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Future Internet, Volume 10, Issue 12 (December 2018)

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Open AccessArticle A Method for Filtering Pages by Similarity Degree based on Dynamic Programming
Future Internet 2018, 10(12), 124; https://doi.org/10.3390/fi10120124
Received: 20 October 2018 / Revised: 8 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
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Abstract
To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of
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To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of the three same relationships. The biggest innovation of MFPSDDP is that it does not need to know the structures of webpages in advance. First, we address the design ideas with queue and double threads. Then, a dynamic programming algorithm for calculating the length of the longest common subsequence and a formula for calculating similarity are proposed. Further, for obtaining detailed information webpages from 200,000 webpages downloaded from the famous website “www.jd.com”, we choose the same relationship Completely Same Relationship (CSR) and set the similarity threshold to 0.2. The Recall Ratio (RR) of MFPSDDP is in the middle in the four filtering methods compared. When the number of webpages filtered is nearly 200,000, the PR of MFPSDDP is highest in the four filtering methods compared, which can reach 85.1%. The PR of MFPSDDP is 13.3 percentage points higher than the PR of a Method for Filtering Pages by Containing Strings (MFPCS). Full article
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Open AccessArticle Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
Future Internet 2018, 10(12), 123; https://doi.org/10.3390/fi10120123
Received: 28 October 2018 / Revised: 28 November 2018 / Accepted: 10 December 2018 / Published: 13 December 2018
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Abstract
Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task
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Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task of NLP and can be considered a classification problem. We propose a bidirectional long short-term memory (LSTM) model for this entity recognition task of the Arabic text. The LSTM network can process sequences and relate to each part of it, which makes it useful for the NER task. Moreover, we use pre-trained word embedding to train the inputs that are fed into the LSTM network. The proposed model is evaluated on a popular dataset called “ANERcorp.” Experimental results show that the model with word embedding achieves a high F-score measure of approximately 88.01%. Full article
(This article belongs to the Special Issue Innovative Topologies and Algorithms for Neural Networks)
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Open AccessArticle A Compact Printed Monopole Antenna for WiMAX/WLAN and UWB Applications
Future Internet 2018, 10(12), 122; https://doi.org/10.3390/fi10120122
Received: 12 November 2018 / Revised: 7 December 2018 / Accepted: 10 December 2018 / Published: 13 December 2018
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Abstract
In this paper, a printed monopole antenna design for WiMAX/WLAN applications in cable-free self-positioning seismograph nodes is proposed. Great improvements were achieved in miniaturizing the antenna and in widening the narrow bandwidth of the high-frequency band. The antenna was fed by a microstrip
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In this paper, a printed monopole antenna design for WiMAX/WLAN applications in cable-free self-positioning seismograph nodes is proposed. Great improvements were achieved in miniaturizing the antenna and in widening the narrow bandwidth of the high-frequency band. The antenna was fed by a microstrip gradient line and consisted of a triangle, an inverted-F shape, and an M-shaped structure, which was rotated 90° counterclockwise to form a surface-radiating patch. This structure effectively widened the operating bandwidth of the antenna. Excitation led to the generation of two impedance bands of 2.39–2.49 and 4.26–7.99 GHz for a voltage standing wave ratio of less than 2. The two impedance bandwidths were 100 MHz, i.e., 4.08% relative to the center frequency of 2.45 GHz, and 3730 MHz, i.e., 64.31% relative to the center frequency of 5.80 GHz, covering the WiMAX high-frequency band (5.25–5.85 GHz) and the WLAN band (2.4/5.2/5.8). This article describes the design details of the antenna and presents the results of both simulations and experiments that show good agreement. The proposed antenna meets the field-work requirements of cable-less seismograph nodes. Full article
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Open AccessReview Exploiting JTAG and Its Mitigation in IOT: A Survey
Future Internet 2018, 10(12), 121; https://doi.org/10.3390/fi10120121
Received: 30 October 2018 / Revised: 29 November 2018 / Accepted: 30 November 2018 / Published: 3 December 2018
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Abstract
Nowadays, companies are heavily investing in the development of “Internet of Things(IoT)” products. These companies usually and obviously hunt for lucrative business models. Currently, each person owns at least 3–4 devices (such as mobiles, personal computers, Google Assistant, Alexa, etc.) that are connected
