Future Internet, Volume 13, Issue 6 (June 2021) – 25 articles

In the field of journalism, the collection and processing of information from different heterogeneous sources are difficult and time-consuming processes. In the context of the theory of journalism 3.0, where multimedia data can be extracted from different sources on the web, the possibility of creating a tool for the exploitation of Earth observation (EO) data, especially images by professionals belonging to the field of journalism, is explored. With the production of massive volumes of EO image data, the problem of their exploitation and dissemination to the public, specifically, by professionals in the media industry, arises. In particular, the exploitation of satellite image data from existing tools is difficult for professionals who are not familiar with image processing. In this scope, this article presents a new innovative platform that automates some of the journalistic practices. This platform includes several mechanisms allowing users to early detect and receive information about breaking news in real-time, retrieve EO Sentinel-2 images upon request for a certain event, and automatically generate a personalized article according to the writing style of the author. Through this platform, the journalists or editors can also make any modifications to the generated article before publishing. This platform is an added-value tool not only for journalists and the media industry but also for freelancers and article writers who use information extracted from EO data in their articles. Full article

Successful responses to societal challenges require sustained behavioral change. However, as responses to the COVID-19 pandemic in the US showed, political partisanship and mistrust of science can reduce public willingness to adopt recommended behaviors such as wearing a mask or receiving a vaccination. To better understand this phenomenon, we explored attitudes toward science using social media posts (tweets) that were linked to counties in the US through their locations. The data allowed us to study how attitudes towards science relate to the socioeconomic characteristics of communities in places from which people tweet. Our analysis revealed three types of communities with distinct behaviors: those in large metro centers, smaller urban places, and rural areas. While partisanship and race are strongly associated with the share of anti-science users across all communities, income was negatively and positively associated with anti-science attitudes in suburban and rural areas, respectively. We observed that emotions in tweets, specifically negative high arousal emotions, are expressed among suburban and rural communities by many anti-science users, but not in communities in large urban places. These trends were not apparent when pooled across all counties. In addition, we found that anti-science attitudes expressed five years earlier were significantly associated with lower COVID-19 vaccination rates. Our analysis demonstrates the feasibility of using spatially resolved social media data to monitor public attitudes on issues of social importance. Full article

The race for the 6th generation of wireless networks (6G) has begun. Researchers around the world have started to explore the best solutions for the challenges that the previous generations have experienced. To provide the readers with a clear map of the current developments, several review papers shared their vision and critically evaluated the state of the art. However, most of the work is based on general observations and the big picture vision, and lack the practical implementation challenges of the Internet of Things (IoT) use cases. This paper takes a novel approach in the review, as we present a sample of IoT use cases that are representative of a wide variety of its implementations. The chosen use cases are from the most research-active sectors that can benefit from 6G and its enabling technologies. These sectors are healthcare, smart grid, transport, and Industry 4.0. Additionally, we identified some of the practical challenges and the lessons learned in the implementation of these use cases. The review highlights the cases’ main requirements and how they overlap with the key drivers for the future generation of wireless networks. Full article

The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic. Full article

The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stems together with derivation and compounding. Furthermore, there is a need to understand which themes linked with misinformation—and the concepts related to it—manifest in different countries and language areas in Twitter discourse. To address this issue, this study explores misinformation and its related concepts: disinformation, fake news, and propaganda in Finnish language tweets. We utilized (1) word cloud clustering, (2) topic modeling, and (3) word count analysis and clustering to detect and analyze misinformation-related concepts and themes connected to those concepts in Finnish language Twitter discussions. Our results are two-fold: (1) those concerning the functional data analysis methods and (2) those about the themes connected in discourse to the misinformation-related concepts. We noticed that each utilized method individually has critical limitations, especially all the automated analysis methods processing for the Finnish language, yet when combined they bring value to the analysis. Moreover, we discovered that politics, both internal and external, are prominent in the Twitter discussions in connection with misinformation and its related concepts of disinformation, fake news, and propaganda. Full article

In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning, and highlight challenges that should be addressed when designing human–machine cooperation in cyber-physical production systems. Full article

The Fourth Industrial Revolution (Industry 4.0), with the help of cyber-physical systems (CPS), the Internet of Things (IoT), and Artificial Intelligence (AI), is transforming the way industrial setups are designed. Recent literature has provided insight about large firms gaining benefits from Industry 4.0, but many of these benefits do not translate to SMEs. The agent-oriented smart factory (AOSF) framework provides a solution to help bridge the gap between Industry 4.0 frameworks and SME-oriented setups by providing a general and high-level supply chain (SC) framework and an associated agent-oriented storage and retrieval (AOSR)-based warehouse management strategy. This paper presents the extended heuristics of the AOSR algorithm and details how it improves the performance efficiency in an SME-oriented warehouse. A detailed discussion on the thorough validation via scenario-based experimentation and test cases explain how AOSR yielded 60–148% improved performance metrics in certain key areas of a warehouse. Full article

Nowadays, the majority of everyday computing devices, irrespective of their size and operating system, allow access to information and online services through web browsers. However, the pervasiveness of web browsing in our daily life does not come without security risks. This widespread practice of web browsing in combination with web users’ low situational awareness against cyber attacks, exposes them to a variety of threats, such as phishing, malware and profiling. Phishing attacks can compromise a target, individual or enterprise, through social interaction alone. Moreover, in the current threat landscape phishing attacks typically serve as an attack vector or initial step in a more complex campaign. To make matters worse, past work has demonstrated the inability of denylists, which are the default phishing countermeasure, to protect users from the dynamic nature of phishing URLs. In this context, our work uses supervised machine learning to block phishing attacks, based on a novel combination of features that are extracted solely from the URL. We evaluate our performance over time with a dataset which consists of active phishing attacks and compare it with Google Safe Browsing (GSB), i.e., the default security control in most popular web browsers. We find that our work outperforms GSB in all of our experiments, as well as performs well even against phishing URLs which are active one year after our model’s training. Full article

Picking reliable partners, negotiating synchronously with all partners, and managing similar proposals are challenging tasks for any manager. This challenge is even harder when it concerns small and medium enterprises (SMEs) who need to deal with short budgets and evident size limitations, often leading them to avoid handling very large contracts. This size problem can only be mitigated by collaboration efforts between multiple SMEs, but then again this brings back the initially stated issues. To address these problems, this paper proposes a collaborative negotiation system that automates the outsourcing part by assisting the manager throughout a negotiation. The described system provides a comprehensive view of all negotiations, facilitates simultaneous bilateral negotiations, and provides support for ensuring interoperability among multiple partners negotiating on a task described by multiple attributes. In addition, it relies on an ontology to cope with the challenges of semantic interoperability, it automates the selection of reliable partners by using a lattice-based approach, and it manages similar proposals by allowing domain experts to define a satisfaction degree for each SME. To showcase this method, this research focused on small and medium-size dairy farms (DFs) and describes a negotiation scenario in which a few DFs are able to assess and generate proposals. Full article

The central thesis of this paper is that memetic practices can be crucial to understanding deception at present when hoaxes have increased globally due to COVID-19. Therefore, we employ existing memetic theory to describe the qualities and characteristics of meme hoaxes in terms of the way they are replicated by altering some aspects of the original, and then shared on social media platforms in order to connect global and local issues. Criteria for selecting the sample were hoaxes retrieved from and related to the local territory in the province of Alicante (Spain) during the first year of the pandemic (n = 35). Once typology, hoax topics and their memetic qualities were identified, we analysed their characteristics according to form in terms of Shifman (2014) and, secondly, their content and stance concordances both within and outside our sample (Spain and abroad). The results show, firstly, that hoaxes are mainly disinformation and they are related to the pandemic. Secondly, despite the notion that local hoaxes are linked to local circumstances that are difficult to extrapolate, our conclusions demonstrate their extraordinary memetic and “glocal” capacity: they rapidly adapt other hoaxes from other places to local areas, very often supplanting reliable sources, and thereby demonstrating consistency and opportunism. Full article

