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Multi-modal news event detection with external knowledge Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-28 Zehang Lin, Jiayuan Xie, Qing Li
News event detection involves the identification and categorization of significant happenings or occurrences from social media data. Recent work has typically relied on datasets collected solely based on event-related keywords. However, datasets collected with such keywords tend to oversimplify the task. They reduce the contribution of non-text modalities and do not fully capture real-world scenarios
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From coarse to fine: Enhancing multi-document summarization with multi-granularity relationship-based extractor Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-28 Ming Zhang, Jiyu Lu, Jiahao Yang, Jun Zhou, Meilin Wan, Xuejun Zhang
Multi-Document Summarization (MDS) is a challenging task due to the fact that multiple documents not only have extremely long inputs but may also be overlapping, complementary, or contradictory to each other. In this paper, we propose to capture complex cross-document interactions to handle lengthy inputs for better multi-document summarization. Specifically, we present , a coarse-to-fine MDS framework
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Unveiling the impacts of performance-contingent incentivized reviews on subsequent supplementary reviews Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-28 Yuanhong Ma, Zhong Yao, Jing Zhang, Pengfei Tang
In this paper, we track 856 products with 309,385 online reviews over eight weeks on a leading e-commerce platform to investigate the effects of Performance-contingent Incentivized Reviews (PIRs) on subsequent Supplementary Reviews (SRs). Our results demonstrate that the emergence of PIRs in an online review system has a positive spillover effect on subsequent SRs. We further conduct a randomized controlled
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MLRN: A multi-view local reconstruction network for single image restoration Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-28 Qingbo Hao, Wenguang Zheng, Chundong Wang, Yingyuan Xiao, Luotao Zhang
Limited by storage conditions, the degradation of old photos exhibits complex and diverse features. Existing image restoration methods heavily rely on features extracted from a single view, allowing them to effectively handle a certain type of noise; however, they lack the versatility to address multiple types of noise. This poses great challenges in accurately detecting various noises and integrating
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Forecasting tourism demand with search engine data: A hybrid CNN-BiLSTM model based on Boruta feature selection Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-27 Ji Chen, Zhihao Ying, Chonghui Zhang, Tomas Balezentis
Using search engine data (SED) to forecast tourist flow is essential for management and security warnings at tourist attractions. Existing prediction models cannot effectively handle noise in the SED and external uncertainties. Thus, insufficient feature extraction may hinder the fitting of tourist flow time series. To improve the prediction accuracy, a forecasting method combining the Boruta algorithm
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Flavor analysis and region prediction of Chinese dishes based on food pairing Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-26 Jie Zhou, Xing Xin, Wei Li, Haohan Ding, Shuai Yu, Xiaohui Cui
There are thirty-four provincial administrative regions in China, and each region possesses its own distinct food culture. However, existing studies on flavor-based research often treat Chinese dishes from various regions as a single entity, resulting in biases when examining regional distinctions and the variety of recipes. To explore the potential correlation between food ingredients and regions
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Strong robust copy-move forgery detection network based on layer-by-layer decoupling refinement Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-24 Jingyu Wang, Xuesong Gao, Jie Nie, Xiaodong Wang, Lei Huang, Weizhi Nie, Mingxing Jiang, Zhiqiang Wei
This paper proposes an all-encompassing methodology called Strong Robust Copy-Move Forgery Detection Network based on Layer-by-Layer Decoupling Refinement (DRNet) which concentrates on detecting a pair of structurally complete similar areas (the source and the tampered area) in the copy-move forgery image by fully extracting the semantically irrelevant shallow information. The DRNet consists of two
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Online attention versus knowledge utilization: Exploring how linguistic features of scientific papers influence knowledge diffusion Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-23 Kejun Chen, Ningyuan Song, Yuehua Zhao, Jiaer Peng, Ye Chen
Knowledge diffusion breeds technological innovation and promotes scientific development. In modern times, knowledge is disseminated in both the academic community and on social media. Despite a rich body of researches on factors influencing knowledge diffusion, they pay less attention to linguistic features and mechanisms behind different kinds of knowledge diffusion. To address the research gaps,
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FairCare: Adversarial training of a heterogeneous graph neural network with attention mechanism to learn fair representations of electronic health records Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-23 Yan Wang, Ruochi Zhang, Qian Yang, Qiong Zhou, Shengde Zhang, Yusi Fan, Lan Huang, Kewei Li, Fengfeng Zhou
