Reviewerly/exHarmony huggingface dataset:
Research on Blockchain Smart Contract Optimization for Power Business Scenarios

In the process of promoting the transformation and development of power grid, blockchain technology fits the development needs of energy Internet, and the combination of blockchain and electric power business is conducive to promoting the transformation of power grid production management and operation mode to the direction of intelligence and networking, which has a positive effect on the restructuring of electric power industry. However, due to the wide coverage of electric power industry, many kinds of terminal devices, large quantity, rich business scenarios and high requirements for real-time security, there are still many technologies that blockchain needs to break through in energy and electric power application scenarios. Smart contract, as one of the key technologies of blockchain, has low performance, inflexible transplantation, low execution efficiency and imperfect application mode in the power industry, which needs to be further optimized in conjunction with the requirements of power business scenarios. This paper proposes the customized development of smart contracts for electric power business through smart contract performance optimization technology, independent non-conflicting contract parallel execution technology, and smart contract cross-platform deployment technology, so as to realize process automation and intelligence, and improve business process efficiency under the premise of ensuring business security compliance.
Handwriting Image Recognition Based on a GAN Model

Big data is a new driver of world economic and societal changes. However, our research is based on using the GAN to solve the real application Deepfakes, which are created by artificial intelligence programmed to replace one person’s likeness with another in recorded video, photo, pr audio. To tackle this issue, this paper proposed a GAN model based handwriting image recognition which can be generated new celebrities after showing it pictures of many real celebrities. Typically, Deepfakes are made using a neural network-based architecture, the most capable of which utilizes generative adversarial networks (GANs). This involves placing two neural networks in contest with one another: the first new data from the same statistical distribution as the training set, and the second attempts to discriminate data produced by the first from data in the original training set. The experimental results demonstrated that using the GAN model to solve Deepfakes can give individuals new tools for self-expression and integration in the online world.
Kernel Factorisation Machines

This paper explores a generalisation of factorisation machines via kernels, which we call Kernel Factorisation Machines (“KFM”). It is well-known that functions in reproducing kernel Hilbert spaces can be understood as a linear combination of features in very high-dimensional (or infinite-dimensional) spaces while being computed in a finite-dimensional space, thanks to the representer theorem. Simultaneously, it has been shown recently that the dot product operation was a key component behind the success of a number of recommender systems, while the recent literature has been preoccupied with enriching factorisation machines. There is thus a need for a framework able to interpolate between factorisation machines that tend to outperform other techniques on sparse datasets and more advanced models that perform well on large and dense datasets. One of the drawbacks of kernel methods is their high dimensionality when the number of observations is large, which is typical of recommender systems. It is thus extremely important to be able to reduce the dimensionality, which we do in two different ways: first, we find a representation of the input features in a lower-dimensional space, and, second, we consider inducing points, i.e., surrogate inputs that are optimised upon training to avoid building (kernel) interactions between each pair of observations in the dataset. In short, we propose a method that adapts kernels to the set up of high-dimensional and potentially sparse datasets. To illustrate our approach, we test it on four well-known datasets and benchmark its results against most available models. While comparisons are difficult and should be interpreted carefully, KFM is able to perform well and obtains the best performance overall. Our methodology is not limited to recommender systems and can be applied to other settings, which we illustrate on a heart disease classification task.
Style-transfer Autoencoder for Efficient Deep Voice Conversion

We consider the problem of voice cloning, which is desirable in many film-related industries, and developed a new modification of the AutoVC state-of-the-art model in the task of voice conversion. We studied the replacement of recurrent modules with convolutional layers while maintaining the quality of the original model. The result of our work showed the speed improvement on longer voice tracks and faster training with the tiniest deterioration in sound quality, as evidenced by the reconstitution loss and Mel-cepstral distortion.
A novel framework for online transaction fraud detection system based on deep neural network

