Reviewerly/exHarmony huggingface dataset:
The Mobile Robot Anti-disturbance vSLAM Navigation Algorithm based on RBF Neural Network

The demand of Auto Guidance Vehicles (AGVs) is increasing while the technique can be popularized to apply on industrial flexible path optimization in intelligent manufacturing and industry 4.0. In the general industrial environment, AGVs navigation is based on fixed guidance markers such as black line and QR code regarding to the cost-effectiveness. As deep learning technique carried out, Vision based Simultaneous Localization and Mapping (vSLAM) technique is widely used on unmanned vehicle auto-driving and navigation for the next generation of AGVs navigation. However, the challenges of vSLAM navigation technology are facing: 1) the vSLAM accuracy is up to 5~10cm, which cannot satisfy the parts of industrial requirements and 2) the problems of anti-disturbance ability are under complex environment, such as lighting, dynamic object, field of vision, therefore, those noise factors will affect the precision level of AGVs navigation. This paper focuses on the anti-disturbance problem of mobile robot’s vSLAM navigation and finds out the solution. An optimization algorithm is developed to minimize robot path following error and enhance the performance of AGVs navigation. The algorithm is based on Lyapunov direct method controller with RBF neural network estimator.
Dense Encoder-Decoder Network based on Two-Level Context Enhanced Residual Attention Mechanism for Segmentation of Breast Tumors in Magnetic Resonance Imaging

Aiming to effective early detection of breast cancer, automatic tumor segmentation based on breast Magnetic Resonance Imaging (MRI) is concentrated by more and more researchers. This paper proposes a dense encoder-decoder network based on two-level context enhanced residual attention mechanism (TLCRAM-DED). With respect to TLCRAM-DED, we design the encoding structure combining two-level residual attention structure with dense block to extract and refine the features of different layers. Meanwhile, a dense multi-scale atrous convolution is used at the end of the encoder to obtain a larger receptive field and enrich the extracted semantic information. Moreover, residual attention structure (RAS) is also used for the refinement during decoding stage, while a long connection formed with the encoder RAS output is applied to supplement the features and to gradually recover the segmentation details. We validated prosed model in the DCE sequence of challenging breast cancer MRI dataset. The average Dice coefficient is up to 81.04%, which outperforms compared state-of-the-arts.
E-crime Management System for Future Smart City

Architecture of an E-crime management system for future smart city is proposed in this paper. The architecture is divided into four main components: Police Station, Server, Internet and Citizen of smart city. Citizen of smart city can use any type of microcomputer such as: smart phone, tablet, laptop or desktop to register a case from online. Specification of server to make it secure and reliable is listed in this paper. Investigation center is equipped with Forensic Services, Artificial intelligence and Analytics module and connected with the main server. All information related to investigation and criminals can be stored in this system for further proceeding. At present, part of the proposed architecture is designed and implemented which is also described in this paper.
Rotation invariant descriptors for galaxy morphological classification

The detection of multi-oriented objects is a difficult pattern recognition problem. In this paper, we propose to evaluate the performance of different families of descriptors for the classification of galaxy morphologies. We investigate the performance of the Hu moments, Flusser moments, Zernike moments, Fourier–Mellin moments, and ring projection techniques based on 1D moment and the Fourier transform. We consider two main datasets for the performance evaluation. The first dataset is an artificial dataset based on representative templates from 11 types of galaxies, which are evaluated with different transformations (noise, smoothing), alone or combined. The evaluation is based on image retrieval performance to estimate the robustness of the rotation invariant descriptors with this type of images. The second dataset is composed of real images extracted from the Galaxy Zoo 2 project. The binary classification of elliptical and spiral galaxies is achieved with pre-processing steps including morphological filtering and a Laplacian pyramid. For the binary classification, we compare the different set of features with Support Vector Machines, Extreme Learning Machine, and different types of linear discriminant analysis techniques. The results support the conclusion that the proposed framework for the binary classification of elliptical and spiral galaxies provides an area under the receiver operating characteristic curve reaching 99.54%, proving the robustness of the approach for helping astronomers to study galaxies.
Learning deep face representation with long-tail data: An aggregate-and-disperse approach

In this work, we study the problem of deep representation learning on a large face dataset with long-tail distribution. Training convolutional neural networks on such dataset with conventional strategy suffers from imbalance problem which results in biased classification boundary, and the few-shot classes lying in tail parts further make the model prone to overfitting. Aiming to learn more discriminative CNN model from long-tail data, we propose a novel aggregate-and-disperse training schema. Firstly, our proposed method aggregates similar classes in tail part to avoid imbalance problem. Based on the aggregated super classes and those original head classes, a model is pre-trained to capture accurate discrimination in head classes as well as coarse discrinimation in tail classes. Secondly, we selectively disperses those aggregated super classes to learn precise inter-class variations and refine the representation for better generalization. We perform extensive experiments on MS-Celeb-1M, BLUFR and MegaFace. Compared with baselines and existing methods, our method achieves better performance of face recognition, demonstrating its effectiveness of handling long-tail distribution.
A SVM-Based Zero-Watermarking Technique for 3D Videos Traitor Tracing

The watermarking layer has a crucial role in a collusion-secure fingerprinting framework since the hidden information, or the identifier, directly attached to user identification, is implanted in the media as a watermark. In this paper, we propose a new zero watermarking technique for 3D videos based on Support Vector Machine (SVM) classifier. Hence, the proposed scheme consists of two major contributions. The first one is the protection of both the 2D video frames and the depth maps simultaneously and independently. Robust features are extracted from Temporally Informative Representative Images (TIRIs) of both the 2D video frames and depth maps to construct the master shares. Then, the relationship between the identifier and the extracted master shares is generated by performing an Exclusive OR (XOR) operation. The second contribution uses the SVM and the XOR operation to estimate the watermark. Compared to other zero watermarking techniques, the proposed scheme has proven good results of robustness and transparency even for long size watermarks, which makes it suitable for a tracing traitor framework.
Object-Oriented Modeling of Multifaceted Service Delivery System Using Connected Governance

Connected governance is the concept of technology- based governance where electronic services are delivered to citizen using Information and Communication Technology (ICT). As conventional governance delivers services to citizen through manual mode, it encounters various performance issues during its operation. For instance, the occurrence of human errors during record maintenance, loss or damage of official records, delayed delivery of services due to limitations of physical communication, engagement of huge manpower and infrastructure for operations and subsequent financial load over the state, etc., may be mentioned as few. To overcome these performance issues, electronic communication medium may be used for prompt delivery of messages between government and citizen within a connected environment. However, the connected governance has its own issues mainly due to application of public communication medium like Internet, which is highly susceptible to infringement attempts of adversaries. Researchers should focus to eradicate security lapses of electronic message communication. For instance, the verification of communicating parties, securing source and destination of SERVICE REQUEST and SERVICE RESPONSE, protection from unauthorized access of sensitive information during its transit, encrypted transmission of sensitive information through Internet, etc., may be mentioned as few. To achieve these objectives, the digital identity of user should be established uniquely before initiating any electronic transaction using authentic instrument. In India, though citizen is claimed to be uniquely identified using Aadhaar Card, Voter Card, Ration Card, PAN Card, etc., recent initiative of government to interlink these identity instruments implicitly acknowledges the necessity of unified platform for delivery of multifaceted services to citizen. Furthermore, this approach of interlinking multiple identity instruments is providing wide window open to adversaries to validate their fake documents with the help of other documents, thereby leading to complete collapse of system. To resolve this issue as per local parameters, author have proposed a cloud-based multifaceted service delivery model, whose object-oriented modeling (OOM) is performed in this chapter.
An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights

K -means clustering algorithm is one of the most popular technique for clustering in machine learning, however, in the existing k-means clustering algorithm, the ability of the different features and the importance of the different data objects are treated equally; the discriminative ability of the different features and the importance of the different data objects cannot be differentiated effectively.In the light of this limitation, this paper put forward an enhanced regularized k-means type clustering algorithm with adaptive weights in which we introduced an adaptive feature weights matrix and an adaptive data weights vector into the objective function of the k-means clustering algorithm and we developed a new objective function with l 2 -norm regularization to the weights of data objects and features, then we obtained the corresponding scientific updating iterative rules of the weights of the different features, the weights of the different data objects and the cluster centers theoretically.In order to evaluate the performance of the new algorithm put forward, extensive experiments were conducted.Experimental results have indicated that our proposed algorithm can improve the clustering performance significantly and are more effective with respects to three metrics: the successful clustering rate (SCR), normal mutual information (NMI) and RandIndex.
Aspect Based Sentiment Analysis in E-Commerce User Reviews Using Latent Dirichlet Allocation (LDA) and Sentiment Lexicon

