Reviewerly/exHarmony huggingface dataset:
Generating High-Quality Panorama by View Synthesis Based on Optical Flow Estimation

Generating high-quality panorama is a key element in promoting the development of VR content. The panoramas generated by the traditional image stitching algorithm have some limitations, such as artifacts and irregular shapes. We consider solving this problem from the perspective of view synthesis. We propose a view synthesis approach based on optical flow to generate a high-quality omnidirectional panorama. In the first stage, we present a novel optical flow estimation algorithm to establish a dense correspondence between the overlapping areas of the left and right views. The result obtained can be approximated as the parallax of the scene. In the second stage, the reconstructed version of the left and the right views is generated by warping the pixels under the guidance of optical flow, and the alpha blending algorithm is used to synthesize the final novel view. Experimental results demonstrate that the subjective experience obtained by our approach is better than the comparison algorithm without cracks or artifacts. Besides the commonly used image quality assessment PSNR and SSIM, we also calculate MP-PSNR, which can provide accurate high-quality predictions for synthesized views. Our approach can achieve an improvement of about 1 dB in MP-PSNR and PSNR and 25% in SSIM, respectively.
Introduction to this book

Nowadays, there exists a growing amount of devices designed for the measurement of the functional state of human organs, which obtain biosignals as their outputs. Additionally, novel hardware advances are producing more precise and fast devices, and in some cases, both wearable and more affordable. This fact allows the generation of a huge quantity of data. The interest in capturing these signals is due to their large information portability capabilities which are suitable to be used for several purposes like medical diagnostics, biomedical engineering, education, sports medicine, personal safety among others. Here, both computational learning and computational intelligence offer a broad group of techniques for developing biosignals-based pattern recognition systems, which can be built from known patterns or, even, they could be useful to discover non-obvious patterns in the signals. These techniques along with the biosignal processing ones are the main core of this book whose main purview is to show the bridge between biosignal processing and pattern recognition, allowing their understanding and the developing of practical and novel applications that potentially can be created by the readers. Finally, in this chapter, we discuss the main scope of this book along with a short description of all its chapters. Especially, we show a set of interesting applications targeted to the development of tools for computer-assisted diagnostic, rehabilitation systems, biometric authentication systems, among others.
BeautyGAN+: mixed-supervised makeup transfer learning algorithm based on new PMT dataset

As one of the important ways to change the appearance of face image, makeup transfer has received more and more attention in recent years. Makeup transfer networks can translate the makeup style of a reference image to any other non-makeup one while preserving face identity, helping people find the most suitable makeup for them and get the beautified image. BeautyGAN, a makeup transfer network, has successfully demonstrated the results of unsupervised makeup transfer. However, unsupervised learning strategies lack supervised labels that can provide makeup details, leading to problems such as background discoloring, artifacts on edges and illumination disturbance in the experimental results. Constraint of BeautyGAN with makeup and non-makeup pairwise images can solve this problem, but academia lacks such fine-labeled paired dataset. To solve this problem, we propose BeautyGAN+, which adds supervised loss to BeautyGAN to train, and build up a new makeup dataset, the PMT dataset, that consists of makeup and non-makeup pairwise images. Finally, we find that combining unsupervised (migration) and supervised (details) loss can get better results through experiments. The experimental results and user study (14 participants) indicate that BeautyGAN+ significantly improves the results of makeup transfer, solving the problem of lack of paired dataset and various deviations in the transfer results.
FGTD: Face Generation from Textual Description

The majority of current text-to-image generation tasks are limited to creating images like flowers (Oxford 102 Flower), birds (CUB-200–2011), and common objects (COCO) from captions. The existing face datasets such as Labeled Faces in the Wild and MegaFace lack description while datasets like CelebA have attributes associated but do not provide feature descriptions. Thus, in this paper, we build upon an existing algorithm to create captions with the attributes provided in the CelebA dataset, which can not only generate one caption, but it can also be extended to generate N captions per image. We utilize sentence BERT to encode these descriptions into sentence embeddings. We then perform a comparative study of three models-DCGAN, SAGAN, and DFGAN, by using these sentence embeddings along with a latent noise as the inputs to the different architectures. Finally, we calculate the Inception Scores and the FID values to compare the output images across different architectures.
Equivariance and generalization in neural networks

