Journal Description
Information
Information
is a scientific, peer-reviewed, open access journal of information science and technology, data, knowledge, and communication, and is published monthly online by MDPI. The International Society for Information Studies (IS4SI) is affiliated with Information and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, and many other databases.
- Journal Rank: CiteScore - Q2 (Information Systems)
- Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 14.9 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the first half of 2021).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks
Information 2021, 12(9), 351; https://doi.org/10.3390/info12090351 - 28 Aug 2021
Abstract
Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two
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Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future online and real-time multi-objective optimisation of particle accelerators.
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(This article belongs to the Special Issue Machine Learning and Accelerator Technology)
Open AccessArticle
The Relationship between Perceived Health Message Motivation and Social Cognitive Beliefs in Persuasive Health Communication
Information 2021, 12(9), 350; https://doi.org/10.3390/info12090350 - 28 Aug 2021
Abstract
People respond to different types of health messages in persuasive health communication aimed at motivating behavior change. Hence, in human factors design, there is a need to tailor health applications to different user groups rather than change the human characteristics and conditions. However,
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People respond to different types of health messages in persuasive health communication aimed at motivating behavior change. Hence, in human factors design, there is a need to tailor health applications to different user groups rather than change the human characteristics and conditions. However, in the domain of fitness app design, there is limited research on the relationship between users’ perceived motivation of health messages and their social–cognitive beliefs about exercise, and how this relationship is moderated by gender. Knowledge of the gender difference will help in tailoring fitness apps to the two main gender types. Hence, I conducted an empirical study to investigate the types of health messages that are most likely to motivate users and how these messages are related to outcome expectation, self-efficacy, and self-regulation beliefs in the context of exercise modeling. The results of the data analysis show that users are more motivated by illness- and death-related messages compared with obesity-, social stigma-, and financial cost-related messages. Moreover, illness- and death-related messages have a significant relationship with users’ social–cognitive beliefs about bodyweight exercise. These findings indicate that, in the fitness domain, illness- and death-related messages may be employed as a persuasive technique to motivate regular exercise.
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(This article belongs to the Special Issue Designing Digital Health Technologies as Persuasive Technologies)
Open AccessArticle
Obérisk: Cybersecurity Requirements Elicitation through Agile Remote or Face-to-Face Risk Management Brainstorming Sessions
Information 2021, 12(9), 349; https://doi.org/10.3390/info12090349 - 27 Aug 2021
Abstract
Cyberattacks make the news daily. Systems must be appropriately secured. Cybersecurity risk analyses are more than ever necessary, but… traveling and gathering in a room to discuss the topic has become difficult due to the COVID, whilst having a cybersecurity expert working isolated
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Cyberattacks make the news daily. Systems must be appropriately secured. Cybersecurity risk analyses are more than ever necessary, but… traveling and gathering in a room to discuss the topic has become difficult due to the COVID, whilst having a cybersecurity expert working isolated with an electronic support tool is clearly not the solution. In this article, we describe and illustrate Obérisk, an agile, cross-disciplinary and Obeya-like approach to risk management that equally supports face-to-face or remote risk management brainstorming sessions. The approach has matured for the last three years by using it for training and a wide range of real industrial projects. The overall approach is detailed and illustrated on a naval use case, with extensive feedback from the end-users. We show that Obérisk is really time-efficient and effective at managing risks at the early stages of a project, whilst remaining extremely low-cost. As the project grows or when the system is deployed, it may eventually be necessary to shift to a more comprehensive commercial electronic support tool.
