Reviewerly/exHarmony huggingface dataset:
MVQAS

This paper demonstrates a medical visual question answering (VQA) system to address three challenges: 1) medical VQA often lacks large-scale labeled training data which requires huge efforts to build; 2) it is costly to implement and thoroughly compare medical VQA models on self-created datasets; 3) applying general VQA models to the medical domain by transfer learning is challenging due to various visual concepts between general images and medical images. Our system has three main components: data generation, model library, and model practice. To address the first challenge, we first allow users to upload self-collected clinical data such as electronic medical records (EMRs) to the data generation component and provides an annotating tool for labeling the data. Then, the system semi-automatically generates medical VQAs for users. Second, we develop a model library by implementing VQA models for users to evaluate their datasets. Users can do simple configurations by selecting self-interested models. The system then automatically trains the models, conducts extensive experimental evaluation, and reports comprehensive findings. The reports provide new insights into the strengths and weaknesses of selected models. Third, we provide an online chat module for users to communicate with an AI robots for further evaluating the models. The source codes are shared on https://github.com/shyanneshan/VQA-Demo.
Construction of power marketing user knowledge graph based on $\text{BERT}+\text{BILSTM}+\text{CRF}$ model

In order to solve the fierce competition in the power market caused by the continuous improvement of the power consumption level of users, to reasonably deal with the contradiction between power supply and demand and to provide effective marketing strategies, the author proposes to construct the user knowledge graph based on the power marketing data. After preliminary data preprocessing, BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model is selected for entity recognition. Bi-lstm (Bidirectional Long short-term Memory) model and CRF (Conditional Random Field) model are combined to extract the relationship, build triples and establish knowledge graph. The graph can clearly show the relationship between users and power load, address, price package, etc., which is conducive to relevant enterprises to put forward corresponding marketing or emergency measures, and provide theoretical support for optimization schemes such as &#x201C;power demand side management&#x201D; and &#x201C;peak load shifting and valley filling&#x201D; under the new situation.
Metric of choosing the optimal parameter setting for edge aware filtering

Most of the existing edge aware filters have a number of parameters. The optimal settings of these parameters guarantee the best performance but they depend on the input image. It would be difficult for inexperienced users to empirically get the optimal parameters. This paper proposes a new metric for choosing the optimal parameter settings of edge aware filtering, which is called metric of edge aware filtering (MEAF). MEAF evaluates the quality of filtered images from three aspects: the color distance, the distance of the prominent structure, and smoothness. The colour distance is calculated by rooted mean square error. The distance of the prominent structure is calculated by the proposed SSIM map masked by Sobel edges (MASKED-SSIM). In MASKED-SSIM, Sobel detector is used to detect the prominent structure from the input image and the calculation of structure distance is constrained on the prominent structures. Number of gradients and relative total variation are further defined to measure the smoothness of the filtered image. MEAF is an objective metric, which is specially designed for choosing the optimal parameters of edge aware filtering and can be used universally for arbitrary input image. Experiments on 12 state-of-the-art edge aware filters show the effectiveness of MEAF.
Research on the Theory and Application of Deep Interactive Learning

Abstract Knowledge distillation (KD), in which a small network (students) is trained to mimic a larger one(teachers), with high precision, has been widely used in various fields. However, the interaction between teachers and students is still weak. It is found in this study that most existing methods, such as Deep Mutual Learning (DML), mainly construct loss function through soft weight indexes. Few researchers pay attention to the sharing of hard and heavy ones. As an improvement of DML, a new online learning distillation method, namely, Deep Interactive Learning (hereinafter DIL), was proposed in this research, which has deeper interaction than DML. We not only output the features of layers, but also disclose the features of hidden layers. We transfer the features to other models to obtain the corresponding softer distribution or features for distillation. Extensive experiments on various data sets show that the accuracy of our method is improved by almost 3% in CIFAR and 2% in ImageNet, which proves the validity of our method.
Comparison of Face Coverings Detection Methods using Deep Learning

