Reviewerly/exHarmony huggingface dataset:
Korean Font Synthesis with GANs

Font synthesis for CJK based languages that consists of large number of characters and complex structures is still a major challenge and ongoing research problem for computer vision and AI.In this paper, we propose a generative model based on GANs as a solution for Korean font synthesis problem with a small set of characters.Korean Hangul includes 11,172 characters and composes of writing in multiple patterns.Normally font design involves heavy loaded human labor that can easily hit to one year to finish for one style set.Various methods have been proposed to solve this character generation problem using generative models such as GANs, but the results are often blurry or broken and are far from realistic.We generate visually appealing Korean Hangul characters with a skeleton-driven approach.We demonstrate that this approach is effective at synthesizing characters from their corresponding skeletons.With 114 samples, the proposed method automatically generates the rest of the characters in the same given font style.Our approach resolves long overdue shortfalls such as blurriness, breaking, and unrealistic shapes and styles of characters using GANs.We demonstrate via our experiments that our approach has better quality then other methods.
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks

Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single learning rate do not match the well-known notion that lower-level layers extract general features and higher-level layers extract specific features. Based on our discussion, we propose an algorithm that improves fine-tuning performance and reduces network complexity through layer-wise pruning and auto-tuning of layer-wise learning rates. The proposed algorithm has verified the effectiveness by achieving state-of-the-art performance on the image retrieval benchmark datasets (CUB-200, Cars-196, Stanford online product, and Inshop). Code is available at https://github.com/youngminPIL/AutoLR.
Optical preamplifier based simultaneous quantum key distribution and classical communication scheme

That the simultaneous quantum key distribution and classical communication (SQCC) scheme are combined with the continuous variable quantum key distribution (CVQKD) and the classical communication together, will provide an effective method to implement the simultaneous CVQKD and the classical communication in the existing optical networks in the future. However, superimposing a classical signal on the quantum signal will introduce excess noise into the CVQKD system, thus greatly reducing the performance of the system. In this paper, a novel scheme of SQCC based on optical preamplifier (OPA) is proposed, that is, the OPA is inserted into the receiver to improve the performance of the system. On the one hand, under the condition of the same bit error rate, the amplification of the signal by the OPA can reduce the requirement for the modulation amplitude of the classical signal at the sending end, thereby reducing the noise effect of the classical signal on the quantum signal. On the other hand, the OPA can compensate for the imperfection of the receiver detector. Moreover, in the case of locally generated local oscillator, the amplifier can also amplify the weak phase reference pulse, and thus reducing the phase excess noise caused by the shot noise of the weak phase reference pulse. Numerical simulation results show that the proposed scheme has better performance than the original scheme in the sense of security key rate and transmission distance. These results show that this scheme provides an effective and practical method for the further development and practical application of the SQCC scheme.
Convolution network pruning based on the evaluation of the importance of characteristic attributions

Although deep learning models have recently achieved remarkable performance in many tasks, they require massive memory footprint and computing power to achieve efficient inference. The researchers propose a number of compression methods to compress the capacity and computation of the model so that deep learning can be deployed to resource-constrained mobile terminals. Based on the pruning framework, two pruning methods are proposed from the perspective of filter importance evaluation. (1) As every filter can learn unique features, we propose an attribution mechanism to evaluate the correlation between the features learned by a filter and the causal features. We prune the filter with low correlation so as to compress the model and retain the attribution characteristics of the original model; the process is called attribution pruning. (2) The second pruning method uses positive correlation features in a channel and gradient to evaluate the importance of the filter, which is based on an iterative optimization pruning framework. This method, which is called Taylor-guided pruning, can improve the accuracy of pruning redundant filters. We implement two pruning methods in VGGNet and ResNet. Extensive experiments demonstrate that attribution pruning can greatly retain the attribution characteristics of the original model. Moreover, the two pruning methods can achieve better compression than current mainstream pruning methods.
Differentiable Forward and Backward Fixed-Point Iteration Layers