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(This article belongs to the Section Information and Communications Technology)
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Open AccessArticle
Impresso Inspect and Compare. Visual Comparison of Semantically Enriched Historical Newspaper Articles
Information 2021, 12(9), 348; https://doi.org/10.3390/info12090348 - 27 Aug 2021
Abstract
The automated enrichment of mass-digitised document collections using techniques such as text mining is becoming increasingly popular. Enriched collections offer new opportunities for interface design to allow data-driven and visualisation-based search, exploration and interpretation. Most such interfaces integrate close and distant reading and
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The automated enrichment of mass-digitised document collections using techniques such as text mining is becoming increasingly popular. Enriched collections offer new opportunities for interface design to allow data-driven and visualisation-based search, exploration and interpretation. Most such interfaces integrate close and distant reading and represent semantic, spatial, social or temporal relations, but often lack contrastive views. Inspect and Compare (I&C) contributes to the current state of the art in interface design for historical newspapers with highly versatile side-by-side comparisons of query results and curated article sets based on metadata and semantic enrichments. I&C takes search queries and pre-curated article sets as inputs and allows comparisons based on the distributions of newspaper titles, publication dates and automatically generated enrichments, such as language, article types, topics and named entities. Contrastive views of such data reveal patterns, help humanities scholars to improve search strategies and to facilitate a critical assessment of the overall data quality. I&C is part of the impresso interface for the exploration of digitised and semantically enriched historical newspapers.
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(This article belongs to the Special Issue Visual Text Analysis in Digital Humanities)
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Open AccessArticle
DebtG: A Graph Model for Debt Relationship
by
Information 2021, 12(9), 347; https://doi.org/10.3390/info12090347 - 26 Aug 2021
Abstract
Debt is common in daily transactions, but it may bring great harm to individuals, enterprises, and society and even lead to a debt crisis. This paper proposes a weighted directed multi-arc graph model DebtG of debts among a large number of entities, including
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Debt is common in daily transactions, but it may bring great harm to individuals, enterprises, and society and even lead to a debt crisis. This paper proposes a weighted directed multi-arc graph model DebtG of debts among a large number of entities, including individuals, enterprises, banks, and governments, etc. Both vertices and arcs of DebtG have attributes. In further, it defines three basic debt structures: debt path, debt tree, and debt circuit, and it presents algorithms to detect them and basic methods to solve debt clearing problems using these structures. Because the data collection and computation need a third-party platform, this paper also presents the profit analysis of the platform. It carries out a case analysis using the real-life data of enterprises in Huangdao Zone. Finally, it points out four key problems that should be addressed in the future.
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(This article belongs to the Special Issue Data Modeling and Predictive Analytics)
Open AccessArticle
The Acceptance of Independent Autonomous Vehicles and Cooperative Vehicle-Highway Autonomous Vehicles
Information 2021, 12(9), 346; https://doi.org/10.3390/info12090346 - 26 Aug 2021
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The public’s acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles is studied by combining the structural equation model and an artificial neural network. The structural equation model’s output variables are used as the input variables of the artificial neural network, which
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The public’s acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles is studied by combining the structural equation model and an artificial neural network. The structural equation model’s output variables are used as the input variables of the artificial neural network, which compensates for the linear problem of the structural equation model and ensures the accuracy of the input variables of the artificial neural network. In order to summarize the influencing factors of autonomous vehicles acceptance, the Unified Theory of Acceptance and Use of Technology model was expanded by adding two variables: risk expectation and consumer innovation. The results show that social influence is the strongest predictor of the acceptance of independent autonomous vehicles. The most significant factor of the cooperative vehicle-highway autonomous vehicles’ acceptance is effort expectation. Additionally, risks, performance, existing traffic conditions, and personal innovation awareness also significantly affect autonomous driving acceptance. The research results can provide a theoretical basis for technology developers and industry managers to develop autonomous driving technology and policymaking.
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Open AccessSystematic Review
Gamification for Brand Value Co-Creation: A Systematic Literature Review
Information 2021, 12(9), 345; https://doi.org/10.3390/info12090345 - 26 Aug 2021
Abstract
Gamification, commonly defined as the use of game elements in non-game contexts, is a relatively novel term, yet it has been gaining popularity across a wide range of academic and industrial disciplines. In the marketing field, companies are increasingly gamifying their mobile apps
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Gamification, commonly defined as the use of game elements in non-game contexts, is a relatively novel term, yet it has been gaining popularity across a wide range of academic and industrial disciplines. In the marketing field, companies are increasingly gamifying their mobile apps and online platforms to enrich their customers’ digital experiences. Whilst there has been a number of systematic studies examining the influence of gamification on user engagement across different fields, none has reviewed its role in brand value co-creation. Following a systematic literature review procedure via the online research platform EBSCOhost, this paper is the first to survey a set of empirical studies examining the role and impact of gamification on brand value co-creation. A final pool of 32 empirical studies implies the existence of four types of activities that are co-created by online users and positively influenced by gamification, namely: customer service, insights sharing, word-of-mouth, and random task. Moreover, this paper highlights the major game dynamics driving these activities, the key findings of each of the covered studies and their main theoretical underpinnings. Lastly, a set of noteworthy research directions for future related studies are suggested, comprising the exploration of novel game elements, and new co-creation activities related to corporate social responsibilities and physical commercial operations.
