Information, Volume 12, Issue 2 (February 2021) – 48 articles

The application of cosegmentation in remote sensing image change detection can effectively overcome the salt and pepper phenomenon and generate multitemporal changing objects with consistent boundaries. Cosegmentation considers the image information, such as spectrum and texture, and mines the spatial neighborhood information between pixels. However, each pixel in the minimum cut/maximum flow algorithm for cosegmentation change detection is regarded as a node in the network flow diagram. This condition leads to a direct correlation between computation times and the number of nodes and edges in the diagram. It requires a large amount of computation and consumes excessive time for change detection of large areas. A superpixel segmentation method is combined into cosegmentation to solve this shortcoming. Simple linear iterative clustering is adopted to group pixels by using the similarity of features among pixels. Two-phase superpixels are overlaid to form the multitemporal consistent superpixel segmentation. Each superpixel block is regarded as a node for cosegmentation change detection, so as to reduce the number of nodes in the network flow diagram constructed by minimum cut/maximum flow. In this study, the Chinese GF-1 and Landsat satellite images are taken as examples, the overall accuracy of the change detection results is above 0.80, and the calculation time is only one-fifth of the original. Full article

Designing and delivering outcome-based courses that emphasize learner-centric educational discourse and active learning is challenging, especially in online learning environments. Ensuring quality in the design and delivery of such courses in the virtual space requires a well-defined framework with key constituents that interact based on ordered sequences of events. Despite the pressing need for a quality assurance system for today’s virtual, real-time courses, such a system has not been systematically designed. A coherent quality assurance system requires a clear framework that defines the interacting constituents. This work proposes a conceptual and generic “Quality Assurance” (QA) framework, based on experiences primarily in Science, Technology, Engineering, and Mathematics (STEM) fields, for the effective design and delivery of outcome-based virtual, real-time courses that incorporate active learning practices. This Quality Assurance framework may be adjusted to serve as a blueprint that, once adjusted by institutions to accommodate their missions, guides institutions in developing or amending their policies and procedures for the design and delivery of virtual, real-time courses; in addition, such a framework is important for institutions to develop Quality Assurance systems that integrate mechanisms for continuous improvement. The proposed quality assurance framework includes three constituents: a “Teaching and Learning Support” (TLS) that trains educators on pedagogical approaches and the capabilities of the institution’s Learning Management System (LMS); an “Information and Communication Technology Support” (ICTS) that assists educators with the technologies and tools available in the learning management system; and a “Course Management System” (CMS) that encapsulates course design, delivery, and assessment; this study focuses primarily on this “Course Management System” constituent. Full article

Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last. Full article

Research trends in gamification have shown a significant diversity in various areas of e-health, particularly in addressing the issues of rehabilitation and physical activity. Rehabilitation requires better engaging tools that help to increase the patient’s motivation and engagement in particular forms of rehabilitation training. Adopting gamification in rehabilitation offers different treatment and care environments when implementing rehabilitation training. As gamification is increasingly being explored in rehabilitation, one might not realize that using various techniques in gamified applications yields a different effect on gameplay. To date, varied gamification techniques have been utilized to provide useful experiences from the perspective of health applications. However, a limited number of surveys have investigated the gamification of rehabilitation and the use of suitable game techniques for rehabilitation in the literature. The objective of this paper is to examine and analyze the existing gamification techniques for rehabilitation applications. A classification of rehabilitation gamification is developed based on the rehabilitation gamifying requirements and the gamification characteristics that are commonly applied in rehabilitation applications. This classification is the main contribution of this paper. It provides insight for researchers and practitioners into suitable techniques to design and apply gamification with increased motivation and sustainable engagement for rehabilitation treatment and care. In addition, different game elements, selection blocks, and gamification techniques are identified for application in rehabilitation. In conclusion, several challenges and research opportunities are discussed to improve gamification deployment in rehabilitation in the future. Full article