Recently, several studies have proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot capture. However, these methods are still in their infancy and require special treatments, such as the analysis of the Karush-Kuhn-Tucker (KKT) condition, to derive the backpropagation formula. In this paper, we propose a new formulation called the fixed-point iteration (FPI) layer, which facilitates the use of more complicated operations in deep networks. The backward FPI layer, which is motivated by the recurrent backpropagation (RBP) algorithm, is also proposed. However, in contrast to RBP, the backward FPI layer yields the gradient using a small network module without explicitly calculating the Jacobian. In actual applications, both forward and backward FPI layers can be treated as nodes in the computational graphs. All the components of our method are implemented at a high level of abstraction, which allows efficient higher-order differentiations on the nodes. In addition, we present two practical methods, the neural net FPI (FPI_NN) layer and the gradient descent FPI (FPI_GD) layer, whereby the FPI update operations are a small neural network module and a single gradient descent step based on a learnable cost function, respectively. FPI_NN is intuitive and simple, while FPI_GD can be used to efficient train energy function networks that have been studied recently. While RBP and related studies have not been applied to practical examples, our experiments show that the FPI layer can be successfully applied to real-world problems such as image denoising, optical flow, and multi-label classification.
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning

Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For each state encountered, MPC solves an online optimization problem to choose a control action that will minimize future cost. This is a surprisingly effective strategy, but real-time performance requirements warrant the use of simple models. If the model is not sufficiently accurate, then the resulting controller can be biased, limiting performance. We present a framework for improving on MPC with model-free reinforcement learning (RL). The key insight is to view MPC as constructing a series of local Q-function approximations. We show that by using a parameter λ, similar to the trace decay parameter in TD(λ), we can systematically trade-off learned value estimates against the local Q-function approximations. We present a theoretical analysis that shows how error from inaccurate models in MPC and value function estimation in RL can be balanced. We further propose an algorithm that changes λ over time to reduce the dependence on MPC as our estimates of the value function improve, and test the efficacy our approach on challenging high-dimensional manipulation tasks with biased models in simulation. We demonstrate that our approach can obtain performance comparable with MPC with access to true dynamics even under severe model bias and is more sample efficient as compared to model-free RL.
All About the Name: Assigning Demographically Appropriate Names to Data-Driven Entities

We develop a method for assigning demographically appropriate names to data-driven entities, such as personas, chatbots, and virtual agents.The value of this method is removing the time-consuming human effort in this task.To demonstrate our method, we collect four million user profiles with gender, age, and country information from an international online social network.From this dataset, we obtain 1, 031, 667 unique names covering 3, 088 demographic group combinations that our method considers as gender, age, and nationality appropriate.A manual evaluation by raters from 34 countries shows a demographic appropriateness score of 85.6%.The demographically appropriate names can be utilized for data-driven personas, virtual agents, chatbots, and other humanized entities.
Development of The Personnel Monitoring System Using Mobile Application and Real-Time Database During the COVID19 Pandemic

Coronavirus disease 19 (COVID19) is a disease caused by the new coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).This disease has infected almost the entire world with a total of 47.5 million sufferers and a death toll of 1.2 million people so that WHO categorizes it as a global pandemic.The COVID19 case in Indonesia still shows an increasing trend even though various prevention efforts have been made.Proven efforts to reduce the spread of COVID19 include limiting physical interactions between humans or physical distance, maintaining the cleanliness of hands and limbs by washing with soap, and limiting outdoor activities by staying at home.Several government and private agencies have required employees to report their health conditions via web pages.Real-time and accurate mobile applications can help prevent the spread of COVID19.This research will develop a real-time monitoring and command system using mobile applications and cloud computing technology.The application will collect GPS-based location data, the number of people in the vicinity identified via Bluetooth, and the user's body condition in the form of temperature and oxygen levels in the blood.User data is stored and processed in a real time database in cloud computing which can be accessed through an application on the user's smartphone.The database also stores data on Covid19 sufferers and where they live.The application provides alerts when in a crowd and notifies the status of the region the user is in.Advice is given by the app when the recording of the body condition points to the early symptoms of COVID19.
Particle Swarm Optimization Based on a Novel Evaluation of Diversity