The crucial role played by the underlying symmetries of high energy physics and lattice field theories calls for the implementation of such symmetries in the neural network architectures that are applied to the physical system under consideration. In these proceedings, we focus on the consequences of incorporating translational equivariance among the network properties, particularly in terms of performance and generalization. The benefits of equivariant networks are exemplified by studying a complex scalar field theory, on which various regression and classification tasks are examined. For a meaningful comparison, promising equivariant and non-equivariant architectures are identified by means of a systematic search. The results indicate that in most of the tasks our best equivariant architectures can perform and generalize significantly better than their non-equivariant counterparts, which applies not only to physical parameters beyond those represented in the training set, but also to different lattice sizes.
Cuckoo search optimization‐based image color and detail enhancement for contrast distorted images

Abstract Researchers have developed various contrast enhancement techniques to provide perceptually good quality images for many vision‐based applications. An exposure‐based optimally weighted gamma correction framework is proposed in this work to overcome the drawback of low contrast and unclear detail in contrast distorted image. At first, the intensities of the input image are expanded and compressed to increase the fine details and reduce the viewable artifacts. The gamma correction and weighted sum approach are then introduced to provide good contrast and natural‐looking images with optimal gamma values. Besides, the cuckoo search algorithm is employed to calculate the optimum value of the gamma function. In this work, the mean squared error is used as the objective function to maximize the information content of the enhanced image. The simulation results reveal the efficiency of the proposed method in enhancing the image for various levels of contrast distortion. Besides, quantitative and qualitative comparisons with the recently published methods demonstrate the superiority of the proposed algorithm in improving details and natural color of the contrast distorted images.
Handwritten Signature Verification Using Transfer Learning and Data Augmentation

An efficient method for the verification of handwritten signatures using the convolutional neural networks for feature extraction and supervised machine learning techniques is presented. Raw images of signatures are used to train CNN models for extracting features along with data augmentation. CNN architectures used are VGG16, Inception-v3, ResNet50 and Xception. The extracted features are classified into two classes, genuine or forgery using Euclidean distance, cosine similarity and supervised learning algorithm such as Logistic Regression, Random Forest, SVM and its variations. Data used for testing are extracted from ICDAR 2011 Signature Dataset and are organized in pairwise fashion. The database contains signatures of 69 subjects.
Bisimilarity of open terms in stream GSOS

Stream GSOS is a specification format for operations and calculi on infinite sequences. The notion of bisimilarity provides a canonical proof technique for equivalence of closed terms in such specifications. In this paper, we focus on open terms, which may contain variables, and which are equivalent whenever they denote the same stream for every possible instantiation of the variables. Our main contribution is to capture equivalence of open terms as bisimilarity on certain Mealy machines, providing a concrete proof technique. Moreover, we introduce an enhancement of this technique, called bisimulation up-to substitutions, and show how to combine it with other up-to techniques to obtain a powerful method for proving equivalence of open terms.
Research on data processing method of railway safety information based on NLP technology

In order to meet the needs of railway safety supervision, further improve the efficiency and intelligent level of railway safety information data processing, based on unstructured railway safety information text data and NLP (natural language processing) key technology, a safety information data processing system is constructed. By analyzing the application scenario of NLP technology and combining the actual needs of railway safety information data processing, the key technologies of unstructured text full-text retrieval and classification are deeply studied, and the full-text retrieval and intelligent classification of railway safety information data are realized. The establishment of the system can deeply mine the key, regular and tendentious problems hidden in the railway safety information text, improve the data processing efficiency and data utilization rate of railway safety information, and provide support and reference for safety decisionmaking and safety supervision.
Duality for powerset coalgebras

Let CABA be the category of complete and atomic boolean algebras and complete boolean homomorphisms, and let CSL be the category of complete meet-semilattices and complete meet-homomorphisms. We show that the forgetful functor from CABA to CSL has a left adjoint. This allows us to describe an endofunctor H on CABA such that the category Alg(H) of algebras for H is dually equivalent to the category Coalg(P) of coalgebras for the powerset endofunctor P on Set. As a consequence, we derive Thomason duality from Tarski duality, thus paralleling how J\'onsson-Tarski duality is derived from Stone duality.
Smart Contract Model of Sci-Tech Services Transaction Based on Consortium Blockchain

Aiming at the problems of high trust cost, low default cost, and difficult supervision in the traditional science and technology service transaction process, combined with blockchain and smart contract technology, a smart contract model of science and technology service transaction based on the consortium blockchain is proposed. According to the technical characteristics of smart contracts, the contract structure and contract rules are designed for the transaction process. The results show that the model can solve the shortcomings while realizing the science and technology service transaction process.
Progressive Text-to-Face Synthesis with Generative Adversarial Network

