Future Internet — Open Access Journal

In the current media world, there is a huge debate about the importance of the visibility of a news website in order to secure its existence. Thus, search engine optimization (SEO) practices have emerged in the news media systems around the world. This study aimed to expand the current literature about the SEO practices by focusing on examining, via the walkthrough method, the ways that news companies allow the users to comment on their online news articles. The comments on the news websites are related to the notions of social influence, information diffusion, and play an essential role as a SEO practice, for instance, by providing content and engagement. The examined sample was collected by the most visited news websites’ rankings of alexa.com for a global scale and for the countries Greece and Cyprus. The findings reveal that the news websites throughout the globe use similar features and ways to support the comments of the users. In the meantime, though, a high number of the news websites did not allow the users to use their social media accounts in order to comment the provided news articles, or provided multiple comment platforms. This trend goes against the SEO practices. It is believed that this finding is associated with the difficulty of the news organizations to regulate and protect themselves by the users’ comments that promote, in some case harmful rhetoric and polarization. Full article

Air traffic management (ATM) plays an important role in maintaining and promoting air traffic safety, maintaining air traffic order and ensuring smooth air traffic. As the core of air traffic management, it is essential to ensure the safe and stable operation of system-wide information management (SWIM). Facing the complex and ever-changing network environment, a SWIM services dynamic migration method is proposed in this paper. This method combines SWIM core services to select destination nodes and migrate services. The experiment proves that the method can hide the service node while ensuring service continuity and increase the difficulty of malicious detection. By comparing with others, this method is more suitable for SWIM in terms of invulnerability. The throughput and delay performance of the method can meet the needs of SWIM. Full article

The need for smart and sustainable communication systems has led to the development of mobile communication networks. In turn, the vast functionalities of the global system of mobile communication (GSM) have resulted in a growing number of subscribers. As the number of users increases, the need for efficient and effective planning of the “limited” frequency spectrum of the GSM is inevitable, particularly in densely-populated areas. As such, there are ongoing discussions about frequency (channel) allocation methods to resolve the challenges of channel allocation, which is a complete NP (Nondeterministic Polynomial time) problem. In this paper, we propose an algorithm for channel allocation which takes into account soft constraints (co-channel interference and adjacent channel interference). By using the Manhattan distance concept, this study shows that the formulation of the algorithm is correct and in line with results in the literature. Hence, the Manhattan distance concept may be useful in other scheduling and optimization problems. Furthermore, this unique concept makes it possible to develop a more sustainable telecommunication system with ease of connectivity among users, even when several subscribers are on a common frequency. Full article

Named Entity Recognition (NER) on Clinical Electronic Medical Records (CEMR) is a fundamental step in extracting disease knowledge by identifying specific entity terms such as diseases, symptoms, etc. However, the state-of-the-art NER methods based on Long Short-Term Memory (LSTM) fail to exploit GPU parallelism fully under the massive medical records. Although a novel NER method based on Iterated Dilated CNNs (ID-CNNs) can accelerate network computing, it tends to ignore the word-order feature and semantic information of the current word. In order to enhance the performance of ID-CNNs-based models on NER tasks, an attention-based ID-CNNs-CRF model, which combines the word-order feature and local context, is proposed. Firstly, position embedding is utilized to fuse word-order information. Secondly, the ID-CNNs architecture is used to extract global semantic information rapidly. Simultaneously, the attention mechanism is employed to pay attention to the local context. Finally, we apply the CRF to obtain the optimal tag sequence. Experiments conducted on two CEMR datasets show that our model outperforms traditional ones. The F1-scores of 94.55% and 91.17% are obtained respectively on these two datasets, and both are better than LSTM-based models. Full article

In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user’s mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches. Full article

