Reviewerly/exHarmony huggingface dataset:
Matching Faces and Attributes Between the Artistic and the Real Domain: the PersonArt Approach

In this article, we present an approach for retrieving similar faces between the artistic and the real domain. The application we refer to is an interactive exhibition inside a museum, in which a visitor can take a photo of himself and search for a lookalike in the collection of paintings. The task requires not only to identify faces but also to extract discriminative features from artistic and photo-realistic images, tackling a significant domain shift. Our method integrates feature extraction networks which account for the aesthetic similarity of two faces and their correspondences in terms of semantic attributes. Also, it addresses the domain shift between realistic images and paintings by translating photo-realistic images into the artistic domain. Noticeably, by exploiting the same technique, our model does not need to rely on annotated data in the artistic domain. Experimental results are conducted on different paired datasets to show the effectiveness of the proposed solution in terms of identity and attribute preservation. The approach is also evaluated on unpaired settings and in combination with an interactive relevance feedback strategy. Finally, we show how the proposed algorithm has been implemented in a real showcase at the Gallerie Estensi museum in Italy, with the participation of more than 1,100 visitors in just three days.
Extraction of keywords in the abstract of scientifics papers using BERT

Abstract Keywords are used to facilitate searches, inaccurate keyword determination, which can result in searches for journals that do not match the intended title. To overcome this, it is necessary to implement a keyword search in the abstract using deep learning methods from a journal to produce the right keywords that will distinguish journals from one another. The purpose of this study is to implement a keyword search system in abstracts using the Bidirectional Encoder Representations from Transformers (BERT) method as an abstract word search system that produces keyword answers that have context according to the desired keyword results. The results of this study get optimal results in the epoch 500, and from these results using a dataset with of 444 training data and 10 test data for testing.
Solving Organic Chemistry Problems Described with Text and Chemical Structure Figure

Problem solving is a hot research area that develops algorithms for solving exercise problems like people. Hence, such algorithms are the crucial technology of developing digital twins such as digital teacher, digital tutor, and digital student. Solving chemistry problems is one of branch in problem solving. This paper proposes an algorithm for solving the organic chemistry problems described with text and chemical structure figure. The algorithm firstly recognizes chemical structure figure using an OSRA method, and then acquires chemical relation from problem text based on S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> method. Finally, it uses a knowledge conversion and matching procedure to solve the problem based on the knowledge obtained in first two steps. In the experiment, this work collects 373 organic chemistry problems described with text and chemical structure figure from People's Education Press (PEP) version exercise books. The experiment results show that the proposed algorithm in this paper is able to solve 86.33% of the problems.
Adversarial Deep Learning Models With Multiple Adversaries

Adversarial machine learning calculations handle adversarial instance age, producing bogus data information with the ability to fool any machine learning model. As the word implies, “foe” refers to a rival, whereas “rival” refers to a foe. In order to strengthen the machine learning models, this section discusses about the weakness of machine learning models and how effectively the misinterpretation occurs during the learning cycle. As definite as it is, existing methods such as creating adversarial models and devising powerful ML computations, frequently ignore semantics and the general skeleton including ML section. This research work develops an adversarial learning calculation by considering the coordinated portrayal by considering all the characteristics and Convolutional Neural Networks (CNN) explicitly. Figuring will most likely express minimal adjustments via data transport represented over positive and negative class markings, as well as a specific subsequent data flow misclassified by CNN. The final results recommend a certain game theory and formative figuring, which obtain incredible favored ensuring about significant learning models against the execution of shortcomings, which are reproduced as attack circumstances against various adversaries.
Lelantus Spark: Secure and Flexible Private Transactions

We propose a modification to the Lelantus private transaction protocol to provide recipient privacy, improved security, and additional usability features. Our decentralized anonymous payment (DAP) construction, Spark, enables non-interactive one-time addressing to hide recipient addresses in transactions. The modified address format permits flexibility in transaction visibility. Address owners can securely provide third parties with opt-in visibility into incoming transactions or all transactions associated to the address; this functionality allows for offloading chain scanning and balance computation without delegating spend authority. It is also possible to delegate expensive proving operations without compromising spend authority when generating transactions. Further, the design is compatible with straightforward linear multisignature operations to allow mutually non-trusting parties to cooperatively receive and generate transactions associated to a multisignature address. We prove that Spark satisfies formal DAP security properties of balance, non-malleability, and ledger indistinguishability.
Exchange Rate Forecasting with Twitter Sentiment Analysis Technology

This paper attempts to introduce sentiment analysis technology into the task of exchange rate prediction, and studies the impact of sentiment factors on short-term fluctuation of exchange rate based on the theoretical support of behavioral finance. Firstly, the relevant network social media data generated during the new round of trade war between China and the United States were obtained, and the sentiment analysis technology was used to quantify the data to form the sentiment score sequence. Then the nonlinear model LSTM based on machine learning is used to model and predict the high-frequency exchange rate sequence. The empirical results show that the accuracy of the exchange rate forecasting model with the sentiment factors of public opinion is improved, which provides a new idea for the exchange rate forecasting method based on the technical analysis method.
Eye Movement Event Detection Based onPath Signature

Eye movement event detection is a demanding technique in cognitive behavior analysis and HCI. Since an eye movement trajectory is a natural path, we try to introduce path signature (PS) to better explore eye movement events; PS is a feature that can highly summarize path information. For this, a multi-input network (MINN) combining 1D-CNN and bidirectional long short-term memory (BiLSTM) is constructed to classify gaze samples as fixation, saccade, smooth pursuit or noise. MINN requires two inputs of local features and global features respectively. The local features include the speed and direction of the gaze trajectory and the global features are the PS’s of the gaze trajectory. Experiments on GazeCom, the biggest eye movement event detection dataset, show that our approach with PS outperforms the state-of-the-art methods that do not use PS.
YOLOv3-mobile for Real-time Pedestrian Detection on Embedded GPU

Pedestrian detection is one of the challenging tasks in the technology of autonomous driving. Recently, the object detection network of you only look once (YOLO), especially YOLOv3 and YOLOv3-tiny have demonstrated a high level of pedestrian detection performance on a powerful GPU card such as Pascal Titan X. However, it is still challenging to use YOLOv3 and YOLOv3-tiny on embedded GPU system due to their large network size. In this paper, we present a lightweight YOLOv3-mobile network by refining the architecture of YOLOv3-tiny to improve its pedestrian detection efficiency on embedded GPUs such as Nvidia Jetson TX1. The experimental results showed that the proposed framework can accelerate the frame rate per second (FPS) from 18 FPS to 37 FPS with comparable mean average precision (mAP).
A Novel Steganography Scheme for Color Image Based on HLS Translation

Abstract In recent years, steganography has attracted extensive attention, but most of steganography schemes are aimed at grayscale images. Therefore, a novel steganography scheme for color image based on HLS translation is proposed in this paper. Firstly, the color cover image is decomposed from RGB three channels to HLS three channels. Then the image of H channel is selected as the cover image for embedding secret information due to its texture feature of original image. The universal distortion function UNIWARD is utilized to embed the secret information into the image of H channel. Finally, the steg image transmitted in common channel is obtained by inverse HLS transform using the single channel image embedding the secret information. The extensive experimental results indicate that our scheme has high security and can also resist the detection of steganalysis schemes based on CNN.
Achieving Lightweight Image Steganalysis with Content-Adaptive in Spatial Domain

Steganography is a technology that modifies complex regions of digital images to embed secret messages for the purpose of covert communication, while steganalysis is to detect whether secret messages are hidden in a digital image or not. However, the emergence of content-adaptive steganography such as S-UNIWARD prioritizes the embedding of secret messages in areas of textural complexity of images by embedding probability map guidelines. Such ways dramatically improve the security of steganography and impede the process of image steganalysis. Most of the existing steganalysis studies are aimed at improving the network structure to enhance the detection performance of the model, without considering the generation of embedding probability maps which can guide the training of the network model, eliminate some unnecessary distractions, shorten the training time and improve the final detection accuracy simultaneously. Therefore, how to obtain embedded probability maps and use them effectively becomes an important challenge in the field of steganalysis. In this paper, to solve the above problem we propose a content-adaptive lightweight network to implement an embedded probability map combined with steganalysis. Our steganalysis model includes two parts: embedding probability maps generation module and features processing module, which is trained Separately. The generation module adopts the basic framework and modifies the model to make it more suitable for steganography. In the features processing module, we adopt a pseudo-siamese architecture to manipulate two different input images. Next, we use the attention mechanism to assign weights to channel parameters. Finally, We use a simple data augmentation method to enhance our training dataset and improve final performance. Because our proposed model incorporates embedded probability maps as guidelines, experiments show that our proposed CNet has faster convergence speed, higher detection accuracy, and better robustness compared to networks such as Yedroudj-Net, SRNet, and Zhu-Net in the spatial domain.
CDC-Wasserstein generated adversarial network for locally occluded face image recognition

In the practical application of wisdom education classroom teaching, students' faces may be blocked due to various factors (such as clothing, environment, lighting), resulting in low accuracy and low robustness of face recognition. To solve this problem, we introduce a new image restoration and recognition method is based on WGAN (Wasserstein Generated Adversarial Networks). When using the deep convolution generates adversarial networks for unsupervised training, we add the conditional category label c to guide the generator to generate sample data. At the same time, a double discriminant mechanism is introduced to enhance the feature extraction ability of the model. The local discriminant can better repair the details of the occlusion area, and the global discriminant is responsible for judging the authenticity and overall visual coherence of the restored image. Part of the convolution layer of the global discriminator is used to construct a VGG-like structure network as the feature extractor, which is composed of the full connection layer and the sigmoid layer. It can accelerate the convergence speed of the network and improve the robustness of the method. In order to improve the training stability and reduce overfitting, L2 regularization is added on the basis of context loss to enhance the continuity of local and whole images, and improve the quality of restoration and recognition accuracy. We used the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and recognition accuracy as evaluation indexes, and achieved good results on CelebA and CelebA-HQ datasets.
Contracting managed security service: Double moral hazard and risk interdependency

The problem of double moral hazard seriously affects the efficiency of information security outsourcing. The interdependency risk of information security between managed security service providers (MSSPs) and client firms further complicates the double moral hazard problem. In the loss-based contract, both positive and negative risk interdependencies make outsourcing more inefficient in most instances. To solve the problem, a relational contract is proposed. We find that this relational contract leads to a greater social welfare with increase of discount factor, and the double moral hazard problem can be solved within the range that the discount factor is high. Furthermore, both positive and negative risk interdependencies can help relational contract to eliminate double moral hazard within a larger discount range. Finally, as some MSSPs’ efforts are considered to be verifiable, we find that by specifying thresholds in a relational contract, the benefits of an MSSP’s default can be limited, thereby ensuring that the relational contract achieves social optimal outcomes in more general cases.
Adopting AI in the Banking Sector – The Wealth Management Perspective

While interest in understanding the impact of Artificial Intelligence (AI) in the real world has been growing, there is limited research on what digital transformation means in the banking sector. The present paper aims to address this gap in the literature by means of a field study based on 11 interviews with wealth managers from across front, middle and back offices in a large Swiss bank. Our analyses suggest bank employees have relatively positive attitudes toward the adoption of new technologies. According to the research, the top three advantages for adopting AI in wealth management are: efficiency, enhanced clients’ experience and better customer insight. Yet, AI also brings new challenges to banking. Obstacles identified include process complexity, greater maintenance effort, and increased regulatory requirements. Still, AI is expected to change the banking model to an integrated, hybrid and leaner operation with digital processes that offer front-to-back solutions. With respect to the future of the bank workforce, hiring tech-savvy talent will be crucial. AI will be able to free bank employees from repetitive jobs for more interesting and creative tasks.
Word Sense Disambiguation Method Based on Graph Model and Word Vector

Abstract As a basic research of natural language processing, word sense disambiguation (WSD) has a very important influence on machine translation, classification tasks, retrieval tasks, etc. In order to solve the problem that existing disambiguation methods rely too much on knowledge base, a disambiguation method combining graph model and word vector is proposed in this paper. Firstly, in this method, the text data are preprocessed by removing punctuation marks and segmenting words. Secondly, the dependency relation is extracted by using the tool of PYLTP for dependency parsing, the words of dependency parent node are matched and the undirected graph is built, and the context knowledge of ambiguous words is selected according to the minimum path length set by the graph model. Finally, Word2Vec model is used to train Wikipedia corpus to obtain word vectors containing ambiguous words and contextual knowledge, and calculate the cross similarity of the word vector, the high mean similarity is regarded as the correct meaning of the ambiguous word. The effectiveness of the proposed method is verified by comparative experiments on the SEVAL-2007 Task# 5 dataset.
Integration Of Natural Science And Humanities Knowledge In Social Workers Training

At present, when the system of socio-economic and cultural interactions becomes ever more complex, specialists of a new type are needed who have accomplished a comprehensive basic training and possess fundamental knowledge. This calls for creation of such a vocational training system which will produce not the pragmatic narrow-minded specialists, but those who have methodologically invariant knowledge which forms an integral scientific outlook on the world and are conducive to personal development and adaptation of the individual to the fast-changing modern life. The main characteristic of the fundamental training is its comprehensive character which can be achieved by overcoming the fragmented contents of educational programs, by introduction of inter-disciplinary courses, by humanization of technical education and introduction of courses in natural science into humanities programs. The authors have developed the contents of an integrated course “The concept of modern natural science” which aims at formation of a scientific outlook in in future social workers. The anticipated result of taking this course should be creation of a scientific view of the world, integrated knowledge about the Nature and humans’ place in it, the system of humanistic values.
Practical Thinking on Neural Network Phishing Website Detection Research Based on Decision Tree and Optimal Feature Selection

Abstract This article analyzes the detection methods of neural network phishing websites. The research content of this paper includes naive Bayes method, decision tree method, support vector machine method, neural network technology. The author combines the key points of phishing website detection based on decision tree and optimal feature selection to study such as URL feature and HTML feature analysis, website application feature analysis, K-Medoids cluster analysis, feature set screening. The author uses simulation experiments to complete the website performance check. The purpose of this article is to optimize the performance of phishing website detection and improve the security of the website’s operating environment.
A Deep Learning Survey on Content Detection from Images Powered by Computer Vision Framework

In online services, images have a great impact on a customer's move from knowing the product, accessing and then buying it. It is not a one-to-one process of buying but there is involvement of third-party sellers where the content has to be monitored. Though there are cautions and guidelines, irrelevant images pop up, the customer may get a wrong notion. This paper presents an ideology of a computer vision driven image detection system. This paper also deals about deep learning to real-world product images. The proposed research work has shown a number of technical goals such as lack of training data, class imbalance, fine-grained class definitions. The system combines image classification and object detection techniques with budgeted crowdsourcing to develop a solution for huge, varied and continuous evolving product catalogue. The final section deals with implementation.
Anti-focal Loss for Speech Recognition on Small-Scale Datasets

Deep learning models with encoder-decoder architecture become popular in automatic speech recognition systems, due to their success in sequential prediction tasks. Recently, the conformer model has greatly improved the accuracy of speech recognition. However, similar to transformer models, its training relies on a large amount of data. This paper explores an efficient few-shot learning strategy. Specifically, a spec-augment approach is proposed to augment the speech dataset, then a novel loss function, anti-focal loss, is introduced to encourage fast convergence in a small-scale, unbalanced data setting. Extensive experiments on aishell-l dataset show that our model outperforms state-of-the-art approaches under limited support data, in terms of convergence speed and generalization ability.
Development of a Long-distance Multimedia Teaching Platform and Data Resource Library Based on the Multi-site Model

This article first analyzes the current development status of the multimedia distance education system, so as to summarize the two focal issues that this multimedia distance education system needs to solve: the production of distance multimedia teaching and the development of data resource library. This article analyzes the differences, advantages and disadvantages, and application scenarios of the single-site and multi-site models. Finally, this paper develops a new remote multimedia teaching platform and data resource library based on the multi-site model.
Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry

Persian poetry has consistently expressed its philosophy, wisdom, speech, and rationale based on its couplets, making it an enigmatic language on its own to both native and non-native speakers. Nevertheless, the noticeable gap between Persian prose and poems has left the two pieces of literature mediumless. Having curated a parallel corpus of prose and their equivalent poems, we introduce a novel Neural Machine Translation approach for translating prose to ancient Persian poetry using transformer-based language models in an exceptionally low-resource setting. Translating input prose into ancient Persian poetry presents two primary challenges: In addition to being reasonable in conveying the same context as the input prose, the translation must also satisfy poetic standards. Hence, we designed our method consisting of three stages. First, we trained a transformer model from scratch to obtain an initial translations of the input prose. Next, we designed a set of heuristics to leverage contextually rich initial translations and produced a poetic masked template. In the last stage, we pretrained different variations of BERT on a poetry corpus to use the masked language modelling technique to obtain final translations. During the evaluation process, we considered both automatic and human assessment. The final results demonstrate the eligibility and creativity of our novel heuristically aided approach among Literature professionals and non-professionals in generating novel Persian poems.