Electronic health record (EHR) datasets have increasingly been harnessed by artificial intelligence (AI) for predictive modeling, yet the ethnicity fairness of these models remains underexplored. To address this issue, we propose FairCare, a novel deep learning framework for ethnically fair EHR representation. FairCare introduces an ethnicity-heterogeneous graph neural network, enhanced with an attention
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Decoupled domain-specific and domain-conditional representation learning for cross-domain recommendation Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-22 Yu Zhang, Zhiyong Cheng, Fan Liu, Xun Yang, Yuxin Peng
Cross-domain recommendation (CDR) has become popular to alleviate the sparsity problem in target-domain recommendation by utilizing auxiliary domain knowledge. A basic assumption of CDR is that users have shared preferences across domains, but most existing CDR models do not distinguish between users’ unique preferences and shared preferences. We propose a new CDR model, called DRLCDR, which adopts
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Near-future vs. distant-future: Unraveling the effect of knowledge differentiation on customers’ decision to purchase paid knowledge from the temporal distance perspective Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-20 Cheng Zhou, Haoxin Xiu
Audio provides an alternative channel to text for customers to obtain knowledge. The differentiation of audio-based from text-based knowledge may play a vital role in consumers’ decision to purchase knowledge products. However, little attention has been paid to the differentiation of audio-based knowledge and, more importantly, to how to place such differentiation. This study explored the impact of
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Self-augmented sequentiality-aware encoding for aspect term extraction Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-20 Qingting Xu, Yu Hong, Jiaxiang Chen, Jianming Yao, Guodong Zhou
Aspect Term Extraction (ATE) is a natural language processing task which recognizes and extracts aspect terms from sentences. The recent study in this field successfully leverages Pretrained Language Model (PLM) and data augmentation, which contributes to the construction of knowledgeable encoder for ATE. In this paper, we propose a novel method to strengthen ATE encoder, using self-augmented mechanism
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BCE4ZSR: Bi-encoder empowered by teacher cross-encoder for zero-shot cold-start news recommendation Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-19 Muhammad Arslan Rauf, Mian Muhammad Yasir Khalil, Weidong Wang, Qingxian Wang, Muhammad Ahmad Nawaz Ul Ghani, Junaid Hassan
In the realm of news recommendations, the persistent challenge of the cold-start problem continues to impede progress. Existing approaches rely heavily on information exchange between news articles and users to personalize news recommendations and have struggled to adapt to users and news articles without historical interaction data. In this paper, we introduce BCE4ZSR, a novel framework that leverages
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HCCKshell: A heterogeneous cross-comparison improved Kshell algorithm for Influence Maximization Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-15 Yaqiong Li, Tun Lu, Weimin Li, Peng Zhang
Influence maximization (IM) has been extensively researched in the information propagation field and applied in various domains. However, existing studies on the IM have primarily focused on network structure, and lack the in-depth exploration of online network complexities, like personal history or preference. In this paper, a heterogeneous cross-comparison improved Kshell algorithm (HCCKshell) is
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Influence maximization on hypergraphs via multi-hop influence estimation Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-15 Xulu Gong, Hanchen Wang, Xiaoyang Wang, Chen Chen, Wenjie Zhang, Ying Zhang
Influence Maximization (IM) has promising applications in social network marketing and has been extensively researched over the past years. However, previous IM studies mainly focus on ordinary graphs rather than hypergraphs, where edges cannot accurately describe group interactions or relationships. To model group interactions, we investigate the IM problem on hypergraphs under the Susceptible–Infected
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A Token-based transition-aware joint framework for multi-span question answering Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-14 Zhiyi Luo, Yingying Zhang, Shuyun Luo
Multi-span question answering has gained prominence as it aligns more closely with real-world user requirements compared to single-span question answering. The utilization of pretrained language models has shown promise in improving multi-span question answering, particularly for factoid questions that necessitate entity-based answers. However, existing methods tend to overlook critical information
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Does academic engagement with industry come at a cost for early career scientists? Evidence from high-tech enterprises’ Ph.D. funding programs Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-09 Xi Zhang, Dehu Yin, Li Tang, Hongke Zhao