Up until quite recently, our contemporary society has faced various challenges and issues related to accelerated urbanization and industrialization, consumers, and organizations’ rather limited range of possibilities to completely satisfy needs and wants regarding environmental pollution, the capacity of our planet to regenerate its used goods annually and ensuring that the living conditions of future generations are considered alongside those of the current generations [...] Full article

The reputation of companies is one of their key success factors. It is therefore necessary to value this intangible asset. In order to detect possible threats quickly, continuous monitoring of corporate reputation plays an important role in this valuation process. Family businesses are an ideal object for reputation management research, as through their brands they integrate tradition and addressability at the same time. The main aim of the paper is to discuss the issue of innovative approaches to the online reputation management. We performed an in-depth analysis of online reputation through an Advanced sentiment analysis on the significant sample of ten largest family-owned businesses in the world. Taking into account all relevant determinants of reputation such as Google as well as major social networks, namely Facebook, Twitter, YouTube, and LinkedIn. As there is a noticeable difference between the marketing communication of the parent company and the marketing communication of the brand owned by the company, the findings of the analyses will provide a better insight into the issue of sustainable brand development. By identify good practices, as well as highlighting weaknesses, our research has the ambition to contribute to the shift of knowledge in the field of reputation management. Full article

With the increasing number of cybercrimes, the digital forensics team has no choice but to implement more robust and resilient evidence-handling mechanisms. The capturing of digital evidence, which is a tangible and probative piece of information that can be presented in court and used in trial, is very challenging due to its volatility and improper handling procedures. When computer systems get compromised, digital forensics comes into play to analyze, discover, extract, and preserve all relevant evidence. Therefore, it is imperative to maintain efficient evidence management to guarantee the credibility and admissibility of digital evidence in a court of law. A critical component of this process is to utilize an adequate chain of custody (CoC) approach to preserve the evidence in its original state from compromise and/or contamination. In this paper, a practical and secure CustodyBlock (CB) model using private blockchain protocol and smart contracts to support the control, transfer, analysis, and preservation monitoring is proposed. The smart contracts in CB are utilized to enhance the model automation process for better and more secure evidence preservation and handling. A further research direction in terms of implementing blockchain-based evidence management ecosystems, and the implications on other different areas, are discussed. Full article

In recent years, the growing number of devices connected to the internet has increased significantly. These devices can interact with the external environment and with human beings through a wide range of sensors that, perceiving reality through the digitization of some parameters of interest, can provide an enormous amount of data. All this data is then shared on the network with other devices and with different applications and infrastructures. This dynamic and ever-changing world underlies the Internet of Things (IoT) paradigm. To date, countless applications based on IoT have been developed; think of Smart Cities, smart roads, and smart industries. This article analyzes the current architectures, technologies, protocols, and applications that characterize the paradigm. Full article

Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public health crisis was declared in specific regions and nation-wide by governments all around the world. Citizens have gone through a wide range of emotions, such as fear of shortage of food, anger at the performance of governments and health authorities in facing the virus, sadness over the deaths of friends or relatives, etc. We present a monitoring system of citizens’ concerns using emotion detection in Twitter data. We also track public emotions and link these emotions with COVID-19 symptoms. We aim to show the effect of emotion monitoring on improving people’s daily health behavior and reduce the spread of negative emotions that affect the mental health of citizens. We collected and annotated 5.5 million tweets in the period from January to August 2020. A hybrid approach combined rule-based and neural network techniques to annotate the collected tweets. The rule-based technique was used to classify 300,000 tweets relying on Arabic emotion and COVID-19 symptom lexicons while the neural network was used to expand the sample tweets that were annotated using the rule-based technique. We used long short-term memory (LSTM) deep learning to classify all of the tweets into six emotion classes and two types (symptom and non-symptom tweets). The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion. The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms. Our study should help governments and decision-makers to dispel people’s fears and discover new symptoms associated with the symptoms that were declared by the WHO. It can also help in the understanding of people’s mental and emotional issues to address them before the impact of disease anxiety becomes harmful in itself. Full article

