Reviewerly/exHarmony huggingface dataset:
Despeckling of Ultrasound Imagery with Qualitative Filtering Techniques

Sound waves’ usage gives rise to ultrasound imagery as it is an indispensable tool for identification and diagnosis of the inner structure of the body for the detection of eventual sickness or anomalous tissues. Ultrasound imaging is a secure, reliable and economical diagnostic reading test. However, ultrasound imagery has an intrinsic property, i.e., speckle noise which diminishes the contrast and resolution of any imagery as it abolishes adequate details or boundaries which are mandatory to be well-preserved. In recent times, many researchers have contemplated distinct filtering schemes for mitigating noisy speckles to achieve good estimation in biomedical operations which include filtration schemes that transform the multiplicative noise into additive with the usage of algorithmic operations. This paper examines and compiles several schemes that are mostly utilized for the eradication of speckling from ultrasound imageries, and a comparison has been done for all the procedures studied based on experiments to illustrate the advantages of every technique. Since genuine ultrasound imageries are already degraded and actual noiseless imageries do not exist. So, synthetic imageries have been formed for testing the procedures. Nevertheless, the scheme has been offered for despeckling in real imageries.
A Fused LBP Texture Descriptor-Based Image Retrieval System

Texture analysis is critical in a variety of computer vision applications, including object recognition, defect detection on surfaces, pattern recognition, and medical picture analysis. The purpose of this research is to offer a novel method for content-based texture picture classification that is based on the discrete wavelet transformation and several texture properties. Three approaches (LBP, DWT, and Tamura) are combined to build an efficient hybrid function vector capable of extracting the finest texture information. The study extracts LBP and Tamura features in two methods, via wavelet transform and fusion, to create an effective hybrid texture feature vector. Experiments on the Brodatz and MIT-VisTex databases demonstrate that the proposed approach is more precise than a single feature texture algorithm and also than a combination of Tamura texture features and wavelet transform features. Additionally, the technique that employs an SVM classifier achieves a higher level of accuracy, up to 99%.
Activity Recognition from Videos Using Semantic Motion Patterns

In today’s world, human activity recognition plays a major role in video surveillance applications. The novelty of this work involves in identifying the activity of the person in the recursive manner using high-level semantics. Recursive activity is defined as the activity that is performed by the persons continuously or recursively. The motion of each and every pixel in the video can give more semantics while identifying the activity. In this work, recursive motion pattern has been identified based on the optical flow algorithm. Initially, the person in the video has been detected and the visual words are extracted. The global and local motion fields are extracted from the video, and this motion pattern gives more semantic meaning in identifying the activity. The semantic gap has been covered using high-level semantic descriptor, and the descriptor output is directly fed into the conditional random field (CRF). Introducing latent variables in the CRF, the descriptor is learned using discriminative model, and the interaction is recognized. The semantic similarity from the interaction has been identified, and the recursive activity is recognized. The new recursive activities such as greeting, fighting, loitering, and buying have been defined from the dataset videos. The importance of the recursive activity is to identify the behavior of the person in the particular scenario. The videos are considered from BIT Interaction dataset, UT Interaction dataset, and collective activity dataset. This work shows the promising results in identifying the recursive activity.
Improving Search in Go Using Bounded Static Safety

Finding the winner of a game of Go is difficult even on small boards due to the game’s complexity. Static safety detection algorithms can find a winner long before the end of a game, thereby reducing the number of board positions that must be searched. These static algorithms find safe points for each player and so help prove the outcome for positions that would otherwise require a deep search. Our board evaluation algorithm Bounded Static Safety (BSS) introduces two new methods for finding valid lower bounds on the number of safe points: extending liberties and intersecting play. These methods define statically evaluated greedy playing strategies to raise the lower bound of a player’s guaranteed score. BSS can solve positions from a test set of $$6\times 6$$ games at an average of 27.29 plies, a significant improvement over the previous best static safety method of locally alternating play (31.56 plies), and far surpassing Benson’s unconditional safety (42.67 plies).
FairKalah: Towards Fair Mancala Play

Kalah (a.k.a. Mancala) is a two-player game of perfect information that has been a popular game for over half a century despite a strong first player advantage. In this paper, we present initial game states that are fair, as well as optimal play insights from analysis of optimal and suboptimal states.
Distributed Face Biometric with Blockchain Federated Learning