Text-to-Face synthesis has considerable challenges and potentials in the field of public safety. Compared with the Text-to-Image synthesis models, the text descriptions of facial features are more complex and diverse. For the text embedding, most of the previous Text-to-Face synthesis models only deal with a single sentence containing several features of face images, and the generated images are vague and lack of details. In this paper, a novel Progressive Text-to-Face synthesis with Generative Adversarial Network (PFGAN) is proposed to generate natural face images from text descriptions. Firstly, a new text encoding method Convolution-Deconvolution Word Embedding LSTM (CDWE-BLSTM) is leveraged as the text encoder, which tackles more complex sentences and improves the accuracy of text encoding. Secondly, the PFGAN is composed of multiple generators and discriminators arranged in a tree-like structure. Furthermore, face images at multiple scales are progressively generated from different branches of the tree, corresponding to the same descriptions. images at multiple scales corresponding to the same scene are generated from different branches of the tree. By comparing with three existing Text-to-Face synthesis methods, extensive experiments demonstrate that the proposed PFGAN is very competitive in the IS (Inception Scores), FID (Frechet Inception Distance) and resolution of the generated face images.
Multidimensional Time-Series Life Cycle Costs Analysis of Intelligent Substation

Comprehensive and accurate LCC (Life Cycle Costs) of intelligent substations are conducive to improving operation efficiency and reliability of power transmission. To reduce maintenance costs and enhance assets utilization, multiple dimensional time series LCC analysis of intelligent substation based on PCA-LSTM (Principal Component Analysis Long-Term Short-Term Memory) is proposed in this paper. This paper first discusses the differences between smart substations and conventional substations in LCC analysis. Because it has an advantage over RNN in handling gradient vanishing and explosion, LSTM (Long-Term Short-Term) is used instead of RNN (Recurrent Neural Network). Considering that LCC is a comprehensive study which contains large number of variables, PCA (Principal component analysis) is utilized to deal with the data dimensionality reduction preprocessing. Results show that PCA-LSTM performs better in RMSE (Root-Mean-Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), as 0.182, 0.154 and 15.84%, respectively. In our case, the computation time is about half that of the pure LSTM. And the calculation efficiency is 2.2 times compared to the original.
Adaptive Beam Search to Enhance On-device Abstractive Summarization

We receive several essential updates on our smartphones in the form of SMS, documents, voice messages, etc. that get buried beneath the clutter of content. We often do not realize the key information without going through the full content. SMS notifications sometimes help by giving an idea of what the message is about, however, they merely offer a preview of the beginning content. One way to solve this is to have a single efficient model that can adapt and summarize data from varied sources. In this paper, we tackle this issue and for the first time, propose a novel Adaptive Beam Search to improve the quality of on-device abstractive summarization that can be applied to SMS, voice messages and can be extended to documents. To the best of our knowledge, this is the first on-device abstractive summarization pipeline to be proposed that can adapt to multiple data sources addressing privacy concerns of users as compared to the majority of existing summarization systems that send data to a server. We reduce the model size by 30.9% using knowledge distillation and show that this model with a 97.6% lesser memory footprint extracts the same or more key information as compared to BERT.
Research on UAV Target Recognition Technology Based on Federated Learning

This paper analyzes the difficulty of data sharing in the current UAV target recognition model training, and designs a UAV target recognition architecture based on federated learning. And combined with the advantages of YOLOv3 to improve the YOLOv3-Tiny model. Finally, the model is trained in two different scenarios through simulation experiments. The results show that the performance of the model is improved on the basis of protecting data privacy. It provides an index reference for the next step in the in-service assessment of UAV equipment target recognition capabilities.
Educating AI Software Engineers: Challenges and Opportunities

To properly develop, test und use Artificial Intelligence (AI) applications, students and professionals need a well-defined AI software engineering (AISE) process and the appropriate tools. However, AISE, which is today mainly based on the use of deep learning (DL) neural networks, is still under development. This makes the education of AI software engineers particularly challenging, since there are no well-established methodologies, tools and practices, like in traditional Software Engineering (SE) education drawing on decades of experience and methods in all phases of software development, from requirements analysis over design and implementation to integration and testing. We analyze the main differences between traditional SE and AISE education and address challenges in AISE education. Our methodology is based on literature survey, analysis of own industry experience and statistical analysis of students works on AI applications. Our goal is to provide guidelines for an AISE process and propose a curriculum path for AISE education, which can be used to update a traditional SE curriculum. According to results of our analysis, the main challenges for the students are: Dealing with data and taking into account that algorithms change (learn) by data, selection and re-use of AI algorithms, model test, maintenance and automatizing the AISE process. We propose to address these challenges in SE curricula by teaching more statistical thinking with connections to software development, developing re-engineering capabilities, teaching a model-based AI approach and combining AI with virtual reality simulations. In the whole process, we consider an optimal division of work between humans and AI systems by explicitly including humans in the AISE loop.
Stability of the Discrete Time-Crystalline Order in Spin-Optomechanical and Open Cavity QED Systems