Since the introduction of online payment systems, people have started doing online transactions which has also led to the rise of fraudulent transactions causing loss of money to the users and created distrust in the usage of online payment systems. Hence, fraud detection systems are the need of the hour. Among the transactions occurring on daily basis, frauds are less in number as compared to the genuine transactions, so class imbalance naturally exists in fraud detection systems. In this research work, a novel framework for online transaction fraud detection system based on Deep Neural Network (DNN) has been proposed by utilizing algorithm-level method capable to detect frauds from imbalanced data and to maintain the overall performance of the model as well. The proposed system optimizes the decision threshold by utilizing the validation data efficiently for deciding whether an incoming transaction is a Fraud or not. For demonstration of the efficiency of our proposed system, three class imbalanced publicly available datasets have been used. Proposed system has shown better performance than data-level method. The results produced by the proposed fraud detection system have also been compared with existing machine learning techniques-based fraud detection systems. The experimental results show that the deep learning-based fraud detection system is more efficient for detecting frauds from imbalanced datasets having large number of input features as compared to the machine learning models.
Realtime Remote Sensing Image Publishing System Based on COG Technology

In order to solve the problem that the image pre-slice method cannot meet the needs of disaster emergency management and instant release, this paper designs a method for remote sensing image segmentation optimization and rapid release based on Cloud Optimized GeoTIFF (COG) technology and establishes an image real-time release system to achieve multi-band remote sensing image processing, rapid release in the background and front-end combination application. The experiment shows that the rapid release technology of remote sensing image based on COG technology shortens the access time of image data through the efficient data stream transmission, and realizes the large-scale rapid release, which can meet the needs of emergency release of remote sensing image.
Generative Adversarial Network Design for Data Augmentation for Copro-parasite Images

Classifier design highly depends on the quality and quantity of the data set. This issue is a problem in areas like medical image classification because acquiring these images may be expensive, time consuming, and require an expert. Thus, data augmentation, DA, may be important for medical images analysis. This paper presents three main contributions. A complete formalization of the GAN model, usually not found in the literature. The design of a GAN model for data augmentation employing a reduced set of copro-parasite images. And the design of a two-class deep neural network classifiers, parasite/not parasite, to demonstrate that the performance of a classifier using the DA proposed in this work can achieve a performance of 99.25% which is greater than the classifier designed only with real images.
Hybrid Collaborative Filtering Algorithm Based on Sparse Rating Matrix and User Preference

This study presents a hybrid collaborative filtering recommendation algorithm for sparse data (HCFDS) to increase the recommendation impact by addressing the problem of data sparsity in standard collaborative filtering methods. To begin, the similarity calculation divergence is evident in a data sparse environment due to the difference in user scoring standards and the rise in weight of the same score in the overall score. The user similarity algorithm IU-CS and item similarity algorithm II-CS are suggested in this work by incorporating the score difference threshold and the same score penalty factor, in order to address the deviation of similarity computation caused by the excessive dilation. Second, this work offers a filling optimization technique for score prediction to address the issue of missing score matrix data. The II-CS algorithm presented in this work is used to forecast the missing items in the scoring matrix first, and then, the user’s preference score in the item category dimension is utilized to correct the score prediction value and fill the matrix. Finally, the IU-CS method presented in this work is used in this study to provide recommendations on the filled score matrix. Experiments indicate that, when compared to the preoptimization method and other algorithms, the optimized algorithm successfully solves the problem of data sparsity and the recommendation accuracy is considerably increased.
HGposeGUCN: A Lightweight Network for 3D Hand Pose Estimation from a Single RGB Image

3D hand pose estimation from a single RGB image is a challenging task which takes only one RGB image as input and predicts all the hand joints&#x2019; 3D positions as output. Some previous works use two or more stages, while some other adopt some kind of model based methods to observe 3D joints positions. These previous works got relatively precise predictions, while they are always short of model weight or limited by the chosen model. In this paper, we propose a lightweight but efficient network named HGposeGUCN-Net for this problem. In our proposed HGposeGUCN-Net, we use a lightweight 2-stack hourglass network to obtain the image feature maps and 21 hand joints heatmaps first. The feature maps and initial 2D positions are fed into a Res2d module to observe the residual 2D coordination which are adopted to update the initial 2D positions got from heatmaps and bring the network more generalization ability. Finally, the refined 2D coordinates are sent into our Hand Graph U-net which can converse the 2D coordinates to 3D. The whole network can be trained end-to-end and on the published STB and RHD datasets, experiments shows that the proposed net structure has better performance.
Permutation binomials of the form x(x−1 + a) over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