In this paper, we propose a particle swarm optimization variant based on a novel evaluation of diversity (PSO-ED). By a novel encoding of the sub-space of the search space and the hash table technique, the diversity of the swarm can be evaluated efficiently without any information compression. This paper proposes a notion of exploration degree based on the diversity of the swarm in the exploration, exploitation, and convergence states to characterize the degree of demand for the dispersion of the swarm. Further, a disturbance update mode is proposed to help the particles jump to the promising regions while reducing the cost of function evaluations for poor particles. The effectiveness of PSO-ED is validated on the CEC2015 test suite by comparison with seven popular PSO variants out of 12 benchmark functions; PSO-ED achieves six best results for both 10-D and 30-D.
Testing Scalable Bell Inequalities for Quantum Graph States on IBM Quantum Devices

Testing and verifying imperfect multi-qubit quantum devices are important as such noisy quantum devices are widely available today. Bell inequalities are known to be useful for testing and verifying the quality of the quantum devices from their nonlocal quantum states and local measurements. There have been many experiments demonstrating the violations of Bell inequalities, but they are limited in the number of qubits and the types of quantum states. We report violations of Bell inequalities on IBM Quantum devices based on the scalable and robust inequalities maximally violated by graph states as proposed by Baccari et al.. The violations are obtained from the quantum states of path graphs up to 57 and 21 qubits on a 65-qubit and two 27-qubit IBM Quantum devices, respectively, and from those of star graphs up to 11 qubits with quantum readout error mitigation (QREM). We are able to show violations of the inequalities on various graph states by constructing low-depth quantum circuits and by applying the QREM technique. We also point out that quantum circuits for star graph states of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> can be realized with circuits of depth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\sqrt {N})$ </tex-math></inline-formula> on subdivided honeycomb lattices which are the topology of the 65-qubit IBM Quantum device. Our experiments show encouraging results on the ability of existing quantum devices to prepare entangled quantum states and provide experimental evidence on the benefit of scalable Bell inequalities for testing them.
On Maximum Likelihood Training of Score-Based Generative Models.

Score-based generative modeling has recently emerged as a promising alternative to traditional likelihood-based or implicit approaches. Learning in score-based models involves first perturbing data with a continuous-time stochastic process, and then matching the time-dependent gradient of the logarithm of the noisy data density - or score function - using a continuous mixture of score matching losses. In this note, we show that such an objective is equivalent to maximum likelihood for certain choices of mixture weighting. This connection provides a principled way to weight the objective function, and justifies its use for comparing different score-based generative models. Taken together with previous work, our result reveals that both maximum likelihood training and test-time log-likelihood evaluation can be achieved through parameterization of the score function alone, without the need to explicitly parameterize a density function.
COMPLIANCE ACADEMIC INTEGRITY LEARNING ACHIEVEMENTS OF EDUCATIONAL APPLICANTS IN THE PROCESS OF DISTANCE LEARNING

This article deals with the problem of respect for academic integrity. The relevance of this research consists in the quarantine measures, conducted be the Ukrainian government, in other words students could not attend all the classes in person. So that created an interesting problem, laying in the issues of respect for academic integrity. It has been analyzed and discussed by the authors. They also have defined and described the meaning of such concepts as ‘respect for academic integrity’, ‘distance learning’ and ‘e-learning’. It is specially noted main differences between distance learning and e-learning. The academic literature on this topic is analyzed and described in short. Data are given about the poll conducted among the students of V. N. Karazin Kharkiv National University on GoogleForms. The authors prepared ten questions related to respect for academic integrity during distance learning. The survey deals with key issues of respect for academic integrity problems as software and hardware of classes, during work in class, doing homework and term tests, evaluation of the effectiveness of methods of control, main reasons of disrespect for academic integrity and influence of that. Conclusions are drawn about main issues of distance learning, so we provided a reader with possible ways to cope with them. Recommendations are given for teachers and officials. The authors recommended to use some additional applications to control the students’ behavior during classes and doing tests, to conduct teacher training to deal with problems of disrespect for academic integrity, to elaborate official recommendations, to stimulate students to respect for academic integrity. The article is of great interest for pedagogical staff, officials, students and anybody interested in this topic.
Modernization readiness analysis of Belitang irrigation system at region level using analytic hierarchy process (AHP) method

