Reviewerly/exHarmony huggingface dataset:
Sports Industry Trend Analysis in the Fourth Industrial Era : Focusing on news Topic Modeling Analysis

The purpose of this study is to explore issues in sports-fourth industry related news articles by applying text network and topic modeling analysis. The scope of the study is all domestic news reports from 2016, when the concept of the Fourth Industrial Revolution was first raised, to the present (2016-2022). The research method was conducted through text mining, news topic modeling, and social network visualization using Bigkinds and Netminer programs. For topic modeling analysis, the LDA algorithm MCMC method was applied, and topic perplexity was set as an index for optimal number of topics. The conclusions of this study are as follows. First, the high-ranking words in news articles related to sports-4th industry are ‘골프’, ‘우승’, ‘축구’, ‘중국’, ‘구굴’, ‘알파’. In the word cloud, it occupies a high proportion among the key words ‘이세돌’, ‘인공지능’, and ‘바둑’. Second, sports-fourth industry (artificial intelligence) game result prediction and artificial intelligence technology-based monitoring system are major news reports. Sports - (Big Data, Virtual Reality) All of them carry out major research interests in the broadcasting of sports events. Its main research areas include motion guidance, rehabilitation, and training for sports-(wearable) athletes. Third, the technology of the 4th industrial revolution is already subdivided into each area of physical education, and there are positive contents such as the prospect of macroscopic utilization according to technology introduction, and the threat of job paradigm change and replacement of existing jobs is also explained.
Postdigital Citizenship

This entry provides some prevalent considerations, as emerging from the literature, toward (re)defining citizenship under a postdigital view. These considerations include the definition of a postdigital city, the challenge of (re)defining civic rights, duties, and identities, the centrality of social participation and action, as well as the definition and importance of digital justice. The entry concludes with a reflection on the social and digital interdependence of human and nonhuman agents, and the role of postdigital civic identities and performativity toward making such interdependence a positive and productive enterprise.
ODTC: An online darknet traffic classification model based on multimodal self-attention chaotic mapping features

&lt;abstract&gt;&lt;p&gt;Darknet traffic classification is significantly important to network management and security. To achieve fast and accurate classification performance, this paper proposes an online classification model based on multimodal self-attention chaotic mapping features. On the one hand, the payload content of the packet is input into the network integrating CNN and BiGRU to extract local space-time features. On the other hand, the flow level abstract features processed by the MLP are introduced. To make up for the lack of the indistinct feature learning, a feature amplification module that uses logistic chaotic mapping to amplify fuzzy features is introduced. In addition, a multi-head attention mechanism is used to excavate the hidden relationships between different features. Besides, to better support new traffic classes, a class incremental learning model is developed with the weighted loss function to achieve continuous learning with reduced network parameters. The experimental results on the public CICDarketSec2020 dataset show that the accuracy of the proposed model is improved in multiple categories; however, the time and memory consumption is reduced by about 50$ % $. Compared with the existing state-of-the-art traffic classification models, the proposed model has better classification performance.&lt;/p&gt;&lt;/abstract&gt;
An adaptive selective ensemble algorithm for fault classification

Abstract With the aim of improving the classification performance of a single extreme learning machine (ELM) for fault diagnosis, an adaptive selective ensemble classification algorithm is proposed based on the idea of ensemble learning. The proposed algorithm achieves better classification performance than a single model and the selected ELM subnetworks are selected adaptively and the ensemble weights of selected ELM subnetworks are calculated adaptively, which avoids the complex process of manually selecting subnetworks and calculating ensemble weights. First, a regularized error weight adjustment ELM is constructed by introducing regularization parameters and error weight adjustment matrix to a standard ELM, where the error weight adjustment matrix is obtained by the method of adaptive Gaussian kernel density estimation. Then, discrepancy subnetworks are constructed using six different activation functionsand the ensemble weights of subnetworks are obtained adaptively according to the normalized ratio of mean and variance of subnetwork F -scores to achieve the ensemble of subnetworks. Finally, the adaptive selective ensemble classification algorithm is validated using the UCI dataset and experimental acoustic emission signals of gearbox faults. The results show that the adaptive selective ensemble method can improve the stability and accuracy of classification algorithms and the achieved classification accuracy for experimental acoustic emission was 0.9773.
CAMShift-Based Moving Object Tracking System