We present several existence and nonexistence results for permutation binomials of the form xr(xq−1+a), where e≥2 and a∈Fqe⁎. As a consequence, we obtain a complete characterization of such permutation binomials over Fq2, Fq3, Fq4, Fp5, and Fp6, where p is an odd prime.
TCTG:A Controllable Text Generation Method Using Text to Control Text Generation

Text generation is an important research field in natural language processing, and current pre-trained models can generate text with very high readability and fluency. However, the content of the generated text cannot be controlled, and it is easy to generate text with weak relevance. Therefore, a controllable text generation method is urgently needed. Traditional controllable text generation methods often have the disadvantages of high training costs and difficult adjustments. This paper presents a method to control text generation by text. By extracting keywords from the data set built by yourself, the sequence of keywords is organized into text information that is controlled to be generated. Insert a module into the language model, use this module to make the initial text interact with the control text, and then perform subsequent generation. And two different training tasks are designed so that the model learns how to control text generation by controlling text information while maintaining a high degree of fluency and diversity of text generation. Experiments have shown that this method can better complete the task of controlling text generation through text, without greatly reducing the fluency and diversity of the generated text, and has a better control effect.
Study of Quantum Annealing and the type of related applications

Most of the modern day computations are considered as an NP-hard like solving a puzzle or traveling salesman problem. Over the decades, engineers are exploring for many approaches of solution, out of which Quantum Annealing has depicted the efficacy. In this paper, we do a review study of Quantum Annealing (i.e., an approach to find the global mini-mum of a particular function that can provide better heuristics for the NP-hard optimization problem) and discussed briefly about quantum computers. Not only that, we also showcase few applications of quantum annealing which include scheduling problem, lung cancer detection, coherence in quantum annealer, and clustering.
ReSORT: an ID-recovery multi-face tracking method for surveillance cameras

As an improvement over the standard simple online real-time tracking (SORT) method, DeepSORT introduces a cascade matching mechanism to track objects during a certain period of occlusion, effectively reducing the number of identity (ID) switches. However, DeepSORT lacks the capability of lost-identities recovery, which enables robustness and performance in face recognition systems. To address the issue, we propose a novel multi-face tracking method, named ReSORT, that can recover lost identities. Our method removes the cascade matching block in DeepSORT and extends a similarity matching (SM) block after the Kalman filter to assign uncertain tracks to their probable tracking IDs. Such arrangement significantly reduces the processing time while maintaining the longevity of tracking IDs. The SM block functions by storing existing facial features and comparing the similarity between the new and the existing facial features, enabling ReSORT to recover the lost IDs or IDs from other cameras. To benchmark the ID-recovery ability, we introduce three new metrics, calling IDnew, TIDRate, and TReRate. We also produce face tracking annotations for three public surveillance camera datasets, i.e., LAB, MSU-AVIS, and ChokePoint. Extensive experiments conducted on the three datasets with various resolutions and frame-rates settings demonstrate the superiority of ReSORT over DeepSORT, i.e. reducing the identity switches by average 36.38%, and the processing time by 5.19 times. Source code and annotations of all three datasets are available at https://github.com/tantm97/ReSORT.
Performance Analysis of Tag Embedded Based Message Authentication Scheme

Tag-embedded has been proposed in the literature as an efficient message authentication scheme due to low spectrum, power and time resources required as compared to the conventional tag-appending schemes. This work addresses the mathematical analysis of the performance of the tag-embedded message authentication scheme, which has never been presented in the literature. The authentication fail probability, as a performance evaluation metric, has been derived in closed form expressions based on two different detection methods, namely, joint detection and successive detection. The derived formulas are validated through simulation results where exact matching is noted.
Facial Recognition Application with Hyperparameter Optimisation

This chapter explores optimisation techniques for a facial recognition application using an Artificial Neural Network (ANN). Facial recognition applications utilise a certain set of hyperparameters to achieve the best recognition accuracy depending on the data set. There are several approaches to find the best set of these hyperparameters. They can be put into two categories: Manual optimisation and automated optimisation. By experimenting with both approaches in order to find the best, certain patterns could be observed. Manual optimisation achieved the highest recognition accuracy 96.6% with a specific set of parameters used in the ANN. Using automated optimisation algorithms, the accuracy could be improved. The experiments were run in Google Colab using certain classifiers, such as Grid Search and Random Search. The optimisation algorithms that were used demonstrated an increase of the accuracy of almost 2% and a decrease of the prediction error. Based on the findings it can be concluded that the automated optimisation strategies are more efficient and provide a better set of parameters to use within an ANN model, so that an improved prediction accuracy can be achieved. Using the automated optimisation combined with manual approximation to find better values turned out to be the best solution.
Designing Educational Trajectories for Generation Z: Identifying Cognitive Factors