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(This article belongs to the Special Issue Gamification and Game Studies)
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Open AccessArticle
VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification
Information 2021, 12(9), 344; https://doi.org/10.3390/info12090344 - 26 Aug 2021
Abstract
The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially when the data is high-dimensional. The emergence of a
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The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially when the data is high-dimensional. The emergence of a type of computational system known as visual analytics has the potential to address these issues by integrating data analysis techniques with interactive visualizations. This paper introduces a visual analytics system called VERONICA that utilizes the natural classification of features in EHRs to identify the group of features with the strongest predictive power. VERONICA incorporates a representative set of supervised machine learning techniques—namely, classification and regression tree, C5.0, random forest, support vector machines, and naive Bayes to support users in developing predictive models using EHRs. It then makes the analytics results accessible through an interactive visual interface. By integrating different sampling strategies, analytics algorithms, visualization techniques, and human-data interaction, VERONICA assists users in comparing prediction models in a systematic way. To demonstrate the usefulness and utility of our proposed system, we use the clinical dataset stored at ICES to identify the best representative feature groups in detecting patients who are at high risk of developing acute kidney injury.
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(This article belongs to the Special Issue Emerging Trends and Challenges in Supervised Learning Tasks)
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Open AccessArticle
Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing
Information 2021, 12(9), 343; https://doi.org/10.3390/info12090343 - 25 Aug 2021
Abstract
Using reinforcement learning technologies to learn offloading strategies for multi-access edge computing systems has been developed by researchers. However, large-scale systems are unsuitable for reinforcement learning, due to their huge state spaces and offloading behaviors. For this reason, this work introduces the centralized
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Using reinforcement learning technologies to learn offloading strategies for multi-access edge computing systems has been developed by researchers. However, large-scale systems are unsuitable for reinforcement learning, due to their huge state spaces and offloading behaviors. For this reason, this work introduces the centralized training and decentralized execution mechanism, designing a decentralized reinforcement learning model for multi-access edge computing systems. Considering a cloud server and several edge servers, we separate the training and execution in the reinforcement learning model. The execution happens in edge devices of the system, and edge servers need no communication. Conversely, the training process occurs at the cloud device, which causes a lower transmission latency. The developed method uses a deep deterministic policy gradient algorithm to optimize offloading strategies. The simulated experiment shows that our method can learn the offloading strategy for each edge device efficiently.
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(This article belongs to the Special Issue Wireless Edge Computing: Enabling Technologies for the Next Generation of Cloud Computing)
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Open AccessArticle
Feature Extraction Network with Attention Mechanism for Data Enhancement and Recombination Fusion for Multimodal Sentiment Analysis
Information 2021, 12(9), 342; https://doi.org/10.3390/info12090342 - 24 Aug 2021
Abstract
Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal,
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Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal, including language, visual, and audio. Therefore, the traditional unimodal sentiment analysis method is no longer applicable, which requires the establishment of a fusion model of multimodal information to obtain sentiment understanding. In previous studies, scholars used the feature vector cascade method when fusing multimodal data at each time step in the middle layer. This method puts each modal information in the same position and does not distinguish between strong modal information and weak modal information among multiple modalities. At the same time, this method does not pay attention to the embedding characteristics of multimodal signals across the time dimension. In response to the above problems, this paper proposes a new method and model for processing multimodal signals, which takes into account the delay and hysteresis characteristics of multimodal signals across the time dimension. The purpose is to obtain a multimodal fusion feature emotion analysis representation. We evaluate our method on the multimodal sentiment analysis benchmark dataset CMU Multimodal Opinion Sentiment and Emotion Intensity Corpus (CMU-MOSEI). We compare our proposed method with the state-of-the-art model and show excellent results.