Contemporary Service Function Chaining (SFC), and the requirements arising from privacy concerns, call for the increasing integration of security features such as encryption and isolation across Network Function Virtualisation (NFV) domains. Therefore, suitable adaptations of automation and encryption concepts for the development of interconnected data centre infrastructures are essential. Nevertheless, packet isolation constraints related to the current NFV infrastructure and SFC protocols, render current NFV standards insecure. Accordingly, the goal of our work was an experimental demonstration of a new SFC packet forwarding standard that enables contemporary data centres to overcome these constraints. This article presents a comprehensive view of the developed architecture, focusing on the elements that constitute a new forwarding standard of encrypted SFC packets. Through a Proof-of-Concept demonstration, we present our closing experimental results of how the architecture fulfils the requirements defined in our use case. Full article

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content. Full article

Multi-access edge computing (MEC) brings high-bandwidth and low-latency access to applications distributed at the edge of the network. Data transmission and exchange become faster, and the overhead of the task migration between mobile devices and edge cloud becomes smaller. In this paper, we adopt the fine-grained task migration model. At the same time, in order to further reduce the delay and energy consumption of task execution, the concept of the task cache is proposed, which involves caching the completed tasks and related data on the edge cloud. Then, we consider the limitations of the edge cloud cache capacity to study the task caching strategy and fine-grained task migration strategy on the edge cloud using the genetic algorithm (GA). Thus, we obtained the optimal mobile device task migration strategy, satisfying minimum energy consumption and the optimal cache on the edge cloud. The simulation results showed that the task caching strategy based on fine-grained migration can greatly reduce the energy consumption of mobile devices in the MEC environment. Full article

Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models. Full article

The presented work is a result of extended research and analysis on timing methods precision, their efficiency in different virtual environments and the impact of timing precision on the performance of high-speed networks applications. We investigated how timer hardware is shared among heavily CPU- and I/O-bound tasks on a virtualized OS as well as on bare OS. By replacing the invoked timing methods within a well-known application for estimation of available path bandwidth, we provide the analysis of their impact on estimation accuracy. We show that timer overhead and precision are crucial for high-performance network applications, and low-precision timing methods usage, e.g., the delays and overheads issued by virtualization result in the degradation of the virtual environment. Furthermore, in this paper, we provide confirmation that, by using the methods we intentionally developed for both precise timing operations and AvB estimation, it is possible to overcome the inefficiency of standard time-related operations and overhead that comes with the virtualization. The impacts of negative virtualization factors were investigated in five different environments to define the most optimal virtual environment for high-speed network applications. Full article

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines. Full article

With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a lack of research on the cross-site scripting (XSS) attack detection model for defense against attacks. It is extremely important to design a method that can effectively improve the detection model against attack. In this paper, we present a method based on reinforcement learning (called RLXSS), which aims to optimize the XSS detection model to defend against adversarial attacks. First, the adversarial samples of the detection model are mined by the adversarial attack model based on reinforcement learning. Secondly, the detection model and the adversarial model are alternately trained. After each round, the newly-excavated adversarial samples are marked as a malicious sample and are used to retrain the detection model. Experimental results show that the proposed RLXSS model can successfully mine adversarial samples that escape black-box and white-box detection and retain aggressive features. What is more, by alternately training the detection model and the confrontation attack model, the escape rate of the detection model is continuously reduced, which indicates that the model can improve the ability of the detection model to defend against attacks. Full article

A crowdsourcing contest is one of the most popular modes of crowdsourcing and is also an important tool for an enterprise to implement open innovation. The solvers’ active participation is one of the major reasons for the success of crowdsourcing contests. Research on solvers’ participation behavior is helpful in understanding the sustainability and incentives of solvers’ participation in the online crowdsourcing platform. So, how to attract more solvers to participate and put in more effort is the focus of researchers. In this regard, previous studies mainly used the submission quantity to measure solvers’ participation behavior and lacked an effective measure on the degree of participation effort expended by a solver. For the first time, we use solvers’ participation time as a dependent variable to measure their effort in a crowdsourcing contest. Thus, we incorporate participation time into the solver’s participation research. With the data from Taskcn.com, we analyze how participation time is affected four key factors including task design, task description, task process, and environment, respectively. We found that, first, for task design, higher task rewards will attract solvers to invest more time in the participation process and the relationship between participation time and task duration is inverted U-shaped. Second, for task description, the length of the task description has a negative impact on participation time and the task description attachment will positively influence the participation time. Third, for the task process, communication and supplementary explanations in a crowdsourcing process positively affect participation time. Fourth, for environmental factors, the task density of the crowdsourcing platform and the market price of all crowdsourcing contests have respectively negative and positive effects on participation time. Full article