This article is based on the hypothesis that Generation Z’s propensity for undivided perception of digital and physical reality (phygital reality) and their fairly easy absorption of new learning formats, in particular blended learning, are interrelated. To prove the existence of this relationship, the authors, based on the methodology of social constructivism and interpretivism, put forward several hypotheses and conduct an empirical study. For a detailed analysis of the research topic the authors conduct three questionnaire polls among university students in St. Petersburg. The first survey is related to identifying the features of socialization of “digital natives”, the second - to identify the cognitive inclinations of Generation Z, the third - the choice of type of education (traditional/classroom, blended or distance learning). The results of the study confirm the main hypotheses of the study, which are that the characteristics of Generation Z make them maximally adapted to blended learning and that the propensity to blended learning is due to a number of factors, such as satisfaction with the content of disciplines, propensity for self-development, satisfaction with the organization of the educational process, work in the specialty after graduation, group work in classes and the use of sources recommended by the teacher. Factor and regression analysis conducted during the study confirms the data obtained.
Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors

Globally, the COVID-19 pandemic has caused dev-astation and continues to do so even a year after its first outbreak. Behavioral modifications could help to mitigate a mechanism for acquiring and spreading illnesses. Using wearable devices such as smartwatches to recognize face contact has the opportunity to decrease face touching and, therefore, the spread of respiratory disease through fomite transmission. The purpose of this paper is to demonstrate how we can utilize accelerometer data from wristwatch sensors to identify face touching actions using deep learning techniques. We proposed the BiGRU deep learning model for the high-performance recognition of hand-to-face actions. The Face Touching dataset is used as a benchmark for evaluating the recognition accuracy of deep learning networks, including our network model. The experimental findings indicate that the BiGRU surpasses other baseline deep learning models regarding accuracy (98.56%) and F1-score (98.56%).
Deep hashing with self-supervised asymmetric semantic excavation and margin-scalable constraint

Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for images associated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing methods are based upon an oversimplified similarity assignment(i.e., 0 for instance pairs sharing no label, 1 for instance pairs sharing at least 1 label), 2) the exploration in multi-semantic relevance are insufficient or even neglected in many of the existing methods. These problems significantly limit the discrimination of generated hash codes. In this paper, we propose a novel Deep Hashing with Self-Supervised Asymmetric Semantic Excavation and Margin-Scalable Constraint(SADH) approach to cope with these problems. SADH implements a self-supervised network to sufficiently preserve semantic information in a semantic feature dictionary and a semantic code dictionary for the semantics of the given dataset, which efficiently and precisely guides a feature learning network to preserve multi-label semantic information using an asymmetric learning strategy. By further exploiting semantic dictionaries, a new margin-scalable constraint is employed for both precise similarity searching and robust hash code generation. Extensive empirical research on four popular benchmarks validates the proposed method and shows it outperforms several state-of-the-art approaches. The source codes URL of our SADH is: http://github.com/SWU-CS-MediaLab/SADH.
Fake audio detection using Hierarchical Representations Learning and Spectrogram Features

Fake voice of the synthetic speech means a sound generated by using machine or using other techniques. Replay attack is one of the types of fake voice in which a person can use recording of the target speaker to conduct replay attack. All the smart phones and audio recordings devices offer high quality of recording and playback, so they can be used in replay attacks. In this system we have used two types of method to extract the features from the audio, one method is conventional features for this we have used Mel-frequency Cepstral Coefficients (MFCC), and the other method is Convolutional Neural Network (CNN) first we converted audio signal into spectrogram and then we extract features from the last layer of CNN model. We combined all these features and passed it to long short term memory (LSTM). The LSTM model is capable to acquire knowledge from these features and used one dense layer to give the output. For the experiments we used ASVSpoof2019 dataset, this dataset is distributed into two parts, Logical access (LA) and physical access (PA). Physical access used all the replayed audios so we use (PA). The experimental results show that this system gives high performance of 0.046 equal error rate with 98% accuracy on ASVSpoof2019 dataset.