Reviewerly/exHarmony huggingface dataset:
DQN-Based Optimization Framework for Secure Sharded Blockchain Systems

High levels of scalability and reliability are needed to support the massive Internet-of-Things (IoT) services. In particular, blockchains can be effectively used to safely manage data from large-scale IoT networks. However, current blockchain systems have low transactions per second (TPS) rates and scalability limitations that make them unsuitable. To solve the above issues, this article proposes a deep Q network shard-based blockchain (DQNSB) scheme that dynamically finds the optimal throughput configuration. In this article, a novel analysis of sharded blockchain latency and security-level characterization is provided. Using the analysis equations, the DQNSB scheme estimates the level of maliciousness and adapts the blockchain parameters to enhance the security level considering the amount of malicious attacks on the consensus process. To achieve this purpose, deep reinforcement learning (DRL) agents are trained to find the optimal system parameters in response to the network status, and adaptively optimizes the system throughput and security level. The simulation results show that the proposed DQNSB scheme provides a much higher TPS than the existing DRL-enabled blockchain technology while maintaining a high security level.
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations

Personalized recommendations are the backbone machine learning (ML) algorithm that powers several important application domains (e.g., ads, e-commerce, etc) serviced from cloud datacenters. Sparse embedding layers are a crucial building block in designing recommendations yet little attention has been paid in properly accelerating this important ML algorithm. This paper first provides a detailed workload characterization on personalized recommendations and identifies two significant performance limiters: memory-intensive embedding layers and compute-intensive multi-layer perceptron (MLP) layers. We then present Centaur, a chiplet-based hybrid sparse-dense accelerator that addresses both the memory throughput challenges of embedding layers and the compute limitations of MLP layers. We implement and demonstrate our proposal on an Intel HARPv2, a package-integrated CPU+FPGA device, which shows a 1.7-17.2× performance speedup and 1.7-19.5× energy efficiency improvement than conventional approaches.
High‐Resolution Neural Face Swapping for Visual Effects

Abstract In this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. To the best of our knowledge, this is the first method capable of rendering photo‐realistic and temporally coherent results at megapixel resolution. To this end, we introduce a progressively trained multi‐way comb network and a light‐ and contrast‐preserving blending method. We also show that while progressive training enables generation of high‐resolution images, extending the architecture and training data beyond two people allows us to achieve higher fidelity in generated expressions. When compositing the generated expression onto the target face, we show how to adapt the blending strategy to preserve contrast and low‐frequency lighting. Finally, we incorporate a refinement strategy into the face landmark stabilization algorithm to achieve temporal stability, which is crucial for working with high‐resolution videos. We conduct an extensive ablation study to show the influence of our design choices on the quality of the swap and compare our work with popular state‐of‐the‐art methods.
An algorithm and method for sentiment analysis using the text and emoticon

People nowadays use emoticons in their text increasingly in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF–IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.
Small-Object Detection in UAV-Captured Images via Multi-Branch Parallel Feature Pyramid Networks

Small object is one of the primary challenges in the field of object detection, which is notably pronounced to the detection in the images from Unmanned Aerial Vehicles (UAV). Existing detectors based on deep-learning methods usually apply the feature extraction networks with a large down-sampling factor to obtain higher-level features. However, such big stride tends to make the feature information of small objects become the little point or even vanish in the low-resolution feature maps due to the limitation of pixels. Therefore, a novel structure called Multi-branch Parallel Feature Pyramid Networks (MPFPN) is proposed in this article, which aims to extract more abundant feature information of the objects with a small size. Specifically, the parallel branch is designed to recover the features that missed in the deeper layers. Meanwhile, a supervised spatial attention module (SSAM) is applied to weaken the impact of background noise inference and focus object information. Furthermore, we adopt cascade architecture in the Fast R-CNN stage for a more powerful localization capability. Experiments on the public drone-based datasets named VisDrone-DET demonstrate that our method achieves competitive performance compared with other state-of-the-art detection algorithms.
FPDP: Flexible Privacy-Preserving Data Publishing Scheme for Smart Agriculture