Face biometric technology has a wide range of applications in identity recognition. For example, there are already good applications in payment and system login. With the advent of new technologies, face biometric technology still has new development opportunities. This paper mainly studies how to integrate federated learning and blockchain technology into the application of face biometric technology. In this opportunity of face biometric technology, we proposed a solution of distributed face biometric which is kept biometric feature storage and performed authentication over decentralize network using blockchain and federated learning technologies. Federated learning technology provides excellent architecture to protect sensitive private biometric feature, and blockchain can help on data storage on all nodes to enhance the availability and readability of the proposed system. In our evaluation, the experiment explores the key trade-offs involved in integration. We study those through experiments with a theoretical blockchain which will implement biometric template in federated environments. We use popular facial recognition models and dataset to make our experiments, including deep learning methods and a database captured in the field.
Procedural Generation of Rush Hour Levels

Procedural generation of puzzle games allows for more varied and diverse levels, which provides a better gaming experience. However, the generation based on deep-learning approaches is very challenging with this genre of games due to their inherent complexity relative to strict design rules and discrete components. In this work, we propose a framework that is composed of three main modules: a solver and two classifiers. Instances of Rush Hour are generated randomly and the solver aims to assess the playability of the generated levels by classifying solvable and unsolvable levels and by evaluating the difficulty of solvable levels. Two neural networks are used to improve the generation. One network is trained to differentiate between solvable and unsolvable levels and the other is trained to classify the difficulty of levels. The robustness of the framework is examined on 6 $$\times $$ 6 and 7 $$\times $$ 7 grid dimensions. The results obtained showed the effectiveness of our modules in generating interesting and various levels with four degrees of difficulty for the Rush Hour puzzle, where the classifier was able to quickly identify unsolvable levels. Our framework could be easily adapted and extended for procedurally generating other game genres, such as Platformer or RogueLike.
A New Four-Dimensional Discrete Chaotic System Is Used for S-Box Masking

FPGA-based S-box chaotic masking needs to call the multiplier IP core during multiplication, which will take up a lot of resources. Therefore, this paper proposes a new four-dimensional discrete chaotic system for S-box masking. The four-dimensional discrete chaos can avoid calling the multiplier IP core by limiting the elements in the polynomial coefficient matrix for shift operation. The new four-dimensional discrete chaotic system has higher Lyapunov exponent and better randomness. In terms of hardware resource consumption, although the new chaotic system has a higher dimension than the classical chaotic system Logistic map, Tent map and Henon map, it requires almost the same or even less resources as the one-dimensional chaotic system. The experimental results show that the new four-dimensional discrete chaotic system is more suitable for S-box masking in FPGA.
An Algorithm for Multiplayer Games Exploiting Opponents’ Interactions with the Player

In multiplayer games, it is often the case that some actions of one player will not influence the decisions of another, either until much later in the game or not at all. Existing algorithms make use of this idea and arbitrarily limit their models of the opponents, or partially ignore them, in order to explore more of the player’s options. These limitations can go too far in the opposite direction, however, and miss crucial pieces of interaction between player and opponent. This paper attempts to solve both these problems, by introducing an algorithm which guides its search based on the player’s interaction with the opponents. Specifically, it conducts a breadth-first, MaxN style search that builds its search trees by grouping together states that have the same interaction with the player.
Towards Transparent Cheat Detection in Online Chess: An Application of Human and Computer Decision-Making Preferences

Online game providers face the challenge of preventing malicious users (cheaters) from breaking the rules and winning games through illegal means. This issue in particular plagues the online chess scene, where the strongest algorithms have long surpassed the world’s best players [4] – any cheater can beat the best human players through computer assistance. Moreover, recent developments in AI-based chess engines have opened the door to even more human-like engines [33], which are increasingly able to mimic legitimate human players. Unfortunately, because major chess websites do not discuss their cheat detection mechanisms publicly, there is limited scientific literature on how to tackle the pervasive problem of cheating in online chess. Certainly, there is no way to validate whether these mechanisms actually work. We take a first step towards formalizing a proper cheat detection framework for online chess by leveraging a large-scale statistical examination of human and computer decision-making tendencies over millions of chess games played online. Although cheaters are not engines (computer players) but centaurs (computer-assisted human players), the insights into computer play serve as a useful guideline for finding the strongest indicators of cheating. We then demonstrate how these findings may distinguish legitimate human players from cheaters in an automated, rules-based manner. Additionally, we argue that the status quo of hiding cheat detection mechanisms from the public eye is dangerous to the integrity of the game, and that cheat detection is foremost a service to society instead of a competitive advantage for chess websites to attract more users. Consistent with Kerckhoffs’ paradigm [24], we believe that the benefits of an open discussion on cheat detection far outweigh the potential drawbacks of cheaters learning about these methods.
Improving Computer Play in Skat with Hope Cards