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Nowadays, companies are heavily investing in the development of “Internet of Things(IoT)” products. These companies usually and obviously hunt for lucrative business models. Currently, each person owns at least 3–4 devices (such as mobiles, personal computers, Google Assistant, Alexa, etc.) that are connected to the Internet 24/7. However, in the future, there might be hundreds of devices that will be constantly online behind each person, keeping track of body health, banking transactions, status of personal devices, etc. to make one’s life more efficient and streamlined. Thus, it is very crucial that each device should be highly secure since one’s life will become dependent on these devices. However, the current security of IoT devices is mainly focused on resiliency of device. In addition, less complex node devices are easily accessible to the public resulting in higher vulnerability. JTAG is an IEEE standard that has been defined to test proper mounting of components on PCBs (printed circuit boards) and has been extensively used by PCB manufacturers to date. This JTAG interface can be used as a backdoor entry to access and exploit devices, also defined as a physical attack. This attack can be used to make products malfunction, modify data, or, in the worst case, stop working. This paper reviews previous successful JTAG exploitations of well-known devices operating online and also reviews some proposed possible solutions to see how they can affect IoT products in a broader sense. Full article
(This article belongs to the Special Issue IoT Security and Privacy)
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Open AccessArticle Personality and Reputation: A Complex Relationship in Virtual Environments
Future Internet 2018, 10(12), 120; https://doi.org/10.3390/fi10120120
Received: 5 November 2018 / Revised: 25 November 2018 / Accepted: 28 November 2018 / Published: 1 December 2018
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Abstract
Online reputational systems are nowadays widely and effectively adopted by several online platforms to support and improve peoples’ interactions and communication. Despite the research approached and modeled social dynamics of reputational systems in different domains, adopting different frameworks, the role played by psycho-social
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Online reputational systems are nowadays widely and effectively adopted by several online platforms to support and improve peoples’ interactions and communication. Despite the research approached and modeled social dynamics of reputational systems in different domains, adopting different frameworks, the role played by psycho-social factors, and personality traits, determining the individual susceptibility to online reputation is still elusive. To study such mediation effects, we implemented a modified online version of the Ultimatum Game, in which participants (215 adolescents) played before as proposers, and then as responders, always knowing the reputation of their interactors. Furthermore, after the reception phase, participants could evaluate the received offers, giving positive or negative feedback to their proposers. Despite the participants’ belief they were playing with their schoolmates, the interactors’ role was always fulfilled by bots characterized by standardized behaviors. Our results show how psychological traits influence the participants’ behavior in all the game phases, as well as in the rating dynamics. Reputation seems to have a direct effect only in the allocation behavior, while, in regards the other dynamics of the game (i.e., acceptance and rating), it comes into play in a complex interaction with the psychological dimensions. Full article
Open AccessArticle Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
Future Internet 2018, 10(12), 119; https://doi.org/10.3390/fi10120119
Received: 15 October 2018 / Revised: 26 November 2018 / Accepted: 28 November 2018 / Published: 30 November 2018
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Abstract
The introduction of the internet in embedded devices led to a new era of technology—the Internet of Things (IoT) era. The IoT technology-enabled device market is growing faster by the day, due to its complete acceptance in diverse areas such as domicile systems,
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The introduction of the internet in embedded devices led to a new era of technology—the Internet of Things (IoT) era. The IoT technology-enabled device market is growing faster by the day, due to its complete acceptance in diverse areas such as domicile systems, the automobile industry, and beyond. The introduction of internet connectivity in objects that are frequently used in daily life raises the question of security—how secure is the information and the infrastructure handled by these devices when they are connected to the internet? Security enhancements through standard cryptographic techniques are not suitable due to the power and performance constraints of IoT devices. The introduction of virtualization technology into IoT devices is a recent development, meant for fulfilling security and performance needs. However, virtualization augments the vulnerability present in IoT devices, due to the addition of one more software layer—namely, the hypervisor, which enables the sharing of resources among different users. This article proposes the adaptation of ASMI (Architectural Support for Memory Isolation—a general architecture available in the literature for the improvement of the performance and security of virtualization technology) on the popular MIPS (Microprocessor without Interlocked Pipeline Stages) embedded virtualization platform, which could be adopted in embedded virtualization architectures for IoT devices. The article illustrates the performance enhancement achieved by the proposed architecture with the existing architectures. Full article
