Future Internet, Volume 12, Issue 9 (September 2020) – 20 articles

Grinding is a critical technology in the manufacturing of high added-value precision parts, accounting for approximately 20–25% of all machining costs in the industrialized world. It is a commonly used process in the finishing of parts in numerous key industrial sectors such as transport (including the aeronautical, automotive and railway industries), and energy or biomedical industries. As in the case of many other manufacturing technologies, grinding relies heavily on the experience and knowledge of the operatives. For this reason, considerable efforts have been devoted to generating a systematic and sustainable approach that reduces and eventually eliminates costly trial-and-error strategies. The main contribution of this work is that, for the first time, a complete digital twin (DT) for the grinding industry is presented. The required flow of information between numerical simulations, advanced mechanical testing and industrial practice has been defined, thus producing a virtual mirror of the real process. The structure of the DT comprises four layers, which integrate: (1) scientific knowledge of the process (advanced process modeling and numerical simulation); (2) characterization of materials through specialized mechanical testing; (3) advanced sensing techniques, to provide feedback for process models; and (4) knowledge integration in a configurable open-source industrial tool. To this end, intensive collaboration between all the involved agents (from university to industry) is essential. One of the most remarkable results is the development of new and more realistic models for predicting wheel wear, which currently can only be known in industry through costly trial-and-error strategies. Also, current work is focused on the development of an intelligent grinding wheel, which will provide on-line information about process variables such as temperature and forces. This is a critical issue in the advance towards a zero-defect grinding process. Full article

A stream can be thought of as a very large set of data, sometimes even infinite, which arrives sequentially and must be processed without the possibility of being stored. In fact, the memory available to the algorithm is limited and it is not possible to store the whole stream of data which is instead scanned upon arrival and summarized through a succinct data structure in order to maintain only the information of interest. Two of the main tasks related to data stream processing are frequency estimation and heavy hitter detection. The frequency estimation problem requires estimating the frequency of each item, that is the number of times or the weight with which each appears in the stream, while heavy hitter detection means the detection of all those items with a frequency higher than a fixed threshold. In this work we design and analyze ACMSS, an algorithm for frequency estimation and heavy hitter detection, and compare it against the state of the art ASketch algorithm. We show that, given the same budgeted amount of memory, for the task of frequency estimation our algorithm outperforms ASketch with regard to accuracy. Furthermore, we show that, under the assumptions stated by its authors, ASketch may not be able to report all of the heavy hitters whilst ACMSS will provide with high probability the full list of heavy hitters. Full article

Along with the growing threat of cyberattacks, cybersecurity has become one of the most important areas of the Internet of Things (IoT). The purpose of IoT cybersecurity is to reduce cybersecurity risk for organizations and users through the protection of IoT assets and privacy. New cybersecurity technologies and tools provide potential for better IoT security management. However, there is a lack of effective IoT cyber risk management frameworks for managers. This paper reviews IoT cybersecurity technologies and cyber risk management frameworks. Then, this paper presents a four-layer IoT cyber risk management framework. This paper also applies a linear programming method for the allocation of financial resources to multiple IoT cybersecurity projects. An illustration is provided as a proof of concept. Full article

Despite the rapid evolution of Internet protocol-based messaging services, SMS still remains an indisputable communication service in our lives until today. For example, several businesses consider that text messages are more effective than e-mails. This is because 82% of SMSs are read within 5 min., but consumers only open one in four e-mails they receive. The importance of SMS for mobile phone users has attracted the attention of spammers. In fact, the volume of SMS spam has increased considerably in recent years with the emergence of new security threats, such as SMiShing. In this paper, we propose a hybrid deep learning model for detecting SMS spam messages. This detection model is based on the combination of two deep learning methods CNN and LSTM. It is intended to deal with mixed text messages that are written in Arabic or English. For the comparative evaluation, we also tested other well-known machine learning algorithms. The experimental results that we present in this paper show that our CNN-LSTM model outperforms the other algorithms. It achieved a very good accuracy of 98.37%. Full article

The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation. Full article

The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users. Full article

Smartphone applications related to coronavirus disease 2019 (COVID-19) continue to emerge and evolve, but despite a wide variety of different app functions, there has yet to be a comprehensive study of what the most prevalent publicly available apps provide, and there exists no standardized evaluation system for end users to determine the safety and efficacy of an app before they download it. Furthermore, limited oversight means that the rapidly growing space creates challenges for end users trying to find a relevant app. We adapted the M-Health Index and Navigation Database (MIND) from apps.digitalpsych.org that previously has been used to evaluate mental health applications to guide the assessment of COVID apps. Using this framework, we conducted a thorough analysis of the top-100 returned coronavirus apps on two separate dates a month apart to understand the clinical utility and features of COVID-19 apps and how these change in a short period of time. We ultimately identified a significant turnover rate, as well as privacy concerns around lack of privacy policies and disclosure of personal information. Our research offers insight into the current status of COVID-19 apps and provides a comprehensive and adaptable framework to help individuals assess the growing number of such digital tools in the wake of the pandemic. Full article

