Information analysis for dynamic sale planning by AI decision support process     

《Information processing & management》2023,60(3):103319

Marketing is all about finding out what your customers want and need. The information is used to create the model and improve the quality of products and services to satisfy customers. There are two major parts to a sale's planning: containing sales tactics and sales strategy. To aid in business decision-making, a data warehouse (DW) for sales collects and organizes relevant and historical data. DW is also a method for combining data from various heterogeneous databases (DB) and other sources of information for analysis. In this paper, Information analysis for dynamic Sale planning by AI Decision support process was done. Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) algorithms have been used to find optimal MVs for sale planning since several authors prove that these algorithms are better than other existing algorithms. MVs selection is based on three factors: time running of MVs, area of MVs, and access frequency of MVs by manipulating the selection result using a weighted combination of each factor as needed by the designer. For PSO, the most cost of frequency was 1.924 for View (V) 11, while for QPSO, it was 1.722 for V11. For PSO, the most cost of time was 1.931 for V2, while for QPSO, it was 1.221 for V22. For PSO, the most cost of the area was 1.800 for V17, while for QPSO, it was 1.071 for V17. The results revealed that Quantum Particle Swarm Optimization is much more accurate than Particle Swarm Optimization.  相似文献   

Artificial intelligence implication on energy sustainability in Internet of Things: A survey     

《Information processing & management》2023,60(2):103212

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.  相似文献   

Directed collaboration patterns in funded teams: A perspective of knowledge flow     

《Information processing & management》2023,60(2):103237

Collaborations in funded teams are essential for understanding funded research and funding policies, although of high interest, are still not fully understood. This study aims to investigate directed collaboration patterns from the perspective of the knowledge flow, which is measured based on the academic age. To this end, we proposed a project-based team identification approach, which gives particular attention to funded teams. The method is applicable to other funding systems. Based on identified scientific teams, we detected recurring and significant subgraph patterns, known as network motifs, and under-represented patterns, known as anti-motifs. We found commonly occurred motifs and anti-motifs are remarkably characterized by different structures matching certain functions in knowledge exchanges. Collaboration patterns represented by motifs favor hierarchical structures, supporting intensive interactions across academic generations. Anti-motifs are more likely to show chain-like structures, hindering potentially various knowledge activities, and are thus seldom found in real collaboration networks. These findings provide new insights into the understanding of funded collaborations and also the funding system. Meanwhile, our findings are helpful for researchers, the public and policymakers to gain knowledge on research(ers) evolution, particularly in terms of primordial collaboration patterns.  相似文献   

共享经济可持续性评价：社会感知与客观绩效     

董晓松  霍依凡  赵星 《科研管理》2023,44(1):79-88

共享经济行业可持续性评价的社会感知和客观绩效不一致将造成认知错位、投资误判和政策失效等结果。本文在线挖掘2017—2019年我国主要社交媒体上29 771条文本信息，并收集行业统计数据，综合测度共享经济行业的可持续性并比较其差异。研究发现：社会公众并不认为共享经济行业具有显著的总体可持续性，而且行业实际绩效展现的可持续性与社会大众认知截然不同。进一步发现：受限于主观认知能力，社会公众认为共享经济可持续性主要来自可观察到的行业外部经济和外部生态等维度，但客观绩效表明共享经济行业内部的经济和外部的技术优势才是其可持续性的重要支撑。此外，二者评价的差异在住宿与医疗等产能共享内部过程不易被大众观察的行业中最为突出。  相似文献   

A multimodal time-series method for gifting prediction in live streaming platforms     

《Information processing & management》2023,60(3):103254

Viewer gifting is an important business mode in live streaming industry, which closely relates to the income of the platforms and streamers. Previous studies on gifting prediction are often limited to cross-section data and consider the problem from the macro perspective of the whole live streaming. However, the multimodal information and the time accumulation effect of live streaming content on viewer gifting behavior are ignored. In this paper, we put forward a multimodal time-series method (MTM) for predicting real-time gifting. The core module of the method is the multimodal time-series analysis (MTA), which targets at effectively fusing multimodal information. Specifically, the proposed orthogonal projection (OP) model can promote cross-modal information interaction without introducing additional parameters. To achieve the interaction of multi-modal information at the same level, we also design a stackable joint representation layer, which makes each target modality's representation (visual, acoustic and textual modality) can benefit from all the other modalities. The residual connections are introduced as well to ensure the integration of low-level and high-level information. On our dataset, our model shows improved performance compared to other advanced models by at least 8% on F1. Meanwhile, the MTA is able to meet the real-time requirements of the live streaming setting, and has demonstrated its robustness and transferability in other tasks. Our research may offer some insights about how to efficiently fuse multimodal information, and contribute to the research on viewer gifting behavior prediction in the live streaming context.  相似文献   

Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis     

《Information processing & management》2023,60(3):103260

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A novel DL-based algorithm integrating medical knowledge graph and doctor modeling for Q&A pair matching in OHP     

《Information processing & management》2023,60(3):103322

Using AI technology to automatically match Q&A pairs on online health platforms (OHP) can improve the efficiency of doctor-patient interaction. However, previous methods often neglected to fully exploit rich information contained in OHP, especially the medical expertise that could be leveraged through medical text modeling. Therefore, this paper proposes a model named MKGA-DM-NN, which first uses the named entities of the medical knowledge graph (KG) to identify the intention of the problem, and then uses graph embedding technology to learn the representation of entities and entity relationships in the KG. The proposed model also employs the relationship between entities in KG to optimize the hybrid attention mechanism. In addition, doctors' historical Q&A records on OHP are used to learn modeling doctors’ expertise to improve the accuracy of Q&A matching. This method is helpful to bridge the semantic gap of text and improve the accuracy and interpretability of medical Q&A matching. Through experiments on a real dataset from a Chinese well-known OHP, our model has been verified to be superior to the baseline models. The accuracy of our model is 4.4% higher than the best baseline model. The cost-sensitive error of our model is 13.53% lower than that of the best baseline model. The ablation experiment shows that the accuracy rate can be significantly improved by 8.72% by adding the doctor modeling module, and the cost-sensitive error can be significantly reduced by 17.27% by adding the medical KG module.  相似文献   

Auditing fairness under unawareness through counterfactual reasoning     

《Information processing & management》2023,60(2):103224

Artificial intelligence (AI) is rapidly becoming the pivotal solution to support critical judgments in many life-changing decisions. In fact, a biased AI tool can be particularly harmful since these systems can contribute to or demote people’s well-being. Consequently, government regulations are introducing specific rules to prohibit the use of sensitive features (e.g., gender, race, religion) in the algorithm’s decision-making process to avoid unfair outcomes. Unfortunately, such restrictions may not be sufficient to protect people from unfair decisions as algorithms can still behave in a discriminatory manner. Indeed, even when sensitive features are omitted (fairness through unawareness), they could be somehow related to other features, named proxy features. This study shows how to unveil whether a black-box model, complying with the regulations, is still biased or not. We propose an end-to-end bias detection approach exploiting a counterfactual reasoning module and an external classifier for sensitive features. In detail, the counterfactual analysis finds the minimum cost variations that grant a positive outcome, while the classifier detects non-linear patterns of non-sensitive features that proxy sensitive characteristics. The experimental evaluation reveals the proposed method’s efficacy in detecting classifiers that learn from proxy features. We also scrutinize the impact of state-of-the-art debiasing algorithms in alleviating the proxy feature problem.  相似文献   

Being my own gatekeeper,how I tell the fake and the real – Fake news perception between typologies and sources     

《Information processing & management》2023,60(2):103228

The phenomenal spread of fake news online necessitates further research into fake news perception. We stress human factors in misinformation management. This study extends prior research on fake news and media consumption to examine how people perceive fake news. The objective is to understand how news categories and sources influence individuals' perceptions of fake news. Participants (N = 1008) were randomly allocated to six groups in which they evaluated the believability of news from three categories (misinformation, conspiracy, and correction news) coupled with six online news sources whose background (official media, commercial media, and social media) and expertise level varied (the presence or absence of a professional editorial team). Our findings indicated people could distinguish media sources, which have a significant effect on fake news perception. People believed most in conspiracy news and then misinformation included in correction news, demonstrating the backfire of correction news. The significant interaction effects indicate people are more sensitive to misinformation news and show more skepticism toward misinformation on social media. The findings support news literacy that users are capable to leverage credible sources in navigating online news. Meanwhile, challenges of processing correction news require design measures to promote truth-telling news.  相似文献   

Alone with others: Understanding physical environmental needs of students within an academic library setting     

《The Journal of Academic Librarianship》2020,46(2):102098

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