End of the year 2019, the world has been shocked by a novel coronavirus disease 2019 (COVID-19), and the world is facing a huge health crisis due to the rapid transmission of COVID-19. World Health Organization (WHO) advised wearing a face cover in public places and crowded areas as a part of a comprehensive element to preventive and control measures to limit the spread of COVID-19. However, it is not easy to monitor people manually in these areas. In this paper, a detector model using deep learning for face coverings detection will be presented based on DEtection TRansformer (DETR) algorithm. The custom dataset has been used through this research, which comprises different face covering such as face mask, face shield, niqab, and purdah. The results concluded that the proposed model achieved the highest accuracy percentage of 92.38% and the highest recall percentage of 86.21% as a detector model. Finally, a comparative result with other face coverings model has been presented at the end of the research. The proposed model has achieved higher accuracy, precision, recall and specificity than the other model.
Virtual reality (VR) for laser safety training

This contribution presents sample scenarios for laser safety training using virtual reality. The scenarios illustrate activities involving laser devices used in industry, medicine, didactic laboratories and laser shows. The described applications are incorporated into laser safety training conducted by the Central Institute for Labour Protection – National Research institute (CIOP-PIB). The thematic scope of the scenarios was selected based on the analysis of the expectations of participants of training courses conducted at CIOP-PIB. After each training a discussion on the issues of main interest has been carried out and supplemented by the surveys conducted among the participants. The proposed scenarios correspond to the interdisciplinary nature of training (industry, medicine, research, entertainment).
Automatic annotation algorithm based on sliding window moment feature matching

In the era of big data, detecting and identifying targets based on massive data sources is a very important task. At present, most of the labeling of large-scale image data relies on traditional manual labeling methods, which takes a long time and is inefficient. In order to efficiently construct large-scale maritime target image data sets, we propose an automatic annotation algorithms, namely: automatic annotation algorithm based on moment features. Experiments were conducted to verify the accuracy of the automatic annotation algorithm to annotate image data, and finally proved that the two image automatic annotation algorithms proposed by us can construct the marine target image data set more efficiently, and provide good data support for downstream tasks.
An extension of the fpLLL library to Hermitian lattices

We present an Hermitian version of the classical floating-point LLL reduction algorithm of Nguyen and Stehlé[16]. This new variant works on imaginary quadratic fields which are norm-Euclidean and also for some adequate cyclotomic fields. An optimized C++ implementation has been performed, based on the fpLLL code and results show a significant improvement for Hermitian lattices reduction of dimension N when compared to fpLLL reduction on the corresponding Euclidean lattice of dimension 2 N. We demonstrate our implementation in the special case of the Gaussian integers.
Predictive Comparison Between Random Machines and Random Forests

Ensemble techniques have been gaining strength among machine learning models, considering supervised tasks, due to their great predictive capacity when compared with some traditional approaches.The random forest is considered to be one of the off-the-shelf algorithms due to its flexibility and robust performance to both regression and classification tasks.In this paper, the random machines method is applied over simulated data sets and benchmarking datasets in order to be compared with the consolidated random forest models.The results from simulated models show that the random machines method has a better predictive performance than random forest in most of the investigated data sets.Three real data situations demonstrate that the random machines may be used to solve real-world problems with competitive payoff.
Quantization of blackjack: Quantum basic strategy and advantage

Quantum computers that process information by harnessing the remarkable power of quantum mechanics are increasingly being put to practical use. In the future, their impact will be felt in numerous fields, including in online casino games. This is one of the reasons why quantum gambling theory has garnered considerable attention. Studies have shown that the quantum gambling theory often yields nontrivial consequences that classical theory cannot interpret. We devised a quantum circuit reproducing classical blackjack and found possible quantum entanglement between strategies. This circuit can be realized in the near future when quantum computers are commonplace. Furthermore, we showed that the player's expectation increases compared to the classical game using quantum basic strategy, which is a quantum version of the popular basic strategy of blackjack.
Application to Aid Hearing and Speech Impaired People