Abstract Irrigation system management in the Belitang Irrigation System has some constraint that can be overcome by conducting irrigation modernization. Before implementing irrigation modernization, an irrigation system should be assessed to determine the level of irrigation modernization readiness stated by the readiness index of irrigation modernization (IKMI). The assessment of irrigation pillars was carried out in three regions including Belitang I, Belitang II and Belitang III. This study aimed to analyze the readiness of irrigation modernization in the Belitang Irrigation System and to determine the order of irrigation modernization development in three regions. Data was collected using 2 methods, namely walk through survey for irrigation infrastructure pillars and interviews for 4 other pillars including water availability, system management, institution and human resources. The assessment data was recapitulated and processed to obtain the IKMI. Then, decision making for the order of irrigation modernization development was conducted by using the analytic hierarchy process (AHP) method. The result of determining the IKMI was 69.05, so Belitang Irrigation System was categorized as less ready for modernization. This showed that the irrigation modernization needs to be postponed and the irrigation system is improved for 1-2 years. Furthermore, the results of the AHP analysis showed that the order of order of irrigation system development and improvement for irrigation modernization started from the Region of Belitang III, then Belitang I and Belitang II.
Text and Style Conditioned GAN for Generation of Offline Handwriting Lines

This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors. Unlike prior work, which produce stroke points or single-word images, this model generates entire lines of offline handwriting. The model produces variable-sized images by using style vectors to determine character widths. A generator network is trained with GAN and autoencoder techniques to learn style, and uses a pre-trained handwriting recognition network to induce legibility. A study using human evaluators demonstrates that the model produces images that appear to be written by a human. After training, the encoder network can extract a style vector from an image, allowing images in a similar style to be generated, but with arbitrary text.
Visualization of Real World Enterprise Data using Python Django Framework

Abstract In the meantime, startup companies are rising more and more. Companies want to track their prominence and their advancement. Basically, the whole data need to be found and stored in database. They need to check their company’s turnout-catastrophe ratio and their expansion. The company obliged to know how compact it is, with different companies. Since the data is in huge amount, estimating the data is almost difficult. Soon these terms, physically reckoning may become annoyed. Considering the plot, we have a proposal termed visualization. Visualization is the graphical depiction of data in the pattern of graphs, tabulations, charts by which we could know about company widening. Using data visualization, we could form an opinion on company’s stand/rate. So to evolve and to develop web pages, the automations we used are html, css for the data analysis, python modules are used. Few of them are pandas, seaborn, matplotlib, plotly. Implementing the data visualization using IDE’s like jupyter, Sublime Text makes the effort transparent. But integrating web pages with python modules will be the challenging load. So to carry out this, we use DJANGO Framework to visualize the data. DJANGO affords frontend, back-end and database This Data visualization is intended to evolve web pages and scrutinize the data.
Spam Classification Based on Supervised Learning Using Grasshopper Optimization Algorithm and Artificial Neural Network

The electronic mailing system has in recent years become a timely and convenient way for the exchange of multimedia messages across the cyberspace and computer networks in the global sphere. This proliferation has prompted most (if not all) inboxes receiving junk email messages on numerous occasions every day. Due to these surges in spam attacks, a number of approaches have been proposed to lessen the attacks across the globe significantly. The effect of previous detection techniques has been weakened due to the adaptive nature of unsolicited email spam. Hence, resolving spam detection (SD) problem is a challenging task. A regular class of the Artificial Neural Network (ANN) called Multi-Layer Perceptron (MLP) was proposed in this study for email SD. The main idea of this research is to train a neural network by leveraging a new nature-inspired metaheuristic algorithm referred to as a Grasshopper Optimization Algorithm (GOA) to categorize emails as ham and spam. Evaluation of its performance was performed on an often-used standard dataset. The results showed that the proposed MLP model trained by GOA achieves high accuracy of up to 94.25% performance compared to other optimization.
Predicting User Preferences with XGBoost Learning to Rank Method

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from Information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation.