Reviewerly/exHarmony huggingface dataset:
A Human-Robot Interactive Mahjong Playing System Based on Visual Recognition Using a Convolutional Neural Network

Mahjong is a popular tabletop game in China, Japan, and other Asian countries. As an incomplete information game, it is more complicated than other complete information games, such as Go, chess, and shogi. With the rapid development of service robot, there have been several human-robot interactive playing systems for complete information games so far, but mahjong is not the case. In this paper, a human-robot interactive mahjong playing system (HRMPS) was developed. HRMPS consists of five modules, including central host, robot players, visual kit, mahjong conveyor, and interactive software. The central host serves the communication between four robot players, which grab a tile on mahjong conveyor and recognize its face via visual kit, and make an action decision by themselves or by human opponents with the help of the interactive software. In HRMPS, to visually recognize a total of 27 different mahjong faces in uncontrolled conditions, a deep convolutional neural network was adopted to achieve an accuracy of 99.71% with a running time of 29ms. The experimental results tell that HRMPS is applicable in human-robot interactive mahjong game.
Pre-build optimization of Android operating system for running inside container

Android, being the most widely used operating system, created a motive to run its applications on as many platforms, which can even have a different operating system. For Android applications to be used in such environments, Android needs to be present on the platform as well. To make this possible, we decreased Android's memory usage and used lightweight virtualization technology in order to have two coexisting operating systems properly running. This paper aims to provide the solution for the above-mentioned problem using pre-built optimization of the Android operating system and running it inside a Linux container.
Sign Gradient Aggregation for Wireless Federated Learning Using Bussgang LMMSE Estimation

The key challenge of wireless federated learning is to reduce the communication cost for aggregating local gradients computed with personally generated data. The computational complexity for estimating local gradient parameters at an access point can be excessive since the cost increases with the model size and the number of client devices. To alleviate this, we propose a computationally efficient gradient aggregation method for wireless federated learning by employing the Bussgang linear minimum mean squared error (BLMMSE) estimator. The key idea of BLMMSE aggregation is to model the gradient parameters as independent and identically distributed Gaussian random variables. Especially, we derive the closed-form BLMMSE solution for the one-bit quantization and demonstrate that the proposed method maintains the learning performance in the level of soft-signSGD algorithm whose aggregator is the exact minimum mean squared error estimator.
Ceramic product copyright transaction model based on blockchain smart contracts

With the rise of living standards, more and more people pay attention to the grade of ceramic products. The beautiful appearance of two different ceramic products of the same type can bring a price difference of more than 1,000 times. Because the structure of ceramic products is simple, easy to imitate, and difficult to identify, many imitation products have infringed on the rights of customers and original product enterprises. Therefore, copyright protection becomes particularly important. However, traditional methods of copyright registration fail to satisfy the requirements due to their long cycle, non-transferable, and cumbersome in rights confirmation, etc. In this paper, we combine the appearance and structural features of ceramic products, investigate an approach for rights confirmation and infringement tracking with the digital features of ceramic products. As another contribution, we propose to establish a copyright transaction model based on smart contracts. To identify the infringement acts, we invoke smart contracts to extract the rights. Through the computer vision algorithms to compare the similarity between physical products and digital resources. Combined with this research, the registration, confirmation, traceability, and transaction of ceramic product copyright can be well realized.
Dynamic Task Allocation and Pathfinding under Precedence and Temporal Constraints

Under precedence and temporal constraints, a novel framework is proposed to optimize both dynamic task allocation and pathfinding for a multi-robot system. Firstly, the dynamic task allocation is solved by a greedy-based algorithm combined with a metaheuristic algorithm that satisfies precedence and temporal constraints, which dynamic update and re-planning of the tasks also handle during the task execution. Next, a conflict-based search algorithm is exploited to accomplish the multi-robot pathfinding. With the priority-based objective function, the proposed scheme initially maximizes the utility of the tasks, and accordingly minimizes the makespan of all robots. Simulation and experimental results are eventually demonstrated to verify the feasibility of the proposed framework.
Neural Arabic Text Diacritization: State-of-the-Art Results and a Novel Approach for Arabic NLP Downstream Tasks

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF), and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models even those requiring human-crafted language-dependent post-processing steps, unlike ours. Moreover, we show how diacritics in Arabic can be used to enhance the models of downstream NLP tasks such as Machine Translation (MT) and Sentiment Analysis (SA) by proposing novel Translation over Diacritization (ToD) and Sentiment over Diacritization (SoD) approaches.
Viewpoint Selection for DermDrone using Deep Reinforcement Learning

This paper presents an RL-based method to improve the performance of real-time 3D human pose estimation as a positioning feedback for DermDrone which is a micro sized quadrotor designed MetaOptima to capture high resolution full body images for dermatology application. The camera viewpoint is identified as the key parameter in the accuracy of monocular 3D human pose estimation. We present a deep reinforcement learning based method for determining the best viewpoint given the flight trajectory. Our goal is to present a reliable and accurate positioning feedback for DermDrone using a 3D human pose estimation algorithm. DQN and its variants (Double DQN, and Dueling DQN) were employed and their performances were investigated by conducting several simulations. The results confirm that RL-based viewpoint selection improve the performance of 3D human pose estimation.
GANs with Suppressed Mode Collapse Using Intrinsic Rewards

In recent years, generative adversarial net-works (GANs) have attracted much attention because it can generate new data and information by taking advantage of the characteristics of samples. However, there are many problems with GANs, such as difficulty in learning well and instability. In this paper, we proposed a method to suppress mode collapse, which is one of the problems that often occur in GANs, by utilizing intrinsic rewards used in deep reinforcement learning. We have conducted comparative experiments between the pro-posed method and the original GANs in several ways to show the performance of the proposed method.
powerGhosts &amp; defensiveGhosts – Enhanced Ghost Team Controller based on Ant Colony Optimization for Ms. Pac-Man

This paper presents an improved controller based on Ant Colony Optimization for the ghost team of Ms. Pac-Man. The controller is an enhanced version of fairGhosts which is based on the ghostAnt framework. Various improvements were implemented in terms of parameters and concepts. Especially for the explorer ants, fairGhosts uses a simplified version, as the exact reasoning for the proposed concepts could not be determined in the ghostAnt framework. In this paper, two new types of ghost teams are proposed after the modifications were conducted: powerGhosts and defensiveGhosts. powerGhosts take into account the power pill aspect for stopping criteria and solution quality of the explorer ants, and defensiveGhosts additionally involve a threshold of Ms. Pac-Man&#x2019;s distance to the nearest power pill for the hunter ants, so as not to be caught easily. Test results show that the powerGhosts version shows on average 15% better results than the initial fairGhosts setup, while defensiveGhosts performs equal or slightly worse than the initial implementation. It can be concluded that including the power pill aspect in the explorer ants concept shows an improved performance of the ghost team. On the other hand, the concept of distancing ghosts from Ms. Pac-Man when she is near a power pill did not result in any significant improvement.
A New Model of Logo Generator

The paper presents a model of a logo generator. The proposed model uses extended StyleGAN neural network. The architecture of the StyleGAN network is presented with an emphasis on the additional components extending the classical GAN neural network. The configuration of the network parameters is also explained. Unlike the known logo generators, the proposed architecture achieves production of unique and original logos. Experimental results are presented and discussed.
Bayes estimation of autocorrelation coefficient

In this paper,we consider Bayesian inference for the autocorrelation coefficient ρ. Posterior distribution of ρ is obtained and then the performance of the Bayes estimator (posterior mean of ρ) is compared with that of classical estimator of ρ through simulation. It is found from simulation study that the Bayes estimator has lower mean square error than the classical estimator. An example with real life data is also presented.
Analysis of Optical Character Recognition Methods to Extract Time Information on Digital Images

The time information recorded in a vehicle black box or CCTV is displayed in the text time(ex: YYYY/MM/DD hh:mm:ss). However, when restoring images from inactive areas, temporary files, or damaged video files, there could be a problem that the sequence of images restored to frame units is not arranged in that of the actual recorded time. To resolve this issue, we recognize time information recorded in text format at the top or bottom of the image, and rename the image file via the recognized time information. At that time, images that have been renamed and sorted chronologically can be made into continuous videos. In this paper, Optical Character Recognition(OCR) was used to recognize time information. OCR-related research has continued for a long time and has recently been available through many open sources. Thus, time information of the image is extracted by applying Convolutional Recurrent Neural Network(CRNN) based OCR, Tesseract OCR, and EasyOCR. The expression of time information in vehicle black boxes and CCTV is expressed in various ways(ex: text font, background and text size). The performance of each OCR was compared and analyzed through test images of time information presented in various forms. It was shown from the comparative analysis that the CRNN-based method has the highest recognition performance among OCRs used in the experiment. Moreover, it was confirmed that a prediction rate is approximately 5 times faster for predicting a single image.
Image Authentication Application with Blockchain to Prevent and Detect Image Plagiarism

