Reviewerly/exHarmony huggingface dataset:
Named Entity Recognition of Electronic Medical Records Based on Multi-Feature Fusion

Named entity recognition (NER) is a very basic task in natural language processing (NLP). The paper studies the problem of named entity recognition in Chinese electronic medical records, and proposes a method based on the Bert-Bi-LSTM-CRF model. In addition, the model incorporates the functionality of radical components and dictionaries to improve the recognition accuracy. The complexity of Chinese medical record entities, the ambiguity in language expression, and the lack of adequate labeled data make traditional rule-based or machine learning methods less effective. To address this problem, we adopt the Bert-Bi-LSTM-CRF model, which effectively captures contextual information and semantic relationships to improve entity recognition accuracy. Furthermore, to further enhance the model's performance, we introduce the functionality of radical components and dictionaries. Radical components are an important component of Chinese characters and can be used to assist in identifying entities and improve the model's generalization ability. We also utilize medical dictionaries to assist in entity recognition. These dictionaries contain rich medical terms and vocabulary, which can effectively help the model identify entities. The proposed method is evaluated on the public dataset CCKS2019, and the experimental results demonstrate that it outperforms traditional methods, achieving an F1 score improvement of nearly 7 percentage points, and achieves good experimental results.
Research on Sentiment Analysis of Government Short Video Comments based on BERT's Multi Strategy Combination Model

In the field of NLP, pre trained models greatly shorten development time, reduce usage difficulty, and improve model robustness. In this paper, the BERT pre training model is used to analyze the text emotion of Tiktok short video comments. Through experimental comparison, it is found that the BERT pre training model is better than CNN and BiLSTM in emotion classification. Based on the BERT model, the multi strategy hybrid method is used to further input the BERT output dynamic word vector into the CNN and BiLSTM models to further extract local features and features with long distance dependency, improve the accuracy of the model, and obtain a good classification effect on the government short video comment dataset, which is better than the single model.
An Adaptive Variance Reduction Zeroth-Order Algorithm for Finite-Sum Optimization

The unconstrained finite-sum optimization problem is a common type of problem in the field of optimization, and there is currently limited research on zeroth-order optimization algorithms. To solve unconstrained finite-sum optimization problems for non-convex function, we propose a zeroth-order optimization algorithm with adaptive variance reduction, called ZO-AdaSPIDER for short. Then, we analyze the convergence performance of the algorithm. The theoretical results show that ZO-AdaSPIDER algorithm can converge to -stationary point when facing non-convex function, and its convergence rate is .
Algorithm Improvement of Movie Recommendation System based on Hybrid Recommendation Algorithm

In recent years, the Internet has developed rapidly, and in the face of thousands of data and information, it has become very critical for users to find the information that is of high value to them in the mass of information, and the recommendation system is one of the most effective ways to solve this information overload phenomenon. In this paper, the current movie recommendation algorithm is improved by using an item-based collaborative filtering algorithm for the similarity measure of items in the item-based recommendation process; In the recommendation process, two more applicable recommendation methods are considered: collaborative filtering content-based recommendation and matrix decomposition-based recommendation. It saves users time in searching, viewing and filtering, while discovering information about their potential movie preferences.
Context-Aware News Recommendation System: Incorporating Contextual Information and Collaborative Filtering Techniques

Abstract With the increasing volume of news articles available on the internet, personalized news recommendations have become increasingly important for users to discover relevant and interesting news articles. However, traditional recommender systems often fail to capture the dynamic nature of users' preferences and the changing trends in news articles. To address this challenge, this paper proposes a context-aware personalized news recommendation system that incorporates contextual information to enhance the personalization of news recommendations. The approach involves collecting, extracting, exploring, cleaning, and processing a large dataset of news articles from 19 distinct internet news sources, totaling 22,657 English pieces. Four different recommender systems were built using different techniques, including content-based methods such as TF-IDF, Bag-of-Words, and Word2Vec, and a collaborative filtering system based on click behavior. To evaluate the effectiveness of our models, we used a combination of standard comparison metric, including precision and recall, and user feedback. To demonstrate the applicability of the model, a web interface was constructed, and we used RMSE and MAE to evaluate the performance of the collaborative filtering model. In addition, we conducted a comparative study to compare the accuracy of different algorithms with different baseline methods, including random and recency. The evaluation results showed that incorporating contextual information and collaborative filtering can significantly improve the personalization of news recommendations. The study suggests that the collaborative filtering model based on click behavior is the most effective approach, with a mean MAE of 0.0252 and a mean RMSE of 0.0364. The content-based models were also effective approaches for recommending news articles, outperforming the baseline approaches.
Modified Extreme Learning Machine Algorithm with Deterministic Weight Modification for Investment Decisions based on Sentiment Analysis

