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   

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

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

  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

  相似文献   

基于深度学习的MOOC论坛探索型对话识别方法研究     

董庆兴  李华阳  曹高辉  夏立新 《图书情报工作》2019,63(5):92-99

[目的/意义]大规模在线开放课程论坛具有丰富的用户评论数据。从大量未区分的评论数据中，自动识别出知识密度较高的探索型对话并挖掘其潜在价值，对于改善教师教学质量以及提高学生知识水平具有重要影响。[方法/过程]首先利用GloVe方法训练词向量，加强对文本语义的理解，然后利用卷积神经网络自动学习文本特征，提出一种基于深度学习的探索型对话自动识别模型，并在学堂在线平台《心理学概论》课程论坛标注数据集上进行实证与对比研究。[结果/结论]实验结果显示，利用GloVe方法预训练词向量以及在训练过程中不断对词向量进行学习修正能够提高模型效果。该模型识别探索型对话的F1值为0.94，相较于传统的朴素贝叶斯方法（0.88）、逻辑斯谛回归方法（0.89）、决策树方法（0.88）以及随机森林方法（0.88）取得较大提升，具有较高的实用性和较低的学习成本。  相似文献   

A framework for fake review detection in online consumer electronics retailers     

《Information processing & management》2019,56(4):1234-1244

The impact of online reviews on businesses has grown significantly during last years, being crucial to determine business success in a wide array of sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some users use unethical means to improve their online reputation by writing fake reviews of their businesses or competitors. Previous research has addressed fake review detection in a number of domains, such as product or business reviews in restaurants and hotels. However, in spite of its economical interest, the domain of consumer electronics businesses has not yet been thoroughly studied. This article proposes a feature framework for detecting fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain in four different cities based on scraping techniques; (ii) definition of a feature framework for fake review detection; (iii) development of a fake review classification method based on the proposed framework and (iv) evaluation and analysis of the results for each of the cities under study. We have reached an 82% F-Score on the classification task and the Ada Boost classifier has been proven to be the best one by statistical means according to the Friedman test.  相似文献   

The role of a digital engineering platform in appropriating the creation of new work-related mind-set and organisational discourse in a large multi-national company     

《International Journal of Information Management》2019

This paper reports on a case study involving a strategic and innovative approach to creation of an in-house multifaceted digital engineering platform (the DEP) in overcoming a number of organisational problems at a multinational engineering company. The DEP was to be used strategically for simplifying the operational complexity and to create and appropriate new work-related mind-set and new organisational discourse to achieve homogenous working across the organisation, which is a huge challenge. The need for this system emerged from the need to resolve many organisational services related problems that carried phenomenal amount of processes, health and safety risks and to regulate, and, control the running of engineering project. Research data were collected using a longitudinal case study approach over a period of six months. In order to make sense of how the DEP helped the organisation, the study used certain elements of Extended Structuration Theory as a lens to assess the case study. This research discovered that the DEP succeeded in creating and appropriating work-related mind-set and organisational discourse. It also had real influence on working processes and employees at all levels while encouraging transparency, responsiveness, agility and accountability. It continues to help the organisation to govern, manage and maintain good standard of service but many barriers still remain.  相似文献   

Skills policy for growth and development: The merits of local approaches in Vietnam     

《International Journal of Educational Development》2021

  相似文献   

Online portfolio management via deep reinforcement learning with high-frequency data     

《Information processing & management》2023,60(3):103247

Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in the field of portfolio management (PM). However, the built-in quadratic complexity of the Transformer prevents its direct application to the PM task. To solve this problem, in this paper, we propose a deep reinforcement learning-based PM framework called LSRE-CAAN, with two important components: a long sequence representations extractor and a cross-asset attention network. Direct Policy Gradient is used to solve the sequential decision problem in the PM process. We conduct numerical experiments in three aspects using four different cryptocurrency datasets, and the empirical results show that our framework is more effective than both traditional and state-of-the-art (SOTA) online portfolio strategies, achieving a 6x return on the best dataset. In terms of risk metrics, our framework has an average volatility risk of 0.46 and an average maximum drawdown risk of 0.27 across the four datasets, both of which are lower than the vast majority of SOTA strategies. In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation.  相似文献   

An end-to-end deep generative approach with meta-learning optimization for zero-shot object classification     

《Information processing & management》2023,60(2):103233

Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks.  相似文献   

Decoding digital transformational outsourcing: The role of service providers’ capabilities     

《International Journal of Information Management》2021

The business process outsourcing industry has got disrupted, first by the significant shift in value creation activities from the clients to the service providers, and second by pervasive digital penetration, resulting in the emergence of Digital Transformational Outsourcing (DTO). Service providers now play a more significant role, making their capabilities important. In the new context, service providers require a uniquely different set of dynamic capabilities to handle end-to-end business functions on behalf of their clients while delivering digital value propositions.We study 26 of the largest global business process outsourcing providers to conceptualise and identify six dynamic capabilities of service providers salient in the new context, i.e., consultative, orchestration, insights, network management, knowledge access, and standardisation. Interviews conducted with industry experts provided evidence in support of the identified dynamic capabilities. A novel firm capability dataset was created using secondary data, and using fuzzy-set Qualitative Comparative Analysis (fsQCA), we identify configurations for high and low performance and find them to vary by the firm's broad/narrow scope.  相似文献