The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches. Full article

The application of blockchain technology to the energy sector promises to derive new operating models focused on local generation and sustainable practices, which are driven by peer-to-peer collaboration and community engagement. However, real-world energy blockchains differ from typical blockchain networks insofar as they must interoperate with grid infrastructure, adhere to energy regulations, and embody engineering principles. Naturally, these additional dimensions make real-world energy blockchains highly dependent on the participation of grid operators, engineers, and energy providers. Although much theoretical and proof-of-concept research has been published on energy blockchains, this research aims to establish a lens on real-world projects and implementations that may inform the alignment of academic and industry research agendas. This research classifies 131 real-world energy blockchain initiatives to develop an understanding of how blockchains are being applied to the energy domain, what type of failure rates can be observed from recently reported initiatives, and what level of technical and theoretical details are reported for real-world deployments. The results presented from the systematic analysis highlight that real-world energy blockchains are (a) growing exponentially year-on-year, (b) producing relatively low failure/drop-off rates (~7% since 2015), and (c) demonstrating information sharing protocols that produce content with insufficient technical and theoretical depth. Full article

This paper aims to present a possible alternative to direct file transfer in “challenged networks”, by using DTNbox, a recent application for peer-to-peer directory synchronization between DTN nodes. This application uses the Bundle Protocol (BP) to tackle long delays and link intermittency typical of challenged networks. The directory synchronization approach proposed in the paper consists of delegating the transmission of bulk data files to DTNbox, instead of modifying source applications to interface with the API of a specific BP implementation, or making use of custom scripts for file transfers. The validity of the proposed approach is investigated in the paper by considering a Mars to Earth interplanetary environment. Experiments are carried out by means of Virtual Machines running ION, the NASA-JPL implementation of DTN protocols. The results show that the directory synchronization approach is a valid alternative to direct transfer in interplanetary scenarios such as that considered in the paper. Full article

The spectral efficiency of wireless networks can be significantly improved by exploiting spatial multiplexing techniques known as multi-user MIMO. These techniques enable the allocation of multiple users to the same time-frequency block, thus reducing the interference between users. There is ample evidence that user groupings can have a significant impact on the performance of spatial multiplexing. The situation is even more complex when the data packets have priority and deadlines for delivery. Hence, combining packet queue management and beamforming would considerably enhance the overall system performance. In this paper, we propose a combination of beamforming and scheduling to improve the overall performance of multi-user MIMO systems in realistic conditions where data packets have both priority and deadlines beyond which they become obsolete. This method dubbed Reward Per Second (RPS), combines advanced matrix factorization at the physical layer with recently-developed queue management techniques. We demonstrate the merits of the this technique compared to other state-of-the-art scheduling methods through simulations. Full article

The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user’s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session. Full article

An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of a dataset for machine learning by an AI entity. Datasets can be very expensive to purchase, as, depending on both the proper selection of the elements and the homogeneity of the data contained within, constructing and maintaining the integrity of a dataset is difficult. Blockchain as a highly secure storage medium presents a technological quantum leap in maintaining data integrity. Furthermore, blockchain’s immutability constructs a fruitful environment for creating high quality, permanent and growing datasets for deep learning. The combination of AI and blockchain could impact fields like Internet of things (IoT), identity, financial markets, civil governance, smart cities, small communities, supply chains, personalized medicine and other fields, and thereby deliver benefits to many people. Full article