Reviewerly/exHarmony huggingface dataset:
A Cost Analysis of Implementing a Blockchain Architecture in a Smart Grid Scenario Using Sidechains

Smart grid systems have become popular and necessary for the development of a sustainable power grid. These systems use different technologies to provide optimized services to the users of the network. Regarding computing, these systems optimize electrical services by processing a large amount of the data generated. However, privacy and security are essential in this kind of system. With a large amount of data generated, it is necessary to protect the privacy of users, because this data may reveal the users' personal information. Today, blockchain technology has proven to be an efficient architecture for solving privacy and security problems in different scenarios. Over the years, different blockchain platforms have emerged, attempting to solve specific problems in different areas. However, the use of different platforms fragmented the market, which was no different in the smart grid scenario. This work proposes a blockchain architecture that uses sidechains to make the system scalable and adaptable. We used three blockchains to ensure privacy, security, and trust in the system. To universalize the proposed solution, we used the Open Smart Grid Protocol and smart contracts. The results show that architecture security and privacy are guaranteed, making it feasible for implementation in real systems; although scalability issues regarding the storage of the data generated still exist.
A novel integrated diagnosis method for breast cancer detection

The effective detection of breast cancer is particularly essential for recovery and treatment in the initial phases. The existing methods are not successfully diagnosis breast cancer in the initial phases. Thus the initial recognition of breast cancer is expressively a great challenge for health pr ofessionals and scientists. To resolve the problem of initial stages recognition of breast cancer, we recommended a machine learning based diagnosis method which will excellently classify the malignant and benign persons. In the designing of our method machine learning model support vector machine has been applied to classify the malignant and benign persons. To increase the classification performances of the method, we used Minimal Redundancy Maximal Relevance and Chi-square algorithms to choose more appropriate features from the breast cancer dataset. The training/testing splitting technique is used for training and testing of the model. Additionally, the performance of the model has been evaluated by performance assessment metrics. The experimental results demonstrated that the classifier support vector machine obtained best classification performance on the selected subset of features as selected by Minimal Redundancy Maximal Relevance feature selection algorithm. The performances of support vector machine on selected features by Chi square feature selection algorithm are low as compared to Minimal Redundancy Maximal Relevance algorithm. From experimental results analysis, we determined that the integrated system based on Minimal Redundancy Maximal Relevance and support vector machine performances are high due to the selection of more suitable features and obtained 99.71% accuracy. According to McNemar’s statistical test the proposed method is more significant then existing methods. Thus, we recommend that the proposed diagnosis method for effective detection of breast cancer.
Joint Personalized Markov Chains with social network embedding for cold-start recommendation

The primary objective of recommender systems is to help users select their desired items, where a key challenge is providing high-quality recommendations to users in a "cold-start" situation. Recent advances in tackling this problem combine social relations and temporal information and integrate them into a unified framework. However, these methods suffer from a limitation that there not always exist links for the newcomers, thus these users are filtered in related studies. To break the boundary, in this paper, we propose a Joint Personalized Markov Chains (JPMC) model to address the cold-start issues for implicit feedback recommendation system. In our study, we first utilize user embedding to mine Network Neighbors, so that newcomers without relations can be represented by similar users, then we designed a two-level model based on Markov chains at both user level and user group level respectively to model user preferences dynamically. Experimental results on three real-world datasets have shown that our model can significantly outperform the state-of-the-art models.
Federated Learning With Multichannel ALOHA

In this letter, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets. We show that multichannel random access can provide a better performance than sequential polling when some users are unable to compute local updates (due to other tasks) or in dormant state. In addition, for better aggregation in federated learning, the access probabilities of users can be optimized for given local updates. To this end, we formulate an optimization problem and show that a distributed approach can be used within federated learning to adaptively decide the access probabilities.
Two-Stage Sketch Colorization With Color Parsing

We implement high-quality sketch colorization using two-stage conditional generative adversarial network (GAN) training based on different intermediate features.The intermediate features used in autonomous colorization are the grayscale parsing and interval pixel-level color parsing.The autonomous colorization based on grayscale parsing feature can learn the spacial topology of pixels in the first stage to guide the colorization in the second stage.The autonomous colorization based on pixel-level color parsing feature can learn the color information of few feature points in the first stage to guide the colorization of all pixels in the second stage.Additionally, we use the intermediate feature of sampling points as constraint and achieve the color reconstruction using Laplacian mesh editing as a special second stage.Furthermore, the interactive colorization uses the superpixel color parsing as the intermediate feature.Specifically, we use the simple linear iterative cluster (SLIC) to obtain a palette that maintains the edges in the first stage to guide the colorization in the second stage.As for evaluation metrics, we propose a color-coded local binary pattern (CCLBP) score based on color distances from the first-order 8 pixels to the central pixel, to measure the degrees of color blurring and mess.We also propose a light-sensitivity (LS) score based on the reversed grayscale map, to measure the degrees of auto painting and overfitting of the color hint.According to the L1 distances between the original and generated color images based on these scores, compared with state-of-the-art methods including one stage approaches such as pix2pix and PaintsChainer and two-stage approaches such as Style2Paints and DeepColor, our model can achieve the highest-quality autonomous colorization.Moreover, compared with pix2pix, PaintsChainer and Style2Paints with color hints, according to the proposed objective evaluation as well as the user visual study, our model can achieve the highest-quality interactive colorization as well.
Permutation-Invariant Feature Restructuring for Correlation-Aware Image Set-Based Recognition

