Reviewerly/exHarmony huggingface dataset:
Efficient Data Trading and Storage in Internet of Vehicles using Consortium Blockchain

The radically increasing amount and enormous types of data generated by vehicles have brought in the innovated application of data trading in the Internet of Vehicles (IoV). However, the trustless environment in IoV enabled data trading faces conflicting interests and disputes of trading parties. To build trust, we exploit consortium blockchain for secure data trading with information transparency. In addition, a hash list of traded data is maintained by roadside units accompanied by bloom filters for fast lookup, to avoid data duplication. The reliability and integrity of trading data are ensured by using the digital signature scheme based on elliptic curve bilinear pairing. For long term availability of traded data, an external distributed storage, i.e., InterPlanetary File System (IPFS) can provide reliable, high capacity storage resources. The experimental results verified that our proposed solution is efficient for data trading in IoV and reliable for long term availability of data storage.
RE-GCN: Relation Enhanced Graph Convolutional Network for Entity Alignment in Heterogeneous Knowledge Graphs

Entity alignment is a fundamental task of matching synonymous entities from different knowledge graphs (KGs). Most of the existing methods perform this task by evaluating the similarity among entity embeddings learned from heterogeneous KGs, where Graph Convolutional Network (GCN) based embedding is widely adopted for capturing complex network structure. However, the semantics and directional information of relations are ignored in previous GCN based efforts, which affect the integrality of embedding definitely and decrease the efficiency consequently. To overcome this shortcoming, this paper proposes a Relation-Enhanced Graph Convolutional Network (RE-GCN) method for entity alignment including two stages. First, to take advantage of the semantics of the relations, a novel triadic graph is designed to integrate relation nodes into the primal graph by using triadic closure. In a triadic graph, both relations and entities nodes could be organized in a unified network. The corresponding triadic graph convolution is utilized together with the primal one to learn the relation and entity embeddings, simultaneously. Second, in order to make use of direction information of the relations, a bidirectional context aggregation mechanism is proposed to aggregate the embeddings from the first stage. The final aggregation embeddings are utilized for entity alignment. On three real-world multilingual datasets, experimental results demonstrate that RE-GCN produces a more excellent performance compared with some state-of-the-art entity alignment methods.
A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes

The automatic classification of volcano-seismic events is a key problem in volcanology. Due to its complexity, deep learning (DL) techniques have become the tool of choice for this problem, outperforming classical classifiers. The main drawback of this approach, when applied to the classification of volcano-seismic events, is its tendency to overfit because of the small-size available databases. In this work, we propose and analyze the use of the Gaussian processes (GPs) and Deep GPs (DGPs), and their hierarchical extension, for volcano-seismic event classification. We empirically prove the adequacy of the proposed modeling with an insightful and exhaustive comparison with state-of-the-art DL-based methods on a seismic database recorded at “Volcán de Fuego,” Colima, Mexico. The hierarchical structure of DGPs and the reduced number of parameters to be automatically estimated become essential to achieve excellent performance even on small databases, capturing well the complex patterns of seismic signals for all classes and, in particular, for those that have been hardly observed.
Dynamic Resource Provisioning for Sustainable Cloud Computing Systems in the Presence of Correlated Failures

Dependence of computing resources on each other in cloud computing systems (CCS) makes them prone to fail in correlated manner which significantly impacts their service reliability and energy efficiency. Focusing on these two metrics of CCS while considering correlated failures remained an open question, which is the focus of this work. This paper proposes mechanisms for improving reliability and energy efficiency jointly under correlated failures in CCS. In order to model failure correlation, statistical cluster analysis techniques are applied to real failure traces. Then, mathematical models are built to calculate reliability and energy consumption of failure prone CCS. These mathematical models are used to design fault-tolerant and energy-aware resource provisioning mechanisms/policies. In order to further reduce the energy consumption, a correlated failure-aware VM consolidation policy is also proposed in this paper. A simulation based study of the proposed resource management policies and fault tolerance mechanisms is conducted by using real failure traces and Bag-of-Tasks workload. The results demonstrate that by exploiting failure correlation with the proposed resource management policies, we reduce the occurrence of failures on tasks by 34 percent and increase the energy efficiency of the system by 20 percent, approximately in comparison to the environments where failures are handled independently.
Subject Property Inference Attack in Collaborative Learning