Due to its links to computer science (CS), teaching computational thinking (CT) often involves the handling of algorithms in activities, such as their implementation or analysis. Although there already exists a wide variety of different tasks for various learning environments in the area of computer science, there is less material available for CT. In this article, we propose so-called Generic Tasks for algorithms inspired by common programming tasks from CS education. Generic Tasks can be seen as a family of tasks with a common underlying structure, format, and aim, and can serve as best-practice examples. They thus bring many advantages, such as facilitating the process of creating new content and supporting asynchronous teaching formats. The Generic Tasks that we propose were evaluated by 14 experts in the field of Science, Technology, Engineering, and Mathematics (STEM) education. Apart from a general estimation in regard to the meaningfulness of the proposed tasks, the experts also rated which and how strongly six core CT skills are addressed by the tasks. We conclude that, even though the experts consider the tasks to be meaningful, not all CT-related skills can be specifically addressed. It is thus important to define additional tasks for CT that are detached from algorithms and programming. Full article

Many games in which chance plays a role can be simulated as a random walk over a graph of possible configurations of board pieces, cards, dice or coins. The end of the game generally consists of the appearance of a predefined winning pattern; for random walks, this corresponds to an absorbing trap. The strategy of a player consist of betting on a given sequence, i.e., in placing a trap on the graph. In two-players games, the competition between strategies corresponds to the capabilities of the corresponding traps in capturing the random walks originated by the aleatory components of the game. The concept of dominance transitivity of strategies implies an advantage for the first player, who can choose the strategy that, at least statistically, wins. However, in some games, the second player is statistically advantaged, so these games are denoted “intransitive”. In an intransitive game, the second player can choose a location for his/her trap which captures more random walks than that of the first one. The transitivity concept can, therefore, be extended to generic random walks and in general to Markov chains. We analyze random walks on several kinds of networks (rings, scale-free, hierarchical and city-inspired) with many variations: traps can be partially absorbing, the walkers can be biased and the initial distribution can be arbitrary. We found that the transitivity concept can be quite useful for characterizing the combined properties of a graph and that of the walkers. Full article

The Mobile ad hoc Network (MANET) is a collection of mobile devices that forms a self-created, self-administered, and self-organized network. It is an infrastructureless network that does not require an existing infrastructure to operate. MANET suits scenarios where a temporary network is needed, such as emergency rescue, the military field, and disaster areas. MANET is an isolated network, and communication is restricted to the participating nodes’ transmission coverage. In order to increase its connectivity and its application scope, however, MANET requires integration with other networks, forming a hybrid MANET. The integration of MANET and IP networks raises many challenges and issues. Mobility management is one of the main challenges. Traditional mobility management protocols provide seamless mobility in a single hop infrastructure network. Consequently, mobile nodes can maintain their global connectivity without interrupting the ongoing sessions. Mobility management becomes more challenging in a network that relies on multi-hop communication, such as MANET. This paper presents a survey of the use of mobility management systems when integrating MANET with the internet, with the objective of serving as a handy reference in this field of research. It presents, analyzes, and discusses existing mobility management solutions for integrated MANET networks. It also investigates their shortcomings and provides a comparative study of the surveyed proposals. Full article

State of the art: Cinema, because of the eclectic nature of art, technology and mass media, can be manifested as an educational tool in the classroom. In this sense, the educational possibilities detected in the cinema are numerous. The pre-service teacher education in the figure of the teacher determines their educational resources. Purpose: The general objective of this study is to design and validate an instrument to measure the perceptions of students of Pre-School Teacher Degree and Primary Teacher Degree in Spanish universities about cinema as a teaching resource in Pre-School and Primary Education. Design/methodology: For this purpose, a systematic and planned process was developed for the design and validation of the Percepciones sobre las potencialidades del cine como recurso didáctico en las aulas de Infantil y Primaria ((PECID) (perceptions about the potentialities of cinema as a didactic resource in pre-school and primary classrooms)) questionnaire. Main findings: The results obtained showed a good content validity of 25 items after an expert judgement. On the other hand, a reliability of the internal consistency of the instrument of 0.978 was obtained. Furthermore, a three-factor structure was confirmed through factor analysis. Conclusions: It is concluded that the PECID questionnaire is a valid and reliable instrument to measure the perceptions of future teachers in Spain about using cinema as a resource for future teaching. Full article

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments. Full article

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning. Full article

Over the past two decades, social media have become a crucial and omnipresent cultural and economic phenomenon, which has seen platforms come and go and advance technologically. In this study, we explore the further development of social media regarding interactive technologies, platform development, relationships to news media, the activities of institutional and organizational users, and effects of social media on the individual and the society over the next five to ten years by conducting an international, two-stage Delphi study. Our results show that enhanced interaction on platforms, including virtual and augmented reality, somatosensory sense, and touch- and movement-based navigation are expected. AIs will interact with other social media users. Inactive user profiles will outnumber active ones. Platform providers will diversify into the WWW, e-commerce, edu-tech, fintechs, the automobile industry, and HR. They will change to a freemium business model and put more effort into combating cybercrime. Social media will become the predominant news distributor, but fake news will still be problematic. Firms will spend greater amounts of their budgets on social media advertising, and schools, politicians, and the medical sector will increase their social media engagement. Social media use will increasingly lead to individuals’ psychic issues. Society will benefit from economic growth and new jobs, increased political interest, democratic progress, and education due to social media. However, censorship and the energy consumption of platform operators might rise. Full article