Academic engagement with industry is now practiced by more scientists than ever before. Despite broad consensus regarding the positive effect on senior and successful scientists’ research productivity, its effects on early career scientists have remained insufficiently investigated. Utilizing a novel dataset drawn from both awardees and nominees in the case of high-tech enterprises’ Ph.D. funding programs
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Improving extractive summarization with semantic enhancement through topic-injection based BERT model Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-08 Yiming Wang, Jindong Zhang, Zhiyao Yang, Bing Wang, Jingyi Jin, Yitong Liu
In the field of text summarization, extractive techniques aim to extract key sentences from a document to form a summary. However, traditional methods are not sensitive enough to obtain the core semantics of the text, resulting in summaries that contain complicate comprehension. Recently, topic extraction technology extracts core semantics from text, enabling accurate summaries of the main points of
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AI for social science and social science of AI: A survey Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-07 Ruoxi Xu, Yingfei Sun, Mengjie Ren, Shiguang Guo, Ruotong Pan, Hongyu Lin, Le Sun, Xianpei Han
Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize
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Dialogue summarization enhanced response generation for multi-domain task-oriented dialogue systems Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-06 Lifang Wang, Meng Zhao, Hongru Ji, Zejun Jiang, Ronghan Li, Zhongtian Hu, Xinyu Lu
Task-oriented dialogue systems (TOD) are blossoming with the advances in pre-trained language models (PrLM). Recently, research on PrLM-based multi-domain TOD has arisen with many outstanding outcomes. However, three challenges still need to be thoroughly studied. First, most current works regard dialogue state tracking as a generative problem supervised by concatenated slot-value sequences, impairing
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Learning context-aware region similarity with effective spatial normalization over Point-of-Interest data Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-06 Jiahui Jin, Yifan Song, Dong Kan, Binjie Zhang, Yan Lyu, Jinghui Zhang, Hongru Lu
With the increasing availability of Point-of-Interest (PoI) data driven by the widespread adoption of location-based services, there is a growing demand to comprehend the similarities among different regions. Existing works generally regard regions with the similar number or distribution of PoI as similar. However, in practice, the region similarity depends on the surrounding environment as well, owing
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An adaptive category-aware recommender based on dual knowledge graphs Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-06 Yuanbo Xu, Tian Li, Yongjian Yang, Weitong Chen, Lin Yue
Combining the knowledge graph (KG) with the personalized item recommendation has become an important method to improve user experience. In the personalized item recommendation, users have their preferences on categories that influence their choices of items. In order to fully use category information, we explicitly focus on their impact on user preference and run through the whole recommendation process
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Integrating user short-term intentions and long-term preferences in heterogeneous hypergraph networks for sequential recommendation Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-05 Bingqian Liu, Duantengchuan Li, Jian Wang, Zhihao Wang, Bing Li, Cheng Zeng
Sequential recommendation tries to model the binary correlations among users and items in a sequence to provide accurate recommendations. However, user behaviors are influenced by both their intentions and preferences. Existing sequential recommendation models cannot effectively capture the user’s real preferences and intentions just from the interaction data. To tackle this dilemma, we propose IPSRec
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Generative Adversarial Networks for text-to-face synthesis & generation: A quantitative–qualitative analysis of Natural Language Processing encoders for Spanish Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-05 Eduardo Yauri-Lozano, Manuel Castillo-Cara, Luis Orozco-Barbosa, Raúl García-Castro
In recent years, the development of Natural Language Processing (NLP) text-to-face encoders and Generative Adversarial Networks (GANs) has enabled the synthesis and generation of facial images from textual description. However, most encoders have been developed for the English language. This work presents the first study of three text-to-face encoders, namely, the RoBERTa pre-trained model and the
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POI recommendation for random groups based on cooperative graph neural networks Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-05 Zhizhong Liu, Lingqiang Meng, Quan Z. Sheng, Dianhui Chu, Jian Yu, Xiaoyu Song
Group Point-of-Interests (POI) recommendation devotes to find the optimal POIs for groups, which has extracted extensive attention. This work first brings forward a novel POI recommendation model for random groups based on Cooperative Graph Neural Networks (named as CGNN-PRRG). We have done three innovative work. (1) We propose a new fitted presentation learning method for generating the fitted representations
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Study on the impact of recommendation algorithms on user perceived stress and health management behaviour in short video platforms Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-05 Xiwei Wang, Siguleng Wuji, Yutong Liu, Ran Luo, Chengcheng Qiu
