Research

21 pages, 436 KiB

Open AccessEditor’s ChoiceArticle

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian

by Marco Pota, Mirko Ventura, Rosario Catelli and Massimo Esposito

Sensors 2021, 21(1), 133; https://doi.org/10.3390/s21010133 - 28 Dec 2020

Cited by 87 | Viewed by 9836

Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better [...] Read more.

Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps. Firstly, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages. Secondly, the resulting tweets are classified using the language model BERT, but pre-trained on plain text, instead of tweets, for two reasons: (1) pre-trained models on plain text are easily available in many languages, avoiding resource- and time-consuming model training directly on tweets from scratch; (2) available plain text corpora are larger than tweet-only ones, therefore allowing better performance. A case study describing the application of the approach to Italian is presented, with a comparison with other Italian existing solutions. The results obtained show the effectiveness of the approach and indicate that, thanks to its general basis from a methodological perspective, it can also be promising for other languages. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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14 pages, 1763 KiB

Open AccessArticle

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

by Vasyl Teslyuk, Artem Kazarian, Natalia Kryvinska and Ivan Tsmots

Sensors 2021, 21(1), 47; https://doi.org/10.3390/s21010047 - 24 Dec 2020

Cited by 34 | Viewed by 2997

Abstract

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a [...] Read more.

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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26 pages, 8966 KiB

Open AccessArticle

Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

by Bhavya Alankar, Gaurav Sharma, Harleen Kaur, Raul Valverde and Victor Chang

Sensors 2020, 20(24), 7342; https://doi.org/10.3390/s20247342 - 21 Dec 2020

Cited by 19 | Viewed by 5830

Abstract

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load [...] Read more.

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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14 pages, 2195 KiB

Open AccessArticle

Cascaded Cross-Modality Fusion Network for 3D Object Detection

by Zhiyu Chen, Qiong Lin, Jing Sun, Yujian Feng, Shangdong Liu, Qiang Liu, Yimu Ji and He Xu

Sensors 2020, 20(24), 7243; https://doi.org/10.3390/s20247243 - 17 Dec 2020

Cited by 1 | Viewed by 2457

Abstract

We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects: (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semantic features of images and [...] Read more.

We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects: (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semantic features of images and the geometric features of point clouds. (2) The optimization of traditional IoU is not equal to the regression loss of bounding boxes, resulting in biased back-propagation for non-overlapping cases. In this work, we propose a cascaded cross-modality fusion network (CCFNet), which includes a cascaded multi-scale fusion module (CMF) and a novel center 3D IoU loss to resolve these two issues. Our CMF module is developed to reinforce the discriminative representation of objects by reasoning the relation of corresponding LIDAR geometric capability and RGB semantic capability of the object from two modalities. Specifically, CMF is added in a cascaded way between the RGB and LIDAR streams, which selects salient points and transmits multi-scale point cloud features to each stage of RGB streams. Moreover, our center 3D IoU loss incorporates the distance between anchor centers to avoid the oversimple optimization for non-overlapping bounding boxes. Extensive experiments on the KITTI benchmark have demonstrated that our proposed approach performs better than the compared methods. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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20 pages, 3402 KiB

Open AccessEditor’s ChoiceArticle

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

by Vibekananda Dutta, Michał Choraś, Marek Pawlicki and Rafał Kozik

Sensors 2020, 20(16), 4583; https://doi.org/10.3390/s20164583 - 15 Aug 2020

Cited by 81 | Viewed by 7939

Abstract

Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to [...] Read more.

Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient. Therefore, recently developed machine learning techniques, with the emphasis on deep learning, are finding their successful implementations in the detection and classification of anomalies at both the network and host levels. This paper presents an ensemble method that leverages deep models such as the Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and a meta-classifier (i.e., logistic regression) following the principle of stacked generalization. To enhance the capabilities of the proposed approach, the method utilizes a two-step process for the apprehension of network anomalies. In the first stage, data pre-processing, a Deep Sparse AutoEncoder (DSAE) is employed for the feature engineering problem. In the second phase, a stacking ensemble learning approach is utilized for classification. The efficiency of the method disclosed in this work is tested on heterogeneous datasets, including data gathered in the IoT environment, namely IoT-23, LITNET-2020, and NetML-2020. The results of the evaluation of the proposed approach are discussed. Statistical significance is tested and compared to the state-of-the-art approaches in network anomaly detection. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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27 pages, 1196 KiB

Open AccessArticle

Intelligent Controller Design by the Artificial Intelligence Methods

by Jana Nowaková and Miroslav Pokorný

Sensors 2020, 20(16), 4454; https://doi.org/10.3390/s20164454 - 10 Aug 2020

Cited by 9 | Viewed by 3236

Abstract

With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally [...] Read more.

With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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11 pages, 1643 KiB

Open AccessEditor’s ChoiceArticle

Intelligent Data Management and Security in Cloud Computing

by Lidia Ogiela, Marek R. Ogiela and Hoon Ko

Sensors 2020, 20(12), 3458; https://doi.org/10.3390/s20123458 - 18 Jun 2020

Cited by 42 | Viewed by 3579

Abstract

This paper will present the authors’ own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions [...] Read more.

This paper will present the authors’ own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors’ solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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15 pages, 2403 KiB

Open AccessArticle

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

by Roman Tkachenko, Ivan Izonin, Natalia Kryvinska, Ivanna Dronyuk and Khrystyna Zub

Sensors 2020, 20(9), 2625; https://doi.org/10.3390/s20092625 - 04 May 2020

Cited by 87 | Viewed by 3918

Abstract

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network [...] Read more.

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved. Full article

(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))

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