Assessing the business performance is an important aspect of almost all economic decisions at the microeconomic and macroeconomic level, in the short and long term. Information about the partners’ relationship to the business, their interest in the evaluation of investments can be explained by various indicators. It is relevant to understand the dependencies of the business performance and the amount of equity, while negative equity can be considered as critical information of existence. The purpose of quantitative research is to identify the relationship between reported negative equity and the business performance in Slovakia on an exhaustive sample of financial data of businesses with negative equity in the period 2014–2018. The business performance with negative equity is assessed through the Altman Z-score and the IN05 index, by classifying businesses into bankruptcy, prosperity and gray zones. Pearson’s correlation analysis between negative equity and Altman Z-score performance confirms the strong direct relationship between negative equity and the bankruptcy zone, the weaker indirect relationship between negative equity and the gray zone, and almost no dependence of negative equity and prosperity zone. In the case of the IN05 index, a low correlation was found between negative equity and all three zones. Although businesses with negative equity are in a bankruptcy zone, they do not have to close automatically, but they have to improve resource management, in particular to increase equity, for example by making a profit and good financial management. Full article

The field of natural language processing (NLP) has witnessed a boom in language representation models with the introduction of pretrained language models that are trained on massive textual data then used to fine-tune downstream NLP tasks. In this paper, we aim to study the evolution of language representation models by analyzing their effect on an under-researched NLP task: emotion analysis; for a low-resource language: Arabic. Most of the studies in the field of affect analysis focused on sentiment analysis, i.e., classifying text into valence (positive, negative, neutral) while few studies go further to analyze the finer grained emotional states (happiness, sadness, anger, etc.). Emotion analysis is a text classification problem that is tackled using machine learning techniques. Different language representation models have been used as features for these machine learning models to learn from. In this paper, we perform an empirical study on the evolution of language models, from the traditional term frequency–inverse document frequency (TF–IDF) to the more sophisticated word embedding word2vec, and finally the recent state-of-the-art pretrained language model, bidirectional encoder representations from transformers (BERT). We observe and analyze how the performance increases as we change the language model. We also investigate different BERT models for Arabic. We find that the best performance is achieved with the ArabicBERT large model, which is a BERT model trained on a large dataset of Arabic text. The increase in F1-score was significant +7–21%. Full article

Due to the limited bandwidth of Low-Power Wide-Area Networks (LPWAN), the application layer is currently often tied straight above the link layer, limiting the evolution of sensor networks distributed over a large area. Consequently, the highly efficient Static Context Header Compression (SCHC) standard was introduced, where devices can compress the IPv6 and upper layer protocols down to a single byte. This approach, however, assumes that every compression context is distributed before deployment, again limiting the evolution of such networks. Therefore, this paper presents two context registration mechanisms leveraging on the SCHC adaptation layer. This is done by analyzing current registration solutions in order to find limitations and optimizations with regard to very constrained networks. Both solutions and the current State-of-The-Art (SoTA) are evaluated in a Lightweight Machine to Machine (LwM2M) environment. In such situation, both developed solutions decrease the energy consumption already after 25 transmissions, compared with the current SoTA. Furthermore, simulations show that Long Range (LoRa) devices still have a 80% chance to successfully complete the registration flow in a network with a 50% Packet Error Ratio. Briefly, the work presented in this paper delivers bootstrapping tools to constrained, SCHC-enabled networks while still being able to reduce energy consumption. Full article

Computer-aided methods, based on the entropic linear program framework, have been shown to be effective in assisting the study of information theoretic fundamental limits of information systems. One key element that significantly impacts their computation efficiency and applicability is the reduction of variables, based on problem-specific symmetry and dependence relations. In this work, we propose using the disjoint-set data structure to algorithmically identify the reduction mapping, instead of relying on exhaustive enumeration in the equivalence classification. Based on this reduced linear program, we consider four techniques to investigate the fundamental limits of information systems: (1) computing an outer bound for a given linear combination of information measures and providing the values of information measures at the optimal solution; (2) efficiently computing a polytope tradeoff outer bound between two information quantities; (3) producing a proof (as a weighted sum of known information inequalities) for a computed outer bound; and (4) providing the range for information quantities between which the optimal value does not change, i.e., sensitivity analysis. A toolbox, with an efficient JSON format input frontend, and either Gurobi or Cplex as the linear program solving engine, is implemented and open-sourced. Full article