We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the correlations of both inner&inter-set images. Specifically, the residual self-attention can effectively restructure the features using the other features within a set to emphasize the discriminative images and eliminate the redundancy. Then, a sparse/collaborative learning-based dependency-guided representation scheme reconstructs the probe features conditional to the gallery features in order to adaptively align the two sets. This enables our framework to be compatible with both verification and open-set identification. We show that the parametric self-attention network and non-parametric dictionary learning can be trained end-to-end by a unified alternative optimization scheme, and that the full framework is permutation-invariant. In the numerical experiments we conducted, our method achieves top performance on competitive image set/video-based face recognition and person re-identification benchmarks.
Learning Long-Term Temporal Features With Deep Neural Networks for Human Action Recognition

One of challenging tasks in the field of artificial intelligence is the human action recognition.In this paper, we propose a novel long-term temporal feature learning architecture for recognizing human action in video, named Pseudo Recurrent Residual Neural Networks (P-RRNNs), which exploits the recurrent architecture and composes each in different connection among units.Two-stream CNNs model (GoogLeNet) is employed for extracting local temporal and spatial features respectively.The local spatial and temporal features are then integrated into global long-term temporal features by using our proposed two-stream P-RRNNs.Finally, the Softmax layer fuses the outputs of two-stream P-RRNNs for action recognition.The experimental results on two standard databases UCF101 and HMDB51 demonstrate the outstanding performance of proposed method based on architectures for human action recognition.
A Unified Approach to Construct MDS Self-Dual Codes via Reed-Solomon Codes

MDS codes and self-dual codes are important families of classical codes in coding theory. Therefore, it is of interest to investigate MDS self-dual codes. The existence of MDS selfdual codes over finite field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> is completely solved for q is even. In the literature, there are many known constructions of MDS self-dual codes for q is odd. In this paper, we present a unified approach on the existence of MDS self-dual codes with concise statements and simplified proof. It is illustrated that some of known results can also be stated in this framework. Furthermore, we can obtain some new MDS self-dual codes, especially for the case when q is not a square.
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

While node semantics have been extensively explored in social networks, little research attention has been paid to pro le edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to pro le, due to noisy multi-modal signals and limited user-generated ground-truth labels.
Distribution Consistency Loss for Large-Scale Remote Sensing Image Retrieval

Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.
Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval

Recently, the demand for remote sensing image retrieval is growing and attracting the interest of many researchers because of the increasing number of remote sensing images. Hashing, as a method of retrieving images, has been widely applied to remote sensing image retrieval. In order to improve hashing performance, we develop a cohesion intensive deep hashing model for remote sensing image retrieval. The underlying architecture of our deep model is motivated by the state-of-the-art residual net. Residual nets aim at avoiding gradient vanishing and gradient explosion when the net reaches a certain depth. However, different from the residual net which outputs multiple class-labels, we present a residual hash net that is terminated by a Heaviside-like function for binarizing remote sensing images. In this scenario, the representational power of the residual net architecture is exploited to establish an end-to-end deep hashing model. The residual hash net is trained subject to a weighted loss strategy that intensifies the cohesiveness of image hash codes within one class. This effectively addresses the data imbalance problem normally arising in remote sensing image retrieval tasks. Furthermore, we adopted a gradualness optimization method for obtaining optimal model parameters in order to favor accurate binary codes with little quantization error. We conduct comparative experiments on large-scale remote sensing data sets such as UCMerced and AID. The experimental results validate the hypothesis that our method improves the performance of current remote sensing image retrieval.
Covariate Regularized Community Detection in Sparse Graphs