Plagiarism was an act that made a disadvantage for the author because of the use of other people’s work or ideas without mentioning any credit. Especially for image plagiarism cause disadvantage for the author’s income because in this internet era many image authors sold their work for income. The previous research proposed the model that combined DCT hash and Blockchain, but this research didn’t conduct the plagiarism area of the image, so we proposed our model that combined DCT hash and Blockchain to prevent and detect plagiarism images. Our contribution for this research is to prevent plagiarism through the initial authentication process and then to detect image plagiarism on pixel-by-pixel of the image for more accurate plagiarism detection. Blockchain technology also prevented any change to data that was already stored in the Blockchain network, so it can prevent any change to image data and can prevent plagiarism attempts. With this proposed model, we got 100% accuracy for detecting images as plagiarism or not plagiarism. The results of testing the speed of the model on 100 different types of images show the speed of the model in displaying conclusions is 47.90 seconds. We also added some improvement areas for future research.
Edge Detection of Noisy Images Based on Improved Canny and Morphology

In the detection of image edge with noise, it is difficult for the traditional Canny algorithm to filter the noise, and its detection effect is poor. In order to solve this problem, the Canny operator and the morphological edge detection algorithm are improved in this paper. Then a fusion algorithm based on improved Canny operator and morphological edge detection is proposed. First of all, the algorithm improves the traditional Canny operator from three aspects: first, a hybrid filter consisting of an adaptive median filter and Gauss filter is designed; second, the gradient is calculated using the 4-direction Sobel operator; third, the global threshold segmentation algorithm is used to determine the threshold of edge connection. Then, the edge detection is carried out by the mathematical morphological method of multi-directional structure element and adaptive weight calculation. Finally, the resulting images from improved canny operators and improved morphological methods are combined by weighting to obtain the final edge image. Experiments are designed to compare noiseless images with Gaussian and salt and pepper mixed noises. The results show that the denoising effect of the fusion algorithm proposed in this paper is obviously better than that of the improved canny operator and the improved mathematical morphology method alone, and the edge graph is continuously clear and has good performance.
Machine and Deep Learning Algorithms and Applications

This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications.
Detection and Classification of Toxic Content for Social Media Platforms

Over the years the use of the internet has been increased exponentially and it is changing all the time. Recently there are two important evolutions; they are the social media platforms and mobile technology. Through this, the way people communicate with each other has changed significantly [1]. Many social media platforms like Facebook, Instagram, and Twitter, etc. have grown into worldwide networks. Massive amount of information and content arise from these social media platforms as people are engaging themselves to communicate, express their opinions and share their views daily. Even though, this type of virtual communication is highly productive and constructive, a significant part of them will be destructive. That is it is toxic in nature and some of them might include violence, obscene, threat, insult, etc. The toxic content can be either in Image or Text form. So it is essential to recognize the threat and respond to it which makes the online space more healthy and valuable. To address this problem we are proposing a system that makes use of Random Forest Machine learning algorithm and Convolutional neural network to detect toxicity in Text and Image respectively. We have successfully detected and classified toxicity in Text and Image data.
Orthogonal ray scheme: a method for processing interference patterns and reconstructing the shape of a test convex mirror

Abstract This report is devoted to the processing of the interference pattern of the tested mirror, obtained using the orthogonal ray scheme, where the convex testing surface is illuminated by a collimated beam, which is perpendicular to the optical axis of the surface. The interference pattern is created by two wavefronts, one of which is reflected from the mirror, while the other wavefront bypasses the mirror and travels directly to the detector plane. The result of interference pattern processing is a topography map formed by several tangential profiles. The proposed method is suited for large diameter convex spherical and aspherical mirrors and does not require a priori information of surface under the test, such as the vertex radius of curvature and the conical constant. Theoretical foundation of the data processing method are presented.
Text Categorization of Filipino Tweets Using Naïve Byes Algorithm