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(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
Open AccessArticle
A Missing Data Compensation Method Using LSTM Estimates and Weights in AMI System
by
and
Information 2021, 12(9), 341; https://doi.org/10.3390/info12090341 - 24 Aug 2021
Abstract
With the expansion of advanced metering infrastructure (AMI) installations, various additional services using AMI data have emerged. However, some data is lost in the communication process of data collection. Hence, to address this challenge, the estimation of the missing data is required. To
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With the expansion of advanced metering infrastructure (AMI) installations, various additional services using AMI data have emerged. However, some data is lost in the communication process of data collection. Hence, to address this challenge, the estimation of the missing data is required. To estimate the missing values in the time-series data generated from smart meters, we investigated four methods, ranging from a conventional method to an estimation method applying long short-term memory (LSTM), which exhibits excellent performance in the time-series field, and provided the performance comparison data. Furthermore, because power usages represent estimates of data that are missing some values in the middle, rather than regular time-series estimation data, the simple estimation may lead to an error where the estimated accumulated power usage in the missing data is larger than the real accumulated power usage appearing in the data after the end of the missing data interval. Therefore, this study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method, rather than those of conventional methods adopted in the time-series field. The performance of the proposed method is more stable and better than that of other methods.
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Open AccessArticle
Data Security Protocol with Blind Factor in Cloud Environment
Information 2021, 12(9), 340; https://doi.org/10.3390/info12090340 - 24 Aug 2021
Abstract
Compared with the traditional system, cloud storage users have no direct control over their data, so users are most concerned about security for their data stored in the cloud. One security requirement is to resolve any threats from semi-trusted key third party managers.
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Compared with the traditional system, cloud storage users have no direct control over their data, so users are most concerned about security for their data stored in the cloud. One security requirement is to resolve any threats from semi-trusted key third party managers. The proposed data security for cloud environment with semi-trusted third party (DaSCE) protocol has solved the security threat of key managers to some extent but has not achieved positive results. Based on this, this paper proposes a semi-trusted third-party data security protocol (ADSS), which can effectively remove this security threat by adding time stamp and blind factor to prevent key managers and intermediaries from intercepting and decrypting user data. Moreover, the ADSS protocol is proved to provide indistinguishable security under a chosen ciphertext attack. Finally, the performance evaluation and simulation of the protocol show that the ADSS security is greater than DaSCE, and the amount of time needed is lower than DaSCE.
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(This article belongs to the Special Issue Secure Protocols for Future Technologies)
Open AccessArticle
The Expectations of the Residents of Szczecin in the Field of Telematics Solutions after the Launch of the Szczecin Metropolitan Railway
Information 2021, 12(8), 339; https://doi.org/10.3390/info12080339 - 23 Aug 2021
Abstract
Transport is integral to every city, having a crucial impact on its functioning and development. As road infrastructure does not keep up to speed with the constantly growing numbers of vehicles on roads, new solutions are required. Fast urban railway systems are a
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Transport is integral to every city, having a crucial impact on its functioning and development. As road infrastructure does not keep up to speed with the constantly growing numbers of vehicles on roads, new solutions are required. Fast urban railway systems are a solution that can reduce transport congestion, with environmental protection issues also taken into account. Contemporary public transport cannot function without modern communication and information technologies. The use of telematics in public transport allows passenger mobility to be sustainable and efficient. Therefore, it seems justified to conduct research on this issue. The aim of the study is to analyze the perception of the use of telematics solutions to service SKM in Szczecin (Poland) with the use of multivariate correspondence analysis. Results of the research indicate that people living in the area of gravity of the SKM have a positive opinion on the application of telematics solutions in the activities of the Szczecin Metropolitan Railway. The results obtained are local in nature, but show the direction that researchers can take in analyzing public transport in other agglomerations. In addition, the article presents a tool that greatly facilitates the analysis of survey data, even with a large number of results.