Moving object tracking is the process of locating a moving target over time in sequential video frames, and it is an essential research topic in the field of computer vision. As a well-established and efficient algorithm for kernel-based object tracking, CAMShift performs well with simple moving objects, but it is not robust in more complex situations. To overcome the defects in the process of tracking moving objects, a combination of CAMShift and Kalman Filter is widely used in industrial applications and practices. A CAMShift-based tracking system with Kalman Filter-based track window prediction is presented in this paper. When the camera is moving or the object is occluded, the velocity of the moving object is used by Kalman Filter to provide a prediction of the tracking window. Both the original CAMShift-based tracker and the hybrid system are evaluated by the LaSOT benchmark. The results show that the hybrid tracking system has a better success rate on certain categories of objects and situations than the original CAMShift tracker.
Integrating DeblurGAN and CNN to improve the accuracy of motion blur X-Ray image classification

X-rays are common tools used in clinical diagnoses. During the X-ray process, a patient is required to lie still or to hold a deep breath. However, when dealing with patients who may shake involuntarily or with restless children, the required conditions are not always achievable, and blurred images often occur. If these patients receive repeated X-rays, they are exposed to additional amounts of radiation. This research integrates the DeblurGAN model and a convolutional neural network (CNN) to increase the accuracy for classifying clinical X-ray motion blurred images, eliminating the need for repeated clinical X-rays. Results show that classification accuracy of the motion blur group was 87% while the deblurred group was 91%, indicating the process not only improves classification accuracy but also restores the deblurred group images to a 92% level of the original image group. This study verifies the feasibility of applying the DeblurGAN model to medical X-ray image deblurring, helping resolve the challenges presented by a lack of X-ray motion blur image data and allowing InceptionV3 to accurately identify the problem. This method can be further applied to other motion blur medical images (such as MRI and CT) to improve overall clinical diagnosis efficiency.
Improving zero-shot action recognition using human instruction with text description

Zero-shot action recognition, which recognizes actions in videos without having received any training examples, is gaining wide attention considering it can save labor costs and training time. Nevertheless, the performance of zero-shot learning is still unsatisfactory, which limits its practical application. To solve this problem, this study proposes a framework to improve zero-shot action recognition using human instructions with text descriptions. The proposed framework manually describes video contents, which incurs some labor costs; in many situations, the labor costs are worth it. We manually annotate text features for each action, which can be a word, phrase, or sentence. Then by computing the matching degrees between the video and all text features, we can predict the class of the video. Furthermore, the proposed model can also be combined with other models to improve its accuracy. In addition, our model can be continuously optimized to improve the accuracy by repeating human instructions. The results with UCF101 and HMDB51 showed that our model achieved the best accuracy and improved the accuracies of other models.
Book’s materiality:mapping and thematic visualization in the information science literature

Introduction: The book is a symbolic object or a “semiophore”, which denotes its condition of being “beyond information”, involving, in addition, materiality and institutionality. Objective: To map authors, works and themes related or tangent to the subject “book’s materiality” in the scope of information science. Methodology: A mapping of the thematic coverage was carried out in national and international databases, then, the texts identified were analyzed and visualized using Voyant Toolstool. Results: 100 documentsthat covered the period between 1996 and 2020 were analyzed, with the highest occurrence of publication of book’s materiality thematic in the years of 2016, 2017 and 2018. In terms of terminology, expressions that lead to the understanding of the book as a documentary and symbolic support that contains information, stand out, and can compose collections in institutions worried with social memory, as is the case of libraries. The journals that most publish articles on the subject were Encontros Bibliand Journal of Documentation, and the databases that index the most journals that address the topic were BRAPCIand Library and Information Science Abstracts. Conclusion: The book’s materiality theme establishes a bridge between the breadth of the “materiality” concept and a relationship between book and document. The book –when studied based on the multidimensionality of materiality, whether expressed in analog or digital environment –constitutes itself as a symbolic object in addition to being conceived as a mere source of information.
Mining Interest Trends and Adaptively Assigning Sample Weight for Session-based Recommendation

