce a discriminative graph-level anomaly detection framework with dual-students-teacher model, where the teacher model with a heuristic loss are trained to make graph representations more divergent. Then, two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives. Finally, we combine representation errors between two student models to discriminatively distinguish anomalous graphs. Extensive experiment analysis demonstrates that our method is effective for the graph-level anomaly detection task on graph datasets in the real world.(The source code is at https://github.com/whb605/GLADST.git ).
Global Balanced Text Classification for Stable Disease Diagnosis

Disease diagnosis plays an important role in the application of clinical decision system. When applying artificial intelligence models in disease diagnosis, one should be aware of the spurious correlations learned from the data. Such correlations could be introduced by the biased distribution of the data and would be reinforced during the optimization process. The spurious correlations will cause the model ultra-sensitive to some trivial variations in the input, and even introduce some significant errors when applied in non-independent and identically distributed (non-i.i.d) data with the training data. In this paper, we addressed this problem by applying the global balancing method to adjust the distribution balance in the learned representation space of the text via the assignment of global sample weights. By learning the global sample weights that minimized the balance loss, the text representation space had been iteratively optimized by attenuating the strength of the spurious correlations introduced by the training data. Our algorithm, TCGBR (Text Classification with the Global Balance Regularizer), showed the reduced sensitivity to the trivial variations of the input text and increased diagnosis accuracy when tested on the non-i.i.d data. Furthermore, we built the causal graph to investigate the causal relationships in established diagnostic models and found the diagnostic logic of TCGBR was more in line with the clinical common sense. The presented architecture can be used by practitioners to improve the stability of diagnostic models and the technique of global balancing can be naturally generalized to other representation-based deep learning models.
Developing a dynamic obstacle avoidance system for autonomous mobile robots using Bayesian optimization and object tracking: Implementation and testing

This study addresses the challenges faced by autonomous mobile robots in dynamic environments where moving obstacles such as people, cars, and other mobile robots are prevalent. Current motion planning methods for mobile robots focus on stationary obstacles or equivalent static ones at each control moment, resulting in collisions with moving objects or the need for longer distances to avoid them. To address this issue, we propose a system that incorporates dynamic information about moving obstacles into the motion planning decision-making process. We developed an object tracking technique using 2D LiDARs to gather this information and optimized the relevant parameters using Bayesian optimization. The dynamic information of moving obstacles is saved in a costmap representation at each instant, allowing the robot to yield or ignore confronting obstacles based on the prediction of who shall pass first. Simulation and real-world testing demonstrate that our approach saves paths for mobile robots in dynamic environments and prevents them from colliding with dynamic obstacles, improving the practicality of mobile robots working alongside moving humans.
Theoretical Issues and Concerns Around SupTech

As discussed in Chap. 6 , the practitioners, and academia have not yet established a comprehensive theoretical system for SupTech, leaving many fundamental theoretical issues unsolved. In particular, the connotation and scope of SupTech still in controversies and need further clarifies. Therefore, it is of great value and practical significance to clarify the fundamental theoretical issues of SupTech.
AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining

Argument mining is to analyze argument structure and extract important argument information from unstructured text. An argument mining system can help people automatically gain causal and logical information behind the text. As argumentative corpus gradually increases, like more people begin to argue and debate on social media, argument mining from them is becoming increasingly critical. However, argument mining is still a big challenge in natural language tasks due to its difficulty, and relative techniques are not mature. For example, research on non-tree argument mining needs to be done more. Most works just focus on extracting tree structure argument information. Moreover, current methods cannot accurately describe and capture argument relations and do not predict their types. In this paper, we propose a novel neural model called AutoAM to solve these problems. We first introduce the argument component attention mechanism in our model. It can capture the relevant information between argument components, so our model can better perform argument mining. Our model is a universal end-to-end framework, which can analyze argument structure without constraints like tree structure and complete three subtasks of argument mining in one model. The experiment results show that our model outperforms the existing works on several metrics in two public datasets.
CPMFA: A Character Pair-Based Method for Chinese Nested Named Entity Recognition

Chinese Nested Named Entity Recognition (CNNER) faces several challenges due to the language diversity phenomena, the complexity of the language, and the imbalanced distribution of entity types in Chinese text. To address these challenges in CNNER, we propose a new method called CPMFA (Character Pair-based method with Multi-feature representation and Attention mechanism). The CPMFA method predicts the predefined relations of character pairs in a sentence, and identifies nested named entities based on these relations. First, our method utilizes the pre-trained language model LERT (Linguistically-motivated Bidirectional Encoder Representation from Transformer), and Bidirectional Long Short-Term Memory (BiLSTM) to generate comprehensive and precise character representations. Second, our method uses multi-feature representation to capture complex semantic information within the text, and employs the Pyramid Squeeze Attention (PSA) module to emphasize key features. Finally, to overcome the challenge of the imbalanced distribution of entity types, PolyLoss function is integrated into our model training process. Results of experiments show that the proposed CPMFA method achieves an F1 score of 83.79%. Compared to other mainstream span-based methods, the proposed CPMFA method has excellent performance in CNNER.
Deep Knowledge Tracing with Concept Trees

Knowledge tracing aims to diagnose the student's knowledge status and predict the responses to the next questions, which is a critical task in personalized learning. The existing studies consider more academic features, while this paper introduces DKCT, a deep knowledge tracing model with concept trees, to integrate the hierarchical concept tree that describes the structure of concepts in a question. DKCT casts the knowledge concept tree (KCT) in a question from the views of feature, breadth, and difficulty into a KCT representation at first. Then, DKCT is composed of an encoder network with multi-head attention on the question representations and a decoder network with multi-head attention on the interaction embeddings. Finally, DKCT integrates the student embeddings by using fully connected networks to predict the responses to the next questions. Extensive experiments conducted on two real-world educational datasets show that DKCT has a higher prediction accuracy than the currently popular KT models. This work paves the way to consider KCT for knowledge tracing.