One of the most priceless gifts to a natural being is the capability of vision, hear, express and react correspondingly to the situations. Interaction between deaf–dumb and ordinary beings is an inspiring mission. The hearing-impaired and the mute society depends mainly on the hand gestures known as the sign language for communication. The sign language identification is one of the revolutions for serving the specially-abled society. The exploration of identifying sign gestures is successful but involves an exclusive charge to be commercialized. For the sign language identification system to be used widely, the data acquisition process varies largely depending on the cost of the system, the methods used, limitations, etc. The course of learning, recognizing the signs and interacting via the ISL can be simplified by the proposed system that converts speech to the sequence of sign language symbols. Speech processing embraces speech recognition, the learning of identifying the vocabularies being vocalized, irrespective of who the orator is. The proposed system practices template-based detection as the key tactic where the Voice to Sign (V2S) system initially requires to be skilled with a dialogue plan based on the predefined database of signs. It correspondingly translates speech to text via the dictation recognizer of the Unity 3D tool, processes the text and maps the phrases to animations that will assist to convey the desired message. Employing the proposed system trainers will be able to teach sign language effortlessly without explicit training.
Impact of institutional environment on financial development: a literary review

This literary review is focused on outlining approaches to institutional environment and financial development nexus, described in modern Western literature. Its existence is necessitated by the insufficient use of such sources in domestic studies on the subject, which results in inconsistencies in categorical apparatus, methodic approaches and of acquired results. The review is limited to practical Western studies with sufficient statistical apparatus and is concentrated on their choice of representative variables, expected and factual interrelations between said variables and on plausibility of their results. Based on the reviewed literature, it was possible to come up with a number of conclusions, such as: lack of general theoretical model of institutional-financial development nexus behind reviewed regression models; insufficiency of narrowly defined financial depth index to represent financial development as a whole, and need to compliment it with financial institutions’ operational effectiveness indicators, as well as other financial system components’ resource potential indicators, and recipients’ access to financial resources indicators; limitation of currently available institutional development indicators to two sets, both of which are expert-defined aggregated indicators; existence of substantial difference in factors of institutional development influencing financial system in developed and underdeveloped countries. Also, some of the further research perspectives are outlined, such as examining institutional-financial development nexus using PCA-aggregated extended financial development indicators, and researching institutional-financial development nexus using data envelopment analysis based on the statistical data on developed countries.
Differential Privacy for Fair Deep Learning Models

Increasingly, we rely on deep learning to make decisions. Yet, these models may take unfair decisions due to bias in the training data-sets. Most of the time, this bias is discrimination due to sensitive attributes such as gender, race, or ethnicity. In fact, discrimination and inequities are a daily reality that continues to exist in many fields, e.g. hiring processes, tenures, and workplaces. Therefore, exiting data-sets may be biased which results in getting unfair learning models. In this paper, we empirically prove that learning models trained on biased datasets will produce unfair and discriminating models. To handle this problem, we propose a pre-processing approach that takes advantage of differential privacy properties to mitigate bias in sensitive attributes of data-sets. More precisely, in the learning process, we introduce the randomized response mechanism to mitigate the inequity and avoid discrimination from the training data-set. We evaluate our approach in a hiring process using a synthetic data-set of resumes of candidates. Simulation results show that our approach mitigates bias and takes more fair decisions compared to the TensorFlow differential privacy library or a learning model without our pre-processing approach. This is owing to the fact that the TensorFlow library applies differential privacy to all attributes, Whilst in our approach we only apply the differential privacy mechanism to sensitive attributes that are the source of the discrimination.
Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs.The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output.There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits.This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS.In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU).This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model.This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance.The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes.The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method.It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches.The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.
Network Malicious Traffic Identification Method Based On WGAN Category Balancing

Aiming at the problem of data imbalance when in using deep learning model for traffic recognition tasks, a method of using Wasserstein Generative Adversarial Network (WGAN) to generate minority samples based on the image of the original traffic data packets is proposed to achieve a small number of data categories expansion to solve the problem of data imbalance. Firstly, through data preprocessing, the original traffic PCAP data in the dataset is segmented, filled, and mapped into grayscale pictures according to the flow unit. Then, the balance of dataset is achieved by using traditional random over sampling, WGAN confrontation network generation technology, ordinary GAN generation technology and synthetic minority oversampling technology. Finally, public datasets USTC- TFC2016 and CICIDS2017 are adopted to classify the unbalanced dataset and the balanced dataset on classic deep model CNN, and three evaluation indicators of precision, recall, and f1 are applied to evaluate classification effect. Experimental results show that the dataset balanced by the WGAN model is better than the ordinary GAN generation method, traditional oversampling method and the synthesis of the minority class sampling technique method under the same classification model.
Real time SOA based credit card fraud detection system using machine learning techniques