In this article, we investigate community detection in networks in the presence of node covariates. In many instances, covariates and networks individually only give a partial view of the cluster structure. One needs to jointly infer the full cluster structure by considering both. In statistics, an emerging body of work has been focused on combining information from both the edges in the network and the node covariates to infer community memberships. However, so far the theoretical guarantees have been established in the dense regime, where the network can lead to perfect clustering under a broad parameter regime, and hence the role of covariates is often not clear. In this article, we examine sparse networks in conjunction with finite dimensional sub-Gaussian mixtures as covariates under moderate separation conditions. In this setting each individual source can only cluster a nonvanishing fraction of nodes correctly. We propose a simple optimization framework which improves clustering accuracy when the two sources carry partial information about the cluster memberships, and hence perform poorly on their own. Our optimization problem can be solved by scalable convex optimization algorithms. With a variety of simulated and real data examples, we show that the proposed method outperforms other existing methodology. Supplementary materials for this article are available online.
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification

Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures. To tackle this issue, an automatic and robust diagnostic system is required for early detection and treatment. In this paper, an automated deep learning-based method is proposed to distinguish between immature leukemic blasts and normal cells. The proposed deep learning based hybrid method, which is enriched by different data augmentation techniques, is able to extract high-level features from input images. Results demonstrate that the proposed model yields better prediction than individual models for Leukemic Blymphoblast classification with 96.17% overall accuracy, 95.17% sensitivity and 98.58% specificity. Fusing the features extracted from intermediate layers, our approach has the potential to improve the overall classification performance.
Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.
Histogram-Based Algorithm for Building Gradient Boosting Ensembles of Piecewise Linear Decision Trees

One of the most popular machine learning algorithms is gradient boosting over decision trees. This algorithm achieves high quality out of the box combined with comparably low training and inference time. However, modern machine learning applications require machine learning algorithms, that can achieve better quality in less inference time, which leads to an exploration of grading boosting algorithms over other forms of base learners. One of such advanced base learners is a piecewise linear tree, which has linear functions as predictions in leaves. This paper introduces an efficient histogram-based algorithm for building gradient boosting ensembles of such trees. The algorithm was compared with modern gradient boosting libraries on publicly available datasets and achieved better quality with a decrease in ensemble size and inference time. It was proven, that algorithm is independent of a linear transformation of individual features.
Bringing the kid back into YouTube kids

With the advent of child-centric content-sharing platforms, such as YouTube Kids, thousands of children, from all age groups are consuming gigabytes of content on a daily basis. With PBS Kids, Disney Jr. and countless others joining in the fray, this consumption of video data stands to grow further in quantity and diversity. However, it has been observed increasingly that content unsuitable for children often slips through the cracks and lands on such platforms. To investigate this phenomenon in more detail, we collect a first of its kind dataset of inappropriate videos hosted on such children-focused apps and platforms. Alarmingly, our study finds that there is a noticeable percentage of such videos currently being watched by kids with some inappropriate videos having millions of views already. To address this problem, we develop a deep learning architecture that can flag such videos and report them. Our results show that the proposed system can be successfully applied to various types of animations, cartoons and CGI videos to detect any inappropriate content within them.
Detection of SQL Injection Attacks: A Machine Learning Approach

With the rapid growth in online services, hacking (alternatively attacking) on online database applications has become a grave concern now. Attacks on online database application are being frequently reported. Among these attacks, the SQL injection attack is at the top of the list. The hackers alter the SQL query sent by the user and inject malicious code therein. Hence, they access the database and manipulate the data. It is reported in the literature that the traditional SQL injection detection algorithms fail to prevent this type of attack. In this paper, we propose a machine learning based heuristic algorithm to prevent the SQL injection attack. We use a dataset of 616 SQL statements to train and test 23 different machine learning classifiers. Among these classifiers, we select the best five classifiers based on their detection accuracy and develop a Graphical User Interface (GUI) application based on these five classifiers. We test our proposed algorithm and the results show that our algorithm is able to detect the SQL injection attack with a high accuracy (93.8%).
Deep Fuzzy Clustering - A Representation Learning Approach

Fuzzy clustering is a classical approach to provide the soft partition of data. Although its enhancements have been intensively explored, fuzzy clustering still suffers from the difficulties in handling real high-dimensional data with complex latent distribution. To solve the problem, this article proposes a deep fuzzy clustering method by representing the data in a feature space produced by the deep neural network. From the perspective of representation learning, three constraints or objectives are imposed to the neural network to enhance the clustering-friendly representation. At first, as a good representation of data, the mapped data in the new feature space should support the reconstruction of original data. So, the autoencoder architecture is applied to ensure that the original data can be recovered by decoding the encoded representation with another neural network. Second, to solve the clustering problem efficiently, the intracluster compactness and the intercluster separability are to be minimized and maximized, respectively, in the new feature space. At last, considering that the data in the same class should be close to each other, the affinities between new representations are tuned in accordance with the discriminative information. Altogether, we design a graph-regularized deep normalized fuzzy compactness and separation clustering model to conduct representation learning and soft clustering simultaneously. The learning algorithm based on stochastic gradient descent is proposed to the model, and the comparative studies with baseline clustering algorithms on real-world data illustrate the superiority of the proposal.