User ratings on products sold bye-commerce greatly influence the number of purchases. Positive ratings will encourage other buyers to participate in buying the product. While negative ratings given by users will reduce the interest in purchasing products. Nonconformities between rating and user reviews sometimes provide a wrong assessment of a product. This happens because buyers also provide reviews on the quality of delivery services from e-commerce. Based on that issue, the utilization of the Latent Dirichlet Allocation (LDA) could be used on sentiment analysis of the user reviews. Sentiment analysis of the user reviews aims to facilitate e-commerce in informing the product quality as rating supporters that have been given by users. This research aims to determine the classification performance of sentiment analysis on e-commerce user reviews using the LDA algorithm with input data in the form of e-commerce user reviews. Then, compare the application of sentiment analysis of the user reviews with the use of general training data and per category training data. The result of this research showed that in the first iteration the best architecture was produced by the application of LDA with a combination of parameters of alpha 0.001, beta 0.001, and number of topics 15. The architecture had 67,5% accuracy level. From the best architecture then training data input is given based on each product review category. The result showed that the combination of the usage of general data and per category data indicate an increase in the average accuracy of 0,82 % from the three-test data. Therefore, in order to produce the best performance of building a classification model of sentiment analysis of the user reviews, it should be performed by applying LDA with a combination of general data and per category data usage.
BNReLU: Combine Batch Normalization and Rectified Linear Unit to Reduce Hardware Overhead

With the pursuit of higher accuracy, the convolutional neural network has become deeper. Thus, the hardware overhead occupied have increased. Batch Normalization (BN) operation is an indispensable part of the network, and its occupied hardware resources cannot be ignored. In this paper, aiming to reduce hardware overhead caused by BN operation, we combine BN operation and ReLU activation function and propose the BNReLU operation without retraining the network. The experiment results show that compared with traditional BN+ReLU layer, hardware overhead of BNReLU operation is reduced by about 50%. With 32-bit float point input for each layer, the hardware overhead of BRAM, DSP, FF, and LUT for convolutional layer in the FPGA is reduced by 17.16%, 23.08%, 17.64%, and 16.05%.
Early Prediction of Vital Signs Using Generative Boosting via LSTM Networks

Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process. Abnormal vital signs help to alert medical practitioners to potential health problems. Effective and long-range early prediction of vital signs may prevent adverse health outcomes and reduce cost. In this paper, we suggest a new approach called generative boosting, in order to effectively perform long-range early prediction of vital signs. Generative boosting consists of a generative model, to generate synthetic data for the next few time steps, and a predictive model, to directly make long-range predictions based on observed and generated data. We explore generative boosting via long short-term memory (LSTM) for both the predictive and generative models, leading to a scheme called generative LSTM (GLSTM). Our experiments indicate that GLSTM outperforms a diverse range of strong benchmark models, with and without generative boosting. As expected, the results also indicate that more accurately generated data leads to more accurate long-range predictions. In light of this, we use a mutual information based clustering algorithm to select a more representative dataset to train the generative model. This leads to significantly improved accuracy of the long-range prediction of high variation vital signs such as heart rate and systolic blood pressure. Overall, our best results indicate that the proposed method is able to predict heart rate and systolic blood pressure 20 minutes in advance, with a mean absolute percentage error of 7.41% and 6.17%, respectively.
Texture-based Deep Learning for Effective Histopathological Cancer Image Classification

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time- and cost-consuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable morphological features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CAT-Net can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CAT-Net, captures morphological patterns of interest in hierarchical levels in the model. The proposed method out-performed the current state-of-the-art benchmark methods on accuracy, precision, recall, and F1 score.
Learned Representation of Satellite Image Series for Data Compression