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignore the differences in importance between different samples, which limits the model-fitting performance. To tackle these issues, we put forward the method, Mining Interest Trends and Adaptively Assigning Sample Weight, abbreviated as MTAW. Specifically, we model users' instant interest based on their present behavior and all their previous behaviors. Meanwhile, we discriminatively integrate instant interests to capture the changing trend of user interest to make more personalized recommendations. Furthermore, we devise a novel loss function that dynamically weights the samples according to their prediction difficulty in the current epoch. Extensive experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our method.
eCom'23: The SIGIR 2023 Workshop on eCommerce

eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the largest web sites (e.g. Airbnb, Alibaba, Amazon, eBay, Facebook, Flipkart, Lowes's, Taobao, Target). SIGIR has for several years seen sponsorship from eCommerce organizations, reflecting the importance of IR research to them. The purpose of this workshop is (1) to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) to determine how to use eCommerce's unique combination of free text, structured data, and customer behavior data to improve search relevance, and (3) to examine how to build datasets and evaluate algorithms in this domain.
T2Ranking: A Large-scale Chinese Benchmark for Passage Ranking

Passage ranking involves two stages: passage retrieval and passage re-ranking, which are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues. To address this problem, we introduce T2Ranking, a large-scale Chinese benchmark for passage ranking. T2Ranking comprises more than 300K queries and over 2M unique passages from real-world search engines. Expert annotators are recruited to provide 4-level graded relevance scores (fine-grained) for query-passage pairs instead of binary relevance judgments (coarse-grained). To ease the false negative issues, more passages with higher diversities are considered when performing relevance annotations, especially in the test set, to ensure a more accurate evaluation. Apart from the textual query and passage data, other auxiliary resources are also provided, such as query types and XML files of documents which passages are generated from, to facilitate further studies. To evaluate the dataset, commonly used ranking models are implemented and tested on T2Ranking as baselines. The experimental results show that T2Ranking is challenging and there is still scope for improvement. The full data and all codes are available at https://github.com/THUIR/T2Ranking/.
A Short Survey on Business Models of Decentralized Finance (DeFi) Protocols

Decentralized Finance (DeFi) services are moving traditional financial operations to the Internet of Value (IOV) by exploiting smart contracts, distributed ledgers, and transactions among different protocols. The exponential increase of the Total Value Locked (TVL) in DeFi foreshadows a bright future for automated money transfers in a plethora of services. In this short survey paper, we describe the business models of various DeFi protocol types—namely, Protocols for Loanable Funds (PLFs), Decentralized Exchanges (DEXs), and Yield Aggregators. We then abstract and compare the general business models of those protocol types. Finally, we provide open research challenges that will involve different domains such as economics, finance, and computer science.
Towards Multi-Interest Pre-training with Sparse Capsule Network

The pre-training paradigm, i.e., learning universal knowledge across a wide spectrum of domains, has increasingly become a new de-facto practice in many fields, especially for transferring to new domains. The recent progress includes universal pre-training solutions for recommendation. However, we argue that the common treatment utilizing the masked language modeling or simple data augmentation via contrastive learning is not sufficient for pre-training a recommender system, since a user's intent could be more complex than predicting the next word or item. It is more intuitive to go a step further by devising the multi-interest driven pre-training framework for universal user understanding. Nevertheless, incorporating multi-interest modeling in recommender system pre-training is non-trivial due to the dynamic, contextual, and temporary nature of the user interests, particularly when the users are from different domains. The limited effort on this line has greatly rendered it as an open question.
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation

The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information. Furthermore, since the items in the user behaviors are intertwined with each other, these models are incomplete to distinguish the inherent periodicity obscured in the time domain. In this work, we shift the perspective to the frequency domain, and propose a novel Frequency Enhanced Hybrid Attention Network for Sequential Recommendation, namely FEARec. In this model, we firstly improve the original time domain self-attention in the frequency domain with a ramp structure to make both low-frequency and high-frequency information could be explicitly learned in our approach. Moreover, we additionally design a similar attention mechanism via auto-correlation in the frequency domain to capture the periodic characteristics and fuse the time and frequency level attention in a union model. Finally, both contrastive learning and frequency regularization are utilized to ensure that multiple views are aligned in both the time domain and frequency domain. Extensive experiments conducted on four widely used benchmark datasets demonstrate that the proposed model performs significantly better than the state-of-the-art approaches.
Learning Fine-grained User Interests for Micro-video Recommendation

Recent years have witnessed the rapid development of online micro-video platforms, in which the recommender system plays an essential role in overcoming the information overloading problem and providing personalized content for users. Although some progress has been achieved in the micro-video recommendation, there are still some limitations in learning the representations of user interests and video features. Specifically, the user modeling in existing works is performed at a coarse-grained level, i.e., video level. However, in micro-video recommendation, the user feedback is at a continuous form---users can skip over a video at each frame---which reveals fine-grained user preferences. In this work, we approach the problem of learning fine-grained user preferences for micro-video recommendation by first collecting two real-world datasets. To address the challenges of preference modeling and weak supervision signal, we propose a solution named FRAME (short for Fine-gRAined preference-modeling for Micro-video rEcommendation). Specifically, we first adopt visual feature extraction and transformation to maintain the fine-grained video embeddings. We then propose graph convolution layers to learn the user preference from complex and fine-grained user-clip relations, and hybrid-supervision objectives for enhancing the supervision signal. The experimental results on two collected real-world datasets demonstrate the effectiveness of our proposed model. We release the datasets and codes in https://github.com/tsinghua-fib-lab/FRAME, which we believe can benefit the community.
Exploring 360-Degree View of Customers for Lookalike Modeling

Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers' different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.
Advancements in extracting social determinants of health information from narrative text

Journal Article Advancements in extracting social determinants of health information from narrative text Get access Kevin Lybarger, Kevin Lybarger Department of Information Sciences and Technology, George Mason University, Fairfax, Virginia, USA Corresponding Author: Kevin Lybarger, PhD, Department of Information Sciences and Technology, George Mason University, 4400 University Dr. MSN 1G8, Fairfax, VA 22030, USA; klybarge@gmu.edu Search for other works by this author on: Oxford Academic PubMed Google Scholar Oliver J Bear Don't Walk, IV, Oliver J Bear Don't Walk, IV Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, Washington, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Meliha Yetisgen, Meliha Yetisgen Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, Washington, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Özlem Uzuner Özlem Uzuner Department of Information Sciences and Technology, George Mason University, Fairfax, Virginia, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of the American Medical Informatics Association, Volume 30, Issue 8, August 2023, Pages 1363–1366, https://doi.org/10.1093/jamia/ocad121 Published: 16 February 2023 Article history Published: 16 February 2023 Editorial decision: 20 June 2023 Received: 20 June 2023
Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?

Abstract The main idea behind artificial intelligence was simple: what if we study living systems to develop new, practical computing systems that possess “lifelike” properties? And that’s exactly how evolutionary computing emerged. Researchers came up with ideas inspired by the principles of evolution to develop intelligent methods to tackle hard problems. The efficacy of these methods made researchers seek inspiration in living organisms and systems and extend the evolutionary concept to other nature-inspired ideas. In recent years, nature-inspired computing has exhibited an exponential increase in the number of algorithms that are presented each year. Authors claim that they are inspired by a behavior found in nature to come up with a lifelike algorithm. However, the mathematical background does not match the behavior in the majority of these cases. Thus the question is, do all nature-inspired algorithms remain lifelike? Also, are there any ideas included that contribute to computing? This study aims to (a) present some nature-inspired methods that contribute to achieving lifelike features of computing systems and (b) discuss if there is any need for new lifelike features.