Binding decentralized data and computing resources together, collaborative learning is currently a booming research area for the application requirements of efficiency and privacy. However, a lot of work has shown that this may still expose record-level or statistical information of private local data. This paper aims to implement subject-level privacy inference during the training phase. The subject is the data source the training data comes from, such as a person or certain environment. Based on auxiliary local data, gradient and intermediate output, we present passive and active property inference attack on the lack of training data of target subject. In the active attack, we choose a very innovative approach - CycleGAN that reconstructs impacts of data with certain properties on the global model. We test our algorithms on 2 public image datasets, and give a comprehensive analysis of the privacy leakage in subject property inference attack.
Using Smart Services as a Key Enabler for Collaboration in Global Production Networks

Collaboration in global production networks becomes more important in times of increased interconnectivity and complexity. However, due to various resistors the collaboration potential has not been realized, yet. At the same time digitalization has become a key enabler in today’s world of high complexity leading to new, disruptive solutions. Part of digitalization are smart services, triggering incentives by including business models. This and further characteristics of smart services have the potential of overcoming the resistors of collaboration. In this paper an approach is proposed for developing collaborative relationships - from strategy and collaboration scenario modelling to a service-oriented implementation.
Identification and quantification of iron oxides by diffuse reflectance spectroscopy with Praying Mantis accessory and integration sphere

Soil color is one of the main attributes used in the Brazilian System of Soil Classification and is related to the presence and concentration of iron oxides. This work aimed to identify and quantify the iron oxides (hematite, goethite and maghemite) present in soils and synthetic minerals and compare the diffuse reflectance spectroscopy (DRS) data equipped with the Integration Sphere (DRS-IS) or the Praying Mantis (DRS-PM) accessories with reference values obtained by XRD. Four samples of synthetic iron oxides (hematite, goethite, maghemite, magnetite) and two samples of aluminosilicates (kaolinite and smectite) were selected along with the A and Bw Horizons from 6 Oxisols of the State of Paraná-Brazil. The soils were characterized on their physical and mineralogical attributes and organic carbon content. All minerals, soil samples, soils clay fractions treated and not treated with the boiling 5 M NaOH procedure and with 1.8 M H2SO4 were analyzed using the integration sphere, the Praying Mantis accessory and color determined in the Munsell scale using a Minolta CR 400 spectroradiometer. The DRS-PM, by means of the second derivative of Kubelka-Munk function was very promising for identification and quantification of hematite for presenting results close to those obtained by XRD. Otherwise, the quantification of goethite was overestimated in both DRS's measurements due to the presence of organic matter. It was not possible to identify and quantify the presence of maghemite in soils and mixture minerals samples using both DRSs accessories due to the superposition of secondary bands of goethite, hematite, and the magnetite on the maghemite spectrum. The presence of maghemite in the soil material induces an overestimation of hematite content by DRS. The estimation of the Hm/(Hm + Gt) and Gt/(Gt + Hm) ratios by both accessories presented good correlation with data obtained by XRD.
V-Museum: A Virtual Museum Based on Augmented and Virtual Realities for Cultural Heritage Mediation

Cultural tourism is a growing sector. It is one of the best ways to discover the cultural heritage and way of life of a region and its people. However, without modernization and digital mediation, this sector can rapidly deteriorate. In this paper, a novel, playful and informative cultural touristic experience is presented. It consists of a virtual Space Door accessible through augmented reality and leading to a virtual museum built using virtual reality. Evaluation of the prototype was conducted in a real environment to confirm usability, ease of use and interest of the prototype.
Determining the Eligibility of Candidates for a Vacancy Using Artificial Neural Networks

With the development of technology, new methods are being created to solve various tasks. Methods that use artificial neural networks (ANN) have become part of our routine and are very rapidly rooted in the Internet and computers. This work uses modern achievements in the ANN and methods of their training to solve the problem of correlations between objects, namely, vacancies and candidate information. The task of recruiting staff to expand or upgrade their staff has been and still is today. With the development of the Internet, this task has made new sense, because many resumes are scattered across different web services. So the question is: How do employers review and process information on all job applicants? This is what artificial neural networks will help, because with their help this process can be completed several times faster.Methods that use artificial neural networks (ANN) have become part of our routine and are very rapidly rooted in the Internet and computers. This work uses modern achievements in the ANN and methods of their training to solve the problem of correlations between the objects, namely a vacancy and information about a candidate. A vast array of data is processed and relations between the specific components are discovered. The suggested approach can be used not only for this specific task but also for a whole class of similar tasks like classification and pattern recognitionAll these tasks can be solved with different ANN parameters which should be configured for every particular set of data. There are many ways to improves. From input text normalization and different vectorization algorithms to ANN topology, parameters, and training algorithms. Considered ANN and its parameters with chosen training algorithm works with high accuracy level only for tasks described in this paper. for every other task, there is a need to experimentally and theoretically proven other parameters and topology to reach high accuracy.
Robust Randomized Autoencoder and Correntropy Criterion-Based One-Class Classification