Short videos have become an ideal platform for global health information dissemination. Past research has tended to overlook the potentially negative experiences associated with recommendation algorithms and their positive coping mechanisms, while focusing too much on short-term effects at the expense of long-term effects. This study integrates multiple theoretical frameworks and proposes a new theoretical
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Cognitive Biases in Fact-Checking and Their Countermeasures: A Review Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-03 Michael Soprano, Kevin Roitero, David La Barbera, Davide Ceolin, Damiano Spina, Gianluca Demartini, Stefano Mizzaro
The increase of the amount of misinformation spread every day online is a huge threat to the society. Organizations and researchers are working to contrast this misinformation plague. In this setting, human assessors are indispensable to correctly identify, assess and/or revise the truthfulness of information items, i.e., to perform the fact-checking activity. Assessors, as humans, are subject to systematic
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Transforming sentiment analysis for e-commerce product reviews: Hybrid deep learning model with an innovative term weighting and feature selection Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-30 Punithavathi Rasappan, Manoharan Premkumar, Garima Sinha, Kumar Chandrasekaran
Improving user satisfaction by analyzing many user reviews found on e-commerce platforms is becoming increasingly significant in this modern world. However, accurately predicting sentiment polarities within these reviews remains challenging due to variable sequence lengths, textual orders, and complex logic within the content. This study introduces a new optimized Machine Learning (ML) algorithm named
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Is gold open access helpful for academic purification? A causal inference analysis based on retracted articles in biochemistry Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-31 Er-Te Zheng, Zhichao Fang, Hui-Zhen Fu
The relationship between transparency and credibility has long been a subject of theoretical and analytical exploration within the realm of social sciences, and it has recently attracted increasing attention in the context of scientific research. Retraction serves as a pivotal mechanism in addressing concerns about research integrity. This study aims to empirically examining the relationship between
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Safety: A spatial and feature mixed outlier detection method for big trajectory data Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-02 Yang Wu, Junhua Fang, Wei Chen, Pengpeng Zhao, Lei Zhao
Trajectories, as sequential data records generated by continuously collecting sample points from positioning sensors, have the capability to effectively depict the motion patterns of mobile entities. The primary objective of trajectory outlier detection is to identify entities that exhibit aberrant behavior. However, outlier detection for massive trajectories still faces challenges in terms of computational
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Unveiling the secrets of online consumer choice: A deep learning algorithmic approach to evaluate and predict purchase decisions through EEG responses Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-01 Yiran Li, Qihua Liu, Jia Wu
This study utilized cognitive neuroscience experiments to assess and predict online individual behavior by evaluating brain activity signals. We conducted an event-related potential (ERP) experiment and analyzed the data obtained from 85 participants. Moreover, we employed a deep learning algorithm to predict purchase decision-making behavior by examining four ERP components as predictive indicators
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Image retrieval using compact deep semantic correlation descriptors Inf. Process. Manag. (IF 8.6) Pub Date : 2024-02-01 Bo-Jian Zhang, Guang-Hai Liu, Zuoyong Li, Shu-Xiang Song
Significant progress has been made in instance image retrieval based on deep feature aggregation. However, existing approaches are limited by two issues: 1) The inability of deep features to localize target objects generates inaccurate feature descriptions and 2) using short vector feature representations provides unsatisfactory retrieval performance. To address these issues, we propose the compact
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A cross modal hierarchical fusion multimodal sentiment analysis method based on multi-task learning Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-29 Lan Wang, Junjie Peng, Cangzhi Zheng, Tong Zhao, Li’an Zhu
Humans often express affections and intentions through multiple forms when communicating, involving text, audio, and vision modalities. Using a single modality to determine the sentiment state may be biased, but combining multiple clues can fully explore more comprehensive information. Effective fusion of heterogeneous data is one of the core problems of multimodal sentiment analysis. Most cross-modal
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The voices of the displaced: Mobility and Twitter conversations of migrants of Ukraine in 2022 Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-30 Richard Lemoine-Rodríguez, Johannes Mast, Martin Mühlbauer, Nico Mandery, Carolin Biewer, Hannes Taubenböck