An Ambient Intelligence responds to user requests based on several contexts. A relevant context is related to what has happened in the ambient; therefore, it focuses a primordial interest on events. These involve information about time, space, or people, which is significant for modeling the context. In this paper, we propose an event-driven approach for context representation based on an ontological model. This approach is extendable and adaptable for academic domains. Moreover, the ontological model to be proposed is used in reasoning and enrichment processes with the context event information. Our event-driven approach considers five contexts as a modular perspective in the model: Person, temporal (time), physical space (location), network (resources to acquire data from the ambient), and academic events. We carried out an evaluation process for the approach based on an ontological model focused on (a) the extensibility and adaptability of use case scenarios for events in an academic environment, (b) the level of reasoning by using competence questions related to events, (c) and the consistency and coherence in the proposed model. The evaluation process shows promising results for our event-driven approach for context representation based on the ontological model. Full article

Since their emergence in the mid-90s, online media have evolved from simple digital editions that merely served to dump content from print newspapers, to sophisticated multi-format products with multimedia and interactive features. In order to discover their visual evolution, this article conducts a longitudinal study of the design of online media by analyzing the front pages of five general-information Spanish newspapers (elpais.com, elmundo.es, abc.es, lavanguardia.com, and elperiodico.com) over the past 25 years (1996–2020). Moreover, some of their current features are listed. To this end, six in-depth interviews were conducted with managers of different online media outlets. The results indicate that the media analysed have evolved from a static, rigid format, to a dynamic, mobile, and multi-format model. Regarding the language used, along with increased multimedia and interactive possibilities, Spanish online media currently display a balance between text and images on their front pages. Lastly, audience information consumption habits, largely superficial and sporadic, and the increasing technification and speed of production processes, means that news media have lost in terms of the design part of the individual personality they had in their print editions. However, they maintain their index-type front pages as one of their most characteristic elements, which are very vertical and highly saturated. Full article

As an emerging network architecture, Information-Centric Networking (ICN) is considered to have the potential to meet the new requirements of the Fifth Generation (5G) networks. ICN uses a name decoupled from location to identify content, supports the in-network caching technology, and adopts a receiver-driven model for data transmission. Existing ICN congestion control mechanisms usually first select a nearby replica by opportunistic cache-hits and then insist on adjusting the transmission rate regardless of the congestion state, which cannot fully utilize the characteristics of ICN to improve the performance of data transmission. To solve this problem, this paper proposes a two-level congestion control mechanism, called 2LCCM. It switches the replica location based on a node state table to avoid congestion paths when heavy congestion happens. This 2LCCM mechanism also uses a receiver-driven congestion control algorithm to adjust the request sending rate, in order to avoid link congestion under light congestion. In this paper, the design and implementation of the proposed mechanism are described in detail, and the experimental results show that 2LCCM can effectively reduce the transmission delay when heavy congestion occurs, and the bandwidth-delay product-based congestion control algorithm has better transmission performance compared with a loss-based algorithm. Full article

Nowadays, Internet of Things (IoT) adoptions are burgeoning and deemed the lynchpin towards achieving ubiquitous connectivity. In this context, defining and leveraging robust IoT security risk management strategies are paramount for secure IoT adoptions. Thus, this study aims to support IoT adopters from any sector to formulate or reframe their IoT security risk management strategies to achieve robust strategies that effectively address IoT security issues. In a nutshell, this article relies on a mixed methods research methodology and proposes a reference model for IoT security risk management strategy. The proposed IoT security risk management strategy reference model (IoTSRM2) relies on the 25 selected IoT security best practices which are outlined using a proposed taxonomic hierarchy, and on the proposed three-phased methodology that consists of nine steps and outputs. The main contribution of this work is the proposed IoTSRM2 which consists of six domains, 16 objectives, and 30 prioritized controls. Furthermore, prior to providing the related work, this article provides a critical evaluation of selected informative references of IoTSRM2 based on their percentage-wise linkage to the IoTSRM2 domains and to the entire IoTSRM2. The findings of the critical evaluation illustrate, inter alia, the selected informative references that are the top three most and least linked to the entire IoTSRM2. Full article

Age, gender, educational background, and so on are the most basic attributes for identifying and portraying users. It is also possible to conduct in-depth mining analysis and high-level predictions based on such attributes to learn users’ preferences and personalities so as to enhance users’ online experience and to realize personalized services in real applications. In this paper, we propose using classification algorithms in machine learning to predict users’ demographic attributes, such as gender, age, and educational background, based on one month of data collected with the Sogou search engine with the goal of making user portraits. A multi-model approach using the fusion algorithms is adopted and hereby described in the paper. The proposed model is a two-stage structure using one month of data with demographic labels as the training data. The first stage of the structure is based on traditional machine learning models and neural network models, whereas the second one is a combination of the models from the first stage. Experimental results show that our proposed multi-model method can achieve more accurate results than the single-model methods in predicting user attributes. The proposed approach also has stronger generalization ability in predicting users’ demographic attributes, making it more adequate to profile users. Full article

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC. Full article

The proposed paper introduces an innovative methodology useful to assign intelligent scores to web pages. The approach is based on the simultaneous use of User eXperience (UX), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithms, providing the web page scoring and taking into account outlier conditions to construct the training dataset. Specifically, the UX tool analyses different parameters addressing the score, such as navigation time, number of clicks, and mouse movements for page, finding possible outliers, the ANN are able to predict outliers, and the LSTM processes the web pages tags together with UX and user scores. The final web page score is assigned by the LSTM model corrected by the UX output and improved by the navigation user score. This final score is useful for the designer by suggesting the tags typologies structuring a new web page layout of a specific topic. By using the proposed methodology, the web designer is addressed to allocate contents in the web page layout. The work has been developed within a framework of an industry project oriented on the formulation of an innovative AI interface for web designers. Full article

This paper focuses on the study of IoT network deployments, in both unlicensed and licensed bands, considering LoRaWAN and NB-IoT standards, respectively. The objective is to develop a comprehensive and detailed network planning and coverage dimensioning methodology for assessing key metrics related to the achieved throughput and capacity for specific requirements in order to identify tradeoffs and key issues that are related to the applicability of IoT access technologies for representative use case types. This paper will provide a concise overview of key characteristics of IoT representative IoT access network standards that are considered for being deployed in unlicensed and licensed bands and will present a methodology for modeling the characteristics of both access network technologies in order to assess their coverage and capacity considering different parameters. Full article

A reliable log system is a prerequisite for many applications. Financial systems need to have transactions logged in a precise manner, medical systems rely on having trusted medical records and security logs record system access requests in order to trace malicious attempts. Keeping multiple copies helps to achieve availability and reliability against such hackers. Unfortunately, maintaining redundant copies in a distributed manner in a byzantine setting has always been a challenging task, however it has recently become simpler given advances in blockchain technologies. In this work, we present a tamper-resistant log system through the use of a blockchain. We leverage the immutable write action and distributed storage provided by the blockchain as a basis to develop a secure log system, but we also add a privacy preserving layer that is essential for many applications. We detail the security and privacy aspects of our solution, as well as how they relate to performance needs in relevant settings. Finally, we implement our system over Hyperledger Fabric and demonstrate the system’s value for several use cases. In addition, we provide a scalability analysis for applying our solution in a large-scale system. Full article

So-called ‘fake news’—deceptive online content that attempts to manipulate readers—is a growing problem. A tool of intelligence agencies, scammers and marketers alike, it has been blamed for election interference, public confusion and other issues in the United States and beyond. This problem is made particularly pronounced as younger generations choose social media sources over journalistic sources for their information. This paper considers the prospective solution of providing consumers with ‘nutrition facts’-style information for online content. To this end, it reviews prior work in product labeling and considers several possible approaches and the arguments for and against such labels. Based on this analysis, a case is made for the need for a nutrition facts-based labeling scheme for online content. Full article