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(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)
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Open AccessArticle
Making Information Measurement Meaningful: The United Nations’ Sustainable Development Goals and the Social and Human Capital Protocol
Information 2021, 12(8), 338; https://doi.org/10.3390/info12080338 - 23 Aug 2021
Abstract
Drucker’s saying that “What gets measured gets managed” is examined in the context of corporate social responsibility. The United Nations’ Sustainable Development Goals have encouraged sustainability reporting, and a reporting tool, the Social and Human Capital Protocol, has been developed to assist measurement
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Drucker’s saying that “What gets measured gets managed” is examined in the context of corporate social responsibility. The United Nations’ Sustainable Development Goals have encouraged sustainability reporting, and a reporting tool, the Social and Human Capital Protocol, has been developed to assist measurement and provide information to support the achievement of sustainability. This information should be valid and reliable; however, it is not easy to measure social and human capital factors. Additionally, companies use a large number of methodologies and indicators that are difficult to compare, and they may sometimes only present positive outcomes as a form of greenwashing. This lack of full transparency and comparability with other companies has the potential to discredit their reports, thereby supporting the claims of climate change deniers, free-market idealogues and conspiracy theorists who often use social media to spread their perspectives. This paper will describe the development of environmental reporting and CSR, discuss the natural capital protocol, and assess the extent to which the Social and Human Capital Protocol is able to fulfil its purpose of providing SMART objective measurements. It is the first academic article to provide a detailed examination of the Social and Human Capital Protocol.
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(This article belongs to the Special Issue Knowledge Management, Digital Trust, and Corporate Social Responsibility in the Era of Social Media)
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Open AccessArticle
Ranking Algorithms for Word Ordering in Surface Realization
Information 2021, 12(8), 337; https://doi.org/10.3390/info12080337 - 23 Aug 2021
Abstract
In natural language generation, word ordering is the task of putting the words composing the output surface form in the correct grammatical order. In this paper, we propose to apply general learning-to-rank algorithms to the task of word ordering in the broader context
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In natural language generation, word ordering is the task of putting the words composing the output surface form in the correct grammatical order. In this paper, we propose to apply general learning-to-rank algorithms to the task of word ordering in the broader context of surface realization. The major contributions of this paper are: (i) the design of three deep neural architectures implementing pointwise, pairwise, and listwise approaches for ranking; (ii) the testing of these neural architectures on a surface realization benchmark in five natural languages belonging to different typological families. The results of our experiments show promising results, in particular highlighting the performance of the pairwise approach, paving the way for a more transparent surface realization from arbitrary tree- and graph-like structures.
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(This article belongs to the Special Issue Neural Natural Language Generation)
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Open AccessArticle
Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms
Information 2021, 12(8), 336; https://doi.org/10.3390/info12080336 - 22 Aug 2021
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Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy of tomato yield in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by
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Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy of tomato yield in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters and the tomato yield is the prediction output. The GA is adopted to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation (BP) neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability compared with the BP and the WNN prediction models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs.
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Open AccessArticle
Interference Alignment Inspired Opportunistic Communications in Multi-Cluster MIMO Networks with Wireless Power Transfer
Information 2021, 12(8), 335; https://doi.org/10.3390/info12080335 - 21 Aug 2021
Abstract
In this work, we jointly investigate the issues of node scheduling and transceiver design in a sensor network with multiple clusters, which is endowed with simultaneous wireless information and power transfer. In each cluster of the observed network, S out of N nodes
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In this work, we jointly investigate the issues of node scheduling and transceiver design in a sensor network with multiple clusters, which is endowed with simultaneous wireless information and power transfer. In each cluster of the observed network, S out of N nodes are picked, each of which is capable of performing information transmission (IT) via uplink communications. As for the remaining idle nodes, they can harvest energy from radio-frequency signals around their ambient wireless environments. Aiming to boost the intra-cluster performance, we advocate an interference alignment enabled opportunistic communication (IAOC) scheme. This scheme can yield better tradeoffs between IT and wireless power transfer (WPT). With the aid of IAOC scheme, the signal projected onto the direction of the receive combining vector is adopted as the accurate measurement of effective signal strength, and then the high-efficiency scheduling metric for each node can be accordingly obtained. Additionally, an algorithm, based on alternative optimization and dedicated for transceiver design, is also put forward, which is able to promote the achievable sum rate performance as well as the total harvested power. Our simulation results verify the effectiveness of the designed IAOC scheme in terms of improving the performance of IT and WPT in multi-cluster scenarios.
