Article
Information Science & Library Science
Stefan Raff, Stefan Rose, Tin Huynh
Summary: This research addresses the gap in previous studies on resistance and creepiness towards smart home assistants (SHAs). By using a multi-method research design, it explores the inhibiting effect of creepiness on SHAs, analyzes the triggers and mechanisms behind it, and provides design recommendations to mitigate users' perceptions of creepiness associated with SHAs.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Shijun Shi, Kai Hu, Jie Xie, Ya Guo, Huayi Wu
Summary: This research proposes a method to enhance prompt tuning using data augmentation with L2 regularization and demonstrates significant improvements in the accuracy and robustness of language models on two scientific text datasets.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Chenxi Hu, Tao Wu, Shuaiqi Liu, Chunsheng Liu, Tao Ma, Fang Yang
Summary: Text clustering aims to organize a vast collection of documents into meaningful and coherent clusters. This study proposes a model named JourTC, which combines unsupervised contrastive learning and robust GMM for text clustering. Experimental results demonstrate that JourTC can be seamlessly integrated into existing frameworks and significantly improve accuracy.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Tayyab Nasir, Muhammad Kamran Malik
Summary: In this study, a novel technique called Efficient CRNN is proposed for printed text recognition in Urdu language. The technique is computationally efficient and achieves better performance compared to existing techniques. A multi-font printed Urdu text lines corpus is also presented to train and evaluate the proposed technique. The Efficient CRNN achieved impressive results and outperforms existing complex techniques and Vision Transformer-based network.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ping Li, Yu Zhang, Li Yuan, Xianghua Xu
Summary: This paper proposes an end-to-end RVOS framework built upon transformers, which explicitly models the object-level spatial context and improves the accuracy of referring video object segmentation. The proposed method achieves superior performance on benchmark datasets and outperforms existing methods in terms of mAP and J&F.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xinyu Sun, Yan Zhang, Juan Feng
Summary: This study investigates the impact of online information on brand reputation and brand premium in the online market. The findings suggest that the presence of online information may change the situation of brand premium, and firms with lower reputation can potentially earn higher profits under certain conditions. Additionally, as the gap in brand reputation increases, the profits of both firms may also increase, leading to a win-win situation in brand competition.
INFORMATION & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Huimin Xu, Meijun Liu, Yi Bu, Shujing Sun, Yi Zhang, Chenwei Zhang, Daniel E. Acuna, Steven Gray, Eric Meyer, Ying Ding
Summary: This paper examines the role of shared leadership in scientific teams and finds that heterogeneous shared leadership has a greater impact on team citation. In particular, when junior leaders are paired with senior leaders, the team citation ranking can significantly increase by 1-2% compared to two leaders of similar age.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Salah Ud Din, Aman Ullah, Cobbinah B. Mawuli, Qinli Yang, Junming Shao
Summary: Data stream mining faces challenges of concept drift and evolution. Existing learning algorithms require class labels for all data points, but the rapid pace of data streams often leads to label scarcity. To address this, we propose an adaptive, data-driven, prototype-based semi-supervised learning framework that uses dynamic prototypes to handle evolving data streams and achieve improved data abstraction and detection of novel classes.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jialiang Gao, Peng Peng, Feng Lu, Christophe Claramunt, Peiyuan Qiu, Yang Xu
Summary: This study proposes a knowledge graph-driven framework for tourism management research, which aims to overcome the limitations of existing methods and provide comprehensive insights. By constructing a tourism-oriented knowledge graph (TKG) and utilizing it for analysis and inference, a comprehensive decision-support system can be established. The system includes symbolic query and distributed reasoning modes, allowing for multiple spatiotemporal analyses and improving the accuracy of downstream inferential tasks.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jieun Lee, Sangmin Seo, Jonghwan Choi, Sanghyun Park
Summary: This article discusses using genetic algorithms to tune the RocksDB parameter feature vector in database configuration.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Dan Song, Yuanxiang Yang, Wenhui Li, Xuanya Li, Min Liu, An-An Liu
Summary: Unsupervised domain adaptation aims to minimize inter-domain sample differences. This paper proposes a structured serialization semantic transfer network (S3TN) to further reduce the domain gap by utilizing structured and serialization information at the semantic level. Experimental results show that the proposed method achieves significant improvements in classification accuracy.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Quirin Demlehner, Sven Laumer
Summary: This article discusses the challenges brought by the rapid development of artificial intelligence in the adoption of technology at an individual level. It focuses on the role of biases and examines their impact on user decision making. Through a case study of three German car manufacturers, the article highlights the importance of the pre-announcement phase in information systems adoption and provides a comprehensive analysis of biases caused by individuals' cognitive limitations. It also reveals a notable spillover effect of users' experiences and opinions on AI from their personal lives to their professional lives, which contradicts previous findings in IS research.
INFORMATION & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Karen Renaud, Merrill Warkentin, Ganna Pogrebna, Karl van der Schyff
Summary: Insider threats can cause significant damage due to insiders' access and trust. To mitigate these threats, organizations must understand different types of insider threats and employ tailored measures.
INFORMATION & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