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Latent representation discretization for unsupervised text style generation Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-29 Yang Gao, Qianhui Liu, Yizhe Yang, Ke Wang
Language models, such as BART and GPT, have been shown to be highly effective at producing quality headlines. However, without clear guidelines for what constitutes a particular writing style, they may generate text that does not meet the desired style criteria (i.e., attention-grabbing), even if the resulting text is grammatically correct and semantically coherent. In this study, we introduce a novel
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Risk disclosure and entrepreneurial resource acquisition in crowdfunding digital platforms: Evidence from digital technology ventures Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-26 Hong Huo, Chen Wang, Chunjia Han, Mu Yang, Wen-Long Shang
The widespread development of digital technology facilitates the emergence of new entrepreneurial modes, of which crowdfunding digital platforms are one. In the digital environment of crowdfunding platforms, digital entrepreneurs can obtain the essential resources necessary for their startups' rapid and cost-effcient development. However, the information asymmetry derived from the digital nature of
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Legal Judgment Prediction via graph boosting with constraints Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-25 Suxin Tong, Jingling Yuan, Peiliang Zhang, Lin Li
Legal Judgment Prediction (LJP) is a multi-task multi-label problem in the civil law system, involving the prediction of law articles, charges, and terms of penalty based on fact descriptions. However, most existing research approaches LJP as a single-label scenario, neglecting the correlations between multiple labels and failing to consider cross-task consistency constraints in a multi-label scenario
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A reversible natural language watermarking for sensitive information protection Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-25 Lingyun Xiang, Yangfan Liu, Zhongliang Yang
Existing methods have evolved from using synonym substitution to incorporating arbitrary word substitution to achieve reversible natural language watermarking. However, a notable limitation is that they are prone to overlook the sensitivity of information associated with the original words, with a tendency to prefer non-sensitive words for substitution. As a result, a potential risk of sensitive information
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VIEMF: Multimodal metaphor detection via visual information enhancement with multimodal fusion Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-23 Xiaoyu He, Long Yu, Shengwei Tian, Qimeng Yang, Jun Long, Bo Wang
In this paper, we study multimodal metaphor detection to obtain real semantic meaning from multiple heterogeneous information sources. The existing approaches mainly suffer from two drawbacks. (1) They focus on textual aspects, overlooking the characteristics of visual metaphor information. (2) Efficient methods for fusing multimodal metaphor features are lacking. To address the first issue, we propose
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Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-24 Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, Prayag Tiwari
Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class
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Cross-domain correlation representation for new fault categories discovery in rolling bearings Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-24 Chenglong Wang, Jie Nie, Weizhi Nie, Peizhe Yin, Di Niu, Xinyue Liang, Shusong Yu
Fault diagnosis technology plays a crucial role in preventing faults and mitigating safety hazards, particularly in domains such as nuclear power, aerospace, and manufacturing. However, obtaining an ample number of fault samples is challenging due to the stringent requirements for safe and reliable equipment operation in real production environments. Currently, various deep learning-based techniques
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Unveiling urban traffic accessibility patterns and phase diagrams of traffic direction through real-time navigation data in Beijing Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-20 Bing Liu, Yifang Ma, Jin Zhang, Yi Kuang, Junjie Bian, Xin Jiang
Urban transportation accessibility plays a crucial role in assessing traffic conditions and gaining insights into urban development. Current research on accessibility patterns often relies on sensor data, focusing predominantly on specific locations or times. Recognizing the need for a more holistic study that considers the interconnected impact of both geographical location and time variables, this
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CoTea: Collaborative teaching for low-resource named entity recognition with a divide-and-conquer strategy Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-20 Zhiwei Yang, Jing Ma, Kang Yang, Huiru Lin, Hechang Chen, Ruichao Yang, Yi Chang
Low-resource named entity recognition (NER) aims to identify entity mentions when training data is scarce. Recent approaches resort to distant data with manual dictionaries for improvement, but such dictionaries are not always available for the target domain and have limited coverage of entities, which may introduce noise. In this paper, we propose a novel Collaborative Teaching (CoTea) framework for