The development of the Internet of Things (IoT) and 5th generation wireless network (5G) is set to push the smart agriculture to the next level since the massive and real-time data can be collected to monitor the status of crops and livestock, logistics management, and other important information. Recently, COVID-19 has attracted more human attention to food safety, which also has a positive impact on smart agriculture market share. However, the security and privacy concern for smart agriculture has become more prominent. Since smart agriculture implies working with large sets of data, which usually sensitive, some are even confidential, and once leakage it can expose user privacy. Meanwhile, considering the data publishing of smart agriculture helps the public or investors to real-timely anticipate risks and benefits, these data are also a public resource. To balance the data publishing and data privacy, in this article, a privacy-preserving data aggregation scheme with a flexibility property uses ElGamal Cryptosystem is proposed. It is proved to be secure, private, and flexible with the analysis and performance simulation.
Behavioral biometrics &amp; continuous user authentication on mobile devices: A survey

This paper offers an up-to-date, comprehensive, extensive and targeted survey on Behavioral Biometrics and Continuous Authentication technologies for mobile devices. Our aim is to help interested researchers to effectively grasp the background in this field and to avoid pitfalls in their work. In our survey, we first present a classification of behavioral biometrics technologies and continuous authentication for mobile devices and an analysis for behavioral biometrics collection methodologies and feature extraction techniques. Then, we provide a state-of-the-art literature review focusing on the machine learning models performance in seven types of behavioral biometrics for continuous authentication. Further, we conduct another review that showed the vulnerability of machine learning models against well-designed adversarial attack vectors and we highlight relevant countermeasures. Finally, our discussions extend to lessons learned, current challenges and future trends.
Technical, Temporal, and Spatial Research Challenges and Opportunities in Blockchain-Based Healthcare: A Systematic Literature Review

Blockchain can be used to build a peer-to-peer, secure, and smart transaction system. As a horizontal technology that has changed several fields of industry, blockchain has tremendous potential to transform healthcare systems as well. In this article, a systematic review is conducted to critically evaluate 64 articles on blockchain-based healthcare systems, published between 2016 and January 2020 in 21 conferences, 33 journals, and ten online sources. The aim of this article is to answer three main questions. First, what are the applications of blockchain in the healthcare systems, and what are the structures and challenges of applying blockchain to a specific healthcare domain? Second, what are the technical, temporal, and spatial aspects of the currently developed blockchain applications for different healthcare domain? Third, what are the future research directions in designing and implementing blockchain-based healthcare systems? Statistical facts about the technical aspects of these 64 articles show that most of the proposed blockchain-based healthcare systems use private blockchain and Ethereum platforms; furthermore, the majority of the authors are affiliated with research institutions in the USA and China. We also discuss potential future research directions, e.g., integrating the blockchain in artificial intelligence based solutions, cloud-computing-based solutions, and parallel blockchain architecture.
Early vs Late Fusion in Multimodal Convolutional Neural Networks

Combining machine learning in neural networks with multimodal fusion strategies offers an interesting potential for classification tasks but the optimum fusion strategies for many applications have yet to be determined. Here we address this issue in the context of human activity recognition, making use of a state-of-the-art convolutional network architecture (Inception I3D) and a huge dataset (NTU RGB+D). As modalities we consider RGB video, optical flow, and skeleton data. We determine whether the fusion of different modalities can provide an advantage as compared to uni-modal approaches, and whether a more complex early fusion strategy can outperform the simpler late-fusion strategy by making use of statistical correlations between the different modalities. Our results show a clear performance improvement by multi-modal fusion and a substantial advantage of an early fusion strategy.
COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction

Generally, there are more non-defective instances than defective instances in the datasets used for software defect prediction (SDP), which is referred to as the class imbalance problem. Oversampling techniques are frequently adopted to alleviate the problem by generating new synthetic defective instances. Existing techniques generate either near-duplicated instances which result in overgeneralization (high probability of false alarm, pf) or overly diverse instances which hurt the prediction model’s ability to find defects (resulting in low probability of detection, pd). Furthermore, when existing oversampling techniques are applied in SDP, the effort needed to inspect the instances with different complexity is not taken into consideration. In this study, we introduce Complexity-based OverSampling TEchnique (COSTE), a novel oversampling technique that can achieve low pf and high pd simultaneously. Meanwhile, COSTE also performs better in terms of Norm(popt) and ACC, two effort-aware measures that consider the testing effort. COSTE combines pairs of defective instances with similar complexity to generate synthetic instances, which improves the diversity within the data, maintains the ability of prediction models to find defects, and takes the different testing effort needed for different instances into consideration. We conduct experiments to compare COSTE with Synthetic Minority Oversampling TEchnique, Borderline-SMOTE, Majority Weighted Minority Oversampling TEchnique and MAHAKIL. The experimental results on 23 releases of 10 projects show that COSTE greatly improves the diversity of the synthetic instances without compromising the ability of prediction models to find defects. In addition, COSTE outperforms the other oversampling techniques under the same testing effort. The statistical analysis indicates that COSTE’s ability to outperform the other oversampling techniques is significant under the statistical Wilcoxon rank sum test and Cliff’s effect size. COSTE is recommended as an efficient alternative to address the class imbalance problem in SDP.
Discriminative subspace matrix factorization for multiview data clustering