Discrete time crystals (DTC) have been demonstrated experimentally in several different quantum systems in the past few years. Spin couplings and cavity losses have been shown to play crucial roles for realizing DTC order in open many-body systems out of equilibrium. Recently, it has been proposed that eternal and transient DTC can be present with an open Floquet setup in the thermodynamic limit and in the deep quantum regime with few qubits, respectively. In this work, we consider the effects of spin damping and spin dephasing on the DTC order in spin-optomechanical and open cavity systems in which the spins can be all-to-all coupled. In the thermodynamic limit, it is shown that the existence of dephasing can destroy the coherence of the system and finally lead the system to its trivial steady state. Without dephasing, eternal DTC is displayed in the weak damping regime, which may be destroyed by increasing the all-to-all spin coupling or the spin damping. By contrast, the all-to-all coupling is constructive to the DTC in the moderate damping regime. We also focus on a model which can be experimentally realized by a suspended hexagonal boron nitride (hBN) membrane with a few spin color centers under microwave drive and Floquet magnetic field. Signatures of transient DTC behavior are demonstrated in both weak and moderate dissipation regimes without spin dephasing. Relevant experimental parameters are also discussed for realizing transient DTC order in such an hBN optomechanical system.
A Double Fittings Based Total Variation Model for Image Denoising

In this paper, weighted image gradients based fitting is introduced into the proposed model for image denoising, which is called double fittings based total variation (DFTV) model. The weighted gradients are designed to maintain image structures during the denoising process. The definition of the weight function makes it better to enhance edges of images and maintain structures. For solving the proposed model, alternating direction method of multipliers (ADMM) is explored. In order to show the effectiveness and efficiency of the proposed model, both of the visual results and quantity evaluation are provided for compared experiments.
The Effect of Oral Practice via Chatbot on Students’ Oral English Accuracy

Traditional EFL (English as a foreign language) learners could find it difficult to practice speaking after class and obtain effective feedback. With the development of technology, chatbot could be one solution to tackle this dilemma. Employing the chatbot Microsoft Xiaoying as the main tool in a teaching experiment, this study aimed at figuring out whether using chatbot in oral English practice would exert a positive effect on learners’ oral English performance in grammar and pronunciation aspects. By analyzing the collected data, the results showed that: daily practice with chatbot could be suggested as part of oral English practice as it was proved to be effective on promoting learners’ oral English accuracy.
Blockchain-Enabled Electronic Medical Record With Hyperledger Composer

Medical records are files that contain records and documents about patients’ identities, examinations, treatments and actions, and other services while the patients are receiving health services. Medical records are personal data that can only be accessed by authorized personnel. Some problems occur, such as the difficulty in managing files for grouping patients’ medical record data based on specific categories such as the documentation year or patients’ biodata. A large storage area is needed to hold medical record data and the absence of backup data in data storage. Blockchain-based solutions help overcome this problem. Hyperledger Composer is an open and extensive development framework for blockchain systems. Hyperledger Composer allows us to quickly model business networks and integrate them into an application or system that we have created easy and save time. Hyperledger consists of 3 main files, model file, script file, and access control file, which have coding flexibility and are easier to understand. Hyperledger Composer uses the JavaScript language for coding and has a client library for node.js. In this study, we propose a medical record sharing model that combines blockchain with the Hyperledger Composer and conducts a performance evaluation of the implementation. The result shows the increase of transactions during testing in which the system could store data into blocks to record medical transactions.
The Effects of Touch in the Geriatric Population

The current study investigated the effects of touch in a population of nursing home residents. Thirty individuals were randomly chosen to participate in a craft session. Sixteen participants were touched while fourteen were not touched. Participants completed a questionnaire immediately after the craft session. Responses to the questionnaire were statistically analyzed by means of chi square. Results found that tactile stimulation positively impacts attitude and motivation in the elderly. The touch group participants indicated strong, positive responses regarding attitude toward the task and the instructor. The touch group showed greater willingness to work for the instructor and complete a task. Touching is a positive influence on the geriatric person’s attitudes and behaviors toward both a task and therapist. More sensitive instruments are needed to determine the relationship between touch and mood since it was not significant in this study.