The uses of social media platform twitter have progressed to a much wider array of uses due to the number of data it processes every day. It has been a storage for unstructured data that could be useful once arranged into meaningful formats and can produce helpful means to identify and categorize uploaded tweets real time. A Filipino Tweets Categorization system that categorizes tweet inputs into 4 different categories such as Politics, Entertainment, Sports and Technology was developed. Different data processing methods were used to Upon evaluation, the system was found to have effective results in terms of accuracy. As for the recommendations, additional data may be added as well as new categories to further improve the system's functionalities.
Research of Metric Learning-Based Method for Person Re-Identification by Intelligent Computer Vision Technology

Abstract Person re-identification technology aims to establish an efficient metric model for similarity distance measurement of pedestrian images. Candidate images captured by different camera views are ranked according to their similarities to the target individual. However, the metric learning-based method, which is commonly used in similarity measurement, often failed in person re-identification tasks due to the drastic variations in appearance. The main reason for its low identification accuracy is that the metric learning method is over-fitting to the training data. Several types of metric learning methods which differ from each other by the distribution of sample pairs were summarized in this article for analysing and easing the metric learning methods’ over-fitting problem. Three different metric learning methods were tested on the VIPeR dataset. The distributions of the distance of the positive/negative training/test pairs are displayed to demonstrate the over-fitting problem. Then, a new metric model was proposed by combining the thoughts of binary classification and multi-class classification. Related verification experiments were conducted on VIPeR dataset. Besides, the semi-supervised metric learning approach was introduced to alleviate the over-fitting problem. The experimental results reflect gap between training pairs and test pairs in the metric subspace. Therefore, reducing the difference between training data and test data is a promising way to improve the identification accuracy of metric learning method.
Expanding the Interaction Space using Hand-Held-AR with Hearing Support

In recent years, Virtual Reality (VR) and Augmented Reality (AR) have become familiar in daily life. In particular, AR using smartphones and tablets is called Hand-Held-AR and is becoming popular. However, Hand-Held-AR gives users AR experiences only on smartphones and tablets displays. Hand-Held-AR is not suitable for AR applications using a wide viewing angle. In this research, we investigate the problem that arises from the narrow viewing angle in Hand-Held-AR. After that, with sound notification, we suggest that the Hand-Held-AR system not make the user aware of the narrow viewing angle. The research goal is to consider expanding AR experiences in Hand-Held-AR. As a result, the narrow viewing angle decrease spatial recognition and inhibits the user’s attention to their surroundings. For this problem, a Hand-Held-AR system with a sense of hearing realizes that users identify the direction of virtual objects and interact with virtual objects in space while hiding the narrow viewing angle.
Performance evaluation of VR/MR missile virtual hoisting training

Using virtual reality (VR)/mixed reality (MR) virtual hoisting training system is a low-cost and low-risk skill training method. However, the research on the performance of VR/MR virtual hoisting training is still relatively scarce. The article comprehensively evaluates the performance of VR/MR virtual hoisting training by integrating three evaluation indicators: objective performance, visual characteristics and subjective experience. The results show that the effect of the MR virtual hoisting training system is better, and the simulation reduction degree is higher. However, the VR virtual hoisting training system has good operability and is easy to master. Long-term use of the VR/MR virtual training system will cause recoverable vision loss, and there is a problem that the user's ability to actively explore the virtual environment using touch is weak. The research results have a positive effect on optimizing the allocation of training resources and improving training effectiveness.
Statistical Learning: A new outlier detection technique for composite data points using a multidimensional distance space

In this paper, we propose a new technique to detect outliers in a composite data(e.g. segmented genomes of flu virus). The technique has the following computational processes: mapping composite data points into different feature spaces using n-grams feature extraction technique, mapping data-vectors of composite data points into variance-covariance structures, using a supervised learning mode to generate distance data-vectors, and finally applying one of well-defined multivariate outlier detection techniques to analyze distance data-vectors in a high dimensional space. Experimentally, the technique shows robustness in finding the true anomalies, as the most dissimilar composite data points from other data points.