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(This article belongs to the Section Information and Communications Technology)
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Open AccessArticle
Goal-Driven Visual Question Generation from Radiology Images
Information 2021, 12(8), 334; https://doi.org/10.3390/info12080334 - 20 Aug 2021
Abstract
Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain
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Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.
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(This article belongs to the Special Issue Neural Natural Language Generation)
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Open AccessArticle
Geometric Regularization of Local Activations for Knowledge Transfer in Convolutional Neural Networks
Information 2021, 12(8), 333; https://doi.org/10.3390/info12080333 - 19 Aug 2021
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In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a “student” model to adapt such that its local
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In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a “student” model to adapt such that its local features exhibit similar geometrical characteristics to those of an “instructor” model, at corresponding layers. The investigated functions, inspired by manifold-to-manifold distance measures, are designed to compare the neighboring information inside the feature space of the involved activations without any restrictions in the features’ dimensionality, thus enabling knowledge transfer between different architectures. Experimental evidence demonstrates that the proposed technique is effective in different settings, including knowledge-transfer to smaller models, transfer between different deep architectures and harnessing knowledge from external data, producing models with increased accuracy compared to a typical training. Furthermore, results indicate that the presented method can work synergistically with methods such as knowledge distillation, further increasing the accuracy of the trained models. Finally, experiments on training with limited data show that a combined regularization scheme can achieve the same generalization as a non-regularized training with 50% of the data in the CIFAR-10 classification task.
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Open AccessArticle
Studying and Clustering Cities Based on Their Non-Emergency Service Requests
Information 2021, 12(8), 332; https://doi.org/10.3390/info12080332 - 19 Aug 2021
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This study offers a new perspective in analyzing 311 service requests (SRs) across the country by representing cities based on the types of their SRs. This not only uncovers temporal patterns of SRs in each city over the years but also detects cities
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This study offers a new perspective in analyzing 311 service requests (SRs) across the country by representing cities based on the types of their SRs. This not only uncovers temporal patterns of SRs in each city over the years but also detects cities with the most or least similarity to other cities based on their SR types. The first challenge is to gather 311 SRs for different cities and standardize their types since they differ in various cities. Implementing our analyses on close to 42 million SR records in 20 cities from 2006 to 2019 is the second challenge. Representing clusters of cities and outliers effectively, and providing justifications for them, is the last challenge. Our attempt resulted in 79 standardized SR types. We applied the principal component analysis to depict cities on a two-dimensional canvas based on their standardized SR types. Among our main findings are the following: many cities are observing a fall in requests regarding the condition of roads and sidewalks but a rise in requests concerning transportation and traffic; requests regarding garbage, cleaning, rodents, and complaints have also been rising in some cities; new types of requests have emerged and soared in recent years, such as requests for information and regarding shared mobility devices; requests about parking meters, information, sidewalks, curbs, graffities, and missed garbage pick up have the highest variance in their rates across different cities, i.e., they have a large rate in some cities while a low rate in others; the most consistent outliers, in terms of SR types, are Washington DC, Baltimore, Las Vegas, Philadelphia, Chicago, and Baton Rouge.
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Deep Learning in Biomedical Informatics
Guest Editor: Andrej KastrinDeadline: 31 August 2021
Special Issue in
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New Trends and Challenges in Intelligent Transportation Systems Optimisation, Modeling and Security
Guest Editors: Nacima Labadie, Lyes KhoukhiDeadline: 16 September 2021
Special Issue in
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Recent Advances in IoT and Cyber/Physical Security
Guest Editor: Shingo YamaguchiDeadline: 30 September 2021
Special Issue in
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Visual Text Analysis in Digital Humanities
Guest Editor: Stefan JänickeDeadline: 10 October 2021
Topical Collections
Topical Collection in
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Pervasive Intelligent Data Systems
Collection Editors: Carlos Filipe Da Silva Portela, Manuel Filipe Vieira Torres dos Santos, Kolomvatsos Kostas