The use of social networks as a social and educational environment emphasizes an approach to social problems (bullying, cyberbullying, sexism, racism) that has emerged in today’s society. Social networks offer a unique opportunity to increase channels for communication and socialization. The aim of this study is to analyze the adolescents’ attitudes in sports practice in their extracurricular environment, and it highlights the importance of didactic communication as a tool for social cohesion to guarantee the interaction between adolescents. This objective has been evaluated through the “Sport and Social Integration. Survey on Secondary Schools in Italy” questionnaire. In this paper, we focus our attention on Section II to go deeper into the participants’ opinion on previously mentioned social problems. The methods of research for this study were conducted through a descriptive, inferential, quantitative, and ex post facto design. The sample consisted of 286 Italian adolescents aged between 12 and 15 years old. Results show that there are positive correlations in the following variables: gender and verbal, psychological, or physical violence (Bullying) (r = 0.260) (Sig. = 0.000); gender and threats, crimes, and persecutions through the Internet (Cyberbullying) (r = 0.226) (Sig. = 0.000); gender and discrimination against women (Sexism) (r = 0.133) (Sig. = 0.025). In conclusion, this article underlines the importance of investing more systematically in the effort to prevent bullying and digital inclusion from an early age for the critical use of mobile devices and social networks. Full article

Currently, education is going through a critical moment due to the 2019 coronavirus disease that has been declared a pandemic. This has forced many organizations to undergo a significant transformation, rethinking key elements of their processes and the use of technology to maintain operations. The continuity of education has become dependent on technological tools, as well as on the ability of universities to cope with a precipitous transition to a remote educational model. That has generated problems that affect student learning. This work proposes the implementation of a Big Data framework to identify the factors that affect student performance and decision-making to improve learning. Similar works cover two main research topics under Big Data in education, the modeling and storage of educational data. However, they do not consider issues such as student performance and the improvement of the educational system with the integration of Big Data. In addition, this work provides a guide for future studies and highlights new insights and directions for the successful use of Big Data in education. Real-world data were collected for the evaluation of the proposed framework, the collection of these being the existing limitation in all research due to generalized rejection of data consent. Full article

Thanks to the popularity of personal mobile devices, more and more of the different types of private content, such as images and videos, are shared on social networking applications. While content sharing may be an effective practice to enhance social relationships, it is also a source of relevant privacy issues. Unfortunately, users find it difficult to understanding the terms and implications of the privacy policies of apps and services. Moreover, taking privacy decisions about content sharing on social networks is cumbersome and prone to errors that could determine privacy leaks. In this paper, we propose two techniques aimed at supporting the user in taking privacy choices about sharing personal content online. Our techniques are based on machine learning and natural language processing to analyze privacy policies, and on computer vision to assist the user in the privacy-conscious sharing of multimedia content. Experiments with real-world data show the potential of our solutions. We also present ongoing work on a system prototype and chatbot for natural language user assistance. Full article

In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors. Full article

The concept of Industry 4.0 is becoming more and more popular all over the world. The implementation of its assumptions in business practice changes the way companies operate. The enormous innovative potential of the Industry 4.0 concept and the intensive use of processes based on the implementation of advanced technologies in its assumptions have an impact on various industries in each country. The article attempts to analyze the critical factors for the implementation of Industry 4.0 in Kazakhstan. Primary and secondary data sources were used for this purpose. The majority of existing government initiatives and reports do not reflect the actual situation. Additionally, these materials do not always represent the experiences or positions of all the parties involved. Opinions of companies and organizations implementing Industry 4.0 solutions remain unexplored. The primary goal of the paper is to fill the cognitive gap by analyzing stakeholder responses and identifying the actual level of their awareness of the development of Industry 4.0. An additional intention of the authors was to empirically establish barriers that companies face while implementing the most desirable technologies, as well as to establish other critical factors, taking into account the specificity of the country under study. The results of the research can be used by policymakers, scientists and other stakeholders to develop forecasts and strategic plans, as well as to develop and conduct further research on the implementation processes of Industry 4.0 in Kazakhstan. Full article