Hierarchical neural network based one-class anomaly detection algorithms generally rely on stacked autoencoders (AEs) for feature learning. But existing AEs are not specifically designed to exploit the discriminative characteristics of target data in one-class classification (OCC), and thus may lead to poor generalization performance. In this brief, a novel randomized AE that imposes the constraint of the within-class scatter information is developed in feature learning. The correntropy criterion is applied to replace the mean square error criterion (MSE) to enhance the algorithm performance in outlier and noise rejection. The algorithm is further extended to kernel learning to improve its generalization capability. Experiments on benchmark datasets are carried out to show the effectiveness of the proposed algorithm.
Security on Blockchain Technology

Block chain (BC) is a relatively new technology; which has attracted much attention recently. It has been developed rapidly and can be used in many applications of various domains such as decentralized approaches. Indeed, many companies, even worldwide governments, are now using BC technology to improve the security, scalability, and efficiency of their services. In this paper, we provide an overview of BC security. We start with the basics of BC technology. Next, we give the primary BC security attacks. Finally, we present the BC countermeasures.
On a type of permutation rational functions over finite fields

Let p be a prime and n be a positive integer. Let fb(X)=X+(Xp−X+b)−1, where b∈Fpn is such that Trpn/p(b)≠0. In 2008, Yuan et al. [12] showed that for p=2,3, fb permutes Fpn for all n≥1. Using the Hasse-Weil bound, we show that when p>3 and n≥5, fb does not permute Fpn. For p>3 and n=2, we prove that fb permutes Fp2 if and only if Trp2/p(b)=±1. We conjecture that for p>3 and n=3,4, fb does not permute Fpn.
Speckle characteristics of vortex beams scattered from rough targets in turbulent atmosphere

In this study, the echo speckle characteristics of the Laguerre-Gauss (LG) vortex beams scattered from random rough surface targets were examined based on the Rytov Approximation Theory. In addition, the expressions of the intensity correlation and coherence functions, along with the normalized intensity variances of the speckle fields, were derived. The coherent lengths and scintillation indexes of the speckle fields were then numerically simulated, and the speckle characteristics of the Laguerre-Gauss vortex beam were compared with those of the Gaussian beams. The effects of the topological charges, rough target radii, initial beam radii, turbulence intensities, and roughness degrees of the targets on the speckle characteristics of the LG vortex beams were analyzed. This study's results showed that in the weak turbulence regions, the target characteristics were the main influencing factors of the speckle fields. Meanwhile, in the strong turbulence regions, the turbulence effects were the main influencing factors of the speckle fields. The influence effects of turbulent atmosphere and target characteristics on the coherent lengths and scintillation effects of the vortex beam echoes were observed to be less than those of the Gaussian beams. In addition, the influence effects of the turbulent atmosphere on coherent lengths and scintillation effects of the vortex beams echoes with small topology were found to be minimal. Therefore, this study's results can potentially provide theoretical and technical guidance for the future applications of laser target detection, target recognition, and speckle measurement processes in space.
Designing a CHAM Block Cipher on Low-End Microcontrollers for Internet of Things

As the technology of Internet of Things (IoT) evolves, abundant data is generated from sensor nodes and exchanged between them. For this reason, efficient encryption is required to keep data in secret. Since low-end IoT devices have limited computation power, it is difficult to operate expensive ciphers on them. Lightweight block ciphers reduce computation overheads, which are suitable for low-end IoT platforms. In this paper, we implemented the optimized CHAM block cipher in the counter mode of operation, on 8-bit AVR microcontrollers (i.e., representative sensor nodes). There are four new techniques applied. First, the execution time is drastically reduced, by skipping eight rounds through pre-calculation and look-up table access. Second, the encryption with a variable-key scenario is optimized with the on-the-fly table calculation. Third, the encryption in a parallel way makes multiple blocks computed in online for CHAM-64/128 case. Fourth, the state-of-art engineering technique is fully utilized in terms of the instruction level and register level. With these optimization methods, proposed optimized CHAM implementations for counter mode of operation outperformed the state-of-art implementations by 12.8%, 8.9%, and 9.6% for CHAM-64/128, CHAM-128/128, and CHAM-128/256, respectively.
Depth-Based 3D Face Reconstruction and Pose Estimation Using Shape-Preserving Domain Adaptation