Skat is a strategy-rich card game, posing several combinatorial questions to advanced computer play for improved cooperation and competition. In this paper we advance trick-taking play by introducing hope cards, namely cards that excel not in all, but in the set of winnable worlds. Besides winning rate and scoring, we evaluate the strength of computer engine mainly based on the proximity of its results to the outcome of the open-card solver. This way, the opponents’ and the overall playing strength of a state-of-the-art automated Skat player has been upgraded significantly, while keeping the response time small for swift play. For the first time, we won an online tournament with 20 series against a top German Skat player.
Fault Detection Method of Insulator Image Based on Gabor Texture and Convolutional Neural Network

Insulator is the main work of daily inspection of circuit inspectors, and the quality of insulator is related to line safety. To enhance the efficiency of insulator fault recognition, an insulator image fault recognition method based on Gabor Texture and convolutional neural network (CNN) is proposed. Firstly, the insulator image is collected, and the appropriate area is selected for regional segmentation to separate the insulator area. Secondly, the fault area of the insulator image is selected as the area of interest to mark, and the corresponding normal area is combined to construct the insulator image data set. Then, the selected regions are filtered using Gabor wavelet to obtain feature images at different scales and orientations. Finally, combined with the original image, a classification model of the CNN is established. Through training and testing, the parameters of the convolutional network are continuously adjusted to build the optimal classification model. Establish Gabor analysis and CNN prediction models for classification and recognition. Experiments show that the method could quickly identify insulator faults, assist circuit inspection, and have strong practicability.
Adaptive Target Tracking Algorithm Based on Template Updating and Graph-Regularized Saliency

At endmost stage of flight for tracking, target often tends to fulfill whole scene. If the target maneuvers rapidly, it is easy to lose the target. In this paper, an adaptive template update tracking algorithm model based on graph-regularized salience is proposed to solve this problem. The proposed algorithm introduces the graph-regularized salience model into the target tracking framework. When the target’s size exceeds the setting threshold or the target will overflow the screen in the setting number of frames, that is, the target’s lifetime, salience model is used to extract the most prominent position of the target to update the template, in order to ensure that the template has a good response to the target and ensure the stability of target tracking.
Solving Impartial SET Using Knowledge and Combinatorial Game Theory

Standard SET is a card game played between any number of players moving simultaneously, where a move means taking a number of cards obeying some predetermined conditions (a Set). It is mainly a game of pattern recognition and speed. The winner is the player obtaining the most Sets. To enable analyzing SET in a more mathematical and game-theoretic sense we transformed the game into an impartial combinatorial game. SET versions may differ depending on the number of characteristics c on the cards and the number of values v of each characteristic. We indicate a particular version of SET for some v and c as SET-v-c. We analyze different versions of Impartial SET using $$\alpha \beta $$ search with several enhancements from mathematical SET theory and from Combinatorial Game Theory. We first show using SET theory that all SET-2-c versions for $$c > 1$$ are second-player wins and that SET-v-2 versions are first-player wins for odd v and second-player wins for even v. We next show how to compute solutions using search. We give some results and discuss how solving efficiencies were dependent on the enhancements used. Especially, the use of a pruning method based on symmetry of positions was pivotal for a large efficiency enhancement. Also the use of two methods based on endgame databases filled with Combinatorial Game Theory values proved very useful in further enhancing the solving efficiency. Using all enhancements together the complex SET-4-3 game was solved, needing the investigation of some 2 billion nodes. This game is (maybe counterintuitive) a first-player win.
Incentivizing Information Gain in Hidden Information Multi-Action Games

Wargames often include fog of war, i.e. hidden information, and multi-action turns, where each turn requires making multiple, sequential action choices. These properties provide unique challenges for Artificial Intelligence agents. Extensions to Monte-Carlo Tree Search (MCTS) allow it to perform well in games with hidden information as well as multi-action games. However, these extensions do not specifically consider both properties simultaneously nor how information-gaining actions could improve agent performance. Information-gaining actions are important in multi-action turns where initial actions can reveal state information, thus improving later action decisions. This paper presents enhancements to MCTS that add an information gain incentive and a risk determinization to balance locating opponent pieces while minimizing exposure to enemy fire. The information gain incentive and risk functions are implemented in Perfect Information-MCTS (PIMCTS) and Information Set-MCTS (ISMCTS) and evaluated on the multi-action hidden information game TUBSTAP. Results show that these additions improve performance over the baseline algorithms and against a Cheating MCTS implementation.
A Method of Constructing Fine-Grained Pose Evaluation Model