In a real-world scenario, an object is easily considered as features combined by multiple views in reality. Thus, multiview features can be encoded into a unified and discriminative framework to achieve satisfactory clustering performance. An increasing number of algorithms have been proposed for multiview data clustering. However, existing multiview methods have several drawbacks. First, most multiview algorithms focus only on origin data in high dimension directly without the intrinsic structure in the relative low-dimensional subspace. Spectral and manifold-based methods ignore pseudo-information that can be extracted from the optimization process. Thus, we design an unsupervised nonnegative matrix factorization (NMF)-based method called discriminative multiview subspace matrix factorization (DMSMF) for clustering. We provide the following contributions. (1) We extend linear discriminant analysis and NMF to a multiview version and connect them to a unified framework to learn in the discriminant subspace. (2) We propose a multiview manifold regularization term and discriminant multiview manifold regularization term that instruct the regularization term to discriminate different classes and obtain the geometry st ructure from the low-dimensional subspace. (3) We design an effective optimization algorithm with proven convergence to obtain an optimal solution procedure for the complex model. Adequate experiments are conducted on multiple benchmark datasets. Finally, we demonstrate that our model is superior to other comparable multiview data clustering algorithms.
Blockchain technology and traceability in the agrifood industry

Abstract Distributed ledgers are becoming commonly used technologies to trace agrifood supply chains in view of their safety, immutability, transparency, and scalability. In the present review, we discuss the most relevant case studies of agrifood supply chain traceability using blockchain (BC) and other distributed ledgers technologies. Considering that each supply chain actually has specific requests of traceability, we here suggest a logical scheme in order to favor the identification of the BC structure that is more appropriate for each agrifood supply chain, including the identification of supply chains where complex BC technologies are actually not necessary.
Faster ILOD: Incremental learning for object detectors based on faster RCNN

The human vision and perception system is inherently incremental where new knowledge is continually learned over time whilst existing knowledge is retained. On the other hand, deep learning networks are ill-equipped for incremental learning. When a well-trained network is adapted to new categories, its performance on the old categories will dramatically degrade. To address this problem, incremental learning methods have been explored which preserve the old knowledge of deep learning models. However, the state-of-the-art incremental object detector employs an external fixed region proposal method that increases overall computation time and reduces accuracy comparing to Region Proposal Network (RPN) based object detectors such as Faster RCNN. The purpose of this paper is to design an efficient end-to-end incremental object detector using knowledge distillation. We first evaluate and analyze the performance of the RPN-based detector with classic distillation on incremental detection tasks. Then, we introduce multi-network adaptive distillation that properly retains knowledge from the old categories when fine-tuning the model for new task. Experiments on the benchmark datasets, PASCAL VOC and COCO, demonstrate that the proposed incremental detector based on Faster RCNN is more accurate as well as being 13 times faster than the baseline detector.
DWFCAT: Dual Watermarking Framework for Industrial Image Authentication and Tamper Localization

The image data received through various sensors are of significant importance in Industry 4.0. Unfortunately, these data are highly vulnerable to various malicious attacks during its transit to the destination. Although the use of pervasive edge computing (PEC) with the Internet of Things (IoT) has solved various issues, such as latency, proximity, and real-time processing, but the security and authentication of data between the nodes is still a significant concern in PEC-based industrial-IoT scenarios. In this article, we present “DWFCAT,” a dual watermarking framework for content authentication and tamper localization for industrial images. The robust and fragile watermarks along with overhead bits related to the cover image for tamper localization are embedded in different planes of the cover image. We have used discrete cosine transform coefficients and exploited their energy compaction property for robust watermark embedding. We make use of a four-point neighborhood to predict the value of a predefined pixel and use it for embedding the fragile watermark bits in the spatial domain. Chaotic and deoxyribonucleic acid encryption is used to encrypt the robust watermark before embedding to enhance its security. The results indicate that DWFCAT can withstand a range of hybrid signal processing and geometric attacks, such as Gaussian noise, salt and pepper, joint photographic experts group (JPEG) compression, rotation, low-pass filtering, resizing, cropping, sharpening, and histogram equalization. The experimental results prove that the DWFCAT is highly efficient compared with the various state-of-the-art approaches for authentication and tamper localization of industrial images.
The Effect of Digital Transformation on Product Innovation: A Critical Review