(This article belongs to the Special Issue IoT Security and Privacy)
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Open AccessFeature PaperArticle DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks
Future Internet 2018, 10(12), 118; https://doi.org/10.3390/fi10120118
Received: 24 September 2018 / Revised: 24 November 2018 / Accepted: 28 November 2018 / Published: 30 November 2018
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Abstract
To address the continuous growth in high-speed ubiquitous access required by residential users and enterprises, Telecommunication operators must upgrade their networks to higher data rates. For optical fiber access networks that directly connect end users to metro/regional network, capacity upgrade must be done
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To address the continuous growth in high-speed ubiquitous access required by residential users and enterprises, Telecommunication operators must upgrade their networks to higher data rates. For optical fiber access networks that directly connect end users to metro/regional network, capacity upgrade must be done in a cost- and energy-efficient manner. 40 Gb/s is the possible lane rate for the next generation passive optical networks (NG-PONs). Ideally, existing 10 G PON components could be reused to support 40 Gb/s lane-rate NG-PON transceiver, which requires efficient modulation format and digital signal processing (DSP) to alleviate the bandwidth limitation and fiber dispersion. The major contribution of this work is to offer insight performance comparisons of 40 Gb/s lane rate electrical three level Duobinary, optical Duobinary, and four-level pulse amplitude modulation (PAM-4) for incorporating low complex DSPs, including linear and nonlinear Volterra equalization, as well as maximum likelihood sequence estimation. Detailed analysis and comparison of the complexity of various DSP algorithms are performed. Transceiver bandwidth optimization is also undertaken. The results show that the choices of proper modulation format and DSP configuration depend on the transmission distances of interest. Full article
(This article belongs to the Special Issue Recent Advances in DSP-Based Optical Communications)
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Open AccessArticle A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce
Future Internet 2018, 10(12), 117; https://doi.org/10.3390/fi10120117
Received: 28 October 2018 / Revised: 23 November 2018 / Accepted: 27 November 2018 / Published: 29 November 2018
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Abstract
A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior
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A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms. Full article
(This article belongs to the Special Issue Data Science for Internet of Things)
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Open AccessArticle A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification
Future Internet 2018, 10(12), 116; https://doi.org/10.3390/fi10120116
Received: 25 October 2018 / Revised: 19 November 2018 / Accepted: 22 November 2018 / Published: 24 November 2018
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Abstract
The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and
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The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods. Full article
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Open AccessArticle Video-Based Human Action Recognition Using Spatial Pyramid Pooling and 3D Densely Convolutional Networks
Future Internet 2018, 10(12), 115; https://doi.org/10.3390/fi10120115
Received: 22 October 2018 / Revised: 17 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
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Abstract
In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video.
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In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video. It is well known that convolutional neural networks require a fixed size image input, which not only limits the network structure but also affects the recognition accuracy. Although this problem has been solved in the field of images, it has not yet been broken through in the field of video. To address the input problem of fixed size video frames in video recognition, we propose a three-dimensional (3D) densely connected convolutional network based on spatial pyramid pooling (3D-DenseNet-SPP). As the name implies, the network structure is mainly composed of three parts: 3DCNN, DenseNet, and SPPNet. Our models were evaluated on a KTH dataset and UCF101 dataset separately. The experimental results showed that our model has better performance in the field of video-based behavior recognition in comparison to the existing models. Full article
(This article belongs to the Section Techno-Social Smart Systems)
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Open AccessArticle Privacy and Security Issues in Online Social Networks
Future Internet 2018, 10(12), 114; https://doi.org/10.3390/fi10120114
Received: 29 September 2018 / Revised: 14 November 2018 / Accepted: 21 November 2018 / Published: 22 November 2018
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Abstract
The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests.
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The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media. Full article
(This article belongs to the Special Issue 10th Anniversary Feature Papers)
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