In the field of computer vision, human pose estimation (HPE) is an important direction. However, in some fields that require professional actions or motions, researchers will deal with poses with high diversity, strongly instantaneous actions and complex behaviors, and with problems such as lack of enough dataset, algorithm iteration lag, and poor robustness, it is difficult for conventional CV methods to recognize many kinds of poses well. Aiming to the problems above, we propose a method of training fine-grained pose estimation model and building pose dataset based multi-views in this paper. And during the work, we use the operation of filtering as optimization approach to improve the accuracy of results. By filtering data, those wrongly recognized key points can be corrected to some extent and the missing key points or poses can be estimated based on the continuity of motion. The filter method can also improve the smoothness of curve, so the pose sequences can be more fluent and conform the kinetics after the operation. Meanwhile, there sometimes may be errors about the results of 3D pose reconstruction based on 2D pose because of occlusions, changes of environment. In this situation, choosing 2D pose results from appropriate views instead of all the views can also improve the accuracy of final results. Moreover, for the environment with several persons, we finish the work of matching human cross-different views by combining with the method of predicting depths of root key points. From the experiments, the method can increase the average precision (AP) up to 2.2%, which demonstrate the feasibility and reusability of our method.
Super Resolution of Unpaired MR Images Based on Domain Migration

High-resolution magnetic resonance imaging (MRI) is very important for doctors to judge the condition. At present, the depth learning method has been widely studied in the super-resolution reconstruction of MR images. However, due to the distribution difference between the real low-resolution MR images and the down sampled MR images used to train the super-resolution model under experimental conditions, the super-resolution performance of the super-resolution method is often seriously degraded when super-resolution is applied to the real low-resolution MR images. Blind super-resolution methods used to solve such problems in natural image super-resolution are lack of image content constraints. It is not advisable to introduce additional information that will interfere with doctors’ diagnosis in the super-resolution process of MR images. These two problems hinder the application of super-resolution method in real MR images. In order to solve these problems, an unpaired MR image super-resolution network based on domain migration is proposed. The proposed DMN can use a real set of low to high-resolution unpaired MR images as input, migrate the real low-resolution MR images to the down sampled MR image domain, and then super-resolution reconstruction is carried out through the paired super-resolution network obtained through supervision training. Experiments on fast MRI datasets verify the effectiveness of this method in solving the super-resolution problem of real MR images.
An Efficient Multilevel Threshold Image Segmentation Approach Based on Rafflesia Optimization Algorithm

Segmentation is an important step from image processing to analysis. The thresholding technique based on entropy has become one of the most commonly used techniques in this field. Among the many thresholding techniques based on entropy, the minimum cross-entropy threshold (MCET) has been dramatically developed. The key to multilevel MCET technology is quickly finding the optimal threshold point. MCET is a relatively time-consuming technique, so we use optimization techniques to search for optimal multilevel thresholds. Rafflesia optimization algorithm (ROA) is an optimization algorithm inspired by rafflesia growth theory to solve real-world multi-parameter optimization problems. In this paper, Rafflesia optimization algorithm, particle swarm optimization, gray wolf optimization, whale optimization algorithm, and multi-version optimizer are used to segment six images and the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) as evaluation metrics. The experimental results show that the ROA algorithm can achieve higher-quality image segmentation.
Spoken Dialogue System Development Without Speech Recognition Towards Language Revitalization

This paper proposes a spoken dialogue system that needs neither a speech recognizer nor a speech synthesizer. It is desired to develop a language learning programme to revitalize an endangered language; however, it is difficult to train language teachers of such languages, which is the motivation for creating a computer-assisted language learning (CALL) system for endangered languages. The current paper describes a technique for choosing a spoken sentence using only the input speech signal without any speech recognizer because we do not have a speech recognizer of an endangered language. We examined the continuous DP matching (CDP) algorithm with the phonetic posteriorgram (PPG) feature, which gave a fair selection result regardless of the gender of the user and the speakers of the database speech.
A Document Management System with Multi-biometric Watermarking for Educational Purpose

With the rapid development of multimedia technology and computer network technology, phenomenon of paperless work is growing. The problem of copyright protection of digital documents is particularly important. The digital watermarking technology is an important means of document protection. In this work, a document watermarking with embedding four multi-biometric features is proposed; it can do watermark embedding with more accuracy and robust. In addition, an educational propose application are designed as well. According our experiment, the accuracy of four type biometric can meet high accuracy with 99.4% after the extraction from watermarking. Because the open source application we provide is for interested learners, this research has a considerable educational purpose.