This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human–computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes. Full article

In the distance estimation scheme using Frequency-Modulated-Continuous-Wave (FMCW) radar, the frequency difference, which was caused by the time delay of the received signal reflected from the target, is calculated to estimate the distance information of the target. In this paper, we propose a distance estimation scheme exploiting the deep learning technology of artificial neural network to improve the accuracy of distance estimation over the conventional Fast Fourier Transform (FFT) Max value index-based distance estimation scheme. The performance of the proposed scheme is compared with that of the conventional scheme through the experiments evaluating the accuracy of distance estimation. The average estimated distance error of the proposed scheme was 0.069 m, while that of the conventional scheme was 1.9 m. Full article

In this study, we proposed a method to improve the safety level of control software (CSW) by managing the CSW’s design information and safety analysis results, and combining failure mode and effects analysis (FMEA) and fault tree analysis (FTA). Here, the CSW is developed using structured analysis and design methodology. In the upper stage of the CSW’s development process, as the input of the preliminary design information (data flow diagrams (DFDs) and control flow diagrams (CFDs)), the causes of undesirable events of the CSW are clarified by FMEA, and the countermeasures are reflected in the preliminary design information. In the lower stage of the CSW’s development process, as the inputs of the detailed design information (DFDs and CFDs in the lower level) and programs, the causes of the specific undesirable event are clarified by FTA, and the countermeasures are reflected in the detailed design specifications and programs. The processes are repeated until the impact of undesirable events become the acceptable safety level. By applying the proposed method to the CSW installed into a communication control equipment on the space system, we clarified several undesirable events and adopted adequate countermeasures. Consequently, a safer CSW is developed by applying the proposed method. Full article

The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpose of people gathering at Skytrax is to make better choices using the actual experiences of other customers who have experienced airlines. Online reviews written by customers with experience using airlines in Asia were collected. The data collected were online reviews from 27 airlines, with more than 14,000 reviews. Topic modeling and sentiment analysis were used with the collected data to figure out what kinds of important words are in the online reviews. As a result of the topic modeling, ‘seat’, ‘service’, and ‘meal’ were significant issues in the flight through frequency analysis. Additionally, the result revealed that delay was the main issue, which can affect customer dissatisfaction while ‘staff service’ can make customers satisfied through sentiment analysis as the result shows the ‘staff service’ with meal and food in the topic modeling. Full article

The concept of information is foundational to many disciplines yet also problematic and contested. This article contributes to the understanding of information through discussion of the findings of the interdisciplinary Difference That Makes a Difference (DTMD) project. DTMD used international conferences and workshops to bring together individuals from a wide range of disciplines to share how their field understands information, to engage in interdisciplinary conversations, and to contribute to edited publications. A simple answer to the question ‘what is information?’ is not forthcoming, but, it is argued, should no more be expected than would be an answer to ‘what is matter?’. Nevertheless, through exploration of the areas of consensus that emerged from the bottom-up process of interdisciplinary dialogue, this paper offers ten assertions about the nature of information narratives for further debate. The assertions range from ‘information requires a body’, through ‘information always has meaning’ and ‘information cannot be stored or communicated’ to ‘information is always shaped by power, authority and hierarchy’. This article finishes by illustrating and testing the assertions against an information case study of a team of medical experts disseminating information to the general public about the COVID-19 virus. Full article

Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network’s operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution. Full article