Financial transactions using credit card are being observed to be commonly accepted by the users as well as by the financial institutions. At the same time, a good number of fraudsters adopt various techniques to create inconvenience to the users and in the process the financial institutions lose goodwill or trustworthiness. However, a number of methodologies have been proposed by various researchers to counter and revoke the fraudulent activities in credit card transactions. The crux of the problem is that the fraudulent activities need to be curbed instantly if at all can be identified, when the fraudsters initiate them. In this study, a real time fraud detection system based on service oriented architecture (SOA) has been proposed to analyze the fraudulent activities in credit card transactions. The architecture is designed on the basis of Apache Kafka tool, which is used for real time streaming of transactional data to detect the fraudulent transactions. This process considers different services which form the backbone of SOA. Further, five different machine learning classifiers namely support vector machine (SVM), multilayer perceptron (MLP), random forest regressor, autoencoder and isolation forest have been considered to identify the fraudulent activities instantly with the help of SOA based real time architecture.
p-Adic valuations associated to fibers and images of polynomial functions

The Chevalley-Warning and the Ax-Katz theorems, which provide the p-adic valuations for the number of solutions of the system of polynomials in finite fields, are stated in terms of the degrees of the associated polynomials. Recently, Clark et al. reformulated the theorems in terms of the cardinalities of fibers and images of the associated polynomial functions, and raised a question in this setting. In this paper, we give a negative answer to that question and improve their results in general case using p-adic lifting via the Teichmüller character.
The cognitive comparison enhanced hierarchical clustering

Abstract The growth of online shopping is rapidly changing the buying behaviour of consumers. Today, there are challenges facing buyers in the selection of a preferred item from the numerous choices available in the market. To improve the consumer online shopping experience, recommender systems have been developed to reduce the information overload. In this paper, a cognitive comparison-enhanced hierarchical clustering (CCEHC) system is proposed to provide personalised product recommendations based on user preferences. A novel rating method, cognitive comparison rating (CCR), is applied to weigh the product attributes and measure the categorical scales of attributes according to expert knowledge and user preferences. Hierarchical clustering is used to cluster the products into different preference categories. The CCEHC model can be used to rank and cluster product data with the input of user preferences and produce reliable customised recommendations for the users. To demonstrate the advantages of the proposed model, the CCR method is compared with the rating approach of the analytic hierarchy process. Two recommendation cases are demonstrated in this paper with two datasets, one collected by this research for laptop recommendation and the other an open dataset for workstation recommendation. The simulation results demonstrate that the proposed system is feasible for providing personalised recommendations. The significance of this research is the provision of a recommendation solution that does not depend on historical purchase records; rather, one wherein the users’ rating preferences and expert knowledge, both of which are measured by CCR, is considered. The proposed CCEHC model could be further applied to other types of similar recommendation cases such as music, books, and movies.
Preserving Hand-Drawn Qualities in Audiovisual Performance Through Sketch-Based Interaction

Live coding-the real-time procedural creation of audiovisual works-suggests opportunities to extend hand-drawn animation; however, existing live coding systems are incompatible with manual animation workflows. Manual input is not a primary datatype in existing live coding languages and live coding tools require using symbolic programming environments. We theorize that by applying direct manipulation to the domain of live coding, we can enable animators to create expressive mappings between hand-drawn animations and audio effects in real time. We present Megafauna, a sketch-based system for audiovisual performance, informed by interviews with professional animators. Megafauna supports the integrated generation and control of hand-drawn animation and audio sequences by enabling animators to directly sketch mapping functions between animation frames and sound oscillators. We demonstrate the expressive potential of Megafauna by reproducing animated compositions from procedural and manual domains and show how the system supports manual expression and exploration in live performance.