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End-to-end approach of multi-grained embedding of categorical features in tabular data Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-22 Han Liu, Qianxi Qiu, Qin Zhang
In recent years, it has been a commonly adopted strategy to transform categorical data into numerical one to suit popular learning approaches, such as neural networks. The above-mentioned transformation has been undertaken popularly through feature embedding within the setting of representation learning, which has led to successful applications in natural language processing and knowledge graph. However
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Deconstructing cultural appropriation in online communities: A multilayer network analysis approach Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-20 Enrico Corradini
In this study, we introduce a novel multilayer network model designed to analyze complex social phenomena in online communities. The model captures intricate relationships between users, content, and specific aspects of social phenomena, providing a comprehensive framework for understanding these interactions. We applied this model to a dataset of over 1 million Reddit comments from January to April
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Conceptualizing and measuring privacy boundary turbulence in technological contexts: Constructing a measurement scale Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-20 Xiaoxiao Meng
Digital platforms have provided convenience, but they increase information misuse risks, which can cause privacy boundary turbulence, a key concept of Communication Privacy Management theory. Contrary to previous measurements of turbulence that mostly focus on actual privacy violation experiences, I propose a new measurement scale incorporating the perceived privacy violations. To construct the scale
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Time-aware multi-behavior graph network model for complex group behavior prediction Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-20 Xiao Yu, Weimin Li, Cai Zhang, Jingchao Wang, Yan Zhao, Fangfang Liu, Quanke Pan, Huazhong Liu, Jihong Ding, Dehua Chen
In the multifaceted landscape of social networks, user behaviors manifest in various patterns, contributing to the diversity of group behaviors. Current research on group behavior modeling often limits its focus to single behavioral types, overlooking the interplay among different behaviors. To bridge this gap, we introduce Time-aware Multi-behavior Graph Network (TMGN) model. This model integrates
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DT4KGR: Decision transformer for fast and effective multi-hop reasoning over knowledge graphs Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-19 Yi Xia, Junyong Luo, Gang Zhou, Mingjing Lan, Xiaohui Chen, Jing Chen
Multi-hop reasoning over knowledge graphs has received plenty of attention from researchers and is being widely applied to facilitate the development of recommender systems, question answering systems, and other information retrieval systems. Existing multi-hop reasoning methods tend to suffer from poor training efficiency as a result of the large search space and have difficulty tackling missing paths
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A Multi-Criteria Decision Support Model for Restaurant Selection Based on Users' Demand Level: The Case of Dianping.com Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-17 Ziwei Shu, Ramón Alberto Carrasco, Manuel Sánchez-Montañés, Javier Portela García-Miguel
The Internet, by offering a variety of information sources such as online reviews, aids people in selecting restaurants. However, it also prolongs their decision-making process due to the need to integrate information across multiple criteria. Existing decision support models for choosing satisfactory restaurants overlook users' varying demand levels for each aspect of the restaurant, making the process
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CAF-ODNN: Complementary attention fusion with optimized deep neural network for multimodal fake news detection Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-14 Alex Munyole Luvembe, Weimin Li, Shaohau Li, Fangfang Liu, Xing Wu
Fake news is a real problem; unfortunately, it seems to worsen. Even though some false news detection methods have made significant progress, current multimodal approaches integrate cross-modal features directly without considering uncorrelated semantic representations may introduce noise into the multimodal features. This phenomenon reduces model accuracy by obscuring subtle differences between text
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Configurational determinants of time-to-win in NSFC youth program funding: Insights from Chinese library and information science Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-13 Shanshan Zhai, Lixin Xia, Maomao Chi, Xuguang Li
While extensive research has delved into various facets of science funding outputs and the determinants of funding approval, prevailing methodologies predominantly rely on descriptive statistics or regression analyses. These approaches often miss a holistic view that integrates the interplay of multiple influential factors. In this study, we leverage the scientific research productivity model to introduce
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Improving the performance and explainability of knowledge tracing via Markov blanket Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Bo Jiang, Yuang Wei, Ting Zhang, Wei Zhang
