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Computers, Volume 9, Issue 2 (June 2020) – 20 articles

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Open AccessArticle
Evaluation of a Cyber-Physical Computing System with Migration of Virtual Machines during Continuous Computing
Computers 2020, 9(2), 42; https://doi.org/10.3390/computers9020042 (registering DOI) - 23 May 2020
Viewed by 115
Abstract
The Markov model of reliability of a failover cluster performing calculations in a cyber-physical system is considered. The continuity of the cluster computing process in the event of a failure of the physical resources of the servers is provided on the basis of [...] Read more.
The Markov model of reliability of a failover cluster performing calculations in a cyber-physical system is considered. The continuity of the cluster computing process in the event of a failure of the physical resources of the servers is provided on the basis of virtualization technology and is associated with the migration of virtual machines. The difference in the proposed model is that it considers the restrictions on the allowable time of interruption of the computational process during cluster recovery. This limitation is due to the fact that, if two physical servers fail, then object management is lost, which is unacceptable. Failure occurs if their recovery time is longer than the maximum allowable time of interruption of the computing process. The modes of operation of the cluster with and without system recovery in the event of a failure of part of the system resources that do not lead to loss of continuity of the computing process are considered. The results of the article are aimed at the possibility of assessing the probability of cluster operability while supporting the continuity of computations and its running to failure, leading to the interruption of the computational (control) process beyond the maximum permissible time. As a result of the calculation example for the presented models, it was shown that the mean time to failure during recovery under conditions of supporting the continuity of the computing process increases by more than two orders of magnitude. Full article
(This article belongs to the Special Issue Selected Papers from MICSECS 2019)
Open AccessArticle
A Unified Methodology for Heartbeats Detection in Seismocardiogram and Ballistocardiogram Signals
Computers 2020, 9(2), 41; https://doi.org/10.3390/computers9020041 - 22 May 2020
Viewed by 132
Abstract
This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance [...] Read more.
This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p < 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors. Full article
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Open AccessArticle
Inertial Sensor Based Solution for Finger Motion Tracking
Computers 2020, 9(2), 40; https://doi.org/10.3390/computers9020040 - 12 May 2020
Viewed by 319
Abstract
Hand motion tracking plays an important role in virtual reality systems for immersion and interaction purposes. This paper discusses the problem of finger tracking and proposes the application of the extension of the Madgwick filter and a simple switching (motion recognition) algorithm as [...] Read more.
Hand motion tracking plays an important role in virtual reality systems for immersion and interaction purposes. This paper discusses the problem of finger tracking and proposes the application of the extension of the Madgwick filter and a simple switching (motion recognition) algorithm as a comparison. The proposed algorithms utilize the three-link finger model and provide complete information about the position and orientation of the metacarpus. The numerical experiment shows that this approach is feasible and overcomes some of the major limitations of inertial motion tracking. The paper’s proposed solution was created in order to track a user’s pointing and grasping movements during the interaction with the virtual reconstruction of the cultural heritage of historical cities. Full article
(This article belongs to the Special Issue Selected Papers from MICSECS 2019)
Open AccessArticle
Quality of Service (QoS) Management for Local Area Network (LAN) Using Traffic Policy Technique to Secure Congestion
Computers 2020, 9(2), 39; https://doi.org/10.3390/computers9020039 - 12 May 2020
Viewed by 237
Abstract
This study presents the proposed testbed implementation for the Advanced Technology Training Center (ADTEC) Batu Pahat, one of Malaysia’s industrial training institutes. The objectives of this study are to discover the issues regarding network congestion, propose a suitable method to overcome such issues, [...] Read more.
This study presents the proposed testbed implementation for the Advanced Technology Training Center (ADTEC) Batu Pahat, one of Malaysia’s industrial training institutes. The objectives of this study are to discover the issues regarding network congestion, propose a suitable method to overcome such issues, and generate output data for the comparison of the results before and after the proposed implementation. The internet is directly connected to internet service providers (ISPs), which neither impose any rule nor filter the traffic components; all connections comply on the basis of the base effort services provided by the ISP. The congestion problem has been raised several times and the information technology (IT) department has been receiving complaints about poor and sometimes intermittent internet connection. Such issues provide some ideas for a possible solution because the end client is a human resource core business. In addition, budget constraints contribute to this problem. After a comprehensive review of related literature and discussion with experts, the implementation of quality of service through add-on rules, such as traffic policing on network traffic, was proposed. The proposed testbed also classified the traffic. Results show that the proposed testbed is stable. After the implementation of the generated solution, the IT department no longer receives any complaints, and thus fulfills the goal of having zero internet connection issues. Full article
Open AccessArticle
Indiscernibility Mask Key for Image Steganography
Computers 2020, 9(2), 38; https://doi.org/10.3390/computers9020038 - 11 May 2020
Viewed by 413
Abstract
Our concern in this paper is to explore the possibility of using rough inclusions for image steganography. We present our initial research using indiscernibility relation as a steganographic key for hiding information into the stego carrier by means of a fixed mask. The [...] Read more.
Our concern in this paper is to explore the possibility of using rough inclusions for image steganography. We present our initial research using indiscernibility relation as a steganographic key for hiding information into the stego carrier by means of a fixed mask. The information can be embedded into the stego-carrier in a semi-random way, whereas the reconstruction is performed in a deterministic way. The information shall be placed in selected bytes, which are indiscernible with the mask to a fixed degree. The bits indiscernible with other ratios (smaller or greater) form random gaps that lead to somehow unpredictable hiding of information presence. We assume that in our technique it can modify bits, the change of which does not cause a visual modification detectable by human sight, so we do not limit ourselves to the least significant bit. The only assumption is that we do not use the position when the mask we define uses it. For simplicity’s sake, in this work we present its operation, features, using the Least Significant Bit (LSB) method. In the experimental part, we have implemented our method in the context of hiding image into the image. The LSB technique in its simplest form is not resistant to stegoanalisys, so we used the well-known LSB matching method to mask the presence of our steganographic key usage. To verify the resistance to stegoanalisys we have conducted and discussed Chi-square and LSB enhancement test. The positive features of our method include its simplicity and speed, to decode a message we need to hide, or pass to another channel, a several-bit mask, degree of indiscernibility and size of the hidden file. We hope that our method will find application in the art of creating steganographic keys. Full article
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Open AccessArticle
Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling
Computers 2020, 9(2), 37; https://doi.org/10.3390/computers9020037 - 11 May 2020
Viewed by 381
Abstract
Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep [...] Read more.
Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences. Full article
Open AccessArticle
Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition
Computers 2020, 9(2), 36; https://doi.org/10.3390/computers9020036 - 02 May 2020
Viewed by 439
Abstract
Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. However, CNN is not suitable for modeling [...] Read more.
Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. However, CNN is not suitable for modeling the long-term context dependencies between speech signal frames. Recently, the recurrent neural networks (RNNs) have shown great abilities for modeling long-term context dependencies. However, the performance of RNNs is not good for low-resource speech recognition tasks, and is even worse than the conventional feed-forward neural networks. Moreover, these networks often overfit severely on the training corpus in the low-resource speech recognition tasks. This paper presents the results of our contributions to combine CNN and conventional RNN with gate, highway, and residual networks to reduce the above problems. The optimal neural network structures and training strategies for the proposed neural network models are explored. Experiments were conducted on the Amharic and Chaha datasets, as well as on the limited language packages (10-h) of the benchmark datasets released under the Intelligence Advanced Research Projects Activity (IARPA) Babel Program. The proposed neural network models achieve 0.1–42.79% relative performance improvements over their corresponding feed-forward DNN, CNN, bidirectional RNN (BRNN), or bidirectional gated recurrent unit (BGRU) baselines across six language collections. These approaches are promising candidates for developing better performance acoustic models for low-resource speech recognition tasks. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Pattern Recognition)
Open AccessArticle
Generating Trees for Comparison
Computers 2020, 9(2), 35; https://doi.org/10.3390/computers9020035 - 29 Apr 2020
Viewed by 429
Abstract
Tree comparisons are used in various areas with various statistical or dissimilarity measures. Given that data in various domains are diverse, and a particular comparison approach could be more appropriate for specific applications, there is a need to evaluate different comparison approaches. As [...] Read more.
Tree comparisons are used in various areas with various statistical or dissimilarity measures. Given that data in various domains are diverse, and a particular comparison approach could be more appropriate for specific applications, there is a need to evaluate different comparison approaches. As gathering real data is often an extensive task, using generated trees provides a faster evaluation of the proposed solutions. This paper presents three algorithms for generating random trees: parametrized by tree size, shape based on the node distribution and the amount of difference between generated trees. The motivation for the algorithms came from unordered trees that are created from class hierarchies in object-oriented programs. The presented algorithms are evaluated by statistical and dissimilarity measures to observe stability, behavior, and impact on node distribution. The results in the case of dissimilarity measures evaluation show that the algorithms are suitable for tree comparison. Full article
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Open AccessArticle
Employing Behavioral Analysis to Predict User Attitude towards Unwanted Content in Online Social Network Services: The Case of Makkah Region in Saudi Arabia
Computers 2020, 9(2), 34; https://doi.org/10.3390/computers9020034 - 20 Apr 2020
Viewed by 508
Abstract
The high volume of user-generated content caused by the popular use of online social network services exposes users to different kinds of content that can be harmful or unwanted. Solutions to protect user privacy from such unwanted content cannot be generalized due to [...] Read more.
The high volume of user-generated content caused by the popular use of online social network services exposes users to different kinds of content that can be harmful or unwanted. Solutions to protect user privacy from such unwanted content cannot be generalized due to different perceptions of what is considered as unwanted for each individual. Thus, there is a substantial need to design a personalized privacy protection mechanism that takes into consideration differences in users’ privacy requirements. To achieve personalization, a user attitude about certain content must be acknowledged by the automated protection system. In this paper, we investigate the relationship between user attitude and user behavior among users from the Makkah region in Saudi Arabia to determine the applicability of considering users’ behaviors, as indicators of their attitudes towards unwanted content. We propose a semi-explicit attitude measure to infer user attitude from user-selected examples. Results revealed that semi-explicit attitude is a more reliable attitude measure to represent users’ actual attitudes than self-reported preferences for our sample. In addition, results show a statistically significant relationship between a user’s commenting behavior and the user’s semi-explicit attitude within our sample. Thus, commenting behavior is an effective indicator of the user’s semi-explicit attitude towards unwanted content for a user from the Makkah region in Saudi Arabia. We believe that our findings can have positive implications for designing an effective automated personalized privacy protection mechanism by reproducing the study considering other populations. Full article
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Open AccessArticle
Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals
Computers 2020, 9(2), 33; https://doi.org/10.3390/computers9020033 - 20 Apr 2020
Viewed by 487
Abstract
There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music [...] Read more.
There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli. Specifically, we investigate the discriminative capacity of the Logarithmic Energy (LogE), Linear Frequency Cepstral Coefficients (LFCC), Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task. We carried out a comparative evaluation with eighteen modifications of the above-mentioned EEG features that cover different frequency bands and use different energy decomposition methods and spectral resolutions. For that purpose, we made use of Naïve Bayes classifier (NB), Classification and regression trees (CART), k-Nearest Neighbors (kNN) classifier, and support vector machines (SVM) classifier with a radial basis function (RBF) kernel trained with the Sequential Minimal Optimization (SMO) method. The experimental evaluation was performed on the well-known and widely used DEAP dataset. A classification accuracy of up to 98.6% was observed for the best performing combination of pre-processing, EEG features and classifier. These results support that the automated detection of like/dislike reactions based on EEG activity is feasible in a personalized setup. This opens opportunities for the incorporation of such functionality in entertainment, healthcare and security applications. Full article
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
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Open AccessArticle
An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution
Computers 2020, 9(2), 32; https://doi.org/10.3390/computers9020032 - 16 Apr 2020
Viewed by 504
Abstract
In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is [...] Read more.
In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP. Full article
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Open AccessArticle
Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting
Computers 2020, 9(2), 31; https://doi.org/10.3390/computers9020031 - 16 Apr 2020
Viewed by 564
Abstract
Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging [...] Read more.
Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB. Full article
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Open AccessArticle
Eliminating Nonuniform Smearing and Suppressing the Gibbs Effect on Reconstructed Images
Computers 2020, 9(2), 30; https://doi.org/10.3390/computers9020030 - 15 Apr 2020
Viewed by 542
Abstract
In this work, the problem of eliminating a nonuniform rectilinear smearing of an image is considered, using a mathematical- and computer-based approach. An example of such a problem is a picture of several cars, moving with different speeds, taken with a fixed camera. [...] Read more.
In this work, the problem of eliminating a nonuniform rectilinear smearing of an image is considered, using a mathematical- and computer-based approach. An example of such a problem is a picture of several cars, moving with different speeds, taken with a fixed camera. The problem is described by a set of one-dimensional Fredholm integral equations (IEs) of the first kind of convolution type, with a one-dimensional point spread function (PSF) when uniform smearing, and by a set of new one-dimensional IEs of a general type (i.e., not the convolution type), with a two-dimensional PSF when nonuniform smearing. The problem is also described by a two-dimensional IE of the convolution type with a two-dimensional PSF when uniform smearing, and by a new two-dimensional IE of a general type with a four-dimensional PSF when nonuniform smearing. The problem of solving the Fredholm IE of the first kind is ill-posed (i.e., unstable). Therefore, IEs of the convolution type are solved by the Fourier transform (FT) method and Tikhonov’s regularization (TR), and IEs of the general type are solved by the quadrature/cubature and TR methods. Moreover, the magnitude of the image smear, Δ, is determined by the original “spectral method”, which increases the accuracy of image restoration. It is shown that the use of a set of one-dimensional IEs is preferable to one two-dimensional IE in the case of nonuniform smearing. In the inverse problem (i.e., image restoration), the Gibbs effect (the effect of false waves) in the image may occur. This may be an edge or an inner effect. The edge effect is well suppressed by the proposed technique, namely, “diffusing the edges”. The inner effect is difficult to eliminate, but the image smearing itself plays the role of diffusion and suppresses the inner Gibbs effect to a large extent. It is shown (in the presence of impulse noise in an image) that the well-known Tukey median filter can distort the image itself, and the Gonzalez adaptive filter also distorts the image (but to a lesser extent). We propose a modified adaptive filter. A software package was developed in MATLAB and illustrative calculations are performed. Full article
(This article belongs to the Special Issue Selected Papers from MICSECS 2019)
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Open AccessArticle
Deep Transfer Learning in Diagnosing Leukemia in Blood Cells
Computers 2020, 9(2), 29; https://doi.org/10.3390/computers9020029 - 15 Apr 2020
Viewed by 492
Abstract
Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional [...] Read more.
Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy. Full article
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Open AccessArticle
Insights into Mapping Solutions Based on OPC UA Information Model Applied to the Industry 4.0 Asset Administration Shell
Computers 2020, 9(2), 28; https://doi.org/10.3390/computers9020028 - 14 Apr 2020
Viewed by 530
Abstract
In the context of Industry 4.0, lot of effort is being put to achieve interoperability among industrial applications. As the definition and adoption of communication standards are of paramount importance for the realization of interoperability, during the last few years different organizations have [...] Read more.
In the context of Industry 4.0, lot of effort is being put to achieve interoperability among industrial applications. As the definition and adoption of communication standards are of paramount importance for the realization of interoperability, during the last few years different organizations have developed reference architectures to align standards in the context of the fourth industrial revolution. One of the main examples is the reference architecture model for Industry 4.0, which defines the asset administration shell as the corner stone of the interoperability between applications managing manufacturing systems. Inside Industry 4.0 there is also so much interest behind the standard open platform communications unified architecture (OPC UA), which is listed as the one recommendation for realizing the communication layer of the reference architecture model. The contribution of this paper is to give some insights behind modelling techniques that should be adopted during the definition of OPC UA Information Model exposing information of the very recent metamodel defined for the asset administration shell. All the general rationales and solutions here provided are compared with the current OPC UA-based existing representation of asset administration shell provided by literature. Specifically, differences will be pointed out giving to the reader advantages and disadvantages behind each solution. Full article
Open AccessArticle
Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction
Computers 2020, 9(2), 27; https://doi.org/10.3390/computers9020027 - 12 Apr 2020
Viewed by 610
Abstract
Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, [...] Read more.
Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks. Full article
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Open AccessReview
Survey on Decentralized Fingerprinting Solutions: Copyright Protection through Piracy Tracing
Computers 2020, 9(2), 26; https://doi.org/10.3390/computers9020026 - 03 Apr 2020
Viewed by 748
Abstract
Copyright protection is one of the most relevant challenges in the network society. This paper focuses on digital fingerprinting, a technology that facilitates the tracing of the source of an illegal redistribution, making it possible for the copyright holder to take legal action [...] Read more.
Copyright protection is one of the most relevant challenges in the network society. This paper focuses on digital fingerprinting, a technology that facilitates the tracing of the source of an illegal redistribution, making it possible for the copyright holder to take legal action in case of copyright violation. The paper reviews recent digital fingerprinting solutions that are available for two particularly relevant scenarios: peer-to-peer distribution networks and broadcasting. After analyzing those solutions, a discussion is carried out to highlight the properties and the limitations of those techniques. Finally, some directions for further research on this topic are suggested. Full article
Open AccessArticle
A Multi-Hop Data Dissemination Algorithm for Vehicular Communication
Computers 2020, 9(2), 25; https://doi.org/10.3390/computers9020025 - 31 Mar 2020
Viewed by 666
Abstract
In vehicular networks, efficient multi-hop message dissemination can be used for various purposes, such a informing the driver about the recent emergency event or propagating the local dynamic map of a predefined region. Dissemination of warning information up to a longer distance can [...] Read more.
In vehicular networks, efficient multi-hop message dissemination can be used for various purposes, such a informing the driver about the recent emergency event or propagating the local dynamic map of a predefined region. Dissemination of warning information up to a longer distance can reduce the accidents on the road. It provides a driver additional time to react to the situations adequately and assists in finding a safe route towards the destination. The adopted V2X standards, ETSI TS’s C-ITS and IEEE 1609/IEEE 802.11p, specify only primitive multi-hop message dissemination schemes. IEEE 1609.4 standard disseminates the broadcast messages using the method of flooding, which causes high redundancy, severe congestion, and long delay during multi-hop propagation. To address these problems, we propose an effective broadcast message dissemination method. It introduces a notion of source Lateral Crossing Line (LCL) algorithm, which elects a set of relay vehicles for each hop based on the vehicle locations in a way that reduces the redundant retransmission and congestion, consequently minimizing the delays. Our simulation results demonstrated that the proposed method can achieve about 15% reduction in delays and 2 times the enhancement in propagation distance compared with the previous methods. Full article
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Open AccessArticle
Comparing Static and Dynamic Weighted Software Coupling Metrics
Computers 2020, 9(2), 24; https://doi.org/10.3390/computers9020024 - 30 Mar 2020
Viewed by 791
Abstract
Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, [...] Read more.
Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, dynamic metrics use runtime data gathered, e.g., by monitoring a system in production. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We study weighted dynamic coupling that takes into account how often a connection (e.g., a method call) is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. To compare the different metrics, we use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses. Full article
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Open AccessArticle
Accelerating Surface Tension Calculation in SPH via Particle Classification and Monte Carlo Integration
Computers 2020, 9(2), 23; https://doi.org/10.3390/computers9020023 - 29 Mar 2020
Viewed by 700
Abstract
Surface tension has a strong influence on the shape of fluid interfaces. We propose a method to calculate the corresponding forces efficiently. In contrast to several previous approaches, we discriminate to this end between surface and non-surface SPH particles. Our method effectively smooths [...] Read more.
Surface tension has a strong influence on the shape of fluid interfaces. We propose a method to calculate the corresponding forces efficiently. In contrast to several previous approaches, we discriminate to this end between surface and non-surface SPH particles. Our method effectively smooths the fluid interface, minimizing its curvature. We make use of an approach inspired by Monte Carlo integration to estimate local normals as well as curvatures, based on which the force can be calculated. We compare different sampling schemes for the Monte Carlo approach, for which a Halton sequence performed best. Our overall technique is applicable, but not limited to 2D and 3D simulations, and can be coupled with any common SPH formulation. It outperforms prior approaches with regard to total computation time per time step in dynamic scenes. Additionally, it is adjustable for higher quality in small scale scenes with dominant surface tension effects. Full article
(This article belongs to the Special Issue Computer Graphics & Visual Computing (CGVC 2019))
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