The first part of this review highlights a brief update on Digital transformation that is considered as the survival of companies. Digital technologies bring major changes in culture, people, business processes, and business models of organizations. A wide range of organization either completed their digital transformation journey or they are in the way to achieve the digital transformation, all of these organizations are expecting appositive critical changes in the way business is conducted. This research aims to test and provide an overview of digital transformation impact on the Organization’s innovation capabilities. The researcher belief the subject of the Impact and results of post digitalization is not addressed by academia in wide scope compare to pre-digitalization. A systematic review approach is selected to test what the new research is talking about and highlighting in the innovation subject in the digital era. For this subject, different database with the related keyword used and a new model of relationship is proposed.
A survey on recent optimal techniques for securing <scp>unmanned aerial vehicles</scp> applications

Abstract Unmanned aerial vehicles (UAVs) or Drones technology has a huge potential for supporting different efficient solutions for the smart applications in our world. The applications include smart things, smart transportation, smart cities, smart healthcare, smart personal care, smart house, smart industries, and so on. Due to the sensitive applications of UAVs, the security has become a major concern, and therefore, efficient techniques are required to protect captured data from hackers and the fictitious activities from illegitimate users. Machine learning (ML) techniques play a vital role in improving UAVs' security intelligently, while blockchain is recent technology for decentralized UAVs and security. Furthermore, watermarking guarantees digital media to be authenticated, protected, and copyright. Therefore, we provide a comprehensive survey of optimal techniques, which are used for securing UAVs applications in terms of blockchain, ML, and watermarking. Furthermore, we introduce each technique with the advantages and suitably used for securing UAVs collaboration applications. This survey contributes to a better understanding of the blockchain, ML, and watermarking techniques for securing UAVs and sheds new light on challenges and opportunities on subject applications.
Detection and classification of breast cancer using logistic regression feature selection and GMDH classifier

• A deep investigation of previous works and a qualitative comparison among them. • Using a new feature selection method based on logistic regression to reduce computational cost. • Using a novel deep neural network, which can be tuned to increase accuracy or decrease computational cost. • Using three different datasets to evaluate the proposed methods. • Using a comprehensive set of evaluation metrics, including false positive rate. • Reaching a high level of accuracy for all three datasets. Breast cancer is the most common cancer among women such that the existence of a precise and reliable system for the diagnosis of benign or malignant tumors is critical. Nowadays, using the results of Fine Needle Aspiration (FNA) cytology and machine learning techniques, detection and early diagnosis of this cancer can be done with greater accuracy. In this paper, we propose a method consisting of two steps: in the first step, to eliminate the less important features, logistic regression has been used. In the second step, the Group Method Data Handling (GMDH) neural network is used for the diagnosis of benign and malignant samples. To evaluate the performance of the proposed method, three datasets WBCD, WDBC and WPBC are investigated with metrics: precision, the Area Under the ROC (AUC), true positive rate, false positive rate, accuracy and F-criteria. Simulation results show that the proposed method reaches a precision of 99.4% for WBCD, 99.6% for WDBC and a precision of 96.9% for WPBC dataset.
Detection of Insulator Defects With Improved ResNeSt and Region Proposal Network

Insulator is an important part of transmission line. Defective insulators will cause potential safety hazard to transmission lines. Image detection technology can improve the efficiency of insulator defect detection and greatly reduce the maintenance cost. However, the existing insulator defect detection technology has the disadvantages of low accuracy and long detection time. An insulator defect detection method based on improved ResNeSt and Region Proposal Network (RPN) was proposed. First, this method builds a new network based on ResNeSt. Secondly, we added the improved RPN to the improved ResNeSt for feature extraction, to better detect minor defects on insulators. Finally, we enhanced the data processing and labeled the open insulator data set. On this data set, the proposed model is tested and a large number of controlled experiments are done. The results show that the proposed network is more accurate and faster than the control group. Moreover, the proposed network has an accuracy rate of 98.38% for insulator defect detection, which can detect 12.8 pictures per second. The proposed method has good efficiency and practicability in aerial photo insulator defect detection.