This article discusses the impact of a lockdown caused by the novel coronavirus disease 2019 on the educational process at a selected faculty of a public university in the Czech Republic focused on economic education. The aim was to capture relevant aspects in the context of impacts on the management of the educational process in the organization. The unique situation brought the possibility of analyzing the flexibility of the organization, its ability to adapt. A questionnaire survey was conducted among academics. We found out how they coped with this situation, their technical equipment, support from the faculty, and whether they encountered any problems. The goal of the article was not to bring an exact evaluation of selected questions, but to show the state of the actual situation, to point out possible problems of users, and to link these things with the approach to the management of the organization. Based on the analysis, we bring suggestions and recommendations for improving the process of transition to online learning as well as distance education management and recommendation to support teaching, regardless of the teacher’s workplace. The basic areas and activities that need to be managed were also identified. Full article

Certified public accounts’ (CPAs) audit opinions of going concern are the important basis for evaluating whether enterprises can achieve normal operations and sustainable development. This study aims to construct going concern prediction models to help CPAs and auditors to make more effective/correct judgments on going concern opinion decisions by deep learning algorithms, and using the following methods: deep neural networks (DNN), recurrent neural network (RNN), and classification and regression tree (CART). The samples of this study are companies listed on the Taiwan Stock Exchange and the Taipei Exchange, a total of 352 companies, including 88 companies with going concern doubt and 264 normal companies (with no going concern doubt). The data from 2002 to 2019 are taken from the Taiwan Economic Journal (TEJ) Database. According to the empirical results, with the important variables selected by CART and modeling by RNN, the CART-RNN model has the highest going concern prediction accuracy (the accuracy of the test dataset is 95.28%, and the average accuracy is 93.92%). Full article

Digital advertising has been frequently used for the promotion of e-commerce among individuals. However, little is known about the function of cultural factors that can outline the effectiveness of digital advertising practices to alter attitude and consumer behavior toward clothing brands. This research examines how norm-congruent attitudes toward digital advertising (hereafter ADA) may operate as a process variable that mediates the relationship between perception about digital advertising (hereafter PDA) and online purchase intention of fashion brands (hereafter OPI). We propose a gender egalitarianism (hereafter GE)-moderated mediation model whereby ADA mediates the relationships between PDA and OPI in two culturally diverse nations: Malaysia and Pakistan. The model was tested by using 2 (GE appeal: present vs. absent) × 2 (nation: Pakistan vs. Malaysia) × 2 (no exposure to ads/exposure to ads) experimental design with data obtained from a sample of 260. Findings show that there is a significant difference in the relationship between PDA and OPI that is mediated by the attitude in both nations. However, the mediation implication of the attitude is significantly dependent on the interaction of the GE. In this way, the study provides some practical recommendations for the marketers by highlighting the salient advertising features that may be more useful in both nations. Full article

Nowadays, industries are implementing heterogeneous systems from different domains, backgrounds, and operating systems. Manufacturing systems are becoming more and more complex, which forces engineers to manage the complexity in several aspects. Technical complexities bring interoperability, risk management, and hazards issues that must be taken into consideration, from the business model design to the technical implementation. To solve the complexities and the incompatibilities between heterogeneous components, several distributed and cosimulation standards and tools can be used for data exchange and interconnection. High-level architecture (HLA) and functional mockup interface (FMI) are the main international standards used for distributed and cosimulation. HLA is mainly used in academic and defense domains while FMI is mostly used in industry. In this article, we propose an HLA/FMI implementation with a connection to an external business process-modeling tool called Papyrus. Papyrus is configured as a master federate that orchestrates the subsimulations based on the above standards. The developed framework is integrated with external heterogeneous components through an FMI interface. This framework is developed with the aim of bringing interoperability to a system used in a power generation company. Full article

The purpose of this review is to describe the landscape of scientific literature enriched by an author’s keyword analysis to develop and test blockchain’s capabilities for enhancing supply chain resilience in times of increased risk and uncertainty. This review adopts a dynamic quantitative bibliometric method called systematic literature network analysis (SLNA) to extract and analyze the papers. The procedure consists of two methods: a systematic literature review (SLR) and bibliometric network analysis (BNA). This paper provides an important contribution to the literature in applying blockchain as a key component of cyber supply chain risk management (CSRM), manage and predict disruption risks that lead to resilience and robustness of the supply chain. This systematic review also sheds light on different research areas such as the potential of blockchain for privacy and security challenges, security of smart contracts, monitoring counterfeiting, and traceability database systems to ensure food safety and security. Full article

Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools. Full article

This paper aims to examine a theoretical framework of digital society and the ramifications of the digital revolution. The paper proposes that more attention has to be paid to cultural studies as a means for the understanding of digital society. The approach is based on the idea that the digital revolution’s essence is fully manifested in the cultural changes that take place in society. Cultural changes are discussed in connection with the digital society’s transformations, such as blurring the distinction between reality and virtuality and among people, nature, and artifacts, and the reversal from informational scarcity to abundance. The presented study develops a general model of culture. This model describes the spiritual, social, and technological facets of culture. Such new phenomena as individualization, transparisation, and so-called cognification (intellectualization of the surrounding environment) are suggested as the prominent trends characterizing the above cultural facets. Full article

Enterprise architecture (EA) is useful for effectively structuring digital platforms with digital transformation in information societies. Moreover, digital platforms in the healthcare industry accelerate and increase the efficiency of drug discovery and development processes. However, there is the lack of knowledge concerning relationships between EA and digital platforms, in spite of the needs of it. In this paper, we investigated and analyzed the process of drug design and development within the healthcare industry, together with related work in using an enterprise architecture framework for the digital era named the Adaptive Integrated Digital Architecture Framework (AIDAF), specifically supporting the design of digital platforms there. Based on this analysis, we evaluate a method and propose a new reference architecture for promoting digital platforms in the healthcare industry, with future specific aspects of them making effective use of Artificial Intelligence (AI). The practical and theoretical contributions include: (1) Streamlined processes through digital platforms in organizations. (2) Informal knowledge supply and sharing among organizational members through digital platforms. (3) Efficiency and effectiveness in planning production and business for drug development. The findings indicate that EA with digital platforms using the AIDAF contribute to digital transformation with effectiveness for new drugs in the healthcare industry. Full article

Previous studies have pointed out that it is hard to achieve the level of herd immunity for the population and then effectively stop disease propagation from the perspective of public health, if individuals just make vaccination decisions based on individualism. Individuals in reality often exist in the form of groups and cooperate in or among communities. Meanwhile, society studies have suggested that we cannot ignore the existence and influence of collectivism for studying individuals’ decision-making. Regarding this, we formulate two vaccination strategies: individualistic strategy and collectivist strategy. The former helps individuals taking vaccination action after evaluating their perceived risk and cost of themselves, while the latter focuses on evaluating their contribution to their communities. More significantly, we propose a reinforcement learning mechanism based on policy gradient. Each individual can adaptively pick one of these two strategies after weighing their probabilities with a two-layer neural network whose parameters are dynamically updated with his/her more and more vaccination experience. Experimental results on scale-free networks verify that the reinforcement learning mechanism can effectively improve the vaccine coverage level of communities. Moreover, communities can always get higher total payoffs with fewer costs paid, comparing that of pure individualistic strategy. Such performance mostly stems from individuals’ adaptively picking collectivist strategy. Our study suggests that public health authorities should encourage individuals to make vaccination decisions from the perspective of their local mixed groups. Especially, it is more worthy of noting that individuals with low degrees are more significant as their vaccination behaviors can more sharply improve vaccination coverage of their groups and greatly reduce epidemic size. Full article

While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered information is urgently needed. We implement an approach based on the mechanism of a metasearch engine to present less-filtered information to users. We develop a practical application named MosaicSearch to select search results from diversified categories of sources collected from multiple search engines. To determine the power of MosaicSearch, we conduct an evaluation to assess retrieval quality. According to the results, MosaicSearch is more intelligent compared to other general-purpose search engines: it generates a smaller number of links while providing users with almost the same amount of objective information. Our approach contributes to transparent information retrieval. This application helps users play a main role in choosing the information they consume. Full article