Knowledge tracing predicts student knowledge acquisition states during learning. Traditional knowledge tracing methods suffer from poor prediction performance; however, recent studies have significantly improved prediction performance through the incorporation of deep neural networks. However, prediction results generated from deep knowledge tracing methods are typically difficult to explain. To solve
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Flexibly utilizing syntactic knowledge in aspect-based sentiment analysis Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-10 Xiaosai Huang, Jing Li, Jia Wu, Jun Chang, Donghua Liu, Kai Zhu
Aspect-based sentiment analysis (ABSA) refers to ascertaining the propensity of sentiment expressed in a text towards a particular aspect. While previous models have utilized dependency graphs and GNNs to facilitate information exchange, they face challenges such as smoothing of aspect representation and a gap between word-based dependency graphs and subword-based BERT. Taking into account the above
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Knowledge graph embedding based on dynamic adaptive atrous convolution and attention mechanism for link prediction Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Weibin Deng, Yiteng Zhang, Hong Yu, Hongxing Li
Knowledge graph embedding (KGE) is essential for various applications, particularly in link prediction and other downstream tasks. While existing convolutional neural network (CNN)-based methods have been effective, they face challenges in comprehensively capturing local and global contextual information from triplets. To address this challenge, we propose a KGE method based on a dynamic adaptive atrous
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Joint inter-word and inter-sentence multi-relation modeling for summary-based recommender system Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Duantengchuan Li, Ceyu Deng, Xiaoguang Wang, Zhifei Li, Chao Zheng, Jing Wang, Bing Li
Review is an essential piece of information that influences users’ decisions, but excessively long reviews not only degrade the user experience but also affect the accuracy of the recommender system. Therefore, Joint Inter-Word and Inter-Sentence Multi-Relation Modeling for the Summary-based Recommender System (MRSR) is proposed in this paper. In MRSR, the concise summary information serves as representation
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User satisfaction with Arabic COVID-19 apps: Sentiment analysis of users’ reviews using machine learning techniques Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Mina Ramzy, Bahaa Ibrahim
Digital technologies such as mobile health (mHealth) apps with a variety of features can be essential tools for controlling pandemics. Therefore, many Arab countries have launched COVID-19 mHealth apps to reduce the spread of infection among their citizens. Recently, empirical studies have shown that user reviews include useful details to develop apps. However, Arab citizens' satisfaction with the
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Integrating GIN-based multimodal feature transformation and multi-feature combination voting for irony-aware cyberbullying detection Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Tingting Li, Ziming Zeng, Qingqing Li, Shouqiang Sun
With the increasing diversity of expressions, irony-aware cyberbullying has emerged as a significant issue in online social networks. However, detecting irony-aware cyberbullying is challenging, as it requires a comprehensive understanding of context and external factors beyond literal meanings. To take full advantage of multiple features of multimodal data to detect challenging irony-aware cyberbullying
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From words to gender: Quantitative analysis of body part descriptions within literature in Portuguese Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Mariana O. Silva, Luiza de Melo-Gomes, Mirella M. Moro
This article presents a quantitative analysis of gender representation within literature in Portuguese, focusing on the descriptions of male and female body parts. We investigate a corpus of 34 literary works from our 80,000-sized dataset. By leveraging Natural Language Processing techniques, we analyze over 50 body part descriptions of 315 unique characters identified through predetermined lists from
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MDLR: A Multi-Task Disentangled Learning Representations for unsupervised time series domain adaptation Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Yu Liu, Duantengchuan Li, Jian Wang, Bing Li, Bo Hang
Unsupervised Time Series Domain Adaptation (UTSDA) is a method for transferring information from a labeled source domain to an unlabeled target domain. The majority of existing UTSDA approaches focus on learning a domain-invariant feature space by reducing the gap between domains. However, the single-task representation learning methods have limited expressive capability, while ignoring the distinctive
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SCREAM: Knowledge sharing and compact representation for class incremental learning Inf. Process. Manag. (IF 8.6) Pub Date : 2024-01-09 Zhikun Feng, Mian Zhou, Zan Gao, Angelos Stefanidis, Zezhou Sui
Methods based on dynamic structures are effective in addressing catastrophic forgetting on Class-incremental learning (CIL). However, they often isolate sub-networks and overlook the integration of overall information, resulting in a performance decline. To overcome this limitation, we recognize the importance of knowledge sharing among sub-networks. On the basis of dynamic network, we established