Background: A significant problem in economics is stock market prediction. Due to the noise and volatility, however, timely prediction is typically regarded as one of the most difficult challenges. A sentiment-based stock price prediction that takes investors' emotional trends into account to overcome these difficulties is essential. Objective: This study aims to enhance the ELM's generalization performance and prediction accuracy. Methods: This article presents a new sentiment analysis based-stock prediction method using a modified extreme learning machine (ELM) with deterministic weight modification (DWM) called S-DELM. First, investor sentiment is used in stock prediction, which can considerably increase the model's predictive power. Hence, a convolutional neural network (CNN) is used to classify the user comments. Second, DWM is applied to optimize the weights and biases of ELM. Results: The results of the experiments demonstrate that the S-DELM may not only increase prediction accuracy but also shorten prediction time, and investors' emotional tendencies are proven to help them achieve the expected results Conclusion: The performance of S-DELM is compared with different variants of ELM and some conventional method
Experience Shaping, Social Cues and Safety: Headphone Use and Non-Use While Hiking in the United States

AbstractWe explore motivations for the use and non-use of headphones while hiking. In order to understand why people bring, or don’t bring, headphones while hiking we analyzed 247 written responses to the question “why do you prefer to bring (or not bring) headphones when hiking?” We study headphones, rather than other forms of interactive computing like mobile phones, because headphone use and non-use splits survey respondents nearly in half with 51.5% stating that they do prefer to bring headphones while hiking and 48.5% stating that they do not. Those who bring headphones did so to enhance their hike by listening to music or other audio and to avoid social interaction. Those who do not bring headphones do so to fully enjoy the sounds of nature, to invite social interaction and to increase safety. Our results suggest that within the context of hiking as constructed in the United States, use and non-use decisions are motivated by shaping the intended hiking experience, social factors and safety. We view these motivations for non-use not as obstacles to be overcome in the proliferation of technology into hiking, but as nuanced themes to be considered as part of understanding technology in the broader context of hiking.Keywords: HeadphoneshikingoutdoorsHCI outdoorsnon-use Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the United States National Science Foundation under grant IIS-1406578.Notes on contributorsZann AndersonZann Anderson is a software engineer for Autonomous Solutions, Inc. He holds a PhD in computer science from Brigham Young University. His research interests include interactive computing in the outdoors and physical visualizations of place and physical activity data.Michael D. JonesMichael D. Jones is a professor of Computer Science at Brigham Young University. He holds a PhD in Computer Science from the University of Utah. His research interests include the use of motion data in sports coaching and interactive computing in outdoor recreation.
Who’s ideating, prototyping, and evaluating? A case study of resource-limited participatory design for health and aging

ABSTRACTWhile participatory design approaches aim to include stakeholders in all design activities, in resource-limited situations, participation is challenging. Participatory designers often make decisions dealing with the tension between the agency of stakeholder participants and the project’s viability. In designing health services, care professionals have limited availability, and patients, such as low-income older adults, have limitations such as lack of transportation and lower access to specialized communication technology or broadband. Yet, it is critical to find innovative ways to engage stakeholders in the design process for services and interventions that aim to improve their health and well-being. This article explores new ways to engage stakeholders in the participatory design process in resource-limited projects. We used a case study approach focused on the design experience of a project focused on social isolation and older adults to understand the complexity of real-world participatory design. While there are tradeoffs between project viability and stakeholders’ agency, stakeholders’ agency is possible in resource-limited projects. However, experts must adjust and balance influence in different activities to achieve meaningful participation of all stakeholders. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Center for Innovation in Healthy and Resilient Aging, Arizona State University .
Ergodic sampling: Acquisition design to maximize information from limited samples

Abstract Data acquisition using equal spacing has been a standard practice in geophysics. The dense uniform sampling derived from Nyquist–Shannon sampling includes redundant samples, and it is sufficient but not necessary to adequately record target signals. We propose an ergodic sampling, which avoids the redundant samples in the dense uniform sampling and possesses the ability to capture the sufficiently similar information content as does Nyquist sampling. Ergodicity means that a key part of the system can represent the average performance of the entire system. Our ergodic samples are a critical subset of dense uniform samples and can represent the full uniform Nyquist samples. To find such a critical subset, we first examine the properties of different sampling patterns, including the sampling interval distribution, sampling angle distribution, areal sample density, and resolution in the spectral domain. We define the information sampling ability of sampling patterns based on these properties. The concept of information sampling ability that we have proposed serves as the criterion to compare different sampling patterns and assess their sampling performances. The sampling patterns with the same information sampling ability have the same capability to gather information, even though the appearance of sampling patterns may be different. We formulate an optimization problem to find this critical subset of sample locations, which has the fewest number of samples but has a similar information sampling ability as that of the desired dense uniform samples. This critical subset is irregularly located, has the optimized properties and forms the ergodic sampling pattern. We define this process of sampling design and associated understanding as the ergodic sampling. Ergodic sampling can be applied to gain two major benefits in practice. First, this approach can save a significant number of samples. We demonstrate ergodic sampling using one‐dimensional synthetic data and a two‐dimensional field geophysical dataset. The simulations confirm that, compared with other sampling strategies, ergodic sampling can use fewer samples to acquire the same amount of information, so that we can save cost. Alternatively, with the same budget, we can use the same number of samples through ergodic sampling to acquire more information. The new ergodic sampling can lead to a new generation of economic and efficient geophysical data acquisition, which could assist in increasing the discovery rate in resource exploration, tackling more earth science problems with a limited budget and can also benefit the environment in the process by reducing the invasiveness in potentially sensitive regions.
Introduction

The ability of the computers to handle larger amounts of texts and the availability of more texts in electronic form led to the rise of data driven research in computational linguistics. In the case of Machine Translation (MT) and other kinds of multilingual Natural Language Processing (NLP), the first source of large data came from collections of translations, initially in the Statistical MT (SMT) approach from IBM [1], which was based on the Proceedings of the Canadian Parliament in English and French as their data source. This research direction was followed by a proliferation of SMT models, which relied on larger and larger collections of parallel data, which consist of exact translations between a pair of languages or several languages at the same time.
An investigation of linked data catalogue features in libraries, archives, and museums: a checklist approach

Purpose In this study, the distinctive functional features of linked data (LD) catalogues were investigated to contrast with existing online public access catalogues (OPACs) and discovery systems using a checklist approach. The checklist was derived from a literature review and is composed of 10 items as follows: self-descriptive and dynamic content for resource description, linkage to external LD sources and online services, aggregation of knowledge contexts into knowledge graphs (KGs), URI-based link discovery, representation and query of LD relationships, URI-based serendipitous discovery, keyword recommendation, faceted limitation and browsing, visualization and openness of data. Design/methodology/approach Ten functional features derived from the literature were checked against existing LD catalogues offered by libraries, archives and museums (LAMs). The LD catalogues were regarded as qualified subjects if they offered functional features that were distinct from current OPACs and discovery systems through URI-based enrichment and aggregation from various LD sources. In addition to individual organizations, LD union catalogues were also included. However, LD hubs, such as ISNI, OCLC WorldCat Entities, VIAF and Wikidata, were excluded. In total, six LD catalogues from LAMs were selected as subjects for examination. Findings First, LD catalogues provide similar KG information through URI combination, and KGs also facilitate information serendipity, including social-document, intellectual, conceptual, spatial and temporal contexts and networks of corporate bodies, persons and families (CPFs). Second, LD catalogues have transformed the “seek first and browse later” paradigm into a “seek or browse” paradigm by refreshing the browsing function of traditional card catalogues with preview and new options to facilitate LD identification and discovery. Third, LD catalogues have refined keyword recommendation with the addition of the following fields: person’s title, CPF relationships, entity type and LD source. Lastly, a virtual union LD catalogue is offered. Research limitations/implications The proposed checklist revealed the unique/improved functional features of LD catalogues, allowing further investigation and comparison. More cases from the fields of medicine, engineering science and so on will be required to make revisions to fine-tune the proposed checklist approach. Originality/value To the best of the author’s knowledge, this is the first study to propose a checklist of functional features for LD catalogues and examine what the results and features of LD catalogues have achieved and are supported by from ontologies across LAMs. The findings suggest that LD provides a viable alternative to catalogues. The proposed checklist and results pave the way for the future development of LD catalogues and next-generation catalogues and also provide a basis for the future study of LD catalogues from other fields to refine the proposed checklist.
Building Comparable Corpora

In a parallel corpus we know which document is a translation of what by design. If the link between documents in different languages is not known, it needs to be established. In this chapter we will discuss methods for measuring document similarity across languages and how to evaluate the results. Then, we will proceed to discussing methods for building comparable corpora of different degrees of comparability and for different tasks.
Complete Equational Theories for the Sum-Over-Paths with Unbalanced Amplitudes