To keep its business reliable, Google is concerned to ensure the quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind it. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve the security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability, and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative, and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7,835,322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication, and availability, while the remaining 77% has a polarity under 0.5. Developers should make a lot of effort in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer a bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that the security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications work without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals the effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated with existing rating solutions to recommend developers exact CIAA aspects to improve within source codes. Full article

The paper is dedicated to solving the problem of optimal text classification in the area of automated detection of typology of texts. In conventional approaches to topicality-based text classification (including topic modeling), the number of clusters is to be set up by the scholar, and the optimal number of clusters, as well as the quality of the model that designates proximity of texts to each other, remain unresolved questions. We propose a novel approach to the automated definition of the optimal number of clusters that also incorporates an assessment of word proximity of texts, combined with text encoding model that is based on the system of sentence embeddings. Our approach combines Universal Sentence Encoder (USE) data pre-processing, agglomerative hierarchical clustering by Ward’s method, and the Markov stopping moment for optimal clustering. The preferred number of clusters is determined based on the “e-2” hypothesis. We set up an experiment on two datasets of real-world labeled data: News20 and BBC. The proposed model is tested against more traditional text representation methods, like bag-of-words and word2vec, to show that it provides a much better-resulting quality than the baseline DBSCAN and OPTICS models with different encoding methods. We use three quality metrics to demonstrate that clustering quality does not drop when the number of clusters grows. Thus, we get close to the convergence of text clustering and text classification. Full article

Information and communication technologies transform modern education into a more available learning matrix. One of the unexplored aspects of open education is the constant communicative interaction within the student group by using social media. The aim of the study was to determine principal functions of student-led communication in the educational process, the method for assessing its strong points and the disadvantages disrupting traditional learning. For the primary study of the phenomenon, we used methods that made it possible to propose approaches to further analysis. Netnography is the main research method defining the essence and characteristics of the student-led peer-communication. In our research, we applied data visualization, analytical and quantitative methods and developed a set of quantitative indicators that can be used to assess various aspects of student communication in chats. The elaborated visual model can serve as a simple tool for diagnosing group communication processes. We revealed that online group chats perform a support function in learning. They provide constant informational resource on educational and organizational issues and create emotional comfort. Identified features serve to define shortcomings (e.g., lack of students’ readiness to freely exchange answers to assignments) and significant factors (e.g., underutilized opportunities for self-organization) that exist in the modern system of higher education. Full article

Aiming at the problem of low interconnection efficiency caused by the wide variety of data in SWIM (System-Wide Information Management) and the inconsistent data naming methods, this paper proposes a new TLC (Type-Length-Content) structure hybrid data naming scheme combined with Bloom filters. This solution can meet the uniqueness and durability requirements of SWIM data names, solve the “suffix loopholes” encountered in prefix-based route aggregation in hierarchical naming, and realize scalable and effective route state aggregation. Simulation verification results show that the hybrid naming scheme is better than prefix-based aggregation in the probability of route identification errors. In terms of search time, this scheme has increased by 17.8% and 18.2%, respectively, compared with the commonly used hierarchical and flat naming methods. Compared with the other two naming methods, scalability has increased by 19.1% and 18.4%, respectively. Full article

Recycling is vital for a sustainable and clean environment. Developed and developing countries are both facing the problem of solid management waste and recycling issues. Waste classification is a good solution to separate the waste from the recycle materials. In this work, we propose a cloud based classification algorithm for automated machines in recycling factories using machine learning. We trained an efficient MobileNet model, able to classify five different types of waste. The inference can be performed in real-time on a cloud server. Various techniques are described and used in order to improve the classification accuracy, such as data augmentation and hyper-parameter tuning. Multiple industrial stations are supported and interconnected via custom data transmission protocols, along with security features. Experimental results indicated that our solution can achieve excellent performance with 96.57% accuracy utilizing a cloud server. Full article

Peer-to-peer (P2P) networks have been under investigation for several years now, with many novel mechanisms proposed as is shown by available articles. Much of the research focused on showing how the proposed mechanism improves system performance. In addition, several applications were proposed to harness the benefits of the P2P networks. Of these applications, online social networks (OSNs) raised much interest particularly because of the scalability and privacy concerns with centralized OSNs, hence several proposals are in existence. However, accompanying studies on the overall performance of the P2P network under the weight of the OSN applications outside simulations are very few, if any. In this paper, the aim is to undertake a systematic evaluation of the performance of a P2P framework for online social networks called LibreSocial. Benchmark tests are designed, taking into account the random behavior of users, effects of churn on system stability and effect of replication factor. We manage to run benchmark tests for up to 2000 nodes and show the performance against costs of the system in general. From the results it is evident that LibreSocial’s performance is capable of meeting the needs of users. Full article