Real-time transmission of satellite video data is one of the fundamentals in the applications of video satellite. Making use of the historical information to eliminate the long-term background redundancy (LBR) is considered to be a crucial way to bridge the gap between the compressed data rate and the bandwidth between the satellite and the Earth. The main challenge lies in how to deal with the variant image pixel values caused by the change of shooting conditions while keeping the structure of the same landscape unchanged. In this paper, we propose a representation learning based method to model the complex evolution of the landscape appearance under different conditions by making use of the historical image series. Under this representation model, the image is disentangled into the content part and the style part. The former represents the consistent landscape structure, while the latter represents the conditional parameters of the environment. To utilize the knowledge learned from the historical image series, we generate synthetic reference frames for the compression of video frames through image translation by the representation model. The synthetic reference frames can highly boost the compression efficiency by changing the original intra-frame prediction to inter-frame prediction for the intra-coded picture (I frame). Experimental results show that the proposed representation learning-based compression method can save an average of 44.22% bits over HEVC, which is significantly higher than that using references generated under the same conditions. Bitrate savings reached 18.07% when applied to satellite video data with arbitrarily collected reference images.
Application of CNN-BiGRU Model in Chinese Short Text Sentiment Analysis

In view of the fact that traditional sentiment analysis[1] techniques require manual annotation and construction of sentiment lexicon, and the current use of deep learning techniques for sentiment analysis, there is a need for manual screening and large workload, and the accuracy and training speed need to be improved. This paper proposes a text sentiment analysis model (CNN-BiGRU) combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (GRU). The model first uses CNN to extract the local static features of the text, and then extracts the sequence features of the text through BiGRU, and then the two unidirectional GRU layers further reduce the extracted features. Finally, the Sigmoid classifier is used for the final sentiment classification. Through the self-built Douban film and television commentary dataset, compared with the CNN-BLSTM model, the classification accuracy rate increased by 2.52%, and the training rate increased by 41.43%, which verified the validity of the model.
Steganography in Social Networks Based on Behavioral Correlation

This paper proposes a secure steganographic method in social networks based on the behavioral correlation between the sender and his friends. A sender transmits secret data to one of his friends in social network by marking “love” on the news published by his friends. To avoid behavioral abnormality, the sender lets the value of “love” marking probability on each news in proportion to the number of “love” marks made by his friends. As a result, the “love” marks carrying secret data are indistinguishable from other normal “love” marks. Although a receiver does not know the “love” marking probabilities, he can still extract the secret data by observing the “love” marks. The security performance of the proposed steganographic method is satisfactory when examined by a proposed behavioral steganalysis method which is able to defeat existing behavioral steganographic methods.
An analysis and comparison of keyword recommendation methods for scientific data

Abstract To classify and search various kinds of scientific data, it is useful to annotate those data with keywords from a controlled vocabulary. Data providers, such as researchers, annotate their own data with keywords from the provided vocabulary. However, for the selection of suitable keywords, extensive knowledge of both the research domain and the controlled vocabulary is required. Therefore, the annotation of scientific data with keywords from a controlled vocabulary is a time-consuming task for data providers. In this paper, we discuss methods for recommending relevant keywords from a controlled vocabulary for the annotation of scientific data through their metadata. Many previous studies have proposed approaches based on keywords in similar existing metadata; we call this the indirect method . However, when the quality of the existing metadata set is insufficient, the indirect method tends to be ineffective. Because the controlled vocabularies for scientific data usually provide definition sentences for each keyword, it is also possible to recommend keywords based on the target metadata and the keyword definitions; we call this the direct method . The direct method does not utilize the existing metadata set and therefore is independent of its quality. Also, for the evaluation of keyword recommendation methods, we propose evaluation metrics based on a hierarchical vocabulary structure, which is a distinctive feature of most controlled vocabularies. Using our proposed evaluation metrics, we can evaluate keyword recommendation methods with an emphasis on keywords that are more difficult for data providers to select. In experiments using real earth science datasets, we compare the direct and indirect methods to verify their effectiveness, and observe how the indirect method depends on the quality of the existing metadata set. The results show the importance of metadata quality in recommending keywords.