Vilmart recently gave a complete equational theory for the balanced sum-over-paths over Toffoli-Hadamard circuits, and by extension Clifford+diag(1, ζ 2 k ) circuits.Their theory is based on the phase-free ZH-calculus which crucially omits the average rule of the full ZH-calculus, dis-allowing the local summation of amplitudes.Here we study the question of completeness in unbalanced path sums which naturally support local summation.We give a concrete syntax for the unbalanced sum-over-paths and show that, together with symbolic multilinear algebra and the interference rule, various formulations of the average and ortho rules of the ZH-calculus are sufficient to give complete equational theories over arbitrary rings and fields.
The Qudit ZH-Calculus: Generalised Toffoli+Hadamard and Universality

We introduce the qudit ZH-calculus and show how to generalise the phase-free qubit rules to qudits.We prove that for prime dimensions d, the phase-free qudit ZH-calculus is universal for matrices over the ring Z[e 2πi/d ].For qubits, there is a strong connection between phase-free ZH-diagrams and Toffoli+Hadamard circuits, a computationally universal fragment of quantum circuits.We generalise this connection to qudits, by finding that the two-qudit |0 -controlled X gate can be used to construct all classical reversible qudit logic circuits in any odd qudit dimension, which for qubits requires the three-qubit Toffoli gate.We prove that our construction is asymptotically optimal up to a logarithmic term.Twenty years after the celebrated result by Shi proving universality of Toffoli+Hadamard for qubits, we prove that circuits of |0 -controlled X and Hadamard gates are approximately universal for qudit quantum computing for any odd prime d, and moreover that phase-free ZH-diagrams correspond precisely to such circuits allowing postselections.
Student Engagement in Online Learning Among Undergraduate Nursing Students

Aims: Student engagement (SE) is a significant factor for success in online learning. The low of SE will impact students' academic achievement. Purpose: This study aimed to describe SE in online learning at the Faculty of Nursing, Universitas Padjadjaran. Methods: This study was quantitative descriptive research. The population was undergraduate nursing students (N=862). The sample criteria were that respondents were active students of the Faculty of Nursing, Universitas Padjadjaran for the 2020/2021 period from the Jatinangor and Pangandaran campuses. The sampling technique was total sampling with a response rate of 64,3% (n=554). Data were collected using the Online Community and Student Engagement Scale. Data were analyzed using descriptive statistics and presented in frequency distribution tables. Results: The results showed that more than half of students (52.7%) had a low category of SE, and nearly half (47.3%) have shown a high category of SE in online learning. Students had low levels of Community Building with the Instructor, Community Building with Classmates, and Engagement with Learning (57.4%; 58.3%; and 51.8%, respectively). The SE level that the respondent currently has has the potential to be increased along with the ongoing process during the lecture period. Conclusion: More than half of students had a low category of SE in online learning this could have a negative impact on student achievement and educational completion rates. Therefore, intense interactions through discussions between students and lecturers, constructive feedback, and motivational support are highly recommended to be provided by the faculty and peers for improving the SE.
Pre-trained Models for Representation Learning

Abstract Pre-training-fine-tuning has recently become a new paradigm in natural language processing, learning better representations of words, sentences, and documents in a self-supervised manner. Pre-trained models not only unify semantic representations of multiple tasks, multiple languages, and multiple modalities but also emerge high-level capabilities approaching human beings. In this chapter, we introduce pre-trained models for representation learning, from pre-training tasks to adaptation approaches for specific tasks. After that, we discuss several advanced topics toward better pre-trained representations, including better model architecture, multilingual, multi-task, efficient representations, and chain-of-thought reasoning.
Word Representation Learning

Abstract Words are the building blocks of phrases, sentences, and documents. Word representation is thus critical for natural language processing (NLP). In this chapter, we introduce the approaches for word representation learning to show the paradigm shift from symbolic representation to distributed representation. We also describe the valuable efforts in making word representations more informative and interpretable. Finally, we present applications of word representation learning to NLP and interdisciplinary fields, including psychology, social sciences, history, and linguistics.
Graph Representation Learning

Abstract Graph structure, which can represent objects and their relationships, is ubiquitous in big data including natural languages. Besides original text as a sequence of word tokens, massive additional information in NLP is in the graph structure, such as syntactic relations between words in a sentence, hyperlink relations between documents, and semantic relations between entities. Hence, it is critical for NLP to encode these graph data with graph representation learning. Graph representation learning, also known as network embedding, has been extensively studied in AI and data mining. In this chapter, we introduce a variety of graph representation learning methods that embed graph data into vectors with shallow or deep neural models. After that, we introduce how graph representation learning helps NLP tasks.