Depth images are widely used in 3D head pose estimation and face reconstruction. The device-specific noise and the lack of textual constraints pose a major problem for estimating a nonrigid deformable face from a single noisy depth image. In this article, we present a deep neural network-based framework to infer a 3D face consistent with a single depth image captured by a consumer depth camera Kinect. Confronted with a lack of annotated depth images with facial parameters, we utilize the bidirectional CycleGAN-based generator for denoising and noisy image simulation, which helps to generalize the model learned from synthetic depth images to real noisy ones. We generate the code regressors in the source (synthetic) and the target (noisy) depth image domains and present a fusion scheme in the parametric space for 3D face inference. The proposed multi-level shape consistency constraint, concerning the embedded features, depth maps, and 3D surfaces, couples the code regressor and the domain adaptation, avoiding shape distortions in the CycleGAN-based generators. Experiments demonstrate that the proposed method is effective in depth-based 3D head pose estimation and expressive face reconstruction compared with the state-of-the-art.
Correcting and reweighting false label masks in brain tumor segmentation

Purpose Recently, brain tumor segmentation has made important progress. However, the quality of manual labels plays an important role in the performance, while in practice, it could vary greatly and in turn could substantially mislead the learning process and decrease the accuracy. We need to design a mechanism to combine label correction and sample reweighting to improve the effectiveness of brain tumor segmentation. Methods We propose a novel sample reweighting and label refinement method, and a novel three‐dimensional (3D) generative adversarial network (GAN) is introduced to combine these two models into an united framework. Results Extensive experiments on the BraTS19 dataset have demonstrated that our approach obtains competitive results when compared with other state‐of‐the‐art approaches when handling the false labels in brain tumor segmentation. Conclusions The 3D GAN‐based approach is an effective approach to handle false label masks by simultaneously applying label correction and sample reweighting. Our method is robust to variations in tumor shape and background clutter.
Long Short Term Memory (LSTM) based Deep Learning for Sentiment Analysis of English and Spanish Data

In recent years, deep neural networks have acquired a super power in the the field of machine learning, mainly for unstructured data types such as text and image, the demands of which are escalating day by day. This paper presents a Long Term Short Memory (LSTM) based Recurrent Neural Network (RNN), a popular deep learning algorithm for sentiment analysis of English and Spanish data. This domain is a complicated, because people's opinions are very subjective in nature and depends on many unpredictable factors. We tackled two primary bottlenecks of deep learning algorithms: optimal values of the parameters were chosen through cross validation and optimal architecture were selected through dropout based regularization method. Experimental results obtain state-of-the-art performance and it also offers very attractive insights.
Going further with model verification and deep learning

Abstract In our recent review paper aiming to introduce deep learning to ecologists, we presented a workflow describing the steps required to create a deep learning model. This figure did not present some of the following steps of model use such as model verification. By ensuring model adequacy, model verification is an important step after model creation in order to answer ecological questions. Adding model verification to a deep learning model development workflow can raise some new issues such as detecting the difference among the multiple datasets or what to do when model verification fails. In the spirit of our previous review, we identify some questions users trying to verify their deep learning model can have and try to find, for each, a solution to help them navigate the steps of deep learning model testing. We provide an additional cheat sheet to quickly help answer common questions regarding using model verification and deep learning. We hope these resources help stimulate further synthesis and coherence in the use of deep learning models in ecology.
Satellite Image Multi-Frame Super Resolution Using 3D Wide-Activation Neural Networks

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as the data augmentation, better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly on a personal laptop.
COVID-19 Data Visualization through Automatic Phase Detection

This article presents a new interactive visualization system to represent and compare rate of spread of COVID-19 pandemic across different countries over time. We surveyed existing visualization techniques used in various websites and media outlets and introduced use of a knee detection algorithm that divides the exponential spread in multiple linear components. A set of use cases demonstrates the utility of the system in